Geospatial Practices in Natural Resources Management (Environmental Science and Engineering) 3031380037, 9783031380037

Citation preview

Environmental Science and Engineering

Pravat Kumar Shit · Dipanwita Dutta · Tapan Kumar Das · Sandipan Das · Gouri Sankar Bhunia · Pulakesh Das · Satiprasad Sahoo   Editors

Geospatial Practices in Natural Resources Management

Environmental Science and Engineering Series Editors Ulrich Förstner, Buchholz, Germany Wim H. Rulkens, Department of Environmental Technology, Wageningen, The Netherlands

The ultimate goal of this series is to contribute to the protection of our environment, which calls for both profound research and the ongoing development of solutions and measurements by experts in the field. Accordingly, the series promotes not only a deeper understanding of environmental processes and the evaluation of management strategies, but also design and technology aimed at improving environmental quality. Books focusing on the former are published in the subseries Environmental Science, those focusing on the latter in the subseries Environmental Engineering.

Pravat Kumar Shit · Dipanwita Dutta · Tapan Kumar Das · Sandipan Das · Gouri Sankar Bhunia · Pulakesh Das · Satiprasad Sahoo Editors

Geospatial Practices in Natural Resources Management

Editors Pravat Kumar Shit Department of Geography Raja Narendra Lal Khan Women’s College Midnapore, West Bengal, India Tapan Kumar Das Department of Geography Cooch Behar College Cooch Behar, West Bengal, India Gouri Sankar Bhunia Department of Geography Seacom Skills University Kolkata, West Bengal, India Satiprasad Sahoo Centre for Environment Indian Institute of Technology (IIT-Guwahati) Guwahati, Assam, India

Dipanwita Dutta Department of Remote Sensing and GIS Vidyasagar University Midnapore, West Bengal, India Sandipan Das Symbiosis Institute of Geoinformatics (SIG) Symbiosis International (Deemed University) Pune, Maharashtra, India Pulakesh Das Sustainable Landscapes and Restoration World Resources Institute India New Delhi, Delhi, India

ISSN 1863-5520 ISSN 1863-5539 (electronic) Environmental Science and Engineering ISBN 978-3-031-38003-7 ISBN 978-3-031-38004-4 (eBook) https://doi.org/10.1007/978-3-031-38004-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Disclaimer: The authors of individual chapters are solely responsible for the ideas, views, data, figures, and geographical boundaries presented in the respective chapters of this book, and these have not been endorsed, in any form, by the publisher, the editor, and the authors of forewords, preambles, or other chapters. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Dedicated to Young Scholars in the field of Earth and Environment

Foreword

Geospatial Practices in Natural Resources Management is a timely publication considering the over-consumption and unsustainable use of natural resources worldwide. It has been a great challenge to the scientific and research community to find a solution for reducing the degradation and rapid depletion of natural resources. Despite industrialization and development, a large mass of the world’s population depends upon natural resources for their livelihood. Unsustainable use of natural resources may break the positive linkage between man and the environment, and it will further exaggerate the socio-economic vulnerability at the micro-level. In this context, the management of land, water, and forest has become a major focus across the world. This book has valuable contributions to the Sustainable Development Goals (SDG) of the United Nations specifically goal number 12.2 aiming for the sustainable management and efficient use of natural resources. Geospatial technology has become essential for precise observation, analysis and management of natural resources. Recent advancements in the fields of image processing and GIS-based tools have made it possible to handle large volumes of datasets efficiently. The significance of geospatial technology for managing natural resources has been well-recognized worldwide. United Nations has developed an Integrated Geospatial Information Framework (UN-IGIF) to address the key issues in the geospatial information network, and the United Nations initiative on Global Geospatial Information Management (UN-GGIM) is playing a pivotal role in policy making for managing the geospatial information system. With its wide range of applications, remote sensing techniques have become crucial for analyzing various types of spatial datasets and managing land, water and forest resources. The book contains 26 chapters covering all aspects of land, water and forest management including advanced techniques for mapping, monitoring and modelling natural resources. The volume will be useful for scientists, academicians, researchers and students of Environment and Earth Sciences. Moreover, the book can be a valuable reference for decision-makers and planners for preserving and managing natural resources through advanced techniques. The book attempts to present a wide range of geospatial techniques for addressing major issues related to land, water and forests

vii

viii

Foreword

of different regions. I heartily congratulate the editorial team for the publication of this book.

Dr. N. R. Patel Scientist ‘G’ Head, Agriculture and Soils Department Indian Institute of Remote Sensing Indian Space Research Organization (ISRO) Dehradun, India

Preface

This book demonstrates the geospatial technology approach to data mining techniques, data analysis, modeling, assessment, and visualization, and management strategies in different aspects of natural resources. The present book explores stateof-the-art techniques based on open-source software and R statistical programming and modeling in modern artificial intelligence techniques specifically focusing on the recent trends in data mining techniques and robust modeling in natural resources. Its covers major topics such as land resources, water resources, and forest resources. We are very much thankful to all the authors who have meticulously completed their documents on a short announcement and played a vital role in building this edifying and beneficial publication. We do believe that this will be a very convenient book for soil science, geographers, ecologists, environmental scientists, and others working in the field of Land-Water-Forest resources management including research scholars, environmentalists, and policymakers. We also acknowledge our deep gratitude to the Springer Publishing House for contracting with us for such timely publication. Midnapore, India Midnapore, India Cooch Behar, India Pune, India Kolkata, India New Delhi, India Guwahati, India

Pravat Kumar Shit Dipanwita Dutta Tapan Kumar Das Sandipan Das Gouri Sankar Bhunia Pulakesh Das Satiprasad Sahoo

Acknowledgments The preparation of this book has been guided by several subject experts. We are obliged to these experts for providing their time to evaluate the chapters published in this book. We thank the anonymous reviewers for their constructive comments that led to substantial improvement to the quality of this book. Because this book was for a long time in the making, we want to thank our family and friends for their continued support . This work would not have been

ix

x

Preface

possible without constant inspiration from our students, knowledge from our teachers, enthusiasm from our colleagues and collaborators, and support from our family. Finally, we also thank our publisher and its publishing editor, Springer, for their continuous support in the publication of this book.

Contents

Part I 1

2

3

4

5

6

Land Resources

Big Data Analysis for Sustainable Land Management on Geospatial Cloud Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gouri Sankar Bhunia and Pravat Kumar Shit

3

Assessing of LULC and Climate Change in Kolkata Urban Agglomeration Using MOLUSCE Model . . . . . . . . . . . . . . . . . . . . . . . . Satiprasad Sahoo and Suprakash Pan

19

Field Survey and Geoinformatic Approaches for Micro-Level Land Capability Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. S. Pawar-Patil, Sainath Aher, Vidya Chougule, Sandipan Das, and Rushikesh Patil Assessment of Potential Land Suitability for Economic Activity Using AHP and GIS Techniques in Drought Prone Gandheswari Watershed, Bankura District in West Bengal . . . . . . . . Ujjal Senapati, Dipankar Saha, and Tapan Kumar Das Spatial–Temporal Changes of Urban Sprawl, LULC and Dynamic Relationship Between Land Surface Temperature (LST) and Bio-Physical Indicators: A Study of Kolkata Municipal Corporation, West Bengal . . . . . . . . . . . . . . . . . Gourab Saha, Sandipan Das, Suvarna Tikle, and Pravat Kumar Shit

31

61

97

Spatio-Temporal Dynamics of Urban Land Use Applying Change Detection and Built-Up Index for Durgapur Municipal Corporation, Paschim Bardhaman, West Bengal . . . . . . . 111 Tapan Kumar Das, Subham Kumar Roy, Masud Karim, and Dipankar Saha

xi

xii

7

Contents

Studies on Impacts of Land Use/Land Cover Changes on Groundwater Resources: A Critical Review . . . . . . . . . . . . . . . . . . . 143 Suvendu Halder, Satiprasad Sahoo, Tumpa Hazra, and Anupam Debsarkar

Part II

Water Resources

8

Crowdsourcing as a Tool for Spatial Planning in Water Resource Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Gouri Sankar Bhunia, Soumen Bramha, Manju Pandey, and Pravat Kumar Shit

9

Spatio-Seasonal Runoff and Discharge Variability in the Ganga River Basin, India: A Hydrometeorological Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Raghunath Pal

10 Appraisal of Drinking Water Quality of Kalahandi District Using Geospatial Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 M. Patnaik, C. Tudu, M. Priyadarshini, and C. Dalai 11 Allocation of Potential Tourism Gradient Sites at Maithon Dam of Damodar Valley Corporation (DVC), India: A Geospatial Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 Manika Saha and Susmita Sengupta 12 Watershed Management Process Under MGNREGA: An Approach to Natural Resource Management Through People’s Participation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 Soumik Halder, Sumit Panja, and Sayani Mukhopadhyay 13 Meteorological and Agricultural Drought Monitoring Using Geospatial Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 Brij Bhushan, Apurva Dhurandher, and Akanksha Sharma 14 GIS Based Delineation of Flood Susceptibility Mapping Using Analytic Hierarchy Process in East Vidarbha Region, India . . . . . . . 305 Kanak Moharir, Manpreet Singh, Chaitanya B. Pande, and Abhay M. Varade 15 Fluoride Contamination in Groundwater—A Review . . . . . . . . . . . . . 331 Riddha Chaudhuri, Satiprasad Sahoo, Anupam Debsarkar, and Sugata Hazra 16 Analysis of Basin Morphometry for the Prioritization Using Geo-Spatial Techniques: A Case Study of Debnala River Basin, Jharkhand, India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355 Abdur Rahman, Jaidul Islam, and Partha Pratim Sarkar

Contents

xiii

17 Geo-Spatial Techniques to Analyze Fluvial Morphometry of River Kangshabati and Some Associated Features, in Selected Parts of Bankura District, West Bengal . . . . . . . . . . . . . . . 383 Ayan Das Gupta 18 Morphometric Analysis of Panzara River Basin Watershed, Maharashtra, India Using Geospatial Approach . . . . . . . . . . . . . . . . . 401 Pranaya Diwate, Firoz Khan, Sanjeev Kumar, Kunal Chinche, Pavankumar Giri, and Varun Narayan Mishra 19 Groundwater Geochemistry and Identification of Hydrogeochemical Processes of Fluoride Enrichment in the Consolidated Aquifer System in a Rain Shadow Area of South India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421 Anadi Gayen, Suparna Datta, A. V. Arun Kumar, V. S. Joji, and V. K. Vijesh Part III Forest Resources 20 Temporal Areal and Greenness Variation of Marichjhapi Island, Sundarban, India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439 Sipra Biswas and Kallol Sarkar 21 Estimation of Crop Coefficients Using Landsat-8 Remote Sensing Image at Field Scale for Maize Crop . . . . . . . . . . . . . . . . . . . . . 463 Nirav Pampaniya, Mukesh K. Tiwari, Vijay J. Patel, M. B. Patel, P. K. Parmar, Sateesh Karwariya, Shruti Kanga, and Suraj Kumar Singh 22 Spatio-Temporal Assessment of Forest Health Dynamics of Sikkim Using MODIS Satellite Data by AHP Method and Geospatial Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 479 Rima Das and Biraj Kanti Mondal 23 Forest Degradation Susceptibility and Sustainability: Case Study of Arganeraie Biosphere Reserve, Atlantic High Atlas, Morocco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507 Sarrah Ezaidi, Mohamed Ait Haddou, Belkacem Kabbachi, Abdelkrim Ezaidi, Asmae Aichi, Pulakesh Das, and Mohamed Abioui 24 Utilization of PISA Model and Deduced Specific Degradation Over Semi-arid Catchment: Case of Abdelmomen Dam in Souss Basin (Morocco) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527 Mohamed Ait Haddou, Youssef Bouchriti, Belkacem Kabbachi, Mustapha Ikirri, Ali Aydda, Hicham Gougueni, and Mohamed Abioui

xiv

Contents

25 Geospatial Practices for Airpollution and Meteorological Monitoring, Prediction, and Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . 549 Suvarna Tikle, Vrinda Anand, and Sandipan Das 26 Empowerment of Geospatial Technologies in Conjunction with Information and Communication Technologies (ICT) . . . . . . . . 567 Aarti Kochhar, Shashikant Patel, Harpinder Singh, P. K. Litoria, and Brijendra Pateriya

About the Editors

Pravat Kumar Shit (Ph.D.) is an Assistant Professor at the P.G. Department of Geography, Raja N. L. Khan Women’s College (Autonomous), West Bengal, India. He received his M.Sc. and Ph.D. degrees in Geography from Vidyasagar University and P.G. Diploma in Remote Sensing and GIS from Sambalpur University. His research interests include applied geomorphology, soil erosion, groundwater, forest resources, wetland ecosystem, environmental contaminants and pollution, and natural resources mapping and modeling. He has published 20 books (Springer—16, Elsevier—03, CRC Press—01) and more than 85 papers in peer-reviewed journals and 78 book chapters. He is also the guest editor of Environmental Science and Pollution Research and Applied Water Science. He is currently the editor of the GIScience and Geo-environmental Modelling (GGM) Book Series, Springer Nature. Dr. Dipanwita Dutta is an Assistant Professor in the Department of Remote Sensing and GIS, Vidyasagar University, India. Her broad area of research includes drought dynamics, dryland issues, crop monitoring, land use dynamics, and urban green space, and her research projects have been funded by UGC, DSTSERB (Government of India). Dr. Dutta has published more than 40 research articles and book chapters in reputed international journals and edited book volumes. She is a reviewer of many national and international journals. Dr. Dutta has been awarded NUFFIC fellowship for pursuing M.Sc. degree in Remote Sensing and GIS at ITC, the Netherlands, jointly with Indian xv

xvi

About the Editors

Institute of Remote Sensing, Dehradun. She has been awarded International Travel Grant from the Department of Science and Technology, Government of India, for visiting the University of Salzburg, Austria. She has also been awarded WMO-ICTP fellowship for attending an international workshop at ICTP, Italy. Dr. Tapan Kumar Das did his M.Sc. in Geography from University of Calcutta in 1997 and had started his teaching profession since 1997. He was awarded Ph.D. from Vidyasagar University in 2012 for his remarkable research work on “Embankment Breaching and its Consequences: A Case Study in North East Sundarban, West Bengal”. He is now serving as Assistant Professor of Geography, Cooch Behar College, and shouldering his administrative responsibility as Co-ordinator, P.G. Department of Geography and Nodal Officer, UGCNSQF Skill Courses in Geoinformatics, Cooch Behar College. He has undergone NNRMS-ISRO Sponsored Certificate Course from IIRS, Dehradun, on “Application of Remote Sensing and GIS in Agriculture and Soils” and P.G. Diploma in “Geoinformatics” from ITT Council, New Delhi. He is actively engaged in research supervision under Cooch Behar Panchanan Barma University. Many research publications in national and international journals of repute are in his credit. He is also involved in campus to community activity under National Service Scheme, and he has been honored with “Best Programme Officer Award” by Cooch Behar Panchanan Barma University on 3rd Convocation on February 14, 2020, for his outstanding contribution to National Service Scheme and for encouraging and nurturing students who have distinguished in this field.

About the Editors

xvii

Dr. Sandipan Das is an Assistant Professor at Symbiosis Institute of Geoinformatics, Symbiosis International (Deemed University), Pune, India. He received his Master’s degree in Geoinformatics from Pune University and Ph.D. from Symbiosis International (Deemed University). His research interests include forest biomass and productivity assessment, geospatial modeling of groundwater, water resources management, drought, and natural resource management. He has more than seven years of teaching and research experience. He has published 18 research papers, one edited book, six book chapters, and four conference proceedings. He has worked on several research projects funded by the Indian Space Research Organisation (ISRO), Department of Science and Technology (DST). He is a reviewer for several scientific journals of the international repute. Gouri Sankar Bhunia received his Ph.D. from the University of Calcutta, India, in 2015. His Ph.D. dissertation work focused on environmental control measures of infectious disease using geospatial technology. His research interests include environmental modeling, risk assessment, natural resources mapping and modeling, data mining, and information retrieval using geospatial technology. Dr. Bhunia is Associate Editor and on the editorial boards of three international journals in Health GIS and Geosciences. Dr. Bhunia has published more than 75 articles in various journals that are Scopus indexed. He is currently the editor of the GIScience and Geo-environmental Modelling (GGM) Book Series, Springer Nature. Pulakesh Das works as a Senior Project Associate with the Sustainable Landscapes and Restoration program at World Resources Institute India (WRI India). He comes with over ten years of experience in analyzing and integrating various satellite, remote sensing and collateral geospatial data using geo-statistical and machine learning approaches. Before joining WRI India, he taught for two years (2017–2019) as an Assistant Professor in the Department of Remote Sensing and GIS at Vidyasagar University, West Bengal. Between 2013 and 2017, Pulakesh worked as a Research Fellow at the Centre for Ocean, River, Atmosphere and Land

xviii

About the Editors

Sciences (CORAL) at the Indian Institute of Technology (IIT) Kharagpur. He holds a Ph.D. from IIT Kharagpur, and a master’s in Remote Sensing and GIS from Vidyasagar University, Midnapore, India. He has published 40 peer-reviewed research articles and book chapters and co-edited three books. Satiprasad Sahoo is currently an Assistant Professor at the International Institute of Geospatial Science and Technology (IIGST), South Asian Institute for Advanced Research and Development (SAIARD), Kolkata. He had completed B.Sc. (Geography) from the University of Calcutta (in 2009), M.Sc. (Remote Sensing and GIS) from the Vidyasagar University (in 2011), and M.Sc. (Geography) from the CSJM University (in 2013). Moreover, He had completed M.S. (by research) in Water Management from School of Water Resources, Indian Institute of Technology Kharagpur, in 2016. He had completed a Ph.D. in Hydroenvironmental Modeling from the Jadavpur University, in 2019. He had also completed Postdoctoral research at Indian Institute of Technology Guwahati and Nalanda University. His research interest lies in the fields of remote sensing, geographic information system, groundwater hydrology, real-time hydroenvironmental modeling, watershed monitoring and development, the effect of climate change, identification of critical reaches, and environmental impact assessment.

Abbreviations

AAI AHP AI AI ALOS ANN AOD API ASTER AUC AVHRR C.G.W.B. CAGR CART CCT CDI CGWB CMIP5 CR CSO CWSI DEM DL DN DTM DVC EC EPA ET ETM+

Aridity Anomaly Index Analytical Hierarchy Process Aridity Index Artificial Intelligence Advanced Land Observing Satellite Artificial Neural Network Aerosol Optical Depth Application Programming Interface Advance Spaceborne Thermal Emission and Reflection Radiometer Area Under the Curve Advanced Very High-Resolution Radiometer Central Ground Water Board Compound Annual Growth Rate Classification and Regression Trees Continuous Contour Trench Crop Drought Index Central Ground Water Board Coupled Model Intercomparison Project 5 Consistency Ratio Civil Society Organisation Crop Water Stress Index Digital Elevation Model Deep Learning Digital Number Digital Terrain Model Damodar Valley Corporation Electrical Conductivity Environmental Protection Agency Evapotranspiration Enhanced Thematic Mapper xix

xx

FAO GAM GCS GEE GIS GLAS GOSAT GPEI GPM GPS GRACE GWQI HGB ICT IDW IMD IoT ISO ISO-DATA ISRIC ISRO JAXA Kc LAI LCC LIDAR LISS LST LULC MCDA MCDM MERIS MGNREGA ML MLP MNDWI MODIS MSS NASA NBSS & LUP NBT NDBI NDVI NRM NRSC

Abbreviations

Food and Agriculture Organization Generalized Additive Model Ground Control Station Google Earth Engine Geographic Information Systems Geoscience Laser Altimeter System Greenhouse Gases Observing SATellite Global Polio Eradication Initiative Global Precipitation Measurement Global Positioning System Gravity Recovery and Climate Experiment Groundwater Quality Index Himalayan Geothermal Belt Information and Communication Technologies Inverse Distance Weighted Indian Meteorological Department Internet of Things International Organization for Standardization Iterative Self-Organizing Data Analysis Technique International Soil Reference and Information Centre Indian Space Research Organizations Japan Aerospace Exploration Agency Crop coefficients Leaf Area Index Land Capability Classification Light Detection and Ranging Linear Imaging Self-Scanning Sensor Land Surface Temperature Land Use and Land Cover Multi-criteria Decision Analysis Multi-criteria Decision Making Medium Resolution Imaging Spectrometer Mahatma Gandhi National Rural Employment Guarantee Act Machine Learning Multilayer Perceptron Modified Normalized Differentiate Water Index Moderate Resolution Imaging Spectroradiometer Multispectral Scanner National Aeronautics and Space Administration National Bureau of Soil Survey and Land Use Planning Nature-Based Tourism Normalized Difference Built-Up Index Normalized Differential Vegetation Index Natural Resource Management National Remote Sensing Centre

Abbreviations

OGC OLI OSAVI PA PDSI PISA PLSI PM PRA QGIS RADAR RCPs RI RMSE ROC RUSLE RVI SAVI SCIAMACHY SDI SDS SMI SOI SPEI SPI SRTM ST SWAT SWE SWIR TCC TIRS TM TOMS TROPOMI TWI UAV UHI USGS UTM VCI VES VIIRS VIs

xxi

Open Geospatial Consortium Operational Land Imager Optimized Soil Adjusted Vegetation Index Protected Areas Palmer Drought Severity Index Previsioni Interimento Serbatoi Artificiali Potential Land Suitability Index Particulate Matter Participatory Rural Appraisal Quantum GIS Radio Detection and Ranging Representative Concentration Pathways Random Consistency Index Root Mean Square Error Receiver Operating Characteristic Revised Universal Soil Loss Equation Ratio Vegetation Index Soil-Adjusted Vegetation Index SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY Spatial Data Infrastructures Spatial Data Science Soil Moisture Index Survey of India Standardized Precipitation ET Index Standardized Precipitation Index Shuttle Radar Topography Mission Staggered Trench Soil and Water Assessment Tool Sensor Web Enablement Short-Wave Infrared Band Tree Canopy Cover Thermal Infrared Sensor Thematic Mapper Total Ozone Mapping Spectrometer TROPOspheric Monitoring Instrument Topographic Wetness Index Unmanned Aerial Vehicle Urban Heat Island United States Geological Survey Universal Transverse Mercator Vegetation Condition Index Vertical Electrical Sounding Visible Infrared Imaging Radiometer Suite Vegetation Indices

xxii

WGS WQI WRF WSN Xgboost XRF

Abbreviations

World Geodetic System Water Quality Index Weather Research and Forecasting Wireless Sensor Networks Extreme Gradient Boosting X-Ray Fluorescence

Part I

Land Resources

Chapter 1

Big Data Analysis for Sustainable Land Management on Geospatial Cloud Framework Gouri Sankar Bhunia and Pravat Kumar Shit

Abstract The advancements of the 1980s led to the creation of various important technologies, including GPS and satellite imagery, which allowed for the sustainable management of land resources. This must be done while preserving sustainable landuse systems and confronting concerns such as climate change, water scarcity, and the risk of increasing erosion and productivity due to extreme weather events. In several areas, geospatial Big Data analytics is transforming the way firms’ function. Although there are many research workson geographic data analytics and realtime data processing of massive spatial data streams in the literature, only a few have covered the entire geospatial big data analytics and geospatial data science project lifecycle. Because of the volume, pace, and variety of the data being analysed, big data analysis differs from typical data analysis. In comparison to conventional data analysis projects, geospatial data science initiatives are likely to be more difficult and require advanced technologies. The current study introduces a novel geographic big data mining and machine learning framework for geospatial data gathering, fusion, storage, management, processing, analysis, visualisation, and land resource modelling and evaluation. Any data science project that has a robust procedure for land resource data analysis and clear instructions for comprehensive analysis is always a positive. It also aids in estimating the amount of time and resources required early in the process to gain a good picture of the land resource challenges that must be overcome. Automation and the use of artificial intelligence (AI), the internet of things (IoT), drones, satellite imagers, and Big Data lay the foundation for a global “Digital Twin,” which will aid in the development of site-specific conservation and management practices that will boost incomes and ensure the long-term sustainability of land use/land cover systems.

G. S. Bhunia (B) Independent Researcher, Paschim Medinipur, West Bengal, India e-mail: [email protected] P. K. Shit Department of Geography, Raja N L Khan Women’s College (Autonomous), Vidyasagar University, Midnapore, West Bengal, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_1

3

4

G. S. Bhunia and P. K. Shit

Keywords Big data · Geospatial · Data mining · Sustainability · Land use/Land cover

1.1 Introduction The extent to which human actions have an impact on environmental processes has long been a focus of land management research. These variations, which frequently have a spatial dimension, can be quantified and qualitatively measured. Nonetheless, charting the temporal and geographical changes of urban and rural land remains a difficult endeavour since technology tools and techniques have not yet been adequate to assist planners’ and decision makers’ everyday practise and spatiotemporal requirements. Field surveys and participatory mapping, for example, are time-consuming, costly, and labor-intensive tools for land use planning (Cao et al. 2019). Advances in data collection methods, as well as increased computer power, have enabled the practical use of algorithms that were previously only considered theoretical remedies that could not be implemented. The approaches to imaging land usage belong under the category of remote sensing (RS), and the procedures by which they are analysed are done using Geographic Information Systems (GIS) technologies. RS sensors and techniques have advanced significantly in recent decades. They can deliver a vast volume of high-quality data with excellent spatial resolution (Samardži´c et al. 2017). The increasing availability of data such as Light Detection and Ranging (LIDAR), Radio Detection and Ranging (RADAR), Multispectral (MSS), Hyperspectral, Unnamed Aerial Vehicle (UAV)-borne data, and other commercially available satellite data as well as data from other airborne platforms has increased land use planning skills and knowledge (Chaturvedi and de Vries 2021). Within the last millennia, roughly three-quarters of the Earth’s terrestrial surfacehave been affected by various anthropogenic activities. Land use change has significant impact on carbon sources and sinks (Arneth et al. 2014), causes habitat loss, and underlies food production (Arneth et al. 2019), so it’s crucial for managing global sustainability issues like climate change, biodiversity loss, and food security (Powers and Jetz 2019). Over the last five decades, the big data revolution, which includes tools for capturing, processing, analysing, and visualising massive datasets in a short amount of time, has resulted in an explosion of data diversity (Runting et al. 2020). Significant advancements in data growth in the bio-geophysical sciences have enabled scientists to detect, analyse, and understand environmental changes at micro to global sizes, separating what is caused by humans from what is not. LULC data are still hampered by fragmented material, various sizes, a dearth of spatial or temporal precision, and unreliable time series, even in the age of satellites, ‘big data,’ and a growing tendency of extending access to information. Land cover (the biophysical features of a land surface, such as grassland) is covered by satellite remote sensing, which has a high geographical resolution but short temporal coverage (Pongratz et al. 2018).

1 Big Data Analysis for Sustainable Land Management on Geospatial …

5

Spatial data sets that exceed the capabilities of typical computer systems are referred to as geospatial big data. They’ve always been large data, and the volume of such data is increasing year after year. The relevance of automated geographic knowledge discovery is highlighted by the increasing growth and widespread use of geospatial data. In a geographical reference system, position coordinates are frequently used in geospatial data to represent how objects and entities relate to geographic space. Geospatial data refers to these things and objects. Existing data mining approaches, which are beyond storage capacity, make it difficult to evaluate large volumes of geographical data. The process of discovering fascinating and previously unknown spatial patterns is defined as geospatial data science. Conventional data mining techniques for extracting geographical distribution are limited by the complexity of spatial data sets. Geographic data mining, spatial machine learning, and spatial statistics are all part of geospatial data science. Because vast amounts of spatial data have been acquired in diverse applications from remote sensors, geospatial data science has recently become an extremely demanding science. The massive development of geographic data and extensive usage of spatial databases need automated spatial knowledge discovery (Saah et al. 2019). Land use and Land cover data is used in national development plans to evaluate changes in a country’s natural capital, which is then used to determine budget priorities and allocations. Land cover maps also serve as the foundation for hydrology models that governments employ to assess flood risk and readiness in order to enhance climate change resilience (Tolentino et al. 2016). Foresters use land cover maps to create sustainable agricultural management policies, incorporate biodiversity conservation, and participate in climate finance approaches like Reducing Emissions from Deforestation and Forest Degradation and the Role of Conservation, Sustainable Forest Management, and Forest Carbon Stock Enhancement in Developing Countries (Potapov et al. 2019).

1.2 Land Resource Monitoring Using Satellite Data: Prospects and Challenges Remote sensing technology has become one of the most effective technologies for surveying the Earth at local, regional, and global spatial scales over the last five decades. The non-destructive nature of these space-based studies enables for quick assessment of the ambient atmosphere, its underneath surface, and the ocean mixed layer (Dubovik et al. 2021). Satellite instrumentation can also study Inhospitable or in-accessible terrainwithout endangering humans or equipment. Comprehensive (but sparse) field observations are supplemented by contiguous satellite observations, which provide information of unparalleled volume and content for theoretical modelling and data integration (Kuemmerle et al. 2013). The architecture of

6

G. S. Bhunia and P. K. Shit

Fig. 1.1 Timeline of selected technological changes; EO—Earth Observation; GPS—Global Positioning System, GNSS—Global Navigational Satellite System

satellite sensors was frequently highly target-specific in the early stages of observational satellite development. For example, Landsat and the Advanced Very HighResolution Radiometer (AVHRR) instruments, were debuted in the 1970s to measure land surfaces and clouds, while the Total Ozone Mapping Spectrometer (TOMS) instruments observed total column ozone and the High-resolution Infrared Radiation Sounder (HIRS) instruments endorsed weather forecasting and climate monitoring (Dubovik et al. 2021). One of the most significant advantages of satellite remote sensing is the capacity to examine large areas of the Earth without taking much time. There is a need to investigate the synergy of complimentary observations because no single sensor can offer extensive information on a targeted object in a complicated environment. Several natural phenomena with higher temporal and spatial variability are not fully caught by polar orbiting imagers in Low Earth Orbit (LEO), which attain global coverage in a minimal level of one day (but usually two or more days). This constraint is addressed by high-orbit geostationary observations (GEO), which provide many daily views of the same target (Kim et al. 2020). Therefore, there is a trade-off between spatial coverage and satellite imageresolution (usually higher coverage results in lower spatial resolution). For several applications, obtaining observations with both large geographic-temporal coverage and high spatial resolution is required, but it is also quite difficult (Fig. 1.1). While satellite observations have demonstrated their excellent capabilities, the latest evidence produced by our satellite equipment has inadequate information content for several situations. Multi-Angular Polarimeters (MAPs) have long been considered as the best data source for measuring precise columnar features of atmospheric aerosol and cloud (Stamnes et al. 2018). Numerous advanced polarimetric sorties are designed to be implemented by European and US space agencies in the coming years, including 3MI (Multi-View Multi-Channel Multi-Polarization Imaging mission) on MetOp-SG satellite (Fougnie et al. 2018), Multi-Angle Imager for Aerosols (MAIA) instrument (Diner et al. 2018), Spex (Spectropolarimeter for Planetary Exploration) and Hyper-Angular Rainbow Polarimeter (HARP) as part of NASA PACE mission (Werdell et al. 2019), Multi Spectral Imaging Polarimeter (MSIP)/Aerosol-UA (Milinevsky et al. 2019), MAP instruments as part of Copernicus CO2 M Mission (Janssens-Maenhout et al. 2020). The MAI/TG-2, CAPI/ TanSat, DPC/GF-5, and SMAC/GFDM are among the polarimetric remote sensing

1 Big Data Analysis for Sustainable Land Management on Geospatial …

7

instruments that CNSA has successfully launched, with the POSP, PCF, and DPC-Lidar to follow in the future years (Dubovik et al. 2014). Similar to this, it is anticipated that there will be more satellite-based lidars and radars since active remote sensing equipment offer detailed data on the vertical variability of the atmosphere (Chand et al. 2008; Dubovik et al. 2021). Almost all of the main space agencies are working on space-based lidar projects. For instance, NASA launched the Geoscience Laser Altimeter System (GLAS) onboard the ICESat satellite in 2003 (Kwok et al. 2004), the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the CALIPSO satellite in 2006 (Winker et al. 2007), the CloudAerosol Transport System (CATS) onboard the International Space Station in 2015 (Yorks et al. 2016). In addition, the ESA started up the Aladin wind lidar onboard the Aeolus satellite as part of the Living Planet Program (LPP) in 2018 (Källén 2008), the CNSA will release the DPC-Lidar onboard the CM-1 satellite in 2021 (Dubovik et al. 2014), and the joint European/Japanese EarthCARE satellite (due to launch in 2023) will include high-performance lidar and radar technology that has never (Illingsworth et al. 2015). Despite recent advancements, recording global land use intensity continues to face significant obstacles. First, fine-scale land use intensity data with worldwide coverage, especially for grazing and forestry systems, is still not widely available (Fritz et al. 2011). Statistical data is frequently only available at the national level, continuous ground-based data gathering only covers a few locations, and remote sensing tends to catch the spectrum impacts of land use intensity variations, which are often modest. Unfortunately, data gaps are more prevalent in emerging nations, many of which suffer fast land use change and are regarded to have significant potential for increasing land-based productivity (Bouwman et al. 2011). Second, existing datasets are frequently contradictory in time (for example, due to changes in survey methodology or data processing), space (for example, due to changes in political boundaries), or map legends, necessitating significant harmonisation operations. Third, emerging land use intensity metrics are frequently ambiguous (e.g., due to positional inaccuracy, unreliable input data, or processing algorithm constraints), as evidenced by large discrepancies between alternative worldwide cropland extent maps or fertiliser application maps, and are primarily unquantified due to a shortage of formal validation (Dendoncker et al. 2008). Fifth, worldwide datasets are often imprecise, resulting in significant bias in area estimations or data downscaling (Ozdogan and Woodcock 2006). Finally, there are significant conceptual issues in framing land use intensity worldwide (Erb et al. this issue).

8

G. S. Bhunia and P. K. Shit

1.3 Open Geospatial Software and Data for Land Resource Management Knowledge is the first and highest level of implementation for the theory and practice of openness. “Knowledge is open if anybody is free to access, use, alter, and distribute it—subject, at most, to procedures that ensure provenance and openness,” according to the Open Knowledge Foundation (Open Knowledge Foundation 2019). The idea of open-source software development is based on a community of developers working together on a software product. Three major international standards development groups have typically generated open standards for geographic information: the International Hydrographic Organization (IHO), the International Organization for Standardization (ISO), and the Open Geospatial Consortium (OGC). Several of the geographic information standards are based on general-purpose IT standards produced by the Internet Engineering Task Force (IETF) and the World Wide Web Consortium (W3C). Several software developers now contribute to opensource geospatial software as part of their employment, which used to be done without any legal commitment or financial recompense. This method can promote innovation by reducing the constraints that typically restrict proprietary computer operating systems and software products, including software licencing prices. Numerous desktop systems are also available on web or mobile platforms (see, for example, QGIS Ecology, or gvSIG mobile and gvSIG Online) and/or are utilised as geospatial processing back-ends for web-based or cloud-based services (e.g., GRASS GIS, QGIS) (Table 1.1). There are rapidly growing geospatial software development projects affiliated with open-source data science languages, analysis and simulation platforms, virtual reality engines, and web services as geospatial data and tools become ubiquitous across scientific disciplines, industries, governments, and communities. “R” has lately risen to prominence as one of the most popular open-source data science languages in remote sensing and geospatial science, thanks to its well-established capability for georeferenced data processing and wide collection of spatial analytic capabilities. To Table 1.1 Open-source geospatial foundation for Land resource management Type

Project

Geospatial libraries

GDAL/OGR, PROJ, GEOS, GeoTools, Orfeo ToolBox degree, GeoMoose, GeoServer, Mapbender, GeoMapFish

Web mapping GIS

MapServer, OpenLayers, PyWPS

Spatial database

PostGIS

Desktop GIS

GRASS GIS, gvSIG Desktop, Marble, QGIS Desktop

Metadata catalogues, Content management system

GeoNetwork, GeoNode, OSGeoLive, pycsw

Modified after Coetzee et al. (2020)

1 Big Data Analysis for Sustainable Land Management on Geospatial …

9

facilitate interoperability between pertinent libraries, projects, and packages, the R and OSGeo communities worked very closely. Python is the most popular scripting and programming language for geospatial software, both private and open source, encompassing multiple OSGeo projects (https://www.osgeo.org/resources/projectgraduation-checklist/). Meanwhile, separate geospatial projects based on Python are increasing dramatically, such as GeoPandas (http://geopandas.org/index.html), the spatial statistics package PySAL, and the landscape simulation libraries landlab, to mention a few (Table 1.2). Point cloud data processing with PDAL and related on-line point cloud visualisation plas.io, along with drone data processing with OpenDroneMap, are among the relatively new libraries and systems that offer 3D mapping and modelling. WebODM (https://www.opendronemap.org/webodm/) is a cloud-based platform for processing and analysing Drone footage and derived 3D models that includes many open-source geospatial software tools. Spatio-Temporal Asset Catalog (STAC) is a communitydriven catalogue endeavour based on JSON. OpenEO (https://openeo.org/) is an example of an “open application programming interface (API) to connect R, python, javascript, and other clients to huge Earth observation cloud back-ends in a simple and consistent way.” Geoscience computing is also assisted by JuliaGeo projects (https://juliageo.org/), a data science language. Geospatial aspects can be found in several open-source agent-based models (CoMSES Network; https://www.comses. net/). Open-source geospatial solutions will be compared to closed source goods, and as the open-source equivalents platform and their functionality improves, justifying the upfront investment in pricey software licences will become increasingly challenging. QGIS is an example of an open-source solution that is rapidly expanding in terms of functionality while also consolidating and stabilising the product. As firms transition from a product-based business strategy to a value-added and service-based business model, more companies that previously favoured “closed source software development” will publish their code as open source. Because there would be no need to persuade customers to pay for licences in advance, the focus will shift away from sales-pitch capability and toward functionality that meets actual and unique user needs. Another noteworthy development is that some open-source geospatial software is increasingly serving as the underlying software architecture for both Table 1.2 Geospatial software development components and support for general open-source scientific computing platforms and current data science languages Platform/Language

Geospatial components/Applications/Libraries

R

Spatial and spatio-temporal packages

Python

GeoPython, GeoPandas, PySAL, Landlab

Javascript

Leaflet, D3, MapBox, NodeJS, Cesium, plas.io, Potree

Blender

Blender for GIS

Modified after Coetzee et al. (2020)

10

G. S. Bhunia and P. K. Shit

open source and closed source products. For managing geographic data and coordinate reference systems, the GDAL and PROJ libraries, as well as PostGIS and SpatiaLite for data storage have been used. Several researchers intend to migrate to a hybrid paradigm in which open-source solutions serve some demands while closed source products address others. The majority of researchers intend to participate to open-source geospatial software solutions in the future in a variety of ways, including directly or indirectly (e.g., through software development funding); building capacity around the use of open-source geospatial software in their organisations; being deeply involved as users and decision makers of open solutions; or encouraging the sharing of prototype code or geospatial software solutions.

1.4 Smart Land Resource Monitoring and Assessment With a fundamental move away from fixed products and toward on-demand manufacturing of user-specified and configurable solutions, cloud-based platforms will revolutionise the way we retrieve and handle geographic data. Making images directly accessible to users is a major changer: rather than downloading pre-packaged image products, users may modify products to meet their own unique needs. On the technological front, this has already resulted in the creation of cloud interoperability standards such as cloud optimised GeoTIFFs, Zarr (used by the UK Met Office to store massive amounts of meteorological data), the STAC for metadata on Earth Observation data, and GeoJSON. The generation and preservation of geospatial datasets will be accelerated by improved processing power and improvements in the efficiency of algorithms for feature extraction from imagery gathered by satellites and unmanned aerial vehicles. This is especially promising for underdeveloped countries and isolated places where field data is either unavailable or impossible to obtain. Furthermore, this will decrease the requirement for time-consuming field data collection. The rapid generation of such geospatial information opens up a world of possibilities for innovation, including better public service, scientific breakthroughs, and new economic prospects, as well as helping emerging countries compete in the global geospatial arena. Some geographic data owners are still hesitant to share their data openly, particularly when it becomes accessible across national borders. In the future, the threat of losing control, which is frequently highlighted as a barrier to data sharing, will also need to be overcome. As a result, advocacy for the formulation and management of spatial data infrastructures that make open geospatial data discoverable, accessible, interoperable, and reusable on a national and global scale will continue to be a top priority. Furthermore, open standards will continue to be critical in supporting the interoperability of massive volumes of open geospatial data. Conversely, for the application of open geospatial standards, capacity building has been recognised as a major challenge. Geospatial software and data will impact each other in a variety of ways in the future. The use of geographically solutions providers will expand

1 Big Data Analysis for Sustainable Land Management on Geospatial …

11

in the future, and the change from analysing discrete data sets to software that can analyse streams of geographically enabled data (e.g., real-time location-based mobile services) will continue. This is in keeping with the current movement towards FAIR, transparent, traceable, and repeatable science, which, as some earth science pioneers have demonstrated, is not just desirable but also feasible. Several machine learning techniques for land use classification and simulation of land use planning procedures have been tested on various datasets (Samardži´c et al. 2017). Support vector machine, neural network, Markov random field, GANS, and random forest are some of the most prominent algorithms. There is continuing research into new machine learning approaches for taking land use mapping to a higher level. Support vector machines (SVMs) have been used in a number of research articles, and their performance in land use classification has been compared to that of other machine learning methods such as random forest (RF) and neural networks (Duque et al. 2017). SVMs are a class of non-parametric machine learning methods. SVMs’ main function is to create a separation hyperplane (i.e., a decision boundary) based on the training samples’ attributes, specifically their distribution in feature space (Table 1.3). Aside from SVM and RF, deep learning methods are another ML approach that has been frequently used for land use classification. Hinton et al. (2012) introduced deep learning in 2006.More research should focus on analysing the performance of empirical data sets and employing algorithms to examine their usefulness for investigating aspects of land use planning. Experiments on land use categorization using deep learning, SVM, random forest, and GANS should assess each algorithm’s overall classification accuracy. Experiments using spectral, texture, structural, and contextual image features should also be conducted to increase classification accuracy. To acquire the best effects in categorization, modelling, and transition of urban land use growth, change, and transition, the hybrid approach’s performance should be evaluated. To take full advantage of real-time decision making, recent technological advancements necessitate real-time spatial data processing, analytics, and visualisation. Following a thorough investigation and analysis of the preceding literature, there are a number of challenges in the processing and analysis of geospatial big data. As a result, this study introduces a novel geospatial Big Data analytics and processing framework for geospatial data gathering, fusion, storage, management, processing, analysis, visualisation, and modelling. Geospatial big data can be found in a variety of formats, shapes, and sizes all around us. Understanding the importance of each of these data types to the business problem is critical to the project’s success. In addition, there are several levels of hidden complexity in geospatial large data that are not obvious by merely inspecting it. We will only focus on creating a large data ecosystem for geospatial data mining and machine learning in this section. In which data can be managed in a distributed environment in order to store large amounts of data. The ability to deal with geospatial data on a large scale is provided by the big data ecosystem for evaluating geospatial data (Fig. 1.2).

12

G. S. Bhunia and P. K. Shit

Table 1.3 Use of machine learning algorithm in land use planning Goals

Data used

Principle method

Major output

References

Land use change modelling

Landsat TM, ETM+, Transportation data, Elevation data, Infrastructure data

Support vector machine

Improve the accuracy and reliability of land use change modelling; The dataset is significantly unbalanced

Xie (2006)

Spatial dynamics Landsat TM and landscape transformation

Cross-tabulation; Logistic regression; CLUE-S regional modelling framework

To simulated Batisani and urban land use Yarnal (2009) location at country level

Urban growth pattern

Population data, Agriculture, Industrial data, Topographic map, DEM

Logistic regression

Vector feature-based analysis; Less demand of computational resources

Urban growth modelling

Landsat image, MachCA model Topographic whichis cellular map automata; Least squares support vector machines

Mastery of the Feng et al. (2016) required mathematical and technical skills for its implementation, as well as an awareness of the mechanisms of urban dynamics

Modelling urbanization pattern

Global training GANs samples of urban footprints

To create realistic metropolitan patterns that reflect the wide range of urban forms

LULC classification

Sentinel-2

Maximum Abdi et al. (2020) classification accuracy produced by SVM, followed by Xgboost, and RF

SVM, RF, Xgboost, and DL

Nong and Du (2011)

Albert et al. (2018)

(continued)

1 Big Data Analysis for Sustainable Land Management on Geospatial …

13

Table 1.3 (continued) Principle method

Major output

Recurrent neural Sentinel-2 network for land use classification

DL; Image classification; Anomaly detection

Understanding Campos-Taberner models to combat et al. (2020) climate change, maintain biodiversity and ecosystems, and secure a fair financial return for farmers are all important

Analysis of land use and land cover

SVM, RF, CART, RF classifiers Loukika et al. LULC, NDVI, outperform both (2021) NDWI SVM and CART classifiers in terms of accuracy

Goals

Data used

Sentinel-2, Landsat-8

References

Note Extreme gradient boosting (Xgboost), deeplearning (DL); Generative adversarial networks (GANs); CART-classification and regression trees, NDVI-normalized difference vegetation index, NDWI-normalized difference water index Modified after Chaturvedi and de Vries (2021)

Fig. 1.2 Proposed geospatial big data mining and machine learning frame work

14

G. S. Bhunia and P. K. Shit

1.5 The Land Sustainability Agenda and Big Data Big data analyses are definitely necessary for exposing changes in the Earth’s ecology and its ability to support people. However, these great advancements will not assist the world or its inhabitants unless we aim to achieve sustainability goals, therefore big data must be integrated with current sustainability initiatives. Consequently, many national and international policy processes are still lacking in this regard. Despite international consensus on the Aichi Biodiversity Targets (which serve as the strategic plan for the Convention on Biological Diversity), many countries are failing to use big data and related goods to fulfil these objectives. Despite the fact that several spatial data layers required for achieving the Aichi Targets exist at the national or global level, 80% of the 5th round of National Reports on these targets lacked actionable maps (Ervin et al. 2017). In most circumstances, a map of landscape events needs to be further analysed and combined with other datasets before it can be used to effectively guide decisions, which may necessitate additional resources (technical and financial) that are difficult to obtain in some less-developed regions.

1.6 Way Finding With the advancement of improved remote sensing and communication technology, alternative sources of geospatial data emerged in a variety of industries, including transportation, utility, and others. The use of these new types of geospatial data is increasing at a rapid rate. Many initiatives have been undertaken by academic and commercial researchers to improve the value of geospatial big data and to make major use of its worth through data science. For every geospatial data science project, having a robust process for data mining and machine learning, as well as clear guidelines, is always a benefit. It also aids in concentrating essential time and resources early in the process in order to gain a comprehensive understanding of the business challenge to be resolved. As a result, the framework is offered to assist in the lifecycle of geospatial data science projects and to bridge the gap between business needs and technical constraints. While the old concept of sustainable land resource management centred on implementing new methods that address ecosystem services, this new, technology-oriented sustainable land study shifts the focus from site-specific management to global sustainability. To facilitate this shift, we introduced the geospatial framework as an organising concept that connects smart land use, a local, site-specific data generator, to a regional and global picture of land resources that may help both land use planners and policymakers in government. Automation, AI, IoT, drones, multispectral and hyperspectral satellites, and Big Data serve as the foundation for “Digital Twins,” which could enable for virtual simulations of novel ideas to determine land

1 Big Data Analysis for Sustainable Land Management on Geospatial …

15

use impact before being implemented in the real world. To put it another way, developing new habits in the virtual world will minimise the time it takes to implement new practises that improve environmental results.

References Albert A, Strano E, Kaur J, Gonzalez M (2018) Modeling urbanization patterns with generative adversarial networks. arXiv:1801.02710 Arneth A, Brown C, Rounsevell MDA (2014) Global models of human decision-making for landbased mitigation and adaptation assessment. Nat Clim Change 4:550–557 Arneth A et al (2019) Framing and context in climate change and land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems (ed Shukla PR et al) Chap. 1 (IPCC 2019) Batisani N, Yarnal B (2009) Urban expansion in centre county, Pennsylvania: spatial dynamics and landscape transformations. Appl Geogr 29:235–249 Bouwman L, Goldewijk KK, Van Der Hoek KW, Beusen AHW, Van Vuuren DP, Willems J, Rufino MC, Stehfest E (2011) Exploring global changes in nitrogen and phosphorus cycles in agriculture induced by livestock production over the 1900–2050 period. Proc Natl Acad Sci U S A. https:// doi.org/10.1073/pnas.1012878108 Campos-Taberner M, García-Haro FJ, Martínez B et al (2020) Understanding deep learning in land use classification based on Sentinel-2 time series. Sci Rep 10:17188. https://doi.org/10.1038/ s41598-020-74215-5 Cao C, Dragi´cevi´c S, Li S (2019) Land-use change detection with convolutional neural network methods. Environments 6:25 Chand D, Anderson T, Wood R, Charlson R, Hu Y, Liu Z et al (2008) Quantifying above-cloud aerosol using spaceborne lidar for improved understanding of cloudy-sky direct climate forcing. J Geophys Res 113:D13206. https://doi.org/10.1029/2007JD009433 Chaturvedi V, de Vries WT (2021) Machine learning algorithms for urban land use planning: a review. Urban Sci 2021(5):68. https://doi.org/10.3390/urbansci5030068 Coetzee S, Ivánová I, Mitasova H, Brovelli MA (2020) Open Geospatial software and data: a review of the current state and a perspective into the future. ISPRS Int J Geo-Inf 9:90. https://doi.org/ 10.3390/ijgi9020090 Dendoncker N, Schmit C, Rounsevell M (2008) Exploring spatial data uncertainties in land-use change scenarios. Int J Geogr Inf Sci 22:1013–1030 Diner DJ, Brauer M, Bruegge C, Burke KA, Chipman R, Di Girolamo L et al (2018) Advances in multiangle satellite remote sensing of speciated airborne particulate matter and association with adverse health effects: from MISR to MAIA. J Appl Remote Sens 12:042603. https://doi.org/ 10.1117/1.JRS.12.042603 Dubovik O, Lapyonok T, Litvinov P, Herman M, Fuertes D, Ducos F et al (2014) GRASP: a versatile algorithm for characterizing the atmosphere. SPIE, Newsroom. https://doi.org/10.1117/2.120 1408.005558 Dubovik O, Schuster GL, Xu F, Hu Y, Bösch H, Landgraf J, Li Z (2021) Grand challenges in satellite remote sensing. Front Remote Sens Sec Satellite Missions. https://doi.org/10.3389/frsen.2021. 619818 Duque JC, Patino JE, Betancourt A (2017) Exploring the potential of machine learning for automatic slum identification from VHRimagery. Remote Sens 9:895 Ervin J et al (2017) Are We Counting on Nature? An Analysis of Spatial Data included in Post-2010 National Biodiversity Strategies and Action Plans and 5th National Reports (UNDP 2017)

16

G. S. Bhunia and P. K. Shit

Feng Y, Liu Y, Batty M (2016) Modeling urban growth with GIS based cellular automata and least squares SVM rules: a case study in Qingpu–Songjiang area of Shanghai, China. Stoch Environ Res Risk Assess 30:1387–1400 Fougnie B, Marbach T, Lacan A, Lang R, Schlüssel P, Poli G et al (2018) The multi-viewing multichannel multi-polarisation imager–overview of the 3MI polari-metric mission for aerosol and cloud characterization. J Quant Spectrosc Radiat Transf 219:23–32. https://doi.org/10.1016/j. jqsrt.2018.07.008 Fritz S, See L, McCallum I, Schill C, Obersteiner M, Velde MVD, Boettcher H, Havlík P, Achard F (2011) Highlighting continued uncertainty in global land cover maps for the user community. Environ Res Lett 6:044005 Illingsworth A, Barker H, Beljaars A, Ceccaldi M, Chepfer H, Clerbaux N et al (2015) The earthcare satellite: the next step forward in global measurements of clouds, aerosols, precipitation, and radiation. Bull Am Meteorol Soc 96:1311–1332. https://doi.org/10.1175/BAMS-D-12-00227.1 Janssens-Maenhout G, Pinty B, Dowell M, Zunker H, Andersson E, Balsamo G et al (2020) Towards an operational anthropogenic CO2 emissions monitoring and verification support capacity. Bull Am Meteorol Soc 101:E1439–E1451. https://doi.org/10.1175/BAMS-D-19-0017.1 Kim J, Jeong U, Ahn M-H, Kim JH, Park RJ, Lee H et al (2020) New Era of air quality monitoring from space geostationary environment monitoring spectrometer (GEMS). Bull Am Meteorol Soc 101(1):E1–E22. https://doi.org/10.1175/BAMS-D-18-0013 Kuemmerle T, Erb K, Meyfroidt P, Müller D, Verburg PH, Estel S, Haber H, Hostert P, Jepsen MR, Kastner T, Levers C, Lindner M, Plutzar C, Verkerk PJ, der Zanden EH, Reenberg A (2013) Challenges and opportunities in mapping land use intensity globally. Curr Opin Environ Sustain 5(5):484–493 Kwok R, Zwally HJ, Yi D (2004) ICESat observations of Arctic sea ice: a first look. Geophys Res Lett 31:L16401. https://doi.org/10.1029/2004GL020309 Källén E (2008) Special issue with manuscripts related to ESA’s atmospheric dynamics mission/ Aeolus. Tellus Dyn Meteorol Oceanogr 60(2):189–190. https://doi.org/10.1111/j.1600-0870. 2007.00296.x Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444 Loukika KN, Keesara VR, Sridhar V (2021) Analysis of land use and land cover using machine learning algorithms on google earth engine for Munneru River Basin, India. Sustainability 13:13758. https://doi.org/10.3390/su132413758 Milinevsky G, Oberemok Y, Syniavskyi I, Bovchaliuk A, Kolomiets I, Fesyanov I et al (2019) Calibration model of polarimeters on board the Aerosol-UA space mission. J Quant Spectrosc Radiait Transf 229:92–105. https://doi.org/10.1016/j.jqsrt.2019.03.007 Nong Y, Du Q (2011) Urban growth pattern modeling using logistic regression. Geo-Spat Inf Sci 14:62–67 Open Knowledge Foundation. The Open Definition. Available online: http://opendefinition.org/. Accessedon 19 Oct 2019 Ozdogan M, Woodcock CE (2006) Resolution dependent errors in remote sensing of cultivated areas. Remote Sens Environ 103:203–217 Pongratz J et al (2018) Models meet data: challenges and opportunities in implementing land management in Earth system models. Glob Change Biol 24:1470–1487 Potapov P, Tyukavina A, Turubanova S, Talero Y, Hernandez-Serna A, Hansen M et al (2019) Annual continuous fields of woody vegetation structure in the lower mekong region from 2000– 2017 landsat time-series. Remote Sens Environ 232:111278. https://doi.org/10.1016/j.rse.2019. 111278 Powers RP, Jetz W (2019) Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat Clim Chang 9:323–329 Runting RK, Phinn S, Xie Z et al (2020) Opportunities for big data in conservation and sustainability. Nat Commun 11:2003. https://doi.org/10.1038/s41467-020-15870-0

1 Big Data Analysis for Sustainable Land Management on Geospatial …

17

Saah D, Tenneson K, Matin M, Uddin K, Cutter P, Poortinga A, Nguyen QH, Patterson M, Johnson G, Markert K, Flores A, Anderson E, Weigel A, Ellenberg WL, Bhargava R, Aekakkararungroj A, Bhandari B, Khanal N, Housman IW, Potapov P, Tyukavina A, Maus P, Ganz D, Clinton N, Chishtie F (2019) Land cover mapping in data scarce environments: challenges and opportunities. Front Environ Sci 7:150. https://doi.org/10.3389/fenvs.2019.00150 Samardži´c M, Kovaˇcevi´c M, Bajat B, Dragi´cevi´c S (2017) Machine learning techniques for modelling short term land-use change. ISPRS Int J Geo-Inf 6:387 Stamnes S, Fan Y, Chen N, Li W, Tanikawa T, Lin Z et al (2018) Advantages of measuring the Q stokes parameter in addition to the total radiance I in the detection of absorbing aerosols. Front Earth Sci 6:34. https://doi.org/10.3389/feart.2018.00034 Tolentino PL, Poortinga A, Kanamaru H, Keesstra S, Maroulis J, David CPC et al (2016) Projected impact of climate change on hydrological regimes in the philippines. PLoS ONE 11:e0163941. https://doi.org/10.1371/journal.pone.0163941 Werdell PJ, Behrenfeld MJ, Bontempi PS, Boss ES, Cairns B, Davis GT et al (2019) The plankton, aerosol, cloud, ocean ecosystem (PACE) mission: status, science, advances. Bull Am Meteorol Soc 100:1775–1794. https://doi.org/10.1175/BAMS-D-18-0056.1 Winker DM, Hunt WH, McGill MJ (2007) Initial performance assessment of CALIOP. Geophys Res Lett 34:L19803. https://doi.org/10.1029/2007GL030135 Xie C (2006) Support vector machines for land use change modeling. UCGE Reports 20243. Available online: http://www.ucalgary.ca/engo_webdocs/BH/06.20243.ChenglinXie.pdf. Accessed 12 June 2020 Yorks JE, McGill MJ, Palm SP, Hlavka DL, Selmer PA, Nowottnick EP et al (2016) An overview of the CATS level 1 processing algorithms and data products. Geophys Res Lett 43:4632–4639. https://doi.org/10.1002/2016GL068006

Chapter 2

Assessing of LULC and Climate Change in Kolkata Urban Agglomeration Using MOLUSCE Model Satiprasad Sahoo and Suprakash Pan

Abstract Land Use and Climate change are interrelated to each other. Understanding the response of land use/land cover (LULC) change and climate change over highly urbanised area has become a priority issue for contemporary urban planning and management. This present study aimed to assess the LULC changes and predict future trends with relation to changing climate scenario at micro scale in Upper Catchment Area of Bagjola Canal, located within the Kolkata Urban Agglomeration. Land Use Change Simulations (MOLUSCE) model of Quantum GIS (QGIS) environment has been used here to detect land use/land cover changes. Future generated data from Model for Interdisciplinary Research on Climate 5 (MIROSC5) model is used for climate change analysis with the Coupled Model Intercomparison Project 5 (CMIP5). Representative Concentration Pathways (RCPs) 2.6, 4.5, 6 and 8.5 data are utilized for urban climate change impact on urban LULC. The analysis revealed that substantial growth of built-up areas in study area over the study period (1980– 2028) resulted significant decrease in the area of water bodies, wetlands and open vacant land. However, the results show that urban sprawl direction is North and South-West to North and South-East. RCPs data revealed that study area will be more wormer and temperature will reach above 40 ºC. Increased rainfall intensity and number of extreme rainfall events will affect the geo-environment of the study area adversely. The impacts of climate change are more significant in study area. The outcomes of this study may help in developing sustainable urban planning and management policy, as well as assist authorities in making informed decisions to improve environmental and hydro-ecological conditions of Upper Catchment Area of Bagjola Canal which will increase the quality of life in an urban agglomeration environment. Keywords Urbanization · Climate change · LULC change · MOLUSCE · QGIS S. Sahoo (B) GeoAgro, International Center for Agriculture Research in the Dry Areas (ICARDA), Cairo 11728, Egypt e-mail: [email protected] S. Pan Department of Geography, Haringhata Mahavidyalaya, Nadia, West Bengal 741249, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_2

19

20

S. Sahoo and S. Pan

2.1 Introduction Land use/Land cover (LULC) change and climate variability are important environmental components influencing water resource management and different socioeconomic aspects of urbanization process. Urban growth and development caused by urbanization also influence the urban microclimate. Ali et al. (2019) observed that 65% of urbanization will be occurred in developing countries by 2050. However, the highest rate of urbanization is projected of Asia and Africa from 2000 to 2030.With the rapid urban development, India’s major cities are facing more serious climate change problems such as additional infrastructure, informal settlements, water logging, traffic congestions, environmental pollution, ecological degradation and scarcity of natural resources. The use of remote sensing technology in conjunction with a Geographic Information System (GIS) has proven to be effective in identifying a variety of environmental characteristics such as vegetation cover, urban sprawls, forest changes and particularly variations in LULC changes over time. Kayet et al. (2016) evaluated urban heat island based on Land Surface Temperature (LST), LULC and multiple vegetation indices using thermal remote sensing at an urban-microclimatic scenario. Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Ratio Vegetation Index (RVI) and Normalized Difference Built-Up Index (NDBI) are used for the identification of environmental risk areas. Mohamed and Worku (2018) quantified LULC dynamics using statistical modelling techniques for sustainable urban land use planning in Addis Ababa and the surrounding zone. They concluded that the role of built-up dynamic is the main controlling factor for urban sprawling. Richard et al. (2018) demonstrated spatio-temporal patterns of surface and urban canopy of heat islands based on LULC data. Climate model used here to identify thermally coherent intra-urban areas. Patra et al. (2018) identified the impact of urbanization on LULC changes based on hydro-meteorological parameters under micro-climatic zone. It is observed that the Normalized Difference Built-Up Index (NDBI) is utilized for quantification of the urban scape of Howrah Municipal Corporation (HMC) in the Indian state of West Bengal, India. Mascarenhas et al. (2019) studied possible consequences of alternative pathways of residential urban development and their effects on land take and ecosystem services in the Lisbon Metropolitan Area, Portugal. This research highlighted both positive and negative effects on the supply of ecosystem services for the compact city model of urban development and planning. Shukla and Jain (2019) analysed rural–urban transitions critically based on a sustainable model using LULC data of the year 1995–2016 in order to measure the extent and growth of urbanization in the Lucknow city, India. Pixel wise urban landscape quantification performed here according to spatial built-up densities. Saxena and Jat (2019) used cellular automate based SLEUTH model by 22 years of multi-spectral remote sensing data for capturing heterogeneous urban growth of Pushkar Town in India. It is a selfmodifying parameter based model for identifying and controlling the temporal urban growth during calibration and validation time periods. Sejati et al. (2019) predicted LULC changes based on Markov chain and stochastic cellular automata model using

2 Assessing of LULC and Climate Change in Kolkata Urban …

21

Landsat satellite data for 20 years of 1998–2018 and 2018–2038 in the Semarang Metropolitan Region (SMR), Indonesia. It is noted that the Gaussian mixture method and supervised classification techniques are used for LULC classification. Most of the research was focused on the future prediction of LULC change using spatiotemporal satellite data in rural and urban areas. No study is available for future prediction of LULC using MOLUSCE of QGIS interface with the correlation between future RCPs 2.6, 4.5, 6 and 8.5 climate data. In this study, a methodology is proposed for the impact of climate change on land use/land cover (LULC) change based on MOLUSCE Model from QGIS Interface ofUpper Catchment Area of Bagjola Canal within Kolkata Urban Agglomeration. It is a new plug-in for pixel by pixel land change modelling. This research work mainly emphasizes on spatiotemporal urban LULC change based on multi-temporal multi-resolution satellite data under the local climatic condition.

2.2 Materials and Methods 2.2.1 Study Area The area considered for this study is the Upper Catchment Area of Bagjola Canal located within the Kolkata Urban Agglomeration and also a part of the area covered by Kolkata Metropolitan Development Authority (KMDA). Kolkata, the city of Joy is a 300-year old city located in the Eastern part of India. This city spread along the bank lines of the Hooghly River. The Bagjola is a major canal in Kolkata Municipal Corporation (KMC) of the Indian State of West Bengal. It is originated in the swamps of Ariadaha, Dakshineshwar, it continues as a narrow ditch till it reaches South DumDum and meets the Kulti-Bidhyadhari River (Bhattacharjee 2014). The Bagjola canal takes the load of drainage water of South Dum Dum, Panihati, Branagan, Kamarhati and North Dum Dum municipalities. It has been cemented in sections till it finally meets the VIP Road near Krishnapur. Beyond the VIP Road, the Bagjola moves into the Rajarhat area, outside the urban limit. It plays significant role in irrigating the areas in the adjoining agricultural lands in the eastern marshes. The geo-environmental settings of Bagjola Canal are not healthy at all. Overflowing water during storm rainfall and pollution are the major concern. State Irrigation Department, Govt. of West Bengal is planning for the improvement of old Bagjola canal up to South 24 Parganas through Bangor. In the present study only Upper Catchment Area of Bagjola Canal has been taken into consideration which is mostly covered by South and North Dum Dum Municipality. The study area is located on the north eastern part of Kolkata with an average elevation of 9 m. The study area is situated between the latitudes ranging from 22°36’ to 22°41’ N and longitudes ranging from 88°22’ to 88°26’ E (Fig. 2.1). The total area of Bagjola Upper Catchmectis 40.86 Km2 with the population of over 6.5 Lakh

22

S. Sahoo and S. Pan

Fig. 2.1 Study area and validation of LULC map with Google earth images of 2018

(Census 2011). The area is densely populated. About 30% of the population resides at Slums. It has a tropical climate (Aw). Temperature ranges between 18 and 30 °C, with an annual mean temperature of 26 °C. The annual rainfall of Upper Catchment of this Canal is 1600 mm and the most precipitation falls in July. The driest month is December. Humidity reaches more than 80% during June to October.

2.2.2 Data Collection and Processing MOLUSCE is an open-source QGIS based new LULC change evaluation model developed for business purpose. Asia Air Survey Co., Ltd. (AAS) has developed for QGIS 2.0 (Gismondi et al. 2014). It is utilized for LULC change, urban analysis and forest applications purpose. This model required seven main components (Gismondi et al. 2014) i.e. i. Inputs ii. Evaluation Correlation iii. Area Changes iv. Transition Potential Modelling v. Cellular Automata Simulation vi. Validation and vii. Messages. Landsat Multispectral Scanner (MSS), the Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+) and Operational Land Imager (OLI) satellite data are utilized for LULC unsupervised classification purpose during 1980 to 2018 from U.S Geological Survey (https://www.usgs.gov). Iterative Self-Organizing Data Analysis Technique (ISO-DATA) is used for identify the LULC features (Ma et al. 2020). MIROC5 Model data (RCP 2.6, RCP 4.5, RCP 6 and RCP 8.5) are utilized for past, present and future climate change analysis (http://gisweb.ciat.cgiar.org) from AR5

2 Assessing of LULC and Climate Change in Kolkata Urban …

23

Fig. 2.2 Neural network learning curve

data (CMIP5). Future predicted climate data are directly utilized for climate change analysis purposes. MOLUSCE model is used in this study for future urban LULC modelling using geospatial techniques. LULC map of 2000 and 2010 are utilized for future prediction of land use change modelling. Neural network learning curves are depicted through Fig. 2.2. Various spatial variables (e.g. elevation, slope, and road) are input in MOLUSCE modelling before run the check geometry. All input file are converted into same spatial geometry (30 m * 30 m) by GIS environment. Artificial Neural Network (ANN) method is used here. Finally, a Cellular-Automata simulation is used for predicting future LULC map. LULC map of 2018 is used for validation purpose with the help of Google earth images (Fig. 2.1). The results show that percentage of correctness is 98% and overall kappa is 0.94. Overall methodology applied in this study has been depicted through following Fig. 2.3.

2.3 Results and Discussion 2.3.1 LULC Change LULC maps have been prepared on the basis of unsupervised classification technique using Landsat series images. LULC maps have been classified into three main categories i.e. (i) water bodies (ii) open land and (iii) built-up land (Table 2.1). LULC results show that percentage of built-up area increased significantly with the passage of time at the cost of water bodies and open vacant land during 1980– 2018. In 1980, Built-up land is about 34% of the study area, where open vacant land 60.68%, and water bodies stand with 5.08% of the area. After one decade there is a massive change observed in this area and built-up land rapidly increased to reach 51.88% where the major impact of this expansion shown in open vacant land which shrinks from about 60.68–43.26% and water bodies have no major changes and it still stands on 4.84%. In 2000, same tendency observed i.e. built-up land increasing occupying open vacant land. In 2010, after 10 years of gap, there is a major change seen in built-up land and it covered 82.30% and this expansion is in the cost of open

24

S. Sahoo and S. Pan

Fig. 2.3 Overall methodology

Table 2.1 Land use/land cover change statistic of upper catchment area of Bagjola canal Land use/Year

% of area 1980

Water bodies

1990

2000

2010

2018

2028

5.08

4.84

4.4

3.56

2.42

1.19

Open land

60.68

43.26

33.88

14.13

9.02

7.24

Built-up land

34.22

51.88

61.71

82.3

88.54

91.55

vacant land which shrinks from 33.88 to 14.13%. In 2018, the land-use categories like open vacant land which has continued shrink along with the time due to expansion of the built-up area. The CA-ANN simulation was performed to obtain the predicted LULC maps of 2028. Figure 2.4 shows the predicted changes in different LULC classes in study area. The percentages of different LULC classes are illustrated in Table 2.1. The observed outcomes elucidated the steady changes within the study period. Due to the increasing rate of built-up area, diminishing trends for open vacant land were observed in the simulated maps. Wet land and water bodies are in sorry state of affairs

2 Assessing of LULC and Climate Change in Kolkata Urban …

25

Fig. 2.4 LULC classification maps for 1980, 1990, 2000, 2010, 2018 and 2028

which is an unpleasant circumstance for sustainable urbandevelopment. The people who migrated from East Bengal (Bangladesh), interstate and intrastate migrants are settled in that area of Greater Kolkata for employment opportunities, career options and education, where house rent is low or the area that is not much developed but well connected with Kolkata Megacity (Fig. 2.4). These results proved that built-up land continuously increased due to huge population pressure (Safia et al. 2018).

2.3.2 Climate Change Representative Concentration Pathways (RCPs), a set of four new pathways mainly used as a basis for near-term modelling experiments. Figures 2.5, 2.6, 2.7, 2.8 and 2.9 shows the projections of maximum and minimum temperature in the study area under all four RCPs for two future periods; 2025 and 2028. Under RCP2.6, the predicted range of temperature will be 19–36 °C and 23–38 °C for the time periods 2025 and 2028. For the 2028 time period, as expected the temperature anomalies under RCP2.6 are, in general, higher than for the 2025 period. The projections of temperature anomalies are, therefore, on the higher side. The projected

26

S. Sahoo and S. Pan

Fig. 2.5 Rainfall and temperature of 2010 in upper catchment area of Bagjola canal

Fig. 2.6 Rainfall and temperature of 2015 in upper catchment area of Bagjola canal

2 Assessing of LULC and Climate Change in Kolkata Urban …

Fig. 2.7 Rainfall and temperature of 2020 in upper catchment area of Bagjola canal

Fig. 2.8 Rainfall and temperature of 2025 in upper catchment area of Bagjola canal

27

28

S. Sahoo and S. Pan

Fig. 2.9 Rainfall and Temperature of 2028 in upper catchment area of Bagjola canal

range of temperature for winter season was higher than in the monsoon season. Under RCP4.5, the temperature ranged between 19 and 35 °C for the 2025 period. Under RCP6 there was only a slight increase in the projected temperature anomalies for the 2028 period compared to the 2025 period. Under RCP8.5—the most extreme emission scenario—the temperature anomalies for the 2025 time period ranged from 19 to 36 °C. A projected increase of 42 °C can be considered to be significantly higher, and could have important implications for many aspects of the geo-environment around the year 2028. It can be seen that the projections of maximum-minimum temperature under RCP8.5 are the highest, whereas the lowest temperature anomalies have been projected under RCP2.6. This pattern of temperature projections is similar for both the future periods, that is, 2025 and 2028. However, the magnitudes of temperature projections for the 2028 period are higher than for the 2025 period. Under all RCPs scenarios, the results dictated that more extreme localized event of heavy rainfall in the future as all scenarios resulted in increasing amount of rainfall. These showed that for this study area will have lesser rain annually in the future due to the effects from increase of emission radiation. Based on the low emission scenario (RCP2.6), the results showed an obvious change throughout study periods. Fluctuation in the amount of rainfall was predicted to occur in the study area. Projected rainfall resulted in a slightly different amount of rainfall in the future throughout the interval year for all the RCPs of ∆2025 and ∆2028. Lesser rainfall was predicted to occur generally throughout the predicted years due to RCP 8.5 contributes the highest concentration in carbon dioxide release. Hence, more effects were predicted to happen when applying RCP 8.5 as carbon dioxide release is the major effect on

2 Assessing of LULC and Climate Change in Kolkata Urban …

29

climate change. The study area will experience more wormer condition and temperature will reach above 40 ºC. Fluctuation in the amount of rainfall with more extreme events will be another character of future climate in the study area (Fig. 2.9).

2.4 Conclusions The study has explored a significant increase in built-up areas by 91% in 2028 if the urban infrastructural growth continues from 1980 to 2018. The rapid built-up area expansion will take place by reducing open land and water bodies areas in 2028. Reduced water bodies and increased urbanization may have an effect on a city’s ecosystem services, drainage and sewage system, urban health, and thermal characteristics. Based on the present study’s results, the LULC categories should be monitored with caution, especially concerning the increase and decrease in the area of these classes over time. If the unplanned urban expansion trend resumes, the consequences of climate change will be magnified, resulting in an upsurge in environmental economic and medical concerns in the study area. Appropriate landuse planning, the protection of water bodies, afforestation and an escalation in urban greeneries will contribute to make Upper Catchment Area of Bagjola Canal more environmentally sustainable by mitigating the future climate change effects. Energy consumption increased greenhouse gas emissions, and air pollution all contribute to climate change effect, posing a threat to aquatic systems (i.e. ponds, tanks, canal) and a threat to human health. Increased greenhouse gas emissions mostly harm human healthiness, degrade urban health superiority and reduce the city’s environmental sustainability. This study will assist concerned authorities in taking precautionary actions to restrict haphazard and hyper urbanization and mitigate future climate change scenario at micro scale.

References Ali R, Bukhsh K, Yasin MA (2019) Imapct of urbanization on CO2 emissions in emerging economy: evidence from Pakistan. Sustain Cities Soc 101553 Al-Rubkhi ANM, Talal AA, Mohammed AB (2017) Land Use Change Analysis and Modeling Using Open Source (QGIS) Case Study: BoasherWillayat Bhatta B (2009) Analysis of urban growth pattern using remote sensing and GIS: a case study of Kolkata, India. Int J Remote Sens 30(18):4733–4746 Bhattacharjee C (2014) Canals and its relevance to the Kolkata Mega City. Abhinav Natl Mon Ref J Res Arts Educ 20–24 Dasgupta S, De UK (2007) Binary logistic regression models for short term prediction of premonsoon convective developments over Kolkata (India). Int J Climatol 27(6):831–836 Dhiman R, VishnuRadhan R, Eldho TI, Inamdar A (2019) Flood risk and adaptation in Indian coastal cities: recent scenarios. Appl Water Sci 9(1):5 Gismondi M, Kamusoko C, Furuya T, Tomimura S, Maya M (2014) MOLUSCE–an open source land use change analyst for QGIS

30

S. Sahoo and S. Pan

Haque I, Mehta S, Kumar A (2019) Towards sustainable and inclusive cities: the case of Kolkata. ORF, New Delhi, India, p 83 Kayet N, Pathak K, Chakrabarty A, Sahoo S (2016) Urban heat island explored by co-relationship between land surface temperature vs multiple vegetation indices. Spat Inf Res 24(5):515–529 Li X, Mitra C, Marzen L, Yang Q (2016) Spatial and temporal patterns of wetland cover changes in East Kolkata Wetlands, India from 1972 to 2011. Int J Appl Geospatial Res (IJAGR) 7(2):1–13 Li X, Mitra C, Dong L, Yang Q (2018) Understanding land use change impacts on microclimate using Weather Research and Forecasting (WRF) model. Phys Chem Earth, Parts a/b/c 103:115–126 Ma Z, Liu Z, Zhao Y, Zhang L, Liu D, Ren T et al (2020) An unsupervised crop classification method based on principal components isometric binning. ISPRS Int J Geo-Inf 9(11):648 Mallick SK (2021) Prediction-Adaptation-Resilience (PAR) approach-a new pathway towards future resilience and sustainable development of urban landscape. Geogr Sustain 2(2):127–133 Mascarenhas A, Haase D, Ramos TB, Santos R (2019) Pathways of demographic and urban development and their effects on land take and ecosystem services: the case of Lisbon metropolitan area, Portugal. Land Use Policy 82:181–194 Mitra C, Shepherd JM, Jordan T (2012) On the relationship between the premonsoonal rainfall climatology and urban land cover dynamics in Kolkata city, India. Int J Climatol 32(9):1443– 1454 Mohamed A, Worku H (2018) Quantification of the land use/land cover dynamics and the degree of urban growth goodness for sustainable urban land use planning in Addis Ababa and the surrounding Oromia special zone. J Urban Manag Parihar SM, Sarkar S, Dutta A, Sharma S, Dutta T (2013) Characterizing wetland dynamics: a post-classification change detection analysis of the East Kolkata Wetlands using open source satellite data. Geocarto Int 28(3):273–287 Patra S, Sahoo S, Mishra P, Mahapatra SC (2018) Impacts of urbanization on land use/cover changes and its probable implications on local climate and groundwater level. J Urban Manag 7(2):70–84 Ray P, Ghosh P (2016) A short review on characteristics of Kolkata soil on the basis of liquefaction susceptibility. Int J Sci Res Educ 4(5):5420–5428 Richard Y, Emery J, Dudek J, Pergaud J, Chateau-Smith C, Zito S et al (2018) How relevant are local climate zones and urban climate zones for urban climate research? Dijon (France) as a case study. Urban Clim 26:258–274 Saxena A, Jat MK (2019) Capturing heterogeneous urban growth using SLEUTH model. Remote Sens Appl: Soc Environ 13:426–434 Schirpke U, Kohler M, Leitinger G, Fontana V, Tasser E, Tappeiner U (2017) Future impacts of changing land-use and climate on ecosystem services of mountain grassland and their resilience. Ecosyst Serv 26:79–94 Sejati AW, Buchori I, Rudiarto I (2019) The spatio-temporal trends of urban growth and surface urban heat islands over two decades in the semarang metropolitan region. Sustain Cities Soc 46:101432 Sen D (2013) Real-time rainfall monitoring and flood inundation forecasting for the city of Kolkata. ISH J Hydraul Eng 19(2):137–144 Shafia A, Gaurav S, Bharath HA (2018) Urban growth modelling using cellular automata coupled with land cover indices for Kolkata metropolitan region. IOP Conf Ser: Earth Environ Sci 169(1):012090. IOP Publishing Sharma R, Chakraborty A, Joshi PK (2015) Geospatial quantification and analysis of environmental changes in urbanizing city of Kolkata (India). Environ Monit Assess 187(1):4206 Shukla A, Jain K (2019) Critical analysis of rural-urban transitions and transformations in Lucknow city, India. Remote Sens Appl: Soc Environ 13:445–456 Sivaramakrishnan L, Bandyopadhyay S, Sarkar S, Dentinho TP (2020) New or renewed town: sustainable urbanisation in Kolkata. SADF-South Asia Democratic Forum Welle T, Birkmann J (2016) Measuring the unmeasurable: comparative assessment of urban vulnerability for coastal megacities—New York, London, Tokyo, Kolkata and Lagos. J Extrem Events 3(03):1650018

Chapter 3

Field Survey and Geoinformatic Approaches for Micro-Level Land Capability Classification V. S. Pawar-Patil, Sainath Aher, Vidya Chougule, Sandipan Das, and Rushikesh Patil

Abstract Land capability classification (LCC) is useful to carry out scientific land evaluations and assess land suitability for agricultural use. In this study, the Sadoli Khalasa area of Karveer tehsil, Kolhapur District, Maharashtra, India has been selected to assess precise LCC at micro-scale (cadastral level) using field survey and advanced Geoinformatics approaches. For this purpose, 35 soil samples were collected from the entire area with geographical coordinates (x, y) using a handheld Global Positioning System (GPS) and empirically analyzed. Physical characteristics of soil, i.e. soil depth, texture, and structure were identified from analyzed soil samples and with the help of NBSS and LUP maps and soil grid database (250 m resolution) in Geographical information system (GIS) software. Regional relief, slope, contour, and drainage analysis were carried out from CartoDEM (30 m resolution), downloaded from https://bhuvan.nrsc.gov.in/home/index.php, and its validations were carried out from 1:50,000 scale Survey of India (SOI) toposheet (47/ L/2), and sample elevations points acquired by GPS. An Object based classification approach was implemented to prepare land use land cover (LULC) of study area in eCognition Developer software with agriculture, harvested land, built-up, lake, fallow land, and barren land classes using Resourcesat-2 LISS-IV data. Finally, all generated multi-layers are integrated with unique projections and intersect overlay V. S. Pawar-Patil (B) · R. Patil Department of Geography, The New College, Kolhapur 416012, India e-mail: [email protected] S. Aher Universal Geotechnica, Nashik 422011, India S.N. Arts, D.J.M. Commerce and B.N.S. Science College (Autonomous), Sangamner 422605, India V. Chougule Department of Geography, Shivaji University, Kolhapur 416004, India S. Das Symbiosis Institute of Geoinformatics, Symbiosis International (Deemed University), Pune 411016, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_3

31

32

V. S. Pawar-Patil et al.

operation was carried out to create the LCC of the area. The analysis reveals that Class II, III, IV, VI, and VII are observed in the study region of which Class II and III comprised about 50% area of the selected region. This result signifies that about 75% of land in the area is suitable for agriculture purpose, of which about 30% of land fall under Class II. The used Geoinformatics approach along with field investigations has proved satisfactorily for assessment of LCC in Sadoli Khalasa area in order to avail sustainable land development and can be used in other parts which have similar physio-pedological characteristics. Keywords Field survey · Geoinformatics · Land capability classification · Land development

3.1 Introduction Soil is a crucial component of the lithosphere as well as the biosphere and known as a pivotal natural resource on which the supporting life system and socio-economic development depend (Mohan 2008; Deshmukh and Aher 2017). Soil needs to be evaluated according to its perspective, characteristic and other capabilities. The sustainable development of a region needs not only protection and reclamation of natural resources, particularly soil and water, but also a scientific basis for the management in harmony with the environment is of utmost essential. These resources should be managed sustainably so that the challenges proposed to meet the requirements of the development area are brought out without diminishing the potential for their future use (Kanwar 1994). Concerning land management, resources are inevitable for both continued agricultural productivity and protection of the environment (Panhalkar 2011). In view of the stage of population explosion, the availability of land is very limited in proportion to the demand of the same. The per capita cultivable land has been declined from 0.32 ha. in the 1950s through 0.14 by the turn of the century to less than 0.1 ha. by 2020 (Mohan 2008). However, every land parcel has its own characteristics with respect to its utilization, every land parcel has different potentials, and that has to be taken into consideration while preparing for the development plan of that particular area. In fact, the land is a storehouse of nutrients utilized for the growth and development of plant species which serves as primary fodder for the animal. The land is also important for agriculture development, and it provides a base to construct houses and buildings, tanks, reservoirs, and many more. With the pace of time, the utilization of land has been tremendously changed, which again accelerates the degradation of the land resource in the world (FAO 2008). The land capability has to be taken into consideration while using the land. It seems that, without considering the potential of land, its utilization would create a serious environmental problem that would adversely affect the health of society (FAO 2000). A detailed account of land resources with their greatest potential and limitations becomes pre-requisites for land use planning (Abou-Najem et al. 2019).

3 Field Survey and Geoinformatic Approaches for Micro-Level Land …

33

Soil and climate are major variables determining the site-specificity for agriculture in any region. The potential of the land is controlled by various land characteristics like physico-chemical parameters of soil, viz. soil depth, texture, structure, permeability, PH electric conductivity, etc. Tideman (2000). Geology and physiographic aspects also play a crucial role in determining the capability of the land of a particular region (Gawali et al. 2017; Khamkar et al. 2021). To procure the greatest benefit from existing land without compromising the future degradation of land resources with appropriate and well-planned management is inevitable for sustainable development. The LCC is the simplistic but widely used approach in the world to evaluate the land use potential of any region (Scopesi et al. 2020; Ippolito et al. 2021).The capability classification is one of the interpretative groupings made primarily for agriculture purposes. As with all groupings, the capability classification begins with the individual soil mapping units, which are building stones of the system. The classification proceeds through arable land, which is again classifieds according to their potentialities and limitations for agriculture crops in four major groups from I to IV and non-arable four classes from V to VIII. The level of limitations and intensity of proposed conservation measures are raising from Class I to Class IV. The classes from V to VIII are not suitable for agriculture but can be utilized for pasture, range, woodland, grazing, and wildlife purposes. The major decisive factor for demarcating each class and subclass of LCC are topographic factor viz. slope or gradient and pedological characteristics of soil viz. soil depth, texture, permeability, drainage status, etc., were used by several scholars (Panhalkar et al. 2014). The common way of determining land quality/capabilities from land characteristics is mainly by assessing and grouping the types in orders and classes according to their aptitude (Panhalkar 2011). At the outset, the United States of Agriculture (1973) has proposed appropriate norms for LCC. Thus, the present study is based on USDA’s LCC norms which are having eight classes shown by Roman numbers from I to VIII (USDA 1973). In this study, field survey and advanced Geoinformatics approaches were used for cadastral level land capability classification in the Sadoli Khalasa area of Maharashtra. These field survey/empirical methods and their integrations with advanced Geoinformatics disciplines would help to address the problems and planning in land resources.

3.2 Study Area The Sadoli Khalasa area of Karveer tehsil, Kolhapur District, Maharashtra, India was selected as a study area for present research work. The geographical location of the area lies in between 740 07' 58.83'' and 740 09' 12.85'' E longitude and 160 35' 24.14'' to 160 37' 11.56'' N latitude, respectively (Fig. 3.1). In this area, microlevel cadastral agriculture and land use planning have paramount importance because of the administrative significance of the area. It has been experiencing agricultural growth/development for a long time. The area is located on the left bank of river Bhogawati, which is one of the important tributaries of River Panchganga. Three

34

V. S. Pawar-Patil et al.

Fig. 3.1 Location map of the study area

sides of the study area are covered by the Bhogawati River, viz. south, east, and north. The village is demarcated by hills on the north-western side with an average elevation of 600 m (MSL) which serves as the main water divide of this region. The geology of the area is profoundly associated with the Deccan trap, where basaltic rock is predominant. The area reveals a monsoon climate with an average annual temperature of 24 °C. The average annual rain of this region is about 110 cm which occurs mainly from June to September (Monsoon seasons), which is the function of monsoon winds in this region. Fertile soil along the course of the river supports to enhance agriculture activities in this region. The soil varied from deep alluvium red soil in the fertile tract immediate to the river course, deep to medium-deep black cotton soil in the intermediate central part of the village. The hilltops and foothills exhibit the shallow brownish to red soil with a stony surface.

3.3 Database and Methods See Fig. 3.2.

3 Field Survey and Geoinformatic Approaches for Micro-Level Land …

35

Geospatial Data and Methods

NRSC/Bhuvan

Carto DEM

LISS IV MX Data

Surface analysis

10 meter Contour + Toposheet Contour

Slope in Percent Raster

Google earth image / Toposheet

Slope Reclassified

Altitudinal Zones MRSAC Village Boundary

Slope Vectorization

LULC Classes GPS Point Data : Boundary

Slope Vector Generalized

GPS Point Data :Soil Samples Demarcation of Village Boundary Empirical observation of Soil Samples and Database Creation LU/LC Map Preparation using Object Based Classification

Soil Texture

Soil Depth

Interpolation of point Data in ArcGIS Environment

Soil Structure

Soil Coarse fragments

Drainage Condition

Intersect Overlay in ArcGIS Environment

Field Check

Generalization of LCC Map in ArcGIS Environment

Fig. 3.2 Pipeline of methodology

3.3.1 Soil Survey and Analysis It is foremost essential to know the physical characteristics of the soil (Deshmukh and Aher 2014). Soil is a storehouse of nutrients that supports growth and enriches plant community on this earth’s surface. Thus, the information of pedological resources is crucial for sustainable natural resource development in any region and LCC. The physical characteristics of soil, viz. depth, texture, structure, availability of course fragments, etc. are played a pivotal role in LCC (FAO 2000). Thus, with respect to that, 35 soil samples were collected through a systematic soil survey from the entire study area along with their geographical coordinates (x, y) using a handheld GPS device to know the soils depth, texture, structure, and availability of coarse fragments information. Generally, variation in the soil is witnessed with the altitudinal zone.

36

V. S. Pawar-Patil et al.

In view of this, to get appropriate soil samples from various altitudinal zones, the area has been divided according to altitudinal zones with the help of a digital elevation model (DEM) accompanied with contours, and accordingly soil samples were collected (Figs. 3.3 and 3.4). The LULC differentiation has also been considered while executing a soil survey to obtain the real ground variability of the soil scenario. The collected soil samples were analysed according to Tideman’s norm (Tideman 2000) which is given in his book entitled ‘Watershed Management: Guidelines for Indian Condition’. Soil depth (Fig. 3.5), texture (Fig. 3.6), structure (Fig. 3.7) and availability of coarse fragments (Fig. 3.8), etc. maps are generated in GIS software from empirically

Fig. 3.3 Location map of soil sample survey

3 Field Survey and Geoinformatic Approaches for Micro-Level Land …

37

Fig. 3.4 Cross sectional profile map

analysed soil samples (Table 3.1) and soil maps of the area collected from the National Bureau of Soil Survey and Land Use Planning (NBSS and LUP), Nagpur as well as soil grid database of International Soil Reference and Information Centre (ISRIC). Georeferencing of soil maps was carried out in the GIS software to combine all data layers at a unique projections system. Georeferencing procedures obtain the diverse datasets to a unified projection and scale (Aher et al. 2011). The digitisation of various soil layers and interpolations employing the inverse distance weighting (IDW) technique was carried out in the GIS environment to produce the detailed multi-layers of a spatial database of the soils.

38

V. S. Pawar-Patil et al.

Fig. 3.5 Soil depth map

3.3.2 Satellite Image Processing and LULC Classification LULC is one of the influential components in determining the soil characteristics, infiltration, sub-surface flow, and amount and quality of groundwater (Deshmukh et al. 2017). In view of this, the study of LULC is essential to evaluate its quantitative status and relationship with surface soils and LCC. Remote sensing image of Indian Remote Sensing (IRS) satellite obtained from Linear Imaging Self-Scanning Sensor (LISS-IV) was used to understand LULC situation of the area. LISS-IV is a remote sensing satellite sensor developed by Indian Space Research Organizations (ISRO), India, which provides multispectral data in 4 bands and has a decent potential

3 Field Survey and Geoinformatic Approaches for Micro-Level Land …

39

Fig. 3.6 Soil texture map

for identifying the LULC of any Indian territory. Google earth pro image and SOI Toposheet were also taken into consideration for visual classification and validations of generated LULC classes. The traditional technique of LULC classification follows the pixel-based approach for the analysis of remotely sensed imagery (Weih and Riggan 2010) which always encounters several limitations. To overcome such difficulties, the modern approach of classification based on objects (segments) is preferred over the single pixels. Object-based classification relied on image segmentation and the creation of a hierarchical network of homogeneous objects that relates to feature boundaries. For the

40

V. S. Pawar-Patil et al.

Fig. 3.7 Soil structure map

present investigation, eCognition Developer software was used for LULC classification, which comprised agriculture, harvested land, built-up, lake, fallow land, and barren land classes (Fig. 3.14).

3.3.3 Topographic Investigations IRS Satellite-derived CartoDEM (30 m resolution) made freely available by National Remote Sensing Centre (NRSC) at Bhuvan web portal was downloaded from https:// bhuvan.nrsc.gov.in/home/index.php site and used for preparations of relief, slope

3 Field Survey and Geoinformatic Approaches for Micro-Level Land …

41

Fig. 3.8 Stony surface map

(Fig. 3.13), contour (Fig. 3.10), and drainage map (Fig. 3.11) of the area with the integrations of ArcGIS software. In the trail of the rapid advancement in the collection practices of elevation data, the emphasis of user’s need for elevation contours has been shifted from accurate elevation assessment to perceptional requirements of topographic relief and slopes. Thus, the CartoDEM has been considered a solution for obtaining contours and topographic maps from DEM. Drainage, relief, and slope of the particular region have pivotal importance in assessing land capability, with respect to that CartoDEM of 30 m resolution data was validated from the toposheet (47/L/2) and GPS point data. Digital Elevation Model (DEM) of the same area and slope with 5 m contour interval were generated in this GIS software to know the

42

V. S. Pawar-Patil et al.

Fig. 3.9 DEM of study area

regional topographic situations and their correlations with the LCC (Figs. 3.12 and 3.13).

Agriculture/ Sugarcane

Agriculture/ Sugarcane

547

547

559

559

569

570

567

567

553

581

2

3

4

5

6

7

8

9

10

11

Barren land/ Open land

Cow land

Cow land

Mango orchard

Mango orchard

Agriculture/ Groundnut

Agriculture/ Sunflower

Agriculture/ Groundnut

Sandy coarse

Sandy coarse

Sandy coarse

Sandy loam

Sandy loam

Sandy loam

Sandy loam

Silty loam

Coarse

Coarse

Coarse

Granular

Granular

Prismatic

Prismatic

Prismatic

Granular

Granular

Granular

Soil texture Soil structure

Grassland/ Clay loam River terraces

549

1

LULC

Height AMSL (m)

Soil survey point

Table 3.1 Empirical soil inventory

15

15

15

25

20

50

75

60

90

75

90

Soil depth

High

High

Moderately high

Moderate

Moderate

Moderate

Moderately high

Moderately high

Low

Low

Moderate

Erosion

Excessive

Excessive

Excessive

Moderate

Moderate

Moderate

Moderate

Moderate

Well

Well

Well

Drainage condition

High

High

High

Moderately high

Moderately high

Moderately high

Moderately high

Moderately high

Low

Low

Moderate

Overland flow (Damaging)

High

High

High

Moderately high

Moderately high

Moderately high

Moderate

Moderate

Low

Low

Low

Stoniness coarse fragments

(continued)

Red

Reddish

Reddish

Red

Red

Red

Brownish

Slightly red

Black

Red

Red

Soil colour

3 Field Survey and Geoinformatic Approaches for Micro-Level Land … 43

Height AMSL (m)

587

594

607

610

551

544

557

572

582

569

569

550

Soil survey point

12

13

14

15

16

17

18

19

20

21

22

23

Table 3.1 (continued)

Agriculture rice

Agriculture rice

Agriculture sugarcane

Foot hill agriculture

Agriculture sugarcane

Agriculture groundnut

Agriculture sugarcane

Agriculture rice

Hill top

Hilly area

Hilly area/ Grass

Silty loam

Sandy loam

Sandy

Sandy

Coarse

Medium silty loam

Silty loam

Silty loam

Sandy coarse

Sandy coarse

Fine

Granular

Coarse

Coarse

Granular

Coarse

Fine

Smooth

Coarse

Coarse

Coarse

Coarse

Soil texture Soil structure

Foot hill area/ Sandy Grass coarse

LULC

90

20

30

15

55

75

90

90

5

5

7

7

Soil depth

Low

High

High

High

Moderately high

Moderate

Low

Low

High

High

High

High

Erosion

Well

Moderate

Moderate

Low

Moderate

Moderate

Well

Well

Excessive

Excessive

Excessive

Excessive

Drainage condition

Low

High

High

High

Moderately high

Low

Low

Low

High

High

High

High

Overland flow (Damaging)

Low

High

High

High

High

Moderate

Low

Low

High

High

High

High

Stoniness coarse fragments

(continued)

Black

Red

Red

Red

Red

Red

Red

Black

Red

Reddish

Reddish

Brownish white

Soil colour

44 V. S. Pawar-Patil et al.

Fallow land grass

Hilly foot hill Sandy coarse

Agriculture

547

561

551

584

568

586

555

584

588

544

26

27

28

29

30

31

32

33

34

35

Source Based on Field work

Agriculture groundnut

542

Agriculture sugarcane

Agriculture rice

Agriculture sunflower

Agriculture rice

Cow land

Agriculture rice

Agriculture sugarcane

Clay loam

Sandy loam

Sandy loam

Silty loam

Sandy loam

Sandy loam

Clay loam

Sandy loam

Silty loam

Clay loam

Silty loam

25

Agriculture groundnut

561

Granular

Prismatic

Prismatic

Granular

Sandy loam

Prismatic

Coarse

Silty loam

Coarse

Granular

Granular

Fine

Soil texture Soil structure

24

LULC

Height AMSL (m)

Soil survey point

Table 3.1 (continued)

90

55

60

90

7

30

15

90

50

90

90

90

Soil depth

Low

Moderately high

Moderately high

Moderate

High

Moderately high

High

Moderate

High

Low

Low

Low

Erosion

Well

Moderate

Moderate

Well

Excessive

Excessive

Excessive

Well

Moderate

Well

Well

Well

Drainage condition

Low

Moderately high

Moderately high

Moderate

High

Low

High

Moderate

Moderately high

Low

Low

Low

Overland flow (Damaging)

Low

Moderate

Moderate

Low

High

High

High

Low

Moderately high

Low

Low

Low

Stoniness coarse fragments

Black

Slightly red

Slightly red

Black

Red

Red

Red

Black

Black

Black

Brownish

Black

Soil colour

3 Field Survey and Geoinformatic Approaches for Micro-Level Land … 45

46

V. S. Pawar-Patil et al.

Fig. 3.10 Contour map

3.3.4 Use of GIS for Data Integrations Demarcation of study area boundary has immense importance for the micro-level (cadastral) assessment of LCC. So, the outset boundary of the area has demarcated

3 Field Survey and Geoinformatic Approaches for Micro-Level Land …

47

Fig. 3.11 Drainage map

with the help of the area boundary map of the Karveer Tehsil published by Maharashtra Remote Sensing Application Centre (MRSAC), Nagpur by proper Georeferencing in ArcGIS environment that crosses verified with SOI Toposheet and fieldwork done by GPS. The attribute data of analysed samples, i.e. soil depth, texture, structure, drainage condition, course fragments, etc., were filled and incorporated in the ArcGIS environment, and a spatial interpolation tool was executed to prepare various spatial

48

V. S. Pawar-Patil et al.

Fig. 3.12 Drainage condition map

maps. In GIS software, spatial interpolations maps were prepared using the IDW interpolation method to identify LCC. IDW is a geostatistical interpolation technique, which estimates unknown values with a specific probability distribution at an arbitrary point of the space (Deshmukh and Aher 2016a, b). Generated interpolated maps were vectorised in the ArcGIS environment (Fig. 3.14). Parameters for land capability classification, viz. LULC, soil depth, soil texture, soil structure, drainage condition, coarse fragments, reliefs, and slope were used to identify land capability classes and sub-classes. All these thematic maps were integrated into ArcGIS 9.3 software, and a land capability map was prepared (Fig. 3.15).

3 Field Survey and Geoinformatic Approaches for Micro-Level Land …

49

Fig. 3.13 Slope map

3.3.5 Land Capability Classes (LCC) The LCC provides a hierarchy of the potential of each land parcel to maintain comprehensive land use (Panhalkar et al. 2014). For obtaining the final result of LCC, topographic, hydrologic, and soil characteristics were considered and identified the LCC for the study area (Fig. 3.15). The generated multi-layered spatial data are integrated with unique projections, scale, and intersect overlay techniques of GIS is used to create the LCC of the study area. Finally, topographic and soil characteristics have been classified and accordingly assigned appropriate rank for each subclass of the same, considering suggested criteria for assessing LCC (Table 3.2). The final LCC

50

V. S. Pawar-Patil et al.

Fig. 3.14 LULC map

map was carried out by intersecting the different generated layers and considering the higher rank of the land parcel. 5 major classes have been recognised by considering this region’s physiographic and soil characteristics. The LCC classes of II, III, IV, VI, and VII are implemented in the study region. The areal extent (%) of land capability classes have been given in Table 3.3, and the potential of land use for different LCC has been shown in Table 3.4. The detailed methodology of LCC has shown in Fig. 3.2. The obtained results of soil, topographic characteristics and LCC have been validated by doing thorough field work (Fig. 3.16).

3 Field Survey and Geoinformatic Approaches for Micro-Level Land …

51

Fig. 3.15 Land capability classes

3.4 Result and Discussion 3.4.1 Soil Characteristics The soil inventory offers noteworthy possibilities to establish a way to resolve agricultural problems, but it leftovers less utilized because of its uneasy and complex scientific nature (Rushemukha et al. 2014). The analysis of soil depth reveals that,

2

Low

2

Granular (Very fine)

1

Very deep

1

Insignificant

1

Soil structure

Assigned rank

Soil depth

Assigned rank

Coarse fragments (Stony surface)

Assigned rank

Well

Very well

1

Assigned rank 3

Moderate

3

Moderate (3–5)

3

Moderate

3

Moderate

3

Prismatic

3

Silty clay loam

Class III

Source Based on USDA (1973), FAO (2000) and Tideman (2000)

2

2

1

Drainage condition

Gentle (1–3)

2

Granular

2

Assigned rank

Slope (in Percent Nearly level rise) (25)

7

Very high

7

Extreme shallow

7

Very soarse

7

Coarse sandy (Rock outcrops)

Class VII

8

Rocky (Hard pan)

Class VIII

52 V. S. Pawar-Patil et al.

3 Field Survey and Geoinformatic Approaches for Micro-Level Land …

53

Table 3.3 SadoliKhalasa area: a real extent of land capability classes Sr. no

LCC class

Area (km2 )

Area (%)

1

Class-II

1.59

31.05

2

Class-III

1.08

21.09

3

Class-IV

1.14

22.26

4

Class-VI

0.90

17.57

5

Class-VII

Total

0.41

8.00

5.12

100%

Source Based on field work and GIS intersect overlay analysis

Table 3.4 SadoliKhalasa area: potential of land use for different land capability classes Class

Cultivation suitability

Pastoral suitability

Land use choice

1

High

High

Many

Medium

Limited

2 3

Medium

4

Low

5

Unsuitable

6

Low

7

Unsuitable

Extremely limited

Source After LUCSH, New Zealand Handbook for Classification of Land,1969

reasonably, depth is seen high along the river course, which is an outcome of valley deepening and relatively shallow soil is observed in the central, northwestern part of the area due to the presence of hills in this part. Around the hilly area, the soil depth is less than 10 cm, while in the plain area, the depth is more than 75 cm (Fig. 3.5). Thus, toward the plain area, more suitable land classes for agriculture are seen as compared to hill tracts. The clay loam to silty clay loam type of fine to moderate textured soil is predominant along the river due to the deposition of soil particles that have been transported from the hilly terrain of this area. Sandy to coarse sandy loam textured soil is observed towards northwestern hilltop and hill slopes. LULC has its own bearing on the availability of stones in the surface soil, it was seen that hill slopes and hilltops are enriched with outcropped rocks on the surface. Low laying areas located in the plain region are endowed with low stoniness. Thus, reasonable correlations occurred among physiography, soil characteristics, and overall land capabilities in the area. The hilly tract of this region is characterized by a steep to strong slope, shallow soil depth with coarse-textured soil possess class VI–VII, while the rest of the area with considerable plain physiography is gifted with LCC II to IV in this region.

54

V. S. Pawar-Patil et al.

Fig. 3.16 Validation of soil inventory and LCC

3.4.2 Physiography (Relief and Slope) The slope is nothing but the horizontal inclination of terrain, which is the most influencing topographic factor in LCC assessment (Abou-Najem et al. 2019). It affects surface runoff, soil texture, depth, and vegetation growth of the region (Shinde et al. 2020). Slope assessment reveals that the low gradient is observed along the river bank due to the flood plain and deposition of mud. The steep to the strong slope is seen towards the hillsides of the northwestern part of the study area (Fig. 3.13, Table 3.5). Nearly level and moderate types of slopes influenced the LCC in the eastern side of the plain area; thus, the maximum area has been fallen under Class III and IV in the

3 Field Survey and Geoinformatic Approaches for Micro-Level Land … Table 3.5 Spatial distribution of topographic slope

55

Slope (Percent rise)

Area (km2 )

Percentage to total area

25

0.007378

0.14

Total = 5.12

100

Source Based on Slope map procured in GIS environment

Table 3.6 SadoliKhalasa area: Land use and land cover classes

Sr. no

LULC class

Area (km2 )

Area (%)

1

Agriculture

2.76

53.89

2

Harvested land

1.43

27.96

3

Fallow land

0.39

7.60

4

Barren land

0.32

6.18

5

Builtup

0.22

4.30

6

Lake

0.003

0.06

5.12

100%

Total

Source Based on Field Work and Object-Based Classification in eCognition software

area. Steep to a strong topographic gradient produces shallow soil with coarse soil texture, which greatly influences the LCC, due to that Class VII is seen in the western and northwestern part of this area which reduces agricultural use of the land parcel in this area. At the foothill zones, the occurrence of moderately high types of slope are responsible for the class IV and somewhat class VI development. In conclusion, the regional physiography and nature of the slope of the area are strongly influenced the LCC in this region (Table 3.6).

3.4.3 Land Use Land Cover (LULC) Classification of LULC is the methodological differentiation of diverse land parcels, relied on convinced comparable attributes, mostly utilized to recognize and comprehend their basic uses in view of satisfying the requirements of human civilization in a certain space and time (Singh et al. 2012). The scenario of LULC is revealed that most of the land is occupied by agriculture that has been developed around the central built-up land in this area. The LCC II and III are associated with the lowlaying agricultural land of this area. The harvested land and fallow land have been

56

V. S. Pawar-Patil et al.

slightly mixed with agricultural land. The barren land is seen in the central-western hilltops and sloping grounds which is strongly unsuitable for agricultural land use. According to LCC norms, encroachment of agriculture towards the central hills is not feasible, which probably will cause an ecological imbalance in thisarea. Agricultural land usehas a larger proportion and is slightly mixed with the built-up, harvested, fallow lands. Thus this low laying area is more capable for agriculture as compared to the hilly area located on the western side. The nature of hilly areas and barren lands have revealed unsuitable classes of land capabilities and not suitable for agricultural land use. Thus, in the central-western edge of the study areaexhibited unsuitable class of LCC (Class-VII). The LULC has greatly influenced the LCC in the study area. The development of agriculture has been seen in the entire area except the hilly tract in the area.

3.4.4 Land Capability Classes Appropriate efficiency and the long-term viability of land needed scientific and logical management of land resources. Five major LCC classes have been recognized in the study area, viz. II, III, IV, VI, and VII. Class I of LCC required permanent ideal topographic, hydrological and pedological condition that is difficult to obtain in this region. Class V is associated with the permanent waterlogging situation. Finally, class VIII is related to the availability of a considerable extent of hard rock pans that are both missing in the study area.

3.4.4.1

Class II

Class II is the prominent class in this area, with a larger area (31.05%) in the overall study area (Table 3.3). This area is traversing along the river course of Bhogawati. It is characterized by relatively low gradient, clay loam texture of the soil very deep surface soil with less availability of coarse fragments. The land resource of this class is being used for agriculture purposes with few limitations that could be controlled with certain mechanical measures to slow down the deterioration of soil (AbouNajem et al. 2019). The plain area, nearly level slope, maximum soil depth, granular soil structure, clay and silty soil texture nature of the area are significantly supportive for the development of Class II in the study area.

3.4.4.2

Class III

This class is spread between Class II and Class IV profoundly intermediate land zone. The area occupied by this class is about 21.09%. Moderate slope, 25–75 cm soil depth, prismatic to granular soil structure, and silty to clay loam soil texture are supportive for the development of class III in the study area. The uneven distributional pattern

3 Field Survey and Geoinformatic Approaches for Micro-Level Land …

57

of this class has manifested in this region (Fig. 3.15). It is seen that the distribution of slope has great bearing on the occurrence and spreading of this class in the study area. The restriction of the use of this land for cultivation is a little bit more than Class II, and soil erosion is predominated (Scopesi et al. 2020). The practices applied to overcome the limitations of use of this land includes strip cropping, rotation of crops, formation of terraces and outlets, contour tillage and mulching are common. The reclamation practices are usually more difficult to apply and maintain the soil.

3.4.4.3

Class IV

This class is the last ranking class for utilization of land for agriculture purposes and having more restrictions than previous classes contributes to about 22.26% area of the study region (Table 3.3). The options of cultivation of plants are also limited, and executions of mechanical measures are prerequisites for sustainable agriculture practice (Yohannes and Soromessa 2019). In general, this class is characterized by moderate to high erosion susceptibility, steep to strong slope, and very shallow as well as stony surface soil. The foothill nature, moderate soil depth, medium soil structure, sandy to silty loam soil texture nature is responsible for the formation of Class IV in this study area.

3.4.4.4

Class VI

This class is unsuitable for arable use covers about 17.57% of the area, having slight to moderate mechanical limitations as well as it seems to be catastrophic under permanent cultivation conditions. The rigid hilly tract, presence of foothill zone, moderate to moderately high drainage, 10–25 cm soil depth, medium soil structure, sandy loam soil texture, etc., topographic and pedological characteristics are responsible for the detection of class VI in the area. A considerable proportion of this class is proved in the study area, which indicates a need for proper land resource management. Class VI is suitable for grazing land, pasture, or forestry. This class is characterized by moderate soil erosion, steep to a very steep slope, shallow soil with stony surface soil. Practices like contouring, fencing, furrowing as well as other more advanced practices are essential for sustainable land use.

3.4.4.5

Class VII

This class is also very much unsuitable for agriculture use. The land underclass VII is posing severe physical limitations but it can be used for grazing with high-intensity mechanical and biological soil conservation measures (Tideman 2000). The study area comprised about 8% of land under this class, and it is found in central-western hilly tracts where the slope is very steep to strong, and soil depth ( Surface tool

30 m Resolution

Multiple ring buffer through ArcGIS platform

Soil depth map (Scale—1:50,000)

MNDWI NDVI

Slope Elevation Curvature Distance from the river

Specification

Data types and method

4.3.2 Methods The Potential land suitability for economic activity ofdrought prone Gandheswari watershed, Bankura, West Bengal has been delineated by five Major steps—(i) Different type of Spatial and Non-spatial data of the study area has been collected from various sources which are related with potential land suitability zone, (ii) Collected datas’ has been converted into thematic layer using ArcMap and ERDAS IMAGINE Software, (iii) The weights of different parameters The weightages of each parameter were specified by the scale of relative importance value. Further, the weights are allotted to the thematic layer on the basis of their importance to the potentiality of land suitability (iv) The potential land suitability for economic activity map prepared using weight overlay method. (v) Finally, the output result is further validated with the help of Receiver Operating Curve (ROC). The methodology flow chart for potential land suitability zone for economic activity is shown in Fig. 4.2.

4 Assessment of Potential Land Suitability for Economic Activity Using …

67

Fig. 4.2 The methodological flowchart

4.3.3 Thematic Layer Preparation The Landsat-8 OLI (Operational Land Imager) satellite imagery was used to create the Landuse and Landcover map, MNDWI and NDVI map for the field of the study. It was pre-processed to remove noise and haze, and then all bands were composited together. The maximum likelihood classification tool has been used to create classifications using the supervised classification method. The rainfall and ground water maps are developed using the Inverse Distance Weighted (IDW) interpolation algorithm. With the aid of the spatial analysis capabilities, the flow direction, flow accumulation, and finally streams were constructed using data from the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) and the Distance from the river map was developed using the Multiple ring buffer tool. Soil texture and Soil depth image used to scan and georeferenced these maps. The Arc GIS 10.3 environmental was used to digitize the data by using The (World Geodetic System) WGS 84 datum and (Universal Transverse Mercator) UTM zone 45 N projection were. Slope, Elevation, Curvature maps have been prepared by using SRTM DEM. All the elevan raster layers are scaled down to 30 m × 30 m pixel size.

68

U. Senapati et al.

4.4 Analytical Hierarchy Process (AHP) AHP is simple and extensively used scientific technique has been used throughout the world for fulfillment of various purposes in research field (Saaty 1980). AHP is one of the best Multiple-criteria decision-making (MCDM) methods for doing scientific research where multi-factors are involved (Das et al. 2018; Mandal and Chatterjee 2021; Chaudhary et al. 2021; Senapati and Das 2022a; Senapati et al. 2023b). In some cases it is very tough to allot relative weights to the different parameters involved in making decision. That’s why it is obvious to adopt one technique which allows the assign weights. One such technique is Analytical Hierarchy Process (Duc et al. 2006). This method has been structured in this manner that it can give the researchers best garb. AHP technique is the synthesis of various judgments for decision making based on mathematics and psychology. It also reduced the bias while making decision (Chakraborty and Kumar 2016). The AHP method allows to estimate the proportion scattering in consequence verdict points with conditioning factors that affect the result. This technique conducts a comparison matrix for the discretion of a judgement categorization. Important differences are disclosed in terms of percentage distribution in decision points (Senapati and Das 2021). In multi-criteria decision analysis (MCDA) method, analytic hierarchy process (AHP) (Saaty 1980) is frequently used to identify the weight of each parameter. AHP methods enables the hierarchical assembly of multiple criteria into a pairwise comparison method for making of decision (Bera et al. 2019). AHP technique offers us an outline for individual decision-making process, assist as a consistency director and makes estimations about the weights of assessment classes and improves the precision among the prediction team also provides the alternative option (Kumru and Kumru 2014; Horˇnáková et al. 2019). For preparing the pair-wise comparison matrix every parameters has been valued compared to all other parameters by providing a relative dominant scale from 1 to 9 (Table 4.2). The relative scale of all the selected factors is given based on different standard and preference (1. equally important; 3. slightly important; 5. quite important; 7. extremely important; 9. absolutely important; 2, 4, 6 and 8 are intermediate values). In this study to identify the potential land suitability for economic activity, eleven thematic layers has been selected such as soil depth, soil texture, river buffer, MNDWI, NDVI, ground water, rainfall, Land use and land cover (LULC), slope, elevation and curvature. Then weights are assigned to the all thematic layer as per their relative importance and influencing capacity in order to prospect of potential land suitability for Economic activity. Weights has been allotted on each factors according to its relative importance (Table 4.4) Consistency ratio (CR) is been derived to check whether the pair-wise comparison were consistent enough to move further. Random consistency Index (RI) from the randomly generated reciprocal matrices of Saaty (1980) (Table 4.3). In this study the Consistency ratio (CR) is 0.012844 which means that the comparisons were very consistent (Table 4.4). Normalized weight also has been calculated and it has used for preparing the weighted overlay which is shown in the Table 4.5.The pairwise comparison matrix and a relative rating of each

4 Assessment of Potential Land Suitability for Economic Activity Using …

69

Table 4.2 Description of scales for pair comparison with AHP. Source Saaty (1990) Scales

Degree of preferences

Descriptions

1

Equally important

The contribution of the two factors is equally important

3

Slightly important

Experiences and judgment slightly tend to certain factor

5

Quite important

Experiences and judgment strongly tend to certain factor

7

Extremely important

Experiences and judgment extremely strongly tend to certain factor

9

Absolutely important

There is sufficient evidence for absolutely tending to certain factor

2,4,6,8

Intermediate values

In between two judgments

Table 4.3 Random index value. Source Saaty (1990) n

1

2

3

4

5

6

7

8

9

10

RI

0.00

0.00

0.58

0.9

1.12

1.24

1.32

1.41

1.45

1.49

subclass were used to assign weights to the subclasses of each unique thematic raster layer (Table 4.6). The Consistency Ratio (CR) is used to determine whether or not pairwise comparisons are consistent. If CR is less than 0.10, it implies that continuity is acceptable for recognizing class weights. Otherwise, the weights in question are re-evaluated to avoid discrepancies (Senapati and Das 2021).

4.4.1 Integration of GIS for Weighted Overlay Analysis This section of the study focuses on computing the appropriateness Potential Land Suitability Index (PLSI) for economic activity development after standardizing and reclassifying all criterion layers based on expert and professional opinions as well as relevant literature. The weight of each criterion was calculated using the AHP approach, and their consistency was assessed subsequently. Following that, to produce the suitability map for economic activities, which was analyzed using the linear combination technique, all of the criteria and their respective weights were taken into consideration (Chaudhary et al. 2021). The PLSI is a dimensionless metric for mapping appropriate economic activity in a given area. The final layer has been created and categorized into seven land suitability zones after conducting weighted overlay analysis in the Arc GIS environment. The final PLSI score for each land’s for economic activity was determined using the following equation: PLSI =

n ∑ ( j

Ax × A y

)

1

0.14

0.13

1

1

0.5

0.33

0.33

0.25

0.2

0.17

0.14

0.13

0.11

Soil depth

Soil texture

River buffer

MNDWI

NDVI

Ground water

Rainfall

LULC

Slope

Elevation

Curvature

0.17

0.2

0.25

0.33

0.33

0.5

1

1

ST

SD

Factors

Table 4.4 Pair-wise matrix table

0.14

0.17

0.2

0.25

0.33

0.33

0.5

1

1

1

2

RB

0.17

0.2

0.25

0.33

0.33

0.5

1

1

1

2

3

MNDWI

0.2

0.25

0.33

0.33

0.5

1

1

1

2

3

3

NDVI

0.25

0.33

0.33

0.5

1

1

1

2

3

3

4

GW

0.33

0.33

0.5

1

1

1

2

3

3

4

5

Rainfall

0.33

0.5

1

1

1

2

3

3

4

5

6

LULC

0.5

1

1

1

2

3

3

4

5

6

7

Slope

1

1

1

2

3

3

4

5

6

7

8

Elevation

1

1

2

3

3

4

5

6

7

8

9

Curvature

70 U. Senapati et al.

0.066

0.049

0.04

0.033

0.028

0.025

0.24

0.12

0.08

0.08

0.06

0.048

0.04

0.034

0.03

0.027

Soil texture

River buffer

MNDWI

NDVI

Ground water

Rainfall

LULC

Slope

Elevation

Curvature

0.021

0.024

0.029

0.036

0.048

0.048

0.072

0.144

0.144

0.144

0.289

RB

0.017

0.02

0.026

0.034

0.034

0.051

0.102

0.102

0.102

0.204

0.307

MNDWI

0.016

0.02

0.026

0.026

0.04

0.079

0.079

0.079

0.159

0.238

0.238

NDVI

Principal Eigen value: 11.194164, Consistency ratio (CR): 0.012844

0.066

0.099

0.198

0.198

0.198

0.24

Soil depth

ST

SD

Factors

Table 4.5 Normalized matrix of for all parameters

0.015

0.02

0.02

0.03

0.061

0.061

0.061

0.122

0.183

0.183

0.244

GW

0.016

0.016

0.024

0.047

0.047

0.047

0.094

0.142

0.142

0.189

0.236

Rainfall

0.012

0.019

0.037

0.037

0.037

0.075

0.112

0.112

0.149

0.186

0.224

LULC

0.015

0.03

0.03

0.03

0.06

0.09

0.09

0.119

0.149

0.179

0.209

Slope

0.024

0.024

0.024

0.049

0.073

0.073

0.098

0.122

0.146

0.171

0.195

Elevation

0.02

0.02

0.041

0.061

0.061

0.082

0.102

0.122

0.143

0.163

0.184

Curvature

0.019

0.023

0.029

0.039

0.051

0.067

0.087

0.113

0.149

0.191

0.233

Weights

4 Assessment of Potential Land Suitability for Economic Activity Using … 71

72

U. Senapati et al.

Table 4.6 Weight of sub classes Factors

Sub-class

1

2

3

4

Soil depth

Very deep

1

2

3

4

Very deep-mod, deep

0.5

1

2

3

0.277181

Shallow-mod, deep

0.33

0.5

1

2

0.160089

Shallow

0.33

0.5

1

4

5

Loamy group

0.5

1

3

4

0.305574

Sandy loamy group

0.25

0.33

1

2

0.124794

500

Rainfall

Landuse/ Landcover

Weights 0.467295

2

Gravelly group 0.2

Ground water

C.R 0.011357

0.25

River buffer 200

NDVI

6

1

Soil texture Clay group

MNDWI

5

0.095435 0.01773

0.25

0.5

1

1

2

5

7

9

0.5

1

2

5

7

0.491834

0.077798 0.018881

0.491934 0.270367

750

0.2

0.5

1

2

5

0.134973

1000

0.14

0.2

0.5

1

2

0.065291

1500

0.11

0.14

0.2

0.5

1

0.037435

0.048 to 0.29

1

2

6

7

9

0.011 to 0.047

0.5

1

2

6

8

0.042798

0.276313

0.496078

0.081 to 0.01

0.17

0.5

1

2

6

0.129708

0.11 to -0.82

0.14

0.17

0.5

1

3

0.066105

0.26 to -0.12

0.11

0.12

0.17

0.33

1

0.031795

0.14 to 0.29

1

2

3

4

5

0.01514

0.418534

0.11 to 0.13

0.5

1

2

3

4

0.262519

0.084 to 0.1

0.33

0.5

1

2

3

0.159926

0.01 to 0.083

0.25

0.33

0.5

1

2

0.097255

0.089 to 0

0.2

0.25

0.33

0.5

1

2.35 -4.28

1

2

4

6

7

4.29 - 4.94

0.5

1

2

4

6

0.061766 0.014366

0.464813 0.269876

4.95 - 5.59

0.25

0.5

1

2

4

0.143939

5.6 - 6.4

0.17

0.25

0.5

1

2

0.075793

6.41 - 7.62

0.14

0.17

0.25

0.5

1

0.045579

1977 -2033

1

2

3

4

5

1938 -1976

0.5

1

2

3

4

0.01514

0.262519

0.418534

1897 -1937

0.33

0.5

1

2

3

0.159926

1852 - 1896

0.25

0.33

0.5

1

2

0.097255

1796 -1851

0.2

0.25

0.33

0.5

1

0.061766

Agricultural Land

1

2

4

5

6

7

0.031882

0.411185 (continued)

4 Assessment of Potential Land Suitability for Economic Activity Using …

73

Table 4.6 (continued) Factors

Slope

Elevation

Curvature

Sub-class

1

2

3

4

5

6

Natural vegetation

0.5

1

2

4

5

6

C.R

Weights 0.258356

Water bodies

0.25

0.5

1

2

4

5

0.153125

Fallow land

0.2

0.25

0.5

1

2

4

0.089964

Settlement

0.17

0.2

0.25

0.5

1

2

0.052528

Industrial area

0.14

0.17

0.2

0.25

0.5

1

0.034841

0–1.3

1

2

3

4

5

0.01514

0.418534

1.4–2.7

0.5

1

2

3

4

0.262519

2.8–4.6

0.33

0.5

1

2

3

0.159926

4.7–13

0.25

0.33

0.5

1

2

0.097255

13–37

0.2

0.25

0.33

0.5

1

72–103

1

2

3

4

5

104–124

0.5

1

2

3

4

0.061766 0.01514

0.418534 0.262519

125–145

0.33

0.5

1

2

3

0.159926

146–200

0.25

0.33

0.5

1

2

0.097255

201–255

0.2

0.25

0.33

0.5

1

0.061766

Concave

1

2

7

Flat

0.5

1

2

0.036401

0.26143

0.630098

Convex

0.14

0.5

1

0.108472

where, PLSI is for Potential Land Suitability Index, and “x” and “y” denote “factor classes”, and “factor subclasses” weight of criteria respectively; So, Ax denotes the rank of each factor class as evaluated by using PLSI, Ay is the weightage value of each factor subclasses as determined by relative standards, and n denotes the total number of factors. After assigning overall weights and scores to estimate the final land suitability zone, which employs the following formula: PLSI =

n ∑ (

S Dx S D y + STx STy + D Rx D R y + N Wx N W y + N Vx N Vy

j

+GWx GW y + R A x R A y + LUx LU y + CUx CU y + S L x S L y + E L x E L y

)

where, SD indicates soil depth; ST denotes soil texture; DR represent distance from river; ND refers to Modified Normalized Differentiate Water Index (MNDWI); NV refers to Normalized Differentiate Vegetation Index (NDVI); GW show ground water; RA indicates rainfall; LU represents land use land cover (LULC); CU indicates curvature; SL denotes slope and EL represents elevation.

74

U. Senapati et al.

4.5 Result 4.5.1 Soil Depth Soil depth is one of the most important parameter in order to assess the land suitability zone. Soil depth fixes the limit of growth of root also the existence of water and air in the soil (Bandyopadhyay et al. 2009). Soil depth has positive and negative relationship with different type of activity with different type of economic activity (Zolekar and Bhagat 2015). The shallow soil is not ideal for agricultural activity because it can restrict the growth of plant root whereas the soil having greater depth support the agricultural activity. Highest weight has been given to the very deep and lowest weight is given to the shallow deep soil to identify the potential land suitability zone. There are four classes of soil depth has been identified in this watershed namely shallow depth soil, shallow-moderately–deep depth soil, very deep-moderately deep depth soil and very deep soil. This classes consist of 1.50% area, 20% area, 7.25% and 71.25% area. These categories cover 6, 80, 29, and 285 km2 of the study area, respectively (Fig. 4.3).

4.5.2 Soil Texture Soil texture refers to the relative percentage of sand, silt and clay. It comes under the physical properties of soil which is one of the leading constituent in terms to identify the potential land suitability zone. Texture has decent effect on management and productivity of soil (Sahai 2004). It fundamentally depends on the parent materials on which soils were developed (Bandyopadhyay et al. 2009). In Gandhswari watershed mainly agricultural activity is been performed because of existence of clay soil in large amount but other economic activity like social forestry building of industry can be done in this watershed. Highest weight has been allotted to the clay group of soil and lowest is given to the gravelly group of soil as it is not suitable for doing any economic activity on land. Soil behavior may be used to estimate soil performance for agricultural purposes. As a result, knowing the soil types is essential when evaluating whether or not a piece of land is suitable for economic activities. The thematic data has been reclassified into four soil texture groups, namely clay groups, gravelly groups, loamy groups and sandy-clay groups. These categories cover 236 km2 (59%), 69 km2 (17.25%), 63 km2 (15.75%) and 32 km2 (8%) of the study area, respectively (Fig. 4.4).

4 Assessment of Potential Land Suitability for Economic Activity Using …

75

Fig. 4.3 Soil depth map of the study area

4.5.3 Distance from River The distance between river and land influences the land use of an area (Aydi et al. 2015). As we know that availability of water is very much important for agricultural activity the land closer to the river are used for the agricultural activity and other economic activity can be done in the further distance from river. In the present study five buffer zone from the river has been created to understand the proximity to river Here we have classified into five zones they have distance of 200, 500,750, 1000 and 1500 m and they are having the area of 154 km2 (38.50%), 174 km2 (43.50%), 64 km2 (16%), 7 km2 (1.75%) and 1 km2 (0.25%) respectively (Fig. 4.5). Highest weight has been assigned to the buffer having the distance of 200 m form the river and the lowest assigned to the 1500 m as it is the farthest from river.

76

U. Senapati et al.

Fig. 4.4 Soil texture map of the study area

4.5.4 Modified Normalized Differentiate Water Index (MNDWI) The normalized difference water index (NDWI) has been widely utilized for mapping surface water bodies. But it has been found that when the indicator is used to map of water bodies, its sensitivity may increase the number of false positives (when a water surface feature is missing, it is identified). To enhance the method’s accuracy using “Modifed Normalized Diference Water Index” (MNDWI). MNDWI concept was conceived by Xu (2005) which uses the Green and SWIR bands. MNDWI used for evaluating the occurrence of water and land cover by removing atmospheric noise

4 Assessment of Potential Land Suitability for Economic Activity Using …

77

Fig. 4.5 Distance from river map of the study area

and terrain conflicts (Biswas et al. 2020) the MNDWI applied using the following equation. MNDWI = (Green − SWIR) / (Green + SWIR) where, Green = green band pixel values and SWIR = short-wave infrared band (SWIR) pixel values. It is one of the key factors that influence different type of economic activity of an area. Greater weight is given to the zone where the agricultural activity and vice-versa. The MNDWI map of the study area has been classified into the following five zones: very low (−0.26 to −0.12), low (−0.11 to −0.082), moderate (−0.081 to 0.01), high (0.011 to 0.047) and very high (0.048–0.29) These categories covered

78

U. Senapati et al.

Fig. 4.6 MNDWI map of the study area

114 km2 (28.50%), 133 km2 (33.25%), 81 km2 (20.25%), 49 km2 (12.25%) and 23 km2 (5.75%) of area respectively (Fig. 4.6).

4.5.5 Normalized Differentiate Vegetation Index (NDVI) NDVI is remote sensing based vegetation index used for detect the condition of vegetation of an area. The NDVI is calculated using the following formula (Rouse et al. 1974). NDVI = (NIR − Red) / (NIR + Red)

4 Assessment of Potential Land Suitability for Economic Activity Using …

79

where, NIR is near infrared band and red represents red band. Red and NIR band used for calculating the NDVI and range varies from −1 to +1 (Senapati and Das 2021). It is one of the key parameter that has been used worldwide for delineating potential suitable zone for economic activity. Highest weight has been assigned to the highest value of NDVI and vice-versa for the fact that healthy condition of vegetation indicates that the every condition is suitable for plant growth and agricultural activity and in the other areas can be used for other altered economic activities, The NDVI map of the study area has been classified into the following five zones: very low (−0.089 to 0), low (0.001 to 0.083), moderate (0.084 to 0.1), high (0.11 to 0.0.13) and very high (0.14–0.29) (Fig. 4.11). These categories covered 10 km2 (2.50%), 57 km2 (14.25%), 177 km2 (44.25%), 132 km2 (33%) and 24 km2 (6%) of area respectively (Fig. 4.7).

4.5.6 Ground Water Presence of Groundwater in the area is one of the important factor for assessing the potential suitable zone for economic activity because water control all type of economic activity either directly or indirectly. Here, Groundwater data is gathered from India’s Central Ground Water Board (CGWB). Groundwater map has been created by averaging the pre-monsoon, post-monsoon (Rabi), and monsoon ground water levels. In the ArcGIS environment, point data is transformed into a thematic layer using the interpolation (IDW) method. This thematic map depicts the basin’s average groundwater scenario. Greater water holding capabilities correspond to superior moisture accessibility which is appropriate for agricultural activity (Juhos et al. 2019). Other areas where the availability of ground water is comparatively less can be used for other economic activity like agro-forestry, small scale Industry etc. Maximum weights given to the higher value of ground water and viceversa because 80% of the area study has been used agricultural activity which governs by groundwater. According to the data of CGWCB five classes of ground water level has been identified they are very low (2.35–4.28 mbgl), low (4.29–4.94 mbgl), medium (4.95– 5.59 mbgl), high (5.60–6.40 mbgl), and very high (6.41–47.62 mbgl), which cover about 34 km2 (8.50%), 125 km2 (31.25%), 163 km2 (40.75%), 48 km2 (12%), and 30 km2 (7.50%) areas, respectively (Fig. 4.8).

4.5.7 Rainfall Rainfall is one of the main parameter that control the land suitability for different activity throughout the world (Ziadat et al. 2012). In the study area agricultural activity is predominant specially amon paddy and maize which requires lots of water

80

U. Senapati et al.

Fig. 4.7 NDVI map of the study area

in the time of farming. Small scale industry like silk production, tassar, matka and garad scarf or chadars are being made here but rainfall doesn’t much to do. Greater weights are given to places where the amount of received rainfall is more and viceversa. According to the data of IMD five classes of rainfall zone has been identified they are very low (1796–1851 mm), low (1852–1896 mm), medium (1897–1937 mm), high (1938–1976 mm), and very high (1977–2033 mm), which cover about 30 km2 (7.5%), 46 km2 (11.5%), 67 km2 (16.75%), 187 km2 (46.75%), and 70 km2 (17.5%) areas, respectively (Fig. 4.9).

4 Assessment of Potential Land Suitability for Economic Activity Using …

81

Fig. 4.8 Ground water map of the study area

4.6 LULC Knowledge of land use and land cover is essential criteria to assess criteria for making land suitability map because its gives the idea about how the local peoples are using their land from the past as a sustainable away (Sachin 2011). It’s may not be the ideal use of the land use and this one important factor for identify the land suitability map. Agricultural land and vegetation cover has been allotted with high weight, fallow land and water bodies allotted with moderate weight whereas settlement and industrial area allotted with lowest weight because of its influencing capability. Six types of land use and land cover classes have been observed in study are; industrial area, settlement, water body, natural vegetation, fallow land and agricultural land (Fig. 4.10). Using the following matrix in Table 4.7, an accurate evaluation of the Land use and Land cover (LULC) has been was generated to 93.37% overall

82

U. Senapati et al.

Fig. 4.9 Rainfall map of the study area

accuracy with a kappa coefficient (k) of 0. 917,699 based on ground reality information verification of 166 sample locations. The industrial area consist of 1.25% area, settlement consists of 9.75% area, water body consists of 6.50% area, natural vegetation consists of 17.75% area, fallow land consists of 17.75%, and agricultural land of 47.00% area. These categories cover 5, 39, 26, 71, 71 and 188 km2 of the study area, respectively.

4.6.1 Curvature Curvature is the quantitative measures of the earth surface. The values of the curvature denotes the inclination of the topography of a particular area it varies from convex

4 Assessment of Potential Land Suitability for Economic Activity Using …

83

Fig. 4.10 LULC map of the study area

to flat to concave (Senapati and Das 2021). Negative, zero and positive values of the slope indicates the concave, flat and convex respectively (Biswas et al. 2020). It is one of the most important parameter to assess the land suitability assessment. Convex region areas having very low rate of infiltration which is not supportive for agricultural activities because it can’t hold the water and opposite condition prevails in concave areas which is very much ideal (Nair et al. 2017). In this study more emphasis given to agricultural activities more than 80% people are doing agriculture as their primary source of income because of its nearer location to the river. Highest weight has been given to the concave areas, flat has been given medium and convex surface has been given lowest subject upon their influences in the suitability. The field of study is basically covered by three categories, namely: convex, flat, and concave. Study area falls under the convex area which is 149 km2

84

U. Senapati et al.

Table 4.7 Landuse and Landcover matrix LULC

NV

WB

SE

FL

AL

IA

Natural vegetation (NV)

43

0

0

0

1

0

Water bodies (WB)

0

22

0

0

0

Settlement (SE)

0

0

25

1

Fallow land (FL)

0

0

0

Agricultural land (AL)

0

0

Industrial area (IA)

0

Total (Producer)

43

Total (user)

Users accuracy

Producer accuracy

44

97.727

100

1

23

95.652

95.652

1

0

27

92.592

100

19

1

1

21

90.476

82.608

0

2

38

0

40

95

90.476

1

0

1

1

8

11

72.727

80

23

25

23

42

10

166

(37.25%). Total 204 km2 (51%) and 47 km2 (11.75%) of the study area is covered with flat area and concave respectively (Fig. 4.11).

4.6.2 Slope Slope analysis is one of the most solitary factor that influences land suitability throughout the world. Slope facts more in the mountainous region as our study area having the moderate to gentle slope that’s why slope is less dominant factor in this study (Zolekar and Bhagat 2015). Approximately all the places are flat where agriculture activity is being performed. Highest weight has been allotted to the less slope areas and vice-versa because of the fact plain areas are suitable to perform any type of economic activities on the land. The slope map of the study area has been categorized into the following five classes, namely: flat slope (0–1.3) covering 126 km2 (31.50%), gentle slope (1.4–2.7) covering 171 km2 (42.75%), moderate slope (2.8–4.6) covering 84 km2 (21%), steep slope (4.7–13) covering 18 km2 (4.50%), and very steep slope (14–37) covering 1 km2 (0.25%) area (Fig. 4.12).

4 Assessment of Potential Land Suitability for Economic Activity Using …

85

Fig. 4.11 Curvature map of the study area

4.6.3 Elevation Elevation specify the unevenness of the surface (Senapati and Das 2021). Elevation is one of the factor that influences the land suitability but not as much as other factors in this study area. Elevation of the study are varies from 72 to 260 m from the mean sea level which indicates it doesn’t have much variability. Highest weight is been given to the less elevated area and vice-versa because of its suitability to perform economic activities. The study area is divided into five classes, which are as follows: terrain height (Fig. 4.12), very low (72–100 m), low (110–120 m), moderate (130–150 m), high (160–200 m), and very high (210–260 m). These classes have covered 80 km2 area

86

U. Senapati et al.

Fig. 4.12 Slope map of the study area

(20%), 134 km2 area (33.50%), 120 area (30%), 64 km2 area (16%), and 2 km2 area (0.50%), respectively (Fig. 4.13).

4.6.4 Potential Land Suitability Zone The main geographical parameter which have feasible effect on potential land suitability for economic activity in study area are soil-depth, soil-texture, river-buffer, MNDWI, NDVI, Groundwater, rainfall, LULC, curvature, slope and elevation. Potential land suitability for economic activity zone mapping has been outlined of the study area based on multi-influencing AHP technique and GIS-weighted overlay analysis using the ArcGIS 10.8 platform. On the basis of the output map the field of

4 Assessment of Potential Land Suitability for Economic Activity Using …

87

Fig. 4.13 Elevation map of the study area

the study exposed with seven suitable zones of economic activities, namely highly suitable for agriculture, moderately suitable for agriculture, low to moderate suitable for agriculture, transition zone-I, suitable for agro-forestry and pastureland, transition zone-II, suitable for industrialization covering an area of 84 (21%), 81 (20.25%), 76 (19%), 57 (14.25), 51 (12.75%), 26 (6.5%) and 25 (6.25%) km2 respectively. The result shows that (Fig. 4.14) north-eastern part of gangaljalghati block, southeastern part of saltora block and eastern part of chhatna is highly suitable for agriculture. South-western part of bankura-II block and north-eastern part of saltora and chhatna block is suitable for Agro-forestry and pastureland where as northern part of saltora and chhatna block is suitable for industrialization. Some parts of all four blocks namely saltora, gangaljalghati, bankura-II and chhatna falls under transitional zone–I means low-moderate suitable for agriculture, agro-forestry and pastureland. Some parts of saltora, chhatna and bankura-II

88

U. Senapati et al.

Fig. 4.14 Potential land suitability map of the study area

falls transitional zone-II means suitable for Agro-forestry and pastureland and industrialization. Transitional parts are the places where both economic activities can be done partially because this places are not suitable for one particular activity.

4 Assessment of Potential Land Suitability for Economic Activity Using …

89

4.7 Discussion This study is an application part of a micro-level land utilization planning. The common people collect all their necessities depending on things like (food, cloth, and habitat) on the land. However, due to the increasing population pressure, scientific planning of land use becomes very necessary. Here the study is planning micro land utilization by dividing it into seven potentially suitable areas with the highest agricultural weight and lowest industrial weights. The land use land cover map of the study area shows (Fig. 4.10) that 47% of the total area falls under agricultural land and 17.75% of the land is considered fallow land at that time. Class I is highly suitable for agriculture, being agriculture dominant region peoples does agriculture for their both ends met. Lacks of knowledge regarding the demand of market are obstructing to earn more because they are only following the traditional method of agriculture and selective crops. They have to incorporate different techniques like manuring, mulching, crop rotation and bush following to maintain this zone as a highly suitable for agriculturefor log time period (Kawecki and Tomaszewska 2006). Class II is moderately suitable for agriculture as the name this arenot highly suitable but agricultural activity can be done with great profit. People are cultivating two crops in each year paddy and mustered on their land depends upon the availability of water. Three crops cropping system is need to be introduce by constricting check dams and other irrigation facilities. (Lefroy and Rydberg 2003) Instead of paddy and mustered paddy, mustered and pulses may cultivate. Class III and IV are Low to moderate suitable for agriculture and Transitional Zone–I respectively. In this two mentioned groups traditional agriculture will not give the satisfactory result for the farmers. Co-operative farming is essential in these two zones it will allow them to do what a big farm can do. Land may be utilized for different crops, vegetables and fruits can be grown and it can be harvested throughout the year. Bankura is well known for growing Fruits like plum, mango, pineapple and dragonfruits this can be used for the purpose of making jam, jelly, and pickle (Roy 2014). It’s a new strategy for the farmers if they can adopt they will be beneficial. Class V namely suitable for Agro-forestry and pastureland the land is suitable for agroforestry and pasturing where different type of trees like teak, sal, mahogini, boab, sandal wood, moringo, acacia etc. can be planted and animals like cow, ship, and goat can be reared. Napier, brachiaria and tall fescue grasses are very suitable for feeding those animals. Basically zone V is for giving the favourable condition for growing trees, grasses and animals. In the class VI transitional zone II peoples must have to build timber, non-timber, diary and slaughter industry based on the product of zone-V. Under this timer industry making of wooden furniture like chair, table, dressing table, cot etc. and under nontimber industry making of glass and bowls from sal leaves, biri from tendu leaves, liquor from mahua and different decorative handicrafts from the branches and leaves of tree can be made.

90

U. Senapati et al.

From the milk of reared animals different dairy products like butter, ghee, milk power, cheese, and paneer can be made and meat can be supplied to the market. This is how the land which is not suitable for agriculture can also be used for earning. Class VII namely suitable for industrialization: It is possible to develop some small-scale iron and steel industries in this region through public or private initiatives. In some PPT models, it is possible to build the ancillary industry like Cement bricks (Block), manufacturing parts, components, sub-assemblies, tools, intermediates, machines, etc. This is because there is a lot of labor and a lot of vacant land in this area. So it is possible to set up industries in this area with the help of the government. Suitable land for agriculture is not available everywhere so in this modern age one has to plan for sustainable use of land resources. So this potential area will be very beneficial to get maximum profit from land resources for sustainable use of the Gandheswari watershed.

4.7.1 Validation Validation is one of the most key part of any scientific research and without this part entire research becomes very much unfeasible. The proposed potential land suitability zone for economic activity of gandheswari watershed has been validated by using 100 points, which were randomly collected by the GPS from the four blocks namely chhatna, bankura-II, gangajalghati and saltora with existing economic activity in particular points. Then we have compared the actual economic activity points with the predicted economic activity model to identify the match and mismatch with the binary code. The value 1 indicate that the condition accepted and 0 indicates condition rejected. The validation has been done by the receiver operating characteristic (ROC) curve, cause the ROC curve is widely used by the researcher to validate the different potential zone (Pontius et al. 2001; Park et al. 2011; Lourdes et al. 2011; Vincent et al. 2019). Depending upon the relationship between AUC values and forecast accuracy, AUC value divided into the subsequent classes: “0.9– 1.0” excellent, “0.8–0.9” very good, “0.7–0.8” good, “0.6–0.7” average, and “0.5– 0.6” poor (Naghibi and Pourghasemi 2015; Rahmati et al. 2016, Guru et al. 2017; Arabameri et al. 2018; Senapati and Das 2020; Senapati and Das 2022b). It has been noticed that the area under curve value (AUC) is 0.72. The result of AUC (0.72) and std. error (0.060) under non-parametric estimates (Fig. 4.15). Relationship between the collected 100 points by GPS and potential land suitability for economic activity zone map of gandeshwari watershed indicate that it is well- predicted model and the accuracy is 72%. Ultimately the potential land suitability for economic activity zones showed that the model has given good result.

4 Assessment of Potential Land Suitability for Economic Activity Using …

91

Fig. 4.15 ROC curve for the potential land suitability zone map of gandheswari watershed

4.8 Conclusion The entire study aims to identify the potential land suitability for economic activity using AHP, remote sensing and GIS techniques in drought prone Gandheswari watershed, Bankura, West Bengal. A map of land suitability zone has been prepared by combining all 11 thematic layers namely soil depth, soil texture, distance from river, modified normalized differentiate water index (MNDWI), normalized differentiate vegetation index (NDVI), ground water, rainfall, land use land cover (LULC), curvature, slope and elevation. The result have been validated with the collected 100 GPS existing points. The assimilated map classified into seven classes they are as follows- Highly suitable for agriculture (84 km2 ), Moderately suitable for agriculture (81 km2 ), Low to Moderately suitable for agriculture (76 km2 ), Transitional zone-I, having less agriculture, agro-forestry and pasture (57 km2 ), Suitable for Agro-forestry and pastureland (51 km2 ), Transitional zone-II (26 km2 ), Class-VII: Suitable for industrialization (25 km2 ).Only 165 km2 of land in the drought-prone gandheswari watershed is suitable for agricultural work and agricultural production of some small and scattered lands may not produce enough grain to meet the growing demand of the region. In addition, overpopulation has led to land degradation and reduced soil fertility in recent years. On the other hand, it is difficult to provide livelihood for

92

U. Senapati et al.

such a large population, after judging the usability of the land and determining the appropriate functions by using the land economically for any purpose. As a result, it is suggested that rapid action must be taken to improve the quality of land efficiency and to develop alternative livelihoods through local resource management by dividing the entire region into seven Land suitability zones. Sustainable farming practices, pasture development and animal husbandry, agro-forestry, social forestry, joint forest management and smartagriculture may be used as alternative methods to protect soil quality, establish cottage industries using local resources to promote biodiversity, and industrialization with government support and some using the PPT model. The establishment of ancillary industries should be considered as a marginal and currently suitable place for a sufficient source of revenue for the people of this region. It is very supportive for planning sustainable management of the land. It will definitely help the local people of gandheswari watershed to use their land in a manner that they can avail the maximum result. This method is very cost effective and operational for land use planning specially in terms of choosing suitable economic activity for their land. Acknowledgements We are thankful to the local villagers and farmers of Puruliya and Bankura district for their tremendous knowledge sharing during collecting ground-level information and opinion on this study. We would like to acknowledge the National Bureau of Soil Survey and Land Use Planning, Central Ground Water Board, India Meteorological Department and United States Geological Survey for available data. Conflict of Interest The authors declare that they have no conflict of interest.

References AbdelRahman MAE, Natarajan A, Hegde R (2016) Assessment of land suitability and capability by integrating remote sensing and GIS for agriculture in Chamarajanagar district, Karnataka, India. Egypt J Remote Sens Space Sci 19(1):125–141. https://doi.org/10.1016/j.ejrs.2016.02.001 Akbari M, Neamatollahi E, Neamatollahi P (2019) Evaluating land suitability for spatial planning in arid regions of eastern Iran using fuzzy logic and multi-criteria analysis. Ecol Indic 98: 587–598. https://doi.org/10.1016/j.ecolind.2018.11.035 Arabameri A, Rezaei K, Pourghasemi HR, Lee S, Yamani M (2018) GIS-based gully erosion susceptibility mapping: a comparison among three data-driven models and AHP knowledgebased technique. Environ Earth Sci 77(17). https://doi.org/10.1007/s12665-018-7808-5 Aydi A, Abichou T, Nasr IH, Louati M, Zairi M (2015) Assessment of land suitability for olive mill wastewater disposal site selection by integrating fuzzy logic, AHP, and WLC in a GIS. Environ Monit Assess 188(1). https://doi.org/10.1007/s10661-015-5076-3 Bagdanaviˇci¯ut˙e I, Vali¯unas J (2012) GIS-based land suitability analysis integrating multi-criteria evaluation for the allocation of potential pollution sources. Environ Earth Sci 68(6):1797– 1812. https://doi.org/10.1007/s12665-012-1869-7 Bandyopadhyay S, Jaiswal RK, Hegde VS, Jayaraman, V (2009) Assessment of land suitability potentials for agriculture using a remote sensing and GIS based approach. Int J Remote Sens 30(4):879–895. https://doi.org/10.1080/01431160802395235

4 Assessment of Potential Land Suitability for Economic Activity Using …

93

Bera A, Mukhopadhyay BP, Das D (2019) Landslide hazard zonation mapping using multi-criteria analysis with the help of GIS techniques: a case study from Eastern Himalayas, Namchi, South Sikkim. Nat Hazards. https://doi.org/10.1007/s11069-019-03580-w Biswas S, Mukhopadhyay BP, Bera A (2020) Delineating groundwater potential zones of agriculture dominated landscapes using GIS based AHP techniques: a case study from Uttar Dinajpur district, West Bengal. Environ Earth Sci 79(12). https://doi.org/10.1007/s12665-020-09053-9 Bojórquez-Tapia LA, Diaz-Mondragón S, Ezcurra E (2001) GIS-based approach for participatory decision making and land suitability assessment. Int J Geogr Inf Sci 15(2):129–151. https://doi. org/10.1080/13658810010005534 Bozda˘g A, Yavuz F, Günay AS (2016) AHP and GIS based land suitability analysis for Cihanbeyli (Turkey) County. Environ Earth Sci 75(9). https://doi.org/10.1007/s12665-016-5558-9 Cao Y, Chan F, Chui YH, Xiao H (2012) Characterization of flax fibres modified by alkaline, enzyme, and steam-heat treatments. BioResources 7(3):4109–4121 Chakraborty S, Kumar RN (2016) Assessment of groundwater quality at a MSW landfill site using standard and AHP based water quality index: a case study from Ranchi, Jharkhand, India. Environ Monit Assess 188(6). https://doi.org/10.1007/s10661-016-5336-x Chaudhary S, Kumar A, Pramanik M, Negi MS (2021) Land evaluation and sustainable development of ecotourism in the Garhwal Himalayan region using geospatial technology and analytical hierarchy process. Environ Dev Sustain. https://doi.org/10.1007/s10668-021-01528-4 Chandio IA, Matori A-N, Lawal DU, Sabri S (2011) GIS-based land suitability analysis using AHP for public parks planning in Larkana city. Mod Appl Sci 5(4). https://doi.org/10.5539/mas.v5n 4p177 Das S (2018) Geographic information system and AHP-based flood hazard zonation of Vaitarna basin, Maharashtra, India. Arab J Geosci 11(19). https://doi.org/10.1007/s12517-018-3933-4 Duc TT (2006) Using GIS and AHP technique for land-use suitability analysis. In International symposium on geoinformatics for spatial infrastructure development in earth and allied sciences, pp 1–6 Ennaji W, Barakat A, El Baghdadi M, Oumenskou H, Aadraoui M, Karroum LA, Hilali A (2018) GIS-based multi-criteria land suitability analysis for sustainable agriculture in the northeast area of Tadla plain (Morocco). J Earth Syst Sci 127(6). https://doi.org/10.1007/s12040-0180980-x Ghosh D, Mandal M, Karmakar M, Banerjee, M, Mandal D (2020) Application of geospatial technology for delineating groundwater potential zones in the Gandheswari watershed, West Bengal. Sustain Water Resour Manag 6(1). https://doi.org/10.1007/s40899-020-00372-0 Government of West Bengal (2014) District statistical handbook, Bankura 2014. Bureau of Applied Economics and Statistics, Kolkata Guru B, Seshan K, Bera S (2017). Frequency ratio model for groundwater potential mapping and its sustainable management in cold desert, India. J King Saud Univ Sci 29(3):333–347. https:// doi.org/10.1016/j.jksus.2016.08.003 Horˇnáková N, Jurík L, Hrablik Chovanová H, Cagáˇnová D, Babˇcanová D (2019) AHP method application in selection of appropriate material handling equipment in selected industrial enterprise. Wirel Netw.https://doi.org/10.1007/s11276-01 Hossain MS, Chowdhury SR, Das NG, Rahaman MM (2007) Multi-criteria evaluation approach to GIS-based land-suitability classification for tilapia farming in Bangladesh. Aquac Int 15(6):425– 443. https://doi.org/10.1007/s10499-007-9109-y Juhos K, Czigány S, Madarász B, Ladányi M (2019) Interpretation of soil quality indicators for land suitability assessment—a multivariate approach for Central European arable soils. Ecol Indic 99:261–272. https://doi.org/10.1016/j.ecolind.2018.11.063 Kawecki Z, Tomaszewska Z (2006) The effect of various soil management techniques on growth and yield in the black chokeberry (Aronia melanocarpa Elliot). J Fruit Ornam Plant Res 14:67 Kumar A, Pramanik M, Chaudhary S, Negi MS, Szabo S (2022) Geospatial multi-criteria evaluation to identify groundwater potential in a Himalayan District, Rudraprayag, India. Environ Dev Sustain 1–42. https://doi.org/10.1007/s10668-021-02107-3

94

U. Senapati et al.

Kumru M, Kumru PY (2014) Analytic hierarchy process application in selecting the mode of transport for a logistics company. J Adv Transp 48(8):974–999. https://doi.org/10.1002/atr.1240 Layomi Jayasinghe S, Kumar L, Sandamali J (2019) Assessment of potential land suitability for tea (Camellia sinensis (L.) O. Kuntze) in Sri Lanka Using a GIS-Based Multi-Criteria Approach. Agriculture 9(7):148. https://doi.org/10.3390/agriculture9070148 Lefroy E, Rydberg T (2003) Emergy evaluation of three cropping systems in southwestern Australia. Ecol Modell 161(3):195–211. https://doi.org/10.1016/S0304-3800(02)00341-1 Lourdes L, Karina Z, Pedro L, Héctor M, Néstor M (2011) A dynamic simulation model of land cover in the Dulce Creek Basin, Argentina. Procedia Environ Sci 7:194–199. https://doi.org/10. 1016/j.proenv.2011.07.034 Mandal M, Das Chatterjee N (2021) Geospatial approach-based delineation of elephant habitat suitability zones and its consequence in Mayurjharna Elephant Reserve, India. Environ Dev Sustain. https://doi.org/10.1007/s10668-021-01412-1 Mendas A, Mebrek A, Mekranfar Z (2020) Comparison between two multicriteria methods for assessing land suitability for agriculture: application in the area of Mleta in western part of Algeria. Environ Dev Sustain 23(6):9076–9089. https://doi.org/10.1007/s10668-020-01012-5 Moisa MB, Tiye FS, Dejene IN, Gemeda DO (2022) Land suitability analysis for maize production using geospatial technologies in the Didessa watershed, Ethiopia. Artif Intell in Agri 6:34–46 Mondal B, Mistri B (2016) Mapping and monitoring of LULCC through Integrated analysis of multi-temporal images and change matrix in Gandheswari river basin Musakwa W (2017) Identifying land suitable for agricultural land reform using GIS-MCDA in South Africa. Environ Dev Sustain https://doi.org/10.1007/s10668-017-9989-6 Naghibi SA, Pourghasemi HR (2015) A comparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods in groundwater potential mapping. Water Resour Manag 29(14):5217–5236. https://doi.org/10. 1007/s11269-015-1114-8 Nair HC, Padmalal D, Joseph A, Vinod PG (2017) Delineation of groundwater potential zones in river basins using geospatial tools—an example from Southern Western Ghats, Kerala, India. J Geovis Spat Anal 1(1–2). https://doi.org/10.1007/s41651-017-0003-5 Nurda N, Noguchi R, Ahamed T (2020) Change detection and land suitability analysis for extension of potential forest areas in Indonesia using satellite remote sensing and GIS. Forests 11(4):398. https://doi.org/10.3390/f11040398 Pal SC, Chakrabortty R (2019) Simulating the impact of climate change on soil erosion in subtropical monsoon dominated watershed based on RUSLE, SCS runoff and MIROC5 climatic model. Adv Space Res 64:352–377. https://doi.org/10.1016/j.asr.2019.04.033 Pal SC, Shit M (2012) Geo-hydrological study of gandheshwari subwatershed using remote sensing and GIS techniques. Int J Geomatics Geosci 3(1):204–218 Park S, Jeon S, Kim S, Choi C (2011) Prediction and comparison of urban growth by land suitability index mapping using GIS and RS in South Korea. Landsc. Urban Plan 99(2):104–114. https:// doi.org/10.1016/j.landurbplan.2010.09.001 Pontius RG, Schneider LC (2001) Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agric Ecosyst Environ 85(1–3):239–248. https://doi. org/10.1016/s0167-8809(01)00187-6 Rahmati O, Haghizadeh A, Pourghasemi HR, Noormohamadi F (2016) Gully erosion susceptibility mapping: the role of GIS based bivariate statistical models and their comparison. Nat Hazards 82:1231–1258. https://doi.org/10.1007/s11069-016-2239-7 Rouse JW, Haas RH, Scell JA, Deering DW, Harlan JC (1974) Monitoring the vernal advancements and retrogradation (green wave effect) of nature vegetation. In: NASA/GSFC final report MD.371. NASA, Greenbelt Roy S, Singha N, Bose A, Basak D, Chowdhury RI (2022) Multi-influencing factor (MIF) and RS– GIS-based determination of agriculture site suitability for achieving sustainable development of Sub-Himalayan region. Environ Dev Sustain, India. https://doi.org/10.1007/s10668-022-023 60-0

4 Assessment of Potential Land Suitability for Economic Activity Using …

95

Roy TN (2014) Minor (under-utilized) Fruits in Coochbehar District of West Bengal, India–an analysis on marketing status for economic viability. Int J Bio-Resour 5(1):122–127. https://doi. org/10.5958/j.0976-4038.5.1.024 Saaty TL (1980) The analytic hierarchy process: planning, priority setting, resource allocation. McGraw Hill, New York Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48(1):9–26. https://doi.org/10.1016/0377-2217(90)90057-I Sachin P (2011) Land capability classification for integrated watershed development by applying remote sensing and GIS techniques. J Agric Biol Sci 6(4):46–55 Safitri S, Wikantika K, Riqqi A, Deliar A, Sumarto I (2021) Spatial allocation based on physiological needs and land suitability using the combination of ecological footprint and SVM (Case Study: Java Island, Indonesia). ISPRS Int J Geo-Inf 10(4):259. https://doi.org/10.3390/ijgi10040259 Sahai VN (2004) Fundamentals of Soil. Kalyani Publishers, Kolkata-09 Senagi K, Jouandeau N, Kamoni P (2017) Using parallel random forest classifier in predicting land suitability for crop production. Jagric Inform 8(3):23–32 Senapati U, Das TK (2020) Assessment of potential land degradation in Akarsha Watershed, using GIS and multi-influencing factor technique. In: Shit P., Pourghasemi H., Bhunia G (eds) Gully erosion studies from India and surrounding regions. Advances in science, technology & innovation (IEREK interdisciplinary series for sustainable development). Springer, Cham, pp 187–205. https://doi.org/10.1007/978-3-030-23243-6_11 Senapati U, Das TK (2021) Assessment of basin-scale groundwater potentiality mapping in drought-prone upper Dwarakeshwar River basin, West Bengal, India, using GIS-based AHP techniques. Arab J Geosci 14(11):1–22. https://doi.org/10.1007/s12517-021-07316-8 Senapati U, Das TK (2022a) Geospatial assessment of agricultural drought vulnerability using integrated three-dimensional model in the Upper Dwarakeshwar River Basin in West Bengal, India. Environ Sci Pollut Res 1–28. https://doi.org/10.1007/s11356-022-23663-9 Senapati U, Das TK (2022b) GIS-based comparative assessment of groundwater potential zone using MIF and AHP techniques in Cooch Behar district, West Bengal. Appl Water Sci 12 (43):1–18. https://doi.org/10.1007/s13201-021-01509-y Senapati U, Raha S, Das TK, Gayen SK (2021) A composite assessment of agricultural drought susceptibility using analytic hierarchy process: case study of western region of West Bengal. In: Rukhsana, Alam A (eds) Agriculture, food and nutrition security. Springer, Cham. https://doi. org/10.1007/978-3-030-69333-6_2 Senapati U, Talukdar D, Saha D, Das TK (2023b) Geo-spatial assessment of groundwater drought risk zone due to drought propagation in the Upper Dwarakeshwar River Basin (UDRB), West Bengal. In: Case studies in geospatial applications to groundwater resources. Elsevier, pp 315– 341. https://doi.org/10.1016/B978-0-323-99963-2.00019-5 Senapati U, Raha S, Das TK, Gayen SK (2023a) Identifying suitable sites for alternative agriculture in drought-prone Akarsa Watershed, West Bengal. In: Alam A, Rukhsana (eds) Climate change, agriculture and society: approaches toward sustainability. Springer, Cham, pp 339–363. https:// doi.org/10.1007/978-3-031-28251-5_18 Singha C, Swain KC (2016) Land suitability evaluation criteria for agricultural crop selection: a review. Agric Rev 37(2) Stoms DM, McDonald JM, Davis FW (2002) Fuzzy assessment of land suitability for scientific research reserves. Environ Manag 29(4):545–558 Tadic L. (2012) Criteria for evaluation of agricultural land suitability for irrigation in Osijek County Croatia. In: Problems, perspectives and challenges of agricultural water management, pp 311– 332 Tashayo B, Honarbakhsh A, Azma A, Akbari M (2020) Combined fuzzy AHP–GIS for agricultural land suitability modeling for a watershed in Southern Iran. Environ Manag 66:364–376. https:// doi.org/10.1007/s00267-020-01310-8 Vincent DR, Deepa N, Elavarasan D, Srinivasan K, Chauhdary SH, Iwendi C (2019) Sensors driven AI-based agriculture recommendation model for assessing land suitability. Sensors 19(17):3667

96

U. Senapati et al.

Wang D, Li C, Song X, Wang J, Yang X, Huang W, Zhou J (2011) Assessment of land suitability potentials for selecting winter wheat cultivation areas in Beijing, China, Using RS and GIS. Agric Sci China 10(9):1419–1430. https://doi.org/10.1016/s1671-2927(11)60135-1 Xu H (2005) Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sens 27(14):3025–3033. https://doi.org/10. 1080/01431160600589179 Ziadat F, Bruggeman A, Oweis T, Haddad N, Mazahreh S, Sartawi W, Syuof M (2012) A participatory GIS approach for assessing land suitability for rainwater harvesting in an arid rangeland environment. Arid Land Res Manag 26(4):297–311. https://doi.org/10.1080/15324982.2012. 709214 Zolekar RB (2018) Integrative approach of RS and GIS in characterization of land suitability for agriculture: a case study of Darna catchment. Arab J Geosci 11(24). https://doi.org/10.1007/s12 517-018-4148-4 Zolekar RB, Bhagat V (2015) Multi-criteria land suitability analysis for agriculture in hilly zone: Remote sensing and GIS approach. Comput Electron Agric 118:300–321. https://doi.org/10. 1016/j.compag.2015.09.016

Chapter 5

Spatial–Temporal Changes of Urban Sprawl, LULC and Dynamic Relationship Between Land Surface Temperature (LST) and Bio-Physical Indicators: A Study of Kolkata Municipal Corporation, West Bengal Gourab Saha, Sandipan Das, Suvarna Tikle, and Pravat Kumar Shit

Abstract The rapid urbanization process has become a primary environmental concern due to environmental impacts, such as the drastic change in land-use/landcover (LULC) and changes in the local climate. Rapidly growing areas such as Kolkata need to develop digital skills, which can be done easily with high-resolution remote sensing data. In this study an effort has been made to find out how changing trends of LULC and bio-physical indicators (NDVI, NDWI and NDBI) effects on Land Surface Temperature (LST) over the period of time. This study also attempts to make non-linear relationship among LST, LULC and bio-physical indicators which creates a significant output to know the exact factors those are affecting on microclimatic change in Kolkata city. Landsat TM and OLI-TIRS image datawere utilized to extract information regarding land-use LST and biophysical indicators of Kolkata city between 1990, 2005, and 2019. In this study, GAM modelusing R programming has been used to analyze the relationship between Land Surface Temperature and bio-Physical indicators (NDVI, NDWI, and NDBI). LULC has dramatically changed in the city center as well as surrounding rural areas, resulting in a significant spatiotemporal impact on LSTs. Sustainable urban planning can be improved through these findings, as well as a reduction of the negative impacts of urbanization on the environment. G. Saha · S. Das (B) Symbiosis Institute of Geoinformatics (SIG), Symbiosis International (Deemed University) (SIU), Pune, Maharashtra, India e-mail: [email protected] S. Tikle Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India P. K. Shit Department of Geography, Raja Narendralal Khan Women’s College (Autonomous), Gope Palace, P.O. Vidyasagar University, Paschim Medinipur, Midnapore 721102, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_5

97

98

G. Saha et al.

Keywords Urbanization · Remote sensing · Land Surface Temperature (LST) · NDBI

5.1 Introduction The fast-growing population and urbanization have become significant contributors to putting pressure on land and modifying the surface (Manoli et al. 2019; Gill et al. 2007). Rapid urbanization is the primary cause of the substantial increase in Land Surface Temperature (LST) (Wang et al. 2019; Keshtkar et al. 2017). The satellite thermal bands are significantly important to get the earth’s surface temperature (Shaban et al. 2020). Thermal bands generally capture the surface temperature, which varies according to the type of earth surface. Kolkata Municipal Corporation faces severe environmental issues in LULC change, leading to drastic LST changes (Parveen and Ilahi 2022). The city’s periphery faces the drasticconversion of vegetation cover, agricultural land and waterbodies into urban areas. Some patches in the city area with vegetation cover and water body area havea relatively low surface temperature. The increase in Land Surface Temperature (LST) can negatively impact human health and the water table in the region (Al-sharif et al. 2015). Ghosh et al. (2017) have worked on Kolkata and the peripheral region to find out the urban sprawl and LST change. Their study found that Kolkata is in the rapid urbanization stage, and the peripheral area of Kolkata is facing rapid LST change. Sikarwar and Chattopadhyay (2014) have discussedthe spatial and temporal change of LULC in seven cities in India. Among them, Kolkata is experiencing drastic LULC change, and the LULC change is mainly happening inthe southern direction due to the expansion of the built-up area. Mukherjee et al. (2018) have discussed how LULC change in Kolkata directly impacts water security. They have analyzed from 1980 to 2014 for change analysis. It is found that population growth and LULC change are happening faster, which can negatively affect the water table and increase the water pressure. This problem is happening because increasing population growth and LULC change directly, creating pressure on the water table. As the built area is expanding, wetlands in eastern Kolkata, which helps to increase the groundwater table and supply water, are fast changing into man-made built-up areas (Sahana et al. 2018). The main aim of this study is to find out the spatial–temporal changes of urban growth and LST from 1990 to 2019 and derive the relationship between biophysical indicators and LST using GIS and Remote sensing techniques. The research study’s findingswill help develop plans and policy formulations that can reduce the current problems.

5 Spatial–Temporal Changes of Urban Sprawl, LULC and Dynamic …

99

5.2 Study Area Kolkata is located in the western part of India and the capital of West Bengal. Kolkata is situated eastern bank of the Hooghly River, and for the strategic location, Kolkata helps to grow faster and become the 3rd largest city in India (Fig. 5.1). There are three municipal corporations in Kolkata urban agglomeration. One of them is Kolkata Municipal Corporation, which has 185 km2 and 4.5 million populations (Census of India 2011). The city experiences tropical dry and wet climate zone. The unprecedented rapid urbanizationsof Kolkata are of serious concern fordrastic LULC change, traffic congestion, LST change, urban health, socio-economic problem, etc. The rapid urbanization growth rate has caused the shrinkage ofwetlands in eastern Kolkata.

Fig. 5.1 Location map of Kolkata

100

G. Saha et al.

Table 5.1 Satellite image details Satellite image details Years

Name of satellite

Sensor’s name

Year

Path and row

Cloud cover

1990

Landsat-5

TM

30–01–90

138/44

0.00

2005

Landsat-5

TM

08–02–05

138/44

0.03

2019

Landsat-8

OLI-TIRS

03–01–19

138/44

1.00

5.3 Materials and Methods 5.3.1 Data Collection The multi-temporal satellite data of Landsat 5 and Landsat-8for the years 1990, 2005, and 2019 were acquired from the USGS Earth Explorer (https://earthexplorer.usgs. gov/) portal and applied in the research study (Table 5.1). These satellite images were used for Land use/Land cover (LULC) analysis, Land Surface Temperature (LST), and biophysical indicators calculations.

5.3.2 Land Use/Land Cover Classification In order to prepare the LULC classification map, satellite images were supervised classified using ERDAS Imagine 2014.The FCC satellite image was classified into five LULC classes: urban, vegetation, open land, water bodies, and grass cover. The spectral signaturesfor every LULC class were taken to prepare the classified map. The GCP points collected from Google Earth Pro softwarewere used to perform theKappa accuracy test to check classification accuracy. ArcMap 10.5 software was used to prepare the LULC for each year, along with LST and biophysical indicators.

5.3.3 Calculation of Land Surface Temperature (LST) Band 6 of Landsat 5 (T. M.) and band 10 for Landsat 8 (EM+ and OLI) were used in the study to prepare the Land Surface Temperature (LST) map. The various stepwise calculations applied to estimate to generate the LST are given below.

5.3.3.1

Conversion of Digital Number to Spectral Radiance    L = (LMAX − LMIN) (QCALMAX − QCALMIN) ∗ (QCAL − QCALMIN) + LMIN

5 Spatial–Temporal Changes of Urban Sprawl, LULC and Dynamic …

101

where, LMAX LMAX LMIN QCALMIN QCALMAX 5.3.3.2

denotes maximum radiance (spectral), denotes minimum radiance (spectral), denotes quantized calibrated pixel (minimum), denotes quantized calibrated pixel (maximum).

Conversion of Spectral Radiance to Brightness Temperature BT = (K2 /(ln (K1 / L) + 1)) − 273.15

where BT represents brightness temperature, K1 and K2 are constant which are available in metadata, L denotes the radiance of spectral bands. 5.3.3.3

Calculation of NDVI NDVI = (NIR − RED)/(NIR + RED)

where NDVI represents normalized difference vegetation index. 5.3.3.4

Calculation of Proportion of Vegetation (PV. ) PV = Square ((NDVI − NDVImin )/(NDVImax − NDVImin ))

where PV represents fractional vegetation. NVDI represents normalized difference vegetation index. Here, max means maximum value of NDVI and minimum value NDVI. 5.3.3.5

Calculation of Emissivity (ε) ε = 0.004Pv + 0.986

where ε is emissivity. Pv is fractional vegetation.

102

5.3.3.6

G. Saha et al.

Calculation of Land Surface Temperature (LST) LST = BT /(1 + (0.00115 ∗ BT /1.4388) ∗ Ln (ε)))

where LST represents Land Surface Temperature. BT represents brightness temperature. ε represents emissivity.

5.3.4 Calculation of Bio-Physical Indicators The biophysical indicators such as NDVI, NDWI, and NDBI indices were prepared from various remote sensing bands combinations. Thesteps to calculate NDVI, NDWI, and NDBI areshown below.

5.3.4.1

Estimation of NDVI

NDVI is used to find out the vegetation cover of the study area from 1990 to 2019. NDVI has been estimated from the near-infrared (NIR) and red (RED) bands. Vegetated areas show NDVI values above +1, while non-vegetated areas show NDVI values below −1. NDVI = (NIR − RED)/(NIR + RED)

5.3.4.2

Estimation of NDWI

NDWI is used to find out the water-covered area of the study area from 1990 to 2019. NDWI has been estimated from NIR and GREEN bands. The highest value of NDWI is +1,which indicates waterbodies area, and the lowest value of NDWI is − 1 indicates non-waterbodies area. NDWI = (GREEN − NIR)/(GREEN + NIR)

5.3.4.3

Estimation of NDBI

NDBI is mapped to generate a built-up cover area of KMC from 1990 to 2019. NDBI is estimated from the near-infrared (NIR) and shortwave infrared (SWIR) bands. The highest value of NDBI is +1 indicates built-up areas and the lowest value of NDBI

5 Spatial–Temporal Changes of Urban Sprawl, LULC and Dynamic …

103

is −1 showing non-built-up areas. NDBI = (SWIR − NIR)/(SWIR + NIR)

5.3.4.4

GAM Model

‘R-programming’ has been applied to buildthe relationship between biophysical indicators and LST using a non-linear regression curve (GAM Model). GAM means Generalized additive Model, which is mainly used to find a relation with the help of linearity and non-linearity curves. In this model, ‘Y’ represents a dependent variable (LST), and ‘X’-axis represents an independent variable (bio-physical indicators). The methodology flowchart of the research is shown in Fig. 5.2.

5.4 Results and Discussion 5.4.1 Spatiotemporal Land Cover Changes Over Time Figure 5.3 and Table 5.2 depict the LULC change over the period. The satellite image has been classified into five major LULC classes: waterbody, open land, urban, grass cover, and vegetation. The urban area has increased from 103 to 140 km2 . From 1990 to 2019. The LULC classes, including waterbody, open land, grass cover, and vegetation, have decreased and transformed into urban areas. The other LULC classes have shown changed negative transformation. Vegetation area has changed from 16 to 7% area coverage, and waterbodies area has decreased from 7 to 5% coverage during the period of study. The waterbodies area has significantly reduced due to the transformation of wetlandsinto the built-uparea in eastern Kolkata. Due to significant built-up, the open land has converted drastically from 8 to 2%. So, Kolkata Municipal Corporation should actively look into it and develop the area more sustainably.

5.4.2 Land Surface Temperature From 1990 to 2019 Figure 5.4 depicts that LST has been changing faster from 1990 to 2019. LST has been increasing towards the southern direction of the Kolkata Municipal Corporation (KMC) area asthe built-up area is expanding rapidly in this direction. In 1990, LST ranged from 28.78 to 18.84 °C in the area. The core city area is experiencing high surface temperature, and the periphery of the city, like southern, eastern, and western areas, are experiencing low surface temperature.

104

G. Saha et al.

Fig. 5.2 Methodology flow chart

In 2005, the Land Surface Temperature (LST) ranged from 27.94 to 17.02 °C and was observed expanding towards the city’s periphery. This is due to the decrease of vegetation cover, waterbody and grass cover area, and rapid built-up in the outer part of the city. In 2019, the Fig. 5.4 showed that the core city area is experiencing the highest surface temperature, and the outer city faces the maximum temperature. The temperature ranges from 42.63 to 26.12 °C. There are few patches in the outer part of the city area which only experience low surface temperature because of patches of vegetation and grass-covered area. The eastern part is also experiencing relatively low

5 Spatial–Temporal Changes of Urban Sprawl, LULC and Dynamic …

105

Fig. 5.3 LULC Map of Kolkata for years 1990, 2005 and 2019

Table 5.2 Change of LULC from 1990 to 2019 (Kolkata) Class

Area (km2 ) 1990

Area (%) 1990

Area (km2 ) 2005

Area (%) 2005

Area (km2 ) 2019

Area (%) 2019

Waterbody

13.40

7.30

10.45

5.71

9.14

4.98

Openland

15.44

8.42

6.76

3.70

4.00

2.18

103.04

56.17

119.00

65.07

140.34

76.50

Grass cover

21.97

11.98

21.00

11.48

17.00

9.27

Vegetation

29.59

16.13

25.65

14.03

12.96

7.06

Urban

Fig. 5.4 Land Surface Temperature (LST) map of Kolkata for years 1990, 2005 and 2019

temperature as the area is covered with wetlands but gradually, surface temperature also increases in this area because of transforming into a man-made surface.

106

G. Saha et al.

Fig. 5.5 NDVI map of Kolkata for year 1990, 2005 and 2019

From the above Fig. 5.4 and discussions, it can be said that the overall phenomenon is that Kolkata Municipal area hasa high surface temperature in the core area for rising settlement density area. The peripheral area also experiences maximum temperature for the destruction of the natural vegetation cover.

5.4.3 NDVI Changes From 1990 to 2019 Figure 5.5 shows the changes of NDVI from 1990 to 2019 in the Kolkata Municipal Corporation area. From the Fig. 5.5, it can be depicted that vegetation coversdecrease towards the southern direction. As discussed earlier, this phenomenon is happening in this direction only because urbanization is rapidly expanding. In 1990, it can be seen that the lowest vegetation cover only was in the core city area and some patches in the outer area. The changing nature of vegetation cover changed in 2019; vegetation cover started rapidly decreasing in the outer part of the city, only a few vegetation patches have remained in the peripheral area. The Eastern side where the wetland is situated started decreasing the vegetation cover in that area.

5.4.4 NDWI Changes From 1990 to 2019 Figure 5.6 depicts that the water index has rapidlydecreased from 1990 to 2019. In 1990, the highest water index was 0.375, but in 2019, the index significantly changed to 0.19. Therefore, NDWI analysis inferred that the water cover area has decreased from 1990 due to rapid urbanization. This phenomenon happens because Kolkata Municipal Corporation is filling up water cover areas for development purposes. In

5 Spatial–Temporal Changes of Urban Sprawl, LULC and Dynamic …

107

Fig. 5.6 NDWI map of Kolkata for year 1990, 2005 and 2019

the present scenario, it is observed that both sides of the Hooghly riverbank are facing rapid development, there by leading to a decrease in the water index in the bank area.

5.4.5 NDBI Changes From 1990 to 2019 Figure 5.7 illustratesthat the built-up area has been increasing across the whole KMC area from 1990 to 2019. The increasing population pressure leads to rapid urbanization in the periphery of the city. The transformation of man-made surfaces like built-up areas is leading to the significant increase of LST trend over the period of time.

Fig. 5.7 NDBI map of Kolkata for year 1990, 2005 and 2019

108

G. Saha et al.

5.4.6 GAM Model (Relationship Between LST and Bio-Physical Indicators from 1990 to 2019) The non-linearity graphs depict the relationship between LST and biophysical indicators. These graphs have been prepared through the GAM model in R programming. Figure 5.8 illustrate that the trend is increasing with the increasing trend of NDBI. This phenomenon is happening in the built-up area because the built-up area absorbs the solar heat mostly and increases the surface temperature in the city and the surrounding area. In the outer city area, vegetation cover areas are converted into open land and barren land.

Year 1990

Year 2005

Year 2019

Fig. 5.8 Relationship between LST and bio-physical indicators from 1990 to 2019 in KMC

5 Spatial–Temporal Changes of Urban Sprawl, LULC and Dynamic …

109

On the other hand, LST decreasesfor high vegetation and water bodies areas. Therefore, NDVI and NDWI are decreasing over the time for which Land Surface Temperature is increasing. In 2019, it can be seen that there are few patches of vegetation cover and water body area in the city area, which reflects the low surface temperature. The eastern part of the city is a good example of the changing nature of LST. In that, part wetland started decreasing and converting into other LULC classes, thereby showing an upward trend for the surface temperature. So, it can be concluded that there need to be more plantationsin order to increase vegetation cover and the water body needs to be conserved in the city. In order to meet the world urbanization trend, KMC also needs to develop in a healthy environment way by allocating green space in the city region.

5.5 Conclusion The study from 1990 to 2019 (29 years) illustrates that LULC and LST have significantly changed in the Kolkata Municipal Corporation area. The city’s periphery is witnessing rapid built-up area at the expense of vegetation cover and waterbody area. The Land Surface Temperature (LST) map for 2019 shows that almost the whole city is covered with high surface temperature. Only a few patches in the different parts of the city area have low surface temperature because of vegetation cover and water body, which acts as a cooling factor in those areas. The agricultural lands drastically are converted into a built-up area, industrial area, etc. This phenomenon is visible in the new town area of Kolkata, where vegetation cover, fallow land, agricultural lands are converted into the man-made surface like high-rise buildings. As per the GAM model, it is clear how built-up area increases LST and vegetation and waterbody areas decrease the LST. The integration of GIS with planning models, visualization, and the Internet has made GIS more useful to urban planners. At present, the main constraints to using GIS in urban planning are not technical issues, but the availability of data, organizational change, and staffing. The planners should develop the area in an eco-environment-friendly way. In this study, the authors suggest developing economies should establish a strong GIS data base so that planners can access data quickly and make informed decisions to promote sustainable urban planning and management.

References Al-sharif AA, Pradhan B (2015) Spatio-temporal prediction of urban expansion using bivariate statistical models: assessment of the efficacy of evidential belief functions andfrequency ratio models. Appl Spatial Anal Ghosh S, Singh P, Kumari M (2017) Assessment of urban sprawl and land use change dynamics, using remote sensing technique. a study of Kolkata and surrounding periphery. WB, India

110

G. Saha et al.

Gill S, Forest TM, Ennos R (2007) Adapting cities for climate change: the role of the green infrastructure. Built Environ 33(1):115–133 India P (2011) Census of India 2011 provisional population totals. New Delhi: Office of the Registrar General and Census Commissioner Keshtkar H, Voigt W, Alizadeh E (2017) Land-cover classification and analysis of change usingmachine-learning classifiers and multi-temporal remote sensing imagery. Arab J Geosci 10:1–15 Manoli G, Fatichi S, Schläpfer M, Yu K, Crowther TW, Meili N, Burlando P, Katul GG, Bou-Zeid E (2019) Magnitude of urban heat islands largely explained by climate and population. Nature 573:55–60. https://doi.org/10.1038/s41586-019-1512-9 Mukherjee S, Bebermeier W, Schütt B (2018) An Overview of the impacts of land use land cover changes (1980–2014) on urban water security of Kolkata. Land 7:91. https://doi.org/10.3390/ land7030091 Parveen MT, Ilahi RA (2022) Assessment of land-use change and its impact on the environment using GIS techniques: a case of Kolkata Municipal Corporation, West Bengal, India. GeoJournal. https://doi.org/10.1007/s10708-022-10581-z Sahana M, Hong H, Sajjad H (2018) Analysing urban spatial patterns and trend of urban growth using urban sprawl matrix: a study on Kolkata urban agglomeration, India. Sci Total Environ 628–629:1557–1566 Shaban A et al (2020) India’s urban system: sustainability and imbalanced growth of cities. Sustainability, MDPI Sikarwar A, Chattopadhyay A (2014) Spatial-temporal analysis of population , land use-land cover and environment : a study of seven most populated city-regions of India, pp 1–23 Wang J, Zhou W, Pickett STA, Yu W (2019) A multiscale analysis of urbanization effect on ecosystem services supply in an urban mega region. Sci Total Environ 662:824–833

Chapter 6

Spatio-Temporal Dynamics of Urban Land Use Applying Change Detection and Built-Up Index for Durgapur Municipal Corporation, Paschim Bardhaman, West Bengal Tapan Kumar Das, Subham Kumar Roy, Masud Karim, and Dipankar Saha

Abstract In eastern India, Durgapur is one of the fastest growing cities. The land cover and land use of Durgapur are shifting very hurriedly due to urbanization, which adversely impacts the local land resources there. This study was done from 1991 to 2021. To an attempt was made in this study to determine how the urban growth area grew over time. Aside from that, an attempt has been made to examine the pattern of urban expansion in Durgapur. Normalized Difference Vegetation Index and Normalized Difference Built up Index, Built up Index were analyzed to evaluate the urban expansion of Durgapur city from 1991 to 2021. According to the findings, vegetative area is decreasing in the city central areas (13.9–6.32%) while built-up area percentages are increasing in the central areas (78.42–84.75%). In 1991, the mean value of the built-up index in the study region was 0.04, which was transformed to 0.1 in 2021. Within a thirty-year time span, the degree of modification in the Built up area is perfectly visible. Keywords Built up index · Normalized Difference Built up Index (NDBI) · Normalized Difference Vegetation Index (NDVI) · Urbanization

T. K. Das Cooch Behar College, Cooch Behar, West Bengal, India S. K. Roy Department of Geography, Syed Nurul Hasan College, ProfFarakka, Murshidabad, West Bengal, India M. Karim Department of Geoinformatics, Cooch Behar College, Cooch Behar, West Bengal, India D. Saha (B) Cooch Behar Panchanan Barma University, Cooch Behar, West Bengal, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_6

111

112

T. K. Das et al.

6.1 Introduction Urbanization is the process of transformation from rural sector to urban sector (UNFPA 2007). In general the growth of population in urban areas is known as ‘Urbanization’. In case of developing countries the rate of urbanization is very high rather than developed countries. Natural growth of population and rural to urban migration are the most important factors for rapid urbanization process. Using highly facilitated effective transport communication network the commuters’ population from city centre to rural urban continuum areas due to changing life style which were mainly emphasized comfortable and environmental friendly life. The expansions of urban areas know as urbanization (Seto et al. 2011). According to UN report 2014, urban areas shares greater half of the total population of the world and within 2050 it will be increased to nearly 66% mainly concentrated over the developing countries such as Africa and Asia. Due to the diverse forms of urbanization occurring on each continent there is also a wide range of urbanization types (Pannell 2002). Numerous countries face environmental and resource threats from unplanned, rapid urbanization, including soil destruction, desertification, water shortage, destruction of natural and semi-natural ecosystems, loss of biodiversity, and landscape fragmentation. (Fang 2009; Wang and Fang 2011; Ruppert et al. 2012; Sala et al. 2000; Song 2014). As urbanization rates rise, urban land use and land cover patterns change drastically and the impact of LULC changes on the environment is the result of the urbanization process (Xiao et al. 2006). Land resources are the most valuable natural resource on the earth (Samant and Subramanyam 1998). Land use, in general, refers to the actual usage of land (De and Jana 1997). A part of the city planning process is the development of land-use plans (Weng and Quattrochi 2006). Land usage typically involves switching from one major use to another (Nanavati 1957). Conversion and modification are two strategies for modifying land usage (Meyer and Turner 1996). In terms of how people use the land, land-use change is a historical process. It affects the availability of different resources such as vegetation, soil, water and negatively affect on urban climate, natural hazards and socio economic pattern on regional parameters (Ahmad 2014; Chakilu and Moges 2017). The difference between Land Cover and Land Use is that Land Cover includes landscapes without human influence; some Land Cover changes may occur from natural processes only. Land Use on the other hand is a result of humans influencing and changing existing Land Cover for various purposes (Rimal 2005). Some landscapes are likely to change on a daily basis, particularly at a local scale as a result of human activity, e.g. building of new structures. When such minor changes accumulate, they alter landscapes at a regional or even national level (Finley 2011). It is crucial to have proper planning, usage, and management of natural resources. Change detection is the act of identifying the pattern of alteration in the condition of an object or phenomenon by watching it at various intervals (Singh 1989; Asselman and Middelkoop 1995). Research on the pattern of land cover trade during the past 30 years is necessary to better understand how LULC change will affect the earth’s surface area. Future changes in landuse should also be anticipated (Ojima

6 Spatio-Temporal Dynamics of Urban Land Use Applying Change …

113

et al. 1994). Due to their repetitive data acquisition and suitability for processing, geographic information systems (GIS) and remote sensing data are the most popular and effective tools source for the detection, quantification, and mapping of LULC patterns. These techniques are applicable and useful for studies that aim to detect changes in land use and cover (Chen et al. 2005; Jensen 1996; Herold et al. 2003; Serra et al. 2008; Yuan et al. 2005). Especially considering the fact that smaller towns or municipalities play an important role in urban development, they are sometimes disregarded (Table 6.1). West Bengal is one of the most urbanized states in India with 31.9% of urban population in 2011, higher than the 2 national averages of 31.16%. An important feature of urbanization in West Bengal is its high degree of spatial concentration in Calcutta holding the pre-eminent position (Datta 2000). Urbanization in West Bengal has shown an increasing trend from 24.45% in 1961 to 27.39% in 1991, 28.03% in 2001 and 31.9% in 2011. The rural areas of Durgapur has been rapidly developed into a modern steel city within a period of one decade with rapid growth of urbanization, the number of administrative units and towns has also increased in Durgapur area. The main construction phases was stared from 1952. During that period DVC was constructing the barrage on river Damodar, near Durgapur with the completion of the Durgapur barrage in August 1955 the region has developed as urban and industrial centre. There are many parameters of study is analyzing the cause of rapid urbanization the development and population growth of Durgapur’s urban region gradual change of land use pattern and industrial pattern made an impact of Durgapur city (Pal 2015). In West Bengal, Durgapur is one of the most important urban areas in terms of secondary economic activity and population. The urban environment of Durgapur is a complex fusion of natural elements and the constructed environment, which has radically altered the city’s landscape pattern over the last few decades. The socio cultural makeup of the population, including their values, behaviors, beliefs, knowledge, attributes, laws, traditions, etc., has an impact on the urban environment as well. Urban policies created by socio-economic factors and the behavioural approach of the city’s socio-cultural group have a significant impact on the protection and maintenance of the physical environment. The extent of urban sprawl and the rate at which urban facilities are being added are directly related to the quality of urban lives, and these factors have really changed the ecology of Bengal’s Durgapur Municipal Region (Tah and Ghosh 2015). The majority of research projects primarily focus on the capital city of state Kolkata and researcher mainly emphasized on land surface temperature, air quality monitoring, landscape dynamics, impact of urbanization of vegetation health or urban service However, the spatiotemporal dynamics of urban expansion have received less attention, direction, patterns and its impact on urban environment on Durgapur Municipal Corporation (DMC). Therefore, this study attempted to show the spatiotemporal changes and the pattern of urban growth as well as present condition of DMC. The primary objectives of this study are: (a) to identify the spatiotemporal dynamism of landuse and land cover categories and its change in DMC from 1991 to 2021; (b) to show the expansion of the built up area in DMC.

114

T. K. Das et al.

Table 6.1 Variables and methods used in related research work Study area

Data use

Statistical parameter

Variables

References

Bhubaneswar, India

Landsat 5/8

Pearson’s correlation and regression model

Land surface temperature (LST), NDVI, NDBI, NDWI, NDLI and LULC

Das et al. (2021)

Egypt

Landsat 5/8

Change detection, Markov and matrix analysis

LULC

Hegazy and Kaloop (2015a)

Kolkata, India

Landsat 5/8

Temperature prediction

NDVI NDBI and LST

Chatterjee et al.

Indianapolis City, USA

Landsat ETM +

Pearson’s correlation and tree algorithms

LST and NDVI

Weng et al. (2004)

Barasat Subdivision, India

Landsat 8

Scatter plot

EBBI, NDBI, UI and NDBaI

Ghosh et al. (2018)

Tirupati, India

LISS III and PAN

Change detection

LULC

Mallupattu and Sreenivasula Reddy (2013)

Bangladesh

Landsat 5/8

Accuracy assessment and change detection

LULC

Hasan et al. (2021)

Kashmir valley, India

Landsat 5/7/ 8

Accuracy assessment and change detection

LULC and Transformation of LULC class

Alam et al. (2019)

Lake Tana Basin, Ethiopia

Landsat 5/8

Accuracy assessment and change detection

LULC

Tewabe and Fentahun (2020)

Temperature profile and accuracy assessment

NDVI, LULC, LST and EMISSIVITY

Kumar et al. (2012)

Vijayawada city, Landsat 7 India Amman City, Jordan

Landsat 5/7/ 8

Change detection and urban growth

LULC

Al-Bilbisi (2019)

Beijing

Landsat 5/8

Evaluation index, Coupling model

LULC

Huang et al. (2019)

Asansol, India

Landsat 5/8

Change detection, Matrix analysis, Index of builtup expansion, Magnitude of landscape change, Percentage of change or trends, Index of largest path and Nearest neighboring index

LULC, Transformation of LULC class and Directonial expension

Maity et al. (2020)

Rize, North-East Landsat 5/7 Turkey and DEM

Accuracy assessment and change detection

LULC

Reis (2008) (continued)

6 Spatio-Temporal Dynamics of Urban Land Use Applying Change …

115

Table 6.1 (continued) Study area

Data use

Statistical parameter

Variables

References

Islamabad, Pakistan

Landsat 5 and SPOT 5

Accuracy assessment, Change detection and temperature profile

LULC and Transformation of LULC class

Hassan et al. (2016)

Bangladesh

Landsat 5/8

LULC, Matrix analysis, Accuracy assessment and RF XG boost classifiers

LULC

Md Abdullah et al. (2019)

Singapure, South East Asia

SPOT images

Techniques of cartography

Day time and night time temperature, LULC

Kardinal et al. (2007)

Pearson’s correlation, regression model and ANOVA

Land surface Connors et al. temperature and (2013) LULC

Phoenix, United Quickbird, States ASTER L2 surface and Temperature V003

6.2 Study Area The area under study is the Durgapur Municipal Corporation, under AsansolDurgapur Sub-Division of Paschim Bardhhaman District (2017) of West Bengal. After Kolkata, Asansol, and Siliguri, it is the fourth-largest urban agglomeration in West Bengal. One of West Bengal’s important planned cities, Durgapur, was established during the second five-year plan. The unique characteristics of Durgapur is that it is an urban space where both industrial and tertiary activities have been combined and can claim the importance next to Kolkata among the cities emerged in the western part of Bardhhaman District. Being a planned city still it is experiencing haphazard growth in the recent time. Geographically, the region encompasses 87º12' 26'' East to 87º24' 32'' East and 23º27' 23'' North to 23º38' 45'' North (Fig. 6.1). Total area of Durgapur Municipal Corporation is 154.20 km2 spread into 43 Wards (Durgapur City Development Plan 2017–2018). Geographically the city is bounded by Kanksa C. D. Block in the south- east, Durgapur-Faridpur C. D. Block on the north-east and north-west, Andal C. D. Block in the west, Mejia C. D. Block (Bankura District) in south-west and Barjora C. D. Block (Bankura District) in the south-east. The River Damodar flows through southern margin of the city. The Grand Trunk Road and the South-Central Railway runs through the eastern part of the city and the Durgapur Highway runs through the centre of the city.

116

Fig. 6.1 Location map of study area

T. K. Das et al.

6 Spatio-Temporal Dynamics of Urban Land Use Applying Change …

117

Table 6.2 Details of used data No

Date acquired

Sensor

Path/Row

Datum and projection

1

1991–02–23

Landsat 5/TM

139/44

2

2021–02–27

Landsat 8

139/44

WGS 84 UTM

6.3 Materials and Methods 6.3.1 Materials The study is established on Secondary data collected from different government and on government sources. Ward map of Durgapur Municipal Corporation (DMC) collected from the municipality office of DMC. The Landsat imageries of 1991 and 2021 (Table 6.2) have been downloaded from USGS Earth Explorer. These data’s have been handled for the fulfillment of the objectives.

6.3.2 Methods

Satellite Image (Landsat 5, 1991 & Landsat 8, 2021)

Layer Stack

Band Rationing

Subset of AOI

NDVI(1991 & 2021)

NDBI(1991 & 2021)

Pan Sharpening

Pixel based Supervised Classification

LULC Map (1991 & 2021)

Built-up Index (NDBI – NDVI) 1991 & 2021

Accuracy Assessment

Growth of Built-up Index Change Detection Analysis

(1991 – 2021)

Spatio-temporal Dynamics of urban area in DMC

118

T. K. Das et al.

Table 6.3 LULC categories of study area LULC category

Description

Waterbody

Rivers, Canals, Lakes

Vegetational area

Gardens, Mixed and deciduous forest lands, Roadside or riverside vegetation areas etc.

Barren land

Permanently fallow area with stony or rocky body and other residual landforms

Built up area

Residential, Commercial and services lands

Open space

Stadium, Play ground, Park, Garden, Project area under construction

Source Prepared by Authors, 2022

6.3.3 Classification Scheme In order to get the all-inclusive output the national LULC classification of NRSC and ISRO has been hired (Arveti et al. 2016). Total five different classes of land types (Table 6.3) found this are Waterbodies, Built-up area, vegetation Cover, Barren land and Open area. Waterbodies and Built-up area under level-I and vegetation Cover, Barren land and Open area under level-II classification. As DMC is one of the main municipality of Asansol-Durgapur subdivision under Paschim Bardhaman with 566,517 number of population only in 154 km2 . area. Because of the large number population in small area it is very obvious that all the classes provided by NRSC and IIRS (Arveti et al. 2016) will not be available. Pixel based classification has been done based on the present LULC types.

6.4 Result and Discussions 6.4.1 Image Preprocessing Satellite image pre-processing techniques basically includes all those methods which are required to execute before we can extract the correct information from raster images. The satellite images were imported into ARC GIS 10.2 for minimize geometric correction. According to our goals, the Area of Interest (AOI) has been subset from geo referenced multi-temporal satellite imagery. GIS and remote sensing methods have been implemented for statistical analysis, mapping purposes and extract various cartographic representation. NDVI, NDBI and Urban Index such index and landuse landcover mapping are extract by the help of Software Arc GIS 10.2 has been utilised.

6 Spatio-Temporal Dynamics of Urban Land Use Applying Change …

119

6.4.2 Classification Image Using a supervised image classification method, the previously processed images are subsequently categorized. The maximum likely hood algorithm, which is included into the Arc GIS programme, classifies images using the supervised image classification approach based on the digital number of available pixels. The user-provided training sets and the maximum-likelihood technique will be used to classify the image. With the help of software, user selects various pixels to create required land use land cover map. Five land use and land cover classes are classified namely water bodies, vegetation area, barren land, built up area and open space are identified in the study area.

6.4.3 Supervised Image Classification Selecting training sites to serve as references for the categorization is part of the user-guided process known as “supervised classification” (Campbell 1996; Jensen 2007). Training input, classification, and output these three stages are helps to create supervised image classification. User are selected the training sites according to their prior knowledge during the training stage. This stage mainly helps to correlated various classes with spectral data. Classification stage is the most important stages of supervised image classification techniques because this stage mainly classifies the numerous spectral bands into a specific class. The most important and widely accepted classification algorithm is maximum likelihood. Output stage is the final stage of image classification this stage mainly helps to image visualization, presentation and interpretation the result. This paper illustrates the change detection over a 30 year period using a supervised image classification technique. Using Arc GIS (10.2) software, each and every image is classified independently using a supervised classification approach with maximum likelihood algorithm.

6.4.4 Accuracy Assessment The term “accuracy” typically refers to the degree of “correctness” of a derived map (classification), which is determined through the creation of error matrices (Foody 2002). The accuracy assessment has been a key part on remote sensing studies to detect level of significance of research work. A common method for assessing accuracy is error matrix analysis (Khorram et al. 2013). The accuracy assessment of individual classifications is essential for effective change detection (Owojori and Xie 2005). In order to estimate the accuracy of image classification, a reference map was compared with a classified map. Consequently, a comprehensive accuracy assessment should contain a report on overall accuracy, user accuracy, and producer accuracy (Table 6.4).

120

T. K. Das et al.

Table 6.4 Kappa value (Rwanga and Ndambuki 2017) Sl. no

Value of K

Status

1

0.34 Very high

0.21

0.14

150.848

100

Total

Area (km2 )

−11.712 0.000054 150.84

9.63 0.8 100

−11.712 0.000054

130

T. K. Das et al.

Table 6.10 Dispersion of NDBI Year

Minimum

Maximum

Mean

Sd

1991

−0.11

0.37

0.13

0.339411255

2021

−0.02

0.45

0.215

0.332340187

Fig. 6.7 Normalized difference built-up Index (NDBI) of 1991 and 2021

6 Spatio-Temporal Dynamics of Urban Land Use Applying Change …

131

Fig. 6.7 (continued)

It shows that in 1991, the share of urban area in the mean city area was 0.13, but by 2021, it had risen to 0.215 (Fig. 6.8). This figure mainly plot of mean value of NDBI and r 2 value is 1 that means it’s extremely correlated the matter rendering to time increasing the built up area.

132

T. K. Das et al.

Fig. 6.8 Temporal change of NDBI 1991–2021

6.5.3 Built-Up Index BUI is a binary image that only has positive values for the built-up and barren pixels, allowing the built-up areas to be mapped automatically (Zha et al. 2003). It is a graphical indicator that is used to measure the growth of metropolitan areas, particularly built-up or artificial structure regions. BUI = NDBI − NDVI Urbanization is linked to urban planning, structure, and morphology, as well as urban ecology, which leads to a variety of urban ecological and environmental concerns that have varied degrees of impact on the city’s ever-increasing human population. BUI indices were used to explore the dynamic growth and outgrowth outlines of built up area in Durgapur city. BUI indices have been calculated from 1991 to 2021. This table showing BUI values for Durgapur. Sharing of the BUI index in Durgapur between 1991 and 2021(Fig. 6.9) are classified into four categories such as 0.04 is very high zone (Table 6.11). In 1991, 1.19% of Durgapur’s population had extremely low BUI levels, while 6.33% had very high BUI values. Between these historical periods, the percentage of urban populations increased dramatically. In the current scenario, the proportion of the built-up area or built-up core area increases from 6.33 to 42.03% of the total area. The value for BUI varies from +0.45 to −0.36, according to BUI photos from Durgapur city in 1991 (Table 6.12). In 1991, the amount of built-up area in the Central Business District, A zone, Benachity, Mayabazar, and Neatji settlement was very high. The rest of the area, with the exception of a few pockets of industrial

6 Spatio-Temporal Dynamics of Urban Land Use Applying Change …

133

units, was not as well developed. In 2021, the vegetation indices range from +0.52 to −0.32. In the Central Business District, A zone, Benachity, Anadpur, Shyampur, Netaji colony, Bidhanagar, B zone, the amount of built-up area is extremely very high. However, the built-up area of Bansola, Raghunathpur, and Parulia is low in the western and northern western parts. It shows that the amount of built-up area in the mean city area was 0.045 in 1991 and rose by 0.1 in 2021 (Fig. 6.10). This figure mainly plot of mean value of BUI and r 2 value is 1 that means it’s extremely correlated the matter according to time growing

Fig. 6.9 Built-up index of 1991 and 2021

134

T. K. Das et al.

Fig. 6.9 (continued) Table 6.11 Area and change of BUI BUI

1991 Area (Km2 )

0.04 High Total

2021 Percent

Area (Km2 )

Percent

Change in area (1991–2021)

1.79

1.19

6.27

4.15

4.48

24.49

16.24

37.67

24.97

13.17

115.01

76.25

43.5

28.85

−71.51

42.03

53.85

9.55

6.33

63.4

150.84

100

150.84

100

6 Spatio-Temporal Dynamics of Urban Land Use Applying Change …

135

Table 6.12 Area and change of BUI Year

Minimum

Maximum

Mean

Sd.

1991

−0.36

0.45

0.045

0.572756493

2021

−0.32

0.52

0.1

0.593969696

Fig. 6.10 Temporal change in Built-up index

the built up area. Present growth of the urban areas of DMC is mainly highlighting the north eastern part of town (Fig. 6.11).

6.5.4 Statistical Measures As a quantitative analytical tool, correlation analysis can be used to investigate the grade of relationship among independent and dependent variables (Schober and Schwarte 2018; Senthilnathan 2019). ∑ (Xi − x)(Yi − y) r= √ ∑ (Xi − x)2 (Yi − y)2 In this study scatter plots were done using regression study with all time-points (1991 and 2021) to show the impacts of the NDVI and NDBI. The parameter values of these points were then extracted from the generated maps of the various periods utilizing sample points for each period under analysis. Where ‘r’ indicates Pearson’s correlation coefficients, x is the NDVI measuring value of xi, and y indicates the NDBI measurement value of yi. xiand yi are single test indices with i. Whereas x and y indicates the sample means. The Pearson product-moment correlation coefficient (PPMCC) evaluates the direction and degree of a link between two variables, whereas

136

T. K. Das et al.

Fig. 6.11 Growth of built-up area

regression analysis estimates the functional relationship between independent (x) and dependent (y) variables (Härdle and Vieu 1992; Zhao 2013). Linear regression is a statistical technique for determining the impact of two independent variables, X1, X2, X3, …, Xi, on a single dependent variable, y.

6 Spatio-Temporal Dynamics of Urban Land Use Applying Change …

137

6.6 Conclusion According to the 2011 Indian census, the total population of DMC is 566937, with a total area under municipal service of roughly 150.84 square kilometers. According to the findings of the study, greenery in Durgapur city has been shrinking rapidly due to the rapid expansion of the urban area. The vegetation area in the peripheral areas is higher than in city areas or urban areas, according to NDVI maps. The extent to which the urban area has encroached into the vegetation area is high in the periphery. The NDBI index, on the other hand, is higher in the city areas than it is outside the city centre. The temperature in the centre section of the city is much higher than in the rest of the city. To alleviate these issues, city officials should take quick action. Officials in Durgapur should be concerned about future urban expansion and design a comprehensive plan for environmentally friendly constriction and green building to limit the warming effect of the urban microclimate. The study also found that urban green spaces (UGS) can contribute to a better quality of life in cities and a more sustainable environment (Table 6.13). During the observed period of 30 years (1991–2021), this table presents the results of correlations and linear regression model LRM (Table 6.9) among research variables. In this diagram mainly identify the relationship between NDVI and NDBI (Fig. 6.12). It is clear that Vegetation and Built up signify a negative connection. In the time of 1991 correlation value is -0.31 (low negative) is because this phase recognize as the first imprint to outgrowth of city and within 30 years’ time span that relationship dramatically changed is −0.65 (medium to high negative) so that value identify the massive change or transformation of city. From 1991 to 2021, the impact of LULC on NDBI and NDVI in Durgapur, Eastern India, was investigated. A linear regression model (LRM) was used to determine the relationship between NDBI, NDVI, and the five land cover indices. DMC witnessed considerable LULC changes during the research years, with the built-up area expanding by 9.54 km2 as a result of population increase and urbanization. During the study period, however, vegetation showed a downward tendency and declined by 11.33 km2 . It is observed that areas with natural cover, particularly green space with an average area of good healthy vegetation, are 19.28 and 14.25 km2 , respectively, the coolest places in the city, whereas built-up land with an average area of good healthy built up area is 4.02 and 15.75 km2 , respectively, the hottest places. Between 1991 and 2021, the largest Table 6.13 Correlations and linear regression

Year

Variables NDVI

NDBI

NDVI

1

−0.31

NDBI

−0.31

1

NDVI

NDBI

NDVI

1

−0.65

NDBI

−0.65

1

1991

2021

138

T. K. Das et al.

Fig. 6.12 Correlations and linear regression

change in temperature was reported for built-up land. However, between 1991 and 2021, the temperature of vegetation cover increased as a result of unplanned rapid urbanization and industrialization. Furthermore, the ecological variables NDVI, NDBI, and BUI demonstrated a statistically significant link with rapid urbanisation across the study’s 30-year period.

References Abd El-Kawya OR, Rød JK, Ismail HA, Suliman AS (2011) Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Appl Geogr 31:483–494. https://www.sciencedirect.com/science/article/pii/S0143622810001451 Aboelnour M, Engel BA (2018) Application of remote sensing techniques and geographic information systems toanalyze land surface temperature in response to landuse/land cover change in greater cairo region Egypt. J Geogr Inf Syst 10(01):57–88. https://doi.org/10.4236/jgis.2018. 101003 Ahmad S (2014) Land use change detection using remote sensing and artificial neural network: application to Birjand, Iran. Comput Ecol Softw 4:276 Alam A, Bhat MS, Maheen M (2020) Using Landsat satellite data for assessing the land use change in Kashmir valley. Springer: GeoJournal 85:1529–1543. https://doi.org/10.1007/s10708-01910037-x Al-Bilbisi H (2019) Spatial monitoring of urban expansion using satellite remote sensing images: a case study of Amman City, Jordan. Sustainability 11(2260):1–14. https://www.mdpi.com/20711050/11/8/2260 Arveti N, Etikala B, Dash P (2016) Land use/land cover analysis based on various comprehensive geospatial data sets: a case study from Tirupati area, south India. Adv Remote Sens 5(02):73 Asselman NE, Middelkoop H (1995) Floodplain sedimentation: quantities, patterns and processes. Earth Surf Proc Land 20(6):481–499 Basu M (2018) Geographical analysis of urbanization, demographic structure and problems in Asansol Durgapur planning area (ADPA). J Emerg Technol Innov Res (JETIR) 5(8):280–289. https://www.jetir.org/view?paper=JETIR1808522 Bhatta B (2015) Remote sensing and GIS, 2nd edn. Oxford University Press, Delhi. https://global. oup.com/academic/product/remote-sensing-and-gis-9780198072393

6 Spatio-Temporal Dynamics of Urban Land Use Applying Change …

139

Boehme P, Berger M, Massier T (2015) Estimating thebuilding based energy consumption as an anthropogeniccontribution to urban heat islands. Sustain Cities Soc 19(3):373–384. https://doi. org/10.1016/j.scs.2015.05.006 Bokaie M, Zarkesh MK, Arasteh PD, Hosseini A (2016) Assessment of urban heat island based on therelationship between land surface temperature and land use/land cover in Tehran. Sustain Cities Soc 23(2):94–104. https://doi.org/10.1016/j.scs.2016.03.009 Bonan GB (2008) Ecological climatology—concepts and applications, 2nd edn. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9780511805530 Briassoulis H (2000) Analysis of land use change: theoretical and modeling approaches. The Web Book of Regional Science, West Virginia University. http://www.rri.wvu.edu/WebBook/Briass oulis/contents.html Cai Y, Zhang H, Zheng P, Pan W (2016) Quantifyingthe impact of land use/land cover changes on the Urban Heat Island: a case study of the natural wetlands distribution area of Fuzhou City. China. Wetlands 36(2):285–298. https://doi.org/10.1007/s13157-016-0738-7 Campbell JB (1996) Introduction to remote sensing, 2nd edn. Taylor and Francis, London Chakilu G, Moges M (2017) Assessing the land use/cover dynamics and its impact on the low flow of Gumara Watershed, Upper Blue Nile Basin, Ethiopia. Hydrol Curr Res 7:2. https://www.res earchgate.net/publication/316057359 Chen X, Vierling L, Deering D (2005) A simple and effective radiometric correction method to improve landscape change detection across sensors and across time. Remote Sens Environ 98(1):63–79. https://pubs.er.usgs.gov/publication/70028231 Choudhury D, Das K, Das A (2019) Assessment of land cover changes and its impact on variations of land surface temperature in Asansol-Durgapur development region. Egypt J Remote Sens Space Sci 22(2):203–2018. https://doi.org/10.1016/j.ejrs.2018.05.004 Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37(1):35–46. https://doi.org/10.1016/0034-4257(91)90048-B Das S, Sahu AS (2020) Monitoring landuse/landcover changes using remote sensing and GIS: a case study on Kanchrapara municipality and its adjoining area, West Bengal, India. Reg Sci Inq XII(2):43–54. http://www.rsijournal.eu/ARTICLES/December_2020/3 Das T, Jana A, Mandal B, Sutradhar A (2021) Spatio-temporal pattern of land use and land cover and its effects on land surface temperature using remote sensing and GIS techniques: a case study of Bhubaneswar city, Eastern India (1991–2021). Springer: GeoJournal. https://doi.org/ 10.1007/sl10708-021-10541-z, https://doi.org/10.1007/sl10708-021-10541-z De NK, Jana NC (1997) The land multi faced appraisal and management. Sribhumi Publication Company, Calcutta De NK, Sarkar AK (1992) Alternative strategies for agriculturak developmnt in India. In: Mohamad N (ed) New dimensions in agricultural geography, p 7 Dutta I, Das A (2019a) Exploring the dynamics of urban sprawl using geospatial indices: a study of English Bazar urban agglomeration, West Bengal. Appl Geomatics 11(2):259–276. https:// doi.org/10.1007/s12518-019-00257-8 Fang C (2009) China’s urbanization process and resources environment protection report. Science Press, Beijing Food and Agriculture Organization (FAO) (1995) Planning for sustainable use of land resources, FAO land and water bulletin 2, Food and Agriculture Organization of the United Nations, Rome. https://www.fao.org/land-water/land/land-governance/land-resources-planning Ghosh DK, Mandal AC, Majumder R, Patra P, Bhunia GK (2018) Analysis for mapping of built-up area using remotely sensed indices a case study of Rajarhat block in Barasat Sadar sub-division in West Bengal (India). J Landsc Ecol 11(2):67–76. https://doi.org/10.2478/jlecol-2018-0007 Ghosh B (2012) Paschim Banger Sanskriti. Prakash Bhaban Halder JC (2013) Land use/land cover and change detection mapping in Binpur II Block, Paschim Medinipur District, West Bengal: a remotesensing and GIS perspective. IOSR J Hum Soc Sci 8(5):20–31. https://www.iosrjournals.org/iosr-jhss/papers/Vol8-issue5/C0852031

140

T. K. Das et al.

Härdle W, Vieu P (1992) Kernel regression smoothing of time series. J Time Ser Anal 13(3):209–232. https://doi.org/10.1111/j.1467-9892.1992.tb00103.x Hasan MM, Islam R, Rahman MS, Ibrahim M, Shamsuzzoha M, Khanam R et al (2021) Analysis of land use and land cover changing patterns of bangladesh using remote sensing technology. Am J Environ Sci 17(3):64–74. https://thescipub.com/abstract/ajessp.2021.64.74 Hassan Z, Shabbir R, Ahmad SS, Malik AH, Aziz N, Butt A et al (2016) Dynamics of land use and land cover change (LULCC) using geospatial techniques:a case study of Islamabad Pakistan. SpringerPlus 5(812):1–11. https://doi.org/10.1186/s40064-016-2414-z Hegazy IR, Kaloop MR (2015) Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. Int J Sustain Built Environ (4):117–124. https://doi.org/10.1016/j.ijsbe.2015.02.005 He CY, Okada N, Zhang QF, Shi PJ, Zhang JS (2006) Modeling urban expansion scenarios by coupling cellular automata model and system dynamic model in Beijing, China. Appl Geogr 26:323–345 Hegazy IR, Kaloop MR (2015) Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. Int J Sustain Built Environ 4(1):117–124. 10.1016/j. ijsbe.2015.02.005 Herold M, Goldstein NC, Clarke KC (2003) The spatiotemporal form of urban growth: measurement, analysis and modeling. Remote Sens Environ 86(3):286–302 Jensen JR (1996) Introductory digital image processing: a remote sensing perspective. Prentice-Hall Inc. Jensen JR, Cowen DC (1999) Remote sensing of urban/suburban infrastructure and socio-economic attributes. Photogramm Eng Remote Sens 65:611–622. http://www.asprs.org/wp-content/upl oads/pers/99journal/may/1999_may_611-622 Khorram S, Nelson S, Cakir H, Wiele CV (2013) Digital image processing: post-processing and data integration. In: Pelton JN, Madry S, Lara SC (eds) Handbook of satellite applications, Springer, Berlin, pp 839–862. https://doi.org/10.1007/978-1-4419-7671-0 Kumar KS, Bhaskar PU, Padmakumai K (2012) Estimation of land surface temperature to study urban heat island effect using landsat etm+ image. Int J Eng Sci Technol (IJEST) 4(02):771–778 Lillesand TM, Kiefer RW, Chipman JW (2015) Remote sensing and image interpretation. Wiley, New York Mahato S, Pal S (2018) Changing land surface temperature of a rural Rarh tract river basin of India. Egypt J Remote Sens Space Sci 10(3):209–223. https://doi.org/10.1016/j.rsase.2018.04.005 Maity B, Mallick SK, Rudra S (2020) Spatiotemporal dynamics of urban landscape in Asansol municipal corporation, West Bengal, India: a geospatial. Geojournal. https://doi.org/10.1007/ s10708-020-10315-z(0123456789().,-volV()0123456789().,-volV) Mallupattu PK, Sreenivasula Reddy JR (2013) Analysis of land use/land cover changes using remote sensing data and GIS at an urban area, Tirupati, India. Sci World J 2013:1–6. https://doi.org/10. 1155/2013/268623 Md Abdullah AY, Masrur A, Gani Adnan MS, Al Baky MAK, Hassan Q, Dewan A (2019) Spatio-temporal patterns of land use/land cover change in the heterogeneous coastal region of bangladesh between 1990 and 2017. Remote Sens 11:1–26. https://www.mdpi.com/20724292/11/7/790 Meyer WB, Turner BL II (1996) Land-Use/land-cover change: challenges for geographers. Geo J 39(3):237–240. https://doi.org/10.1007/BF00188373 Moser SC (1996) A partial instructional module on global and regional land use/cover change: assessing the data and searching for general relationships. Geo J 39(3):241–283 Njoku EG (2014) Encyclopedia of remote sensing. Springer, Berlin. https://doi.org/10.1007/9780-387-36699-9 Ojima DS, Galvin KA, Turner BL (1994) The global impact of land-use change. Bioscience 44(5):300–304. https://doi.org/10.2307/1312379

6 Spatio-Temporal Dynamics of Urban Land Use Applying Change …

141

Pal S, Ziaul SK (2017) Detection of land use and land cover change and land surface temperature in english bazar urban centre. Egypt J Remote Sens Space Sci 20(1):125–145. https://doi.org/ 10.1016/j.ejrs.2016.11.003 Pannell CW (2002) China’s continuing urban transition. Environ Plan 34:1571–1589 Patra S, Gavsker KK (2021) Land use and land cover change-induced landscape dynamics: a geospatial studyof Durgapur Sub-Division, West Bengal (India). AUC Geographica 56(1):79– 94. https://doi.org/10.14712/23361980.2021.3 Pelton JN, Madry S, Lara SC (2013) Handbook of satellite applications. Springer, Berlin. https:// doi.org/10.1007/978-1-4419-7671-0 Prasomsup W, Piyatadsananon P, Aunphoklang W, Boonrang A (2020) Extraction technic for builtup area classification in landsat 8 imagery. Int J Environ Sci Dev 11(01):15–20. http://www. ijesd.org/show-142-1702-1.html Reis S (2008).Analyzing land use/land cover changes using remote sensing and GIS in rize, NorthEast Turkey. Sensors 8:6188–6202. https://www.mdpi.com/1424-8220/8/10/6188 Roy PS, Roy A (2010) Land use and land cover changein India: a remote sensing & GIS prespective. J Indian Inst Sci 90(4):489–502 Roy PS, Giriraj A (2008) Land use and land cover analysis in indian context. J Appl Sci 8:1346–1353 Roy SK, Shaikh AS (2021) Spatio—temporal change—an analytical geospatial study using satellite data—Farakka block, Murshidabad district, West Bengal (India). Territ Della Ric Insediamenti E Ambient Int J Urban Plan 27(2/2021):111–126. http://www.serena.unina.it/index.php/tria/art icle/view/8823 Ruppert V, Ghislain G, Pascal P, Claude N, Jacques L (2012) Detecting threat-ened biodiversity by urbanization at regional and local scales using an urban sprawl simulation approach: application on the French mediterranean region. Landsc Urban Plan 104:343–355 Rwanga SS, Ndambuki JM (2017) Assessment of land use/land cover classification using remote sensing and GIS. Int J Geosci Accuracy 8:611–622. https://doi.org/10.4236/ijg.2017.84033 Sala OE, Chapin FS, Armesto JJ, Berlow E, Bloomfield J, Dirzo R, Huber-Sanwald E, Huenneke LF, Jackson RB, Kinzig A, Leemans R, Lodge DM, Mooney HA, Oesterheld M, Poff NL, Sykes MT, Walker BH, Walker M, Wall DH (2000) Biodiversity global biodiversity scenarios for the year2100. Science 287:1770–1774 Samant HP, Subramanyam V (1998) Land-use/land-cover change in Mumbai-Navi Mumbai cities and its effects on the drainage basins and channels a study using GIS. J Indian Soc Remote Sens 26 Schober P, Schwarte LA (2018) Correlation coefficients: appropriate use and interpretation. Anesth Analg 126(5):1763–1768. https://doi.org/10.1213/ANE.0000000000002864 Seto KC, Fragkias M, Guneralp B, Reilly MK (2011) A meta-analysis of global urban land expansion. PLoS One 6, Article e23777 Serra P, Pons X, Saurí D (2008) Land-cover and land-use change in a mediterranean landscape: a spatial analysis of driving forces integrating biophysical and human factors. Appl Geogr 28(3):189–209. https://doi.org/10.1016/j.apgeog.2008.02.001 Senthilnathan S (2019) Usefulness of correlation analysis. SSRN Electron J. https://doi.org/10. 2139/ssrn.3416918 Singh A (1989) Digital change detection techniques using remotely sensed data. Int J Remote Sens 10(6):989–1003. https://doi.org/10.1080/01431168908903939 Song W (2014) Decoupling cultivated land loss by construction occupation from economic growth in Beijing. Habitat Int 43:198–205 Spruce J, Bolten J, Mohammed IN, Srinivasan R, Lakshmi V (2020) Mapping land use land cover change in the lower Mekong basin from 1997 to 2010. Front Environ Sci 8:21. https://doi.org/ 10.3389/fenvs.2020.00021 Tah S, Ghosh B (2015) The Impact of Urbanization on Environment: a study in Durgapur. Soc Res Found 3(2):112–123. https://www.researchgate.net/publication/306346235

142

T. K. Das et al.

Tewabe D, Fentahun T (2020) Assessing land use and land cover change detection using remote sensing in the Lake Tana Basin, Northwest Ethiopia. Cogent Environ Sci 6:1–11. https://doi. org/10.1080/23311843.2020.1778998 Wang J, Fang C (2011) Growth of urban construction land: progress and prospect. Prog Geogr 30(11):1440–1448 Wang SJ, Ma HT, Zhao YB (2014) Exploring the relationship between urbanization and the ecoenvironment—a case study of Beijing-Tianjin-Hebei region. Ecol Indic 45:171–183 Weng Q, Quattrochi D (2006) Thermal remote sensing of urban areas: An introduction to the special issue. Remote Sens Environ 104(2):119–122. https://www.researchgate.net/publication/ 238376798 Xiao J, Shen Y, Ge J, Ryutaro T, Tang C, Liang Y (2006) Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing. Landsc Urban Plan 75(1–2):69–80 Xue J, Su B (2017) Significant remote sensing vegetation indices: a review of developments and applications. J Sens 41(6):652–665. https://doi.org/10.1155/2017/1353691 Ye C, Chen R, Li Y, Liu T, Diao K, Li J (2019) Characterization of combined effects of urban built-up and vegetated areas on long-term urban heat islands in Beijing. Can J Remote Sens 45(5):634–649. https://doi.org/10.1080/07038992.2019.1644157 Yeon-Hee K, Jong-Jin B (2005) Spatial and temporal structure of the urban heat island in seoul. J Appl Meteorol 44(5):591–605. http://search.proquest.com/docview/224581029?accountid= 26415 Yuan F, Sawaya KE, Loeffelholz BC, Bauer ME (2005) Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing. Remote Sens Environ 98(2–3):317–328. https://www.sciencedirect.com/science/article/pii/S00 34425705002646 Zhao J (2013) The impact on personality knowledge sharing of personality traits: organizational trust as intermediary variable. Inf Stud: Theory Appl 36(5):34–38. http://search.ebscohost.com/ login Zha Y, Gao J, Ni S (2003) Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int J Remote Sens 24:583–594

Chapter 7

Studies on Impacts of Land Use/Land Cover Changes on Groundwater Resources: A Critical Review Suvendu Halder, Satiprasad Sahoo, Tumpa Hazra, and Anupam Debsarkar

Abstract Presently, enormous expansions of the built-up environment have been going on at the fastest rate overtime to fulfill the demand of the world population. In this study, the Landsat satellite images of different times with various sensors were used. Various types of change detection methods like unsupervised, supervised classification, image differencing and discriminant function were used to measure the number of changes in land use/land cover with a spatio-temporal scale. Land use/land cover classes were detected by using the spectral reflectance signature. Results of the previous studies showed that built-up environment growth is at a rapid rate at the cost of agricultural land, fellow land, vegetation and water bodies. Climatic parameter like temperature gradually increases with the expansion of the built-up environment. On the other hand rainfall has decreased overtime at a spatial scale. So temperature and rainfall have also been influencing on depth to groundwater level. Continuous alteration of land use/land cover in an area for the purpose of regional development resulting alarming decrease in ground water level since the underlying surfaces have coverage by impervious materials that reduce the recharge of groundwater affect the cycling of groundwater. Spatial–temporal analysis of groundwater table fluctuation data indicates that it has been gradually declining in the groundwater level in respect to depth. Keywords Land use/land cover · Groundwater · Change detection · Remote sensing

S. Halder Faculty of Interdisciplinary Studies, Law and Management, Jadavpur University, Kolkata, India S. Sahoo (B) GeoAgro, International Center for Agricultural Research in the Dry Areas (ICARDA), Cairo, Egypt e-mail: [email protected] T. Hazra · A. Debsarkar Department of Civil Engineering, Jadavpur University, Kolkata, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_7

143

144

S. Halder et al.

7.1 Introduction Though enormous alteration of the earth’s environment began with the emergence of agro and modernist (urbanized and industrialist) societies. Land cover indicate the physical cover on the earth’s surface (Gregorio 2016, pp. 1–40). While land use demarcated that land has been altered by anthropogenic activities. Land use is generally conducted based on land cover. The term land use/land cover being closely linked and interchangeable. Mainly two parameters such as climate change and population growth have been identified as LULC changes (Hassan et al. 2016). The influence of LULC change causes urbanization, industrialization, and agro farming plays a significant role in climate, hydrological condition and environmental sustainability. The hydrological process in an area is mainly controlled by LULC alteration. At present time the intensity of changes in land use/land cover is an alarming level on the global scale (Nath et al. 2020). Urbanization in an area has grown up mainly due to cutting the trees and vegetation. Also, these areas are transforming into build-up lands such as industries, houses, road networks, and modern infrastructure. People are migrated from rural to urban areas and are responsible for changes in an area through different kinds of activities. The land use/land cover and groundwater were come to be an interior part of present strategies for natural resources and environmental monitoring framework (Kayet et al. 2018). The modification of the earth’s surface due to overgrowing population, and adverse anthropogenic intervention often change the groundwater recharge pathway, level of groundwater table, and alteration of the hydrodynamic framework (McGrane 2016). The resulting freshwater crisis has been examined through water scarcity and groundwater deterioration in various parts of an area day by day (Ozdemir, 2011; Patra et al. 2017; Murmu et al. 2019). In the world for all of the time groundwater is the most signified resource of the earth by which people have been collecting fresh water for drinking (Dar and Dar 2010; Ghosh et al. 2016; Murmu et al. 2019). Annual groundwater withdrawal is about 230 km3 /yr (Biswas et al. 2013) which incorporated domestic use, cultivation, misuse, and unscientific management of water in the Indian aspect. Thereby unprecedented fall in the groundwater table during the last two decades in India (NITI Aayog 2018; Murmu et al. 2019). The analysis of LULC changes also provides a plausible explanation not only of man and environment relationship of the past but also hints about future relationships (Lu et al. 2019). LULC studies can help watershed management planning through which flood plain development is possible. Also, these studies provide solutions to environmental problems like the improvement of groundwater resources and agricultural development. Thus there is an increasing trend in the study of the relationship between LULC and physical variables. The high demand for water resources expand over the area with increasing human intervention in contemporary strategies. The LULC changes have shown to have both direct and indirect impacts on the various aspects of the environment (Patra et al. 2018). In order to examine the combined effect of both human and natural activities have considerable influences on

7 Studies on Impacts of Land Use/Land Cover Changes on Groundwater …

145

land cover, which consequently affects the local ecosystem (Chu et al. 2013; Veldkamp et al. 2016). The changes in landcover also regulate fresh surface water and groundwater recharge. Groundwater resources are most vulnerable due to land use and landcover (LULC) change scenario. Thereby, a huge number of families (3/4th) cannot not have sufficient potable drinking water in India. In this context, groundwater preservation is most significant which steady increase of groundwater table through water restoration and sustainable planning of water use at various regional levels. Normally aquifer system of the earth’s crust is restored as well as controlled by Surface and subsurface flowing water (Khan and Jharia 2018; Andualem and Demeke 2019; Lamichhane and Shakya 2019). Some of the cases are shown few hydrological parameters such as rainfall, surface, and subsurface runoff, geological and geomorphological setup, soil character, and land use/land cover alteration rate have influenced groundwater resource status (Khan and Jharia 2018; Andualem and Demeke 2019). In this study groundwater resources assessment, management and monitoring have been done by remote sensing and geographical information system techniques. Remote sensing and geographical information system are both techniques that influenced to take decisions planning with the help of spatial distribution of groundwater resources in the natural environment (Das et al. 2018; Jasrotia et al. 2019; Das and Pal 2020). Table 7.1 presents the available studies of LULC changes’ impact on groundwater resources. In this study the dataset were collected from references mainly concentrated on land use/land cover changes in different countries. Only the related literature about land use/land cover changes and groundwater resources was selected for analysis. The references were searched directly from “Google” and “Science Direct” database using the key word “land use/land cover changes and its implication on groundwater resources” and “land cover change detection method”. From the results obtained, a total of 41 publications were selected, mainly for the last12 years (2010–2021), which were further classified into two ways. First, the literature was selected based on the detection method; secondly the literature was selected which provided the data analysis and methodology regarding impact analysis of groundwater resources. Studies related to impacts of land use/land cover changes on groundwater resources are presented in Fig. 7.1a, b from 2010 to 2021. Continue increasing trend have observed in research on impacts of land use/land cover change on groundwater resources and related topic during the years in 2019–2020. So, this graph indicates that more interest is being paid to research about land use/land cover changes and ground water resources in India (Table 7.2). Therefore, an attempt was made through this review work to study the implication of land use/land cover changes on groundwater level fluctuation. This attempt helps policymakers for the planning of better agricultural practices and well watershed management framework development.

146

S. Halder et al.

Table 7.1 The available studies of LULC changes’ impact on groundwater resources Sl Name of the no author

Name of the region

Data used

Findings

1

Singh et al. (2010)

Lower Shiwalik hills

Remote sensing • Image classification technology was used to applies to change collection and analysis detection approach of ground-based data. and can perform Also used GIS for based on supervised modeling based on and unsupervised score method approach • The quantity of groundwater increased through natural and artificial recharge due to scientific way alteration in LULC pattern

2

Mallupattu et al. (2013)

Tirupati, India

Topographic and • The study area remote sensing data hasbeen classified on have been used. A field the basis of survey was performed geographical views, by GPS field observation and also using remote sensing (RS) data • LULC was categorized based on the supervised signature • Comparison and Interpret of toposheet and RS denotes an increase in a built-up area, open forest, and plantation (continued)

7 Studies on Impacts of Land Use/Land Cover Changes on Groundwater …

147

Table 7.1 (continued) Sl Name of the no author

Name of the region

Data used

Findings

3

Wang et al.

Heihe River Basin, China Groundwater data were • The groundwater collected by the depth was rises Chinese Academy of gradually on the Sciences. The frontier part of the irrigation data were desert that was collected from the success by the annual water resource surrounding management report farmland’s irrigation (1985–2010). For recharge • Impacts of LULC LULC classification, change on satellite imageswere groundwater depth collected from USGS. falling would All maps and images enhance sustainable were presented in the land use and UTM projection groundwater management

4

Liu et al. (2017)

TaWoer River, China

The data like elevation, • In this basin, LULC slope, hydrology, land and climate changes use, and land coverage have deep impacts on were obtained based on the land-use pattern RS and GIS adjustment, wetland techniques. Also,the protection, and local SWAT model was used social and economic in this study development • SWAT model was used to evaluate the impacts of land-use types on water resources considering climate change • Hydrological stations are the main controllers to LULC in the basin (continued)

148

S. Halder et al.

Table 7.1 (continued) Sl Name of the no author

Name of the region

Data used

Findings

5

Patra et al. (2018)

HMC in WB, India

Landsat TM, landsat ETM + and IRS LISS-III was used in this study. Groundwater data are collected from IWRIS

• LULC changes and NDBI has computed based on RS and GIS techniques • IDW interpolation technique is performed for the analysis of spatial distribution of rainfall, temperature and groundwater level dataset • The Kendalls Tau test was applied to the find it the relationship between hydrological parameters and hydro-meteorological components in an area

6

Wang et al. (2020)

Tarim basin, Northwest China

In this study, • Groundwater hydrological resources losses by parameters like the impacts of streamflow, climatic and human groundwater level, and activities on nature. It degree of can put down mineralization were challenges to local observed. Also, water resources and meteorological data ecological (AT, P, AET), NDVI sustainability • Groundwater tables and LULC data were (GWT) have considerable fluctuated at spatial and temporal scales. That is correlated with streamflow and land use/land cover features • This study leads to an understanding of the groundwater cycling mechanisms in a proper way (continued)

7 Studies on Impacts of Land Use/Land Cover Changes on Groundwater …

149

Table 7.1 (continued) Sl Name of the no author

Name of the region

Data used

Findings

7

Chemura et al. (2020)

Buzi sub-catchment, Zimbabwe

The Landsat images were used to LULC classification. Secondary data were collected from Google Earth, the internet website. Also,water-related vegetation indices like NDVI were calculated

• The interaction of physical, ecological and hydrological elements is the major controller to determine the effects of LC changes on surface and sub-surface hydrology • In this study, showed plantation was the major source of highest ET in the basin regarding Partitioning the annual interception, transpiration and ET

8

Mondal et al. Raipur, Chhattisgarh (2020)

The LANDSAT images with Datum WGS1984 and UTM zone were used to prepare thematic maps using ArcGIS. Also,ground-based data are taken from SOI and Google Earth

• Cultivation area decrease and settlement area increase resulting declining of the water table • Time to time spatial and temporal analysis of groundwater resources with the help of detecting LULC changes using satellite imageries (continued)

150

S. Halder et al.

Table 7.1 (continued) Sl Name of the no author 9

Name of the region

Bhattacharya Kangsabati basin, India et al. (2020)

10 Elmahdy et al. (2020)

Northern part of UAE

Data used

Findings

Groundwater potential • This research has find zones were drawn by out and monitoring some hydrological the effects of land parameters. Landsat, use/land cover toposheet, geological changes on map, and SRTM DEM groundwater were used to generate potential level • In laterite outcrop, the thematic map. fallow land, Also, GPS, GIS settlement, cropland techniques, and has existence statistical softwarewere antithetical land used to determine a change processes that correlation between are indicated by LULCC and outcomes Pearson correlation of simulated model matrix. Contrasted in dense forest, degraded forest and wetland have found positive land changes processes • Groundwater potential level is controlled by the land use/land cover changes and other thematic parameters Remote sensing, • To monitoring and Hydrological assessing the effects information and of land use/land ancillary data cover change regarding annual (LULCC) on population growth, groundwater level annual water using landsat image consumption and water and hydrological usage data were used information • To monitoring of land use changes were used as input of data for an image difference algorithm • In order to examined that built-up area increased lead to groundwater level deplation (continued)

7 Studies on Impacts of Land Use/Land Cover Changes on Groundwater …

151

Table 7.1 (continued) Sl Name of the no author

Name of the region

Data used

Findings

11 Nath et al. (2020)

Guwahati city, India

Landsat 5 TM and Landsat 8 OLI/TIRS data were used. DGL was collected from CGWB. Rainfall data were collected from RMC, Guwahati

• This study indicates the evaluation of land use/land cover changes with a Spatio-temporal scale during 1990-to 2020 • The urban structure distribution has increased at the cost of vegetation, fallow land, and open areas and to some extent wetlands • The increase in concrete and asphalt surface coverage due to urbanization has gradually reduced the areas of high potential groundwater recharge zones (continued)

152

S. Halder et al.

Table 7.1 (continued) Sl Name of the no author

Name of the region

Data used

Findings

12 Samal and Gedam (2021)

Upper Bhima river basin

In the study area, • The SWATH is an LULC maps were open-source prepared from hydrological model. classified EO satellite To simulate the images based on GIS. hydrological A number of sensors processes was used were used in satellite this model and images like TM, ETM, understanding the ETM + , and LISS-III. impacts of LULC The DEM data and changes on secondary datasets hydrological have been used parameters in the basin • In basin area the effects of LULC alteration on hydrological features are small. Contrasted in the sub-basin area the impacts of LULC changes on hydrological parameters increasing up

13 Liaqat et al. (2021)

Al Ain Region, UAE

Landsat satellites for ETM + and OLI sensors were used for LULC mapping. Also, ERDAS, ENVI, and ArcGIS were used for map production. The ground values were taken from field and Google earth images

• Groundwater table is fluctuated by the effects of Spatio-temporal changes of LULC • For LULC changes detection, a semi-supervised hybrid classification method was used for image classification and post-classification techniques • The prepared LULC maps were correlated to Spatio-temporal groundwater table maps which generate from groundwater data

7 Studies on Impacts of Land Use/Land Cover Changes on Groundwater …

153

Fig. 7.1 a Shows the trend of numbers of publication regarding LULCC and groundwater resources during 2010–2021. b Shows the number of different environmental parameters regarding impacts of LULCC on groundwater resources Table 7.2 Land use/ land cover categories change with an aspect of an area and quantities in the Howrah Municipality Corporation during 1975–2015 (Patra et al. 2018) Land use type

Change 1975–2000

Change 2000–2015

Overall change 1975–2015

Area (km2 )

%

Area (km2 )

%

Area (km2 )

%

Agricultural lands

−6.65

−12.68

−5.31

−10.28

−11.96

−23.14

Built-up area

+18.13

+ 35.10

+ 11.93

+ 23.06

+ 30.09

+ 58.16

Vegetation cover

−3.74

−7.24

−3.31

−6.39

−7.05

−13.63

Water bodies

−3.84

−7.44

−2.15

−4.13

−5.99

−11.57

Wet land

−3.92

−7.85

−1.16

−2.25

−5.08

−9.83

154

S. Halder et al.

Three important research questions have arisen related to the fastest alteration of LULC in developing countries like India due to unpre 1. How do Spatio-temporal changes in LULC occur under changing environment? 2. What are the effects on groundwater resources according to changes in the LULC scenario? 3. How a temperature, rainfall, and groundwater level does is interrelated based on the LULC changes scenario? cedented anthropogenic activities. The present study shows the increase built-up environment as well as land covers changes and land-use alteration (LCCLUA) under changing environment in an area as time goes on. Also, this study helps to understand the implication of LCCLUA on groundwater level under various climatic conditions. Figure 7.2 presents how the natural environment is affected by the increasing rate of built-up structure.

7.2 Materials and Method 7.2.1 Data Used In an address, the present paper uses different time series remote sensing datafor the study. The LANDSAT satellite images are used in these studies based on datum WGS1984that’s downloaded from the www.earthexplore website. The Landsat imageries are valuable due to the long-term provider of essential data with different resolutions. The LULC map for some decadal years is prepared from Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Mapper Plus (ETM + ) image, and Operational land imager (OLI). These data are used to generate the thematic map for the survey years using GIS software (Mondal et al. 2020). Several land use/land cover classes have been traced using False-color composite satellite images and highresolution Google Earth images for the studies. The Google earth image is mainly used for improving the classification accuracy of different ground objects. Moreover, assessing the impacts of land use/land cover alteration on groundwater resources like groundwater table, seasonal fluctuation of groundwater data at spatial and temporal scale in the study, and its other variable carried from India-Water Resource Information System (WRIS) (http://www.india-wris.nrsc.gov.in/). Also, groundwater data were collected from the groundwater control board and groundwater office at the state level. For climatic parameters analysis National Centers for Environmental Prediction (NCEP), and Climate Forecast System Reanalysis (CFSR) were used (Rahbeh et al. 2011). Meteorological data like average rainfall and average temperature over the times collected from climate data (Kayet et al. 2018). The selected sample points were received by the Global Positioning System (GPS).

7 Studies on Impacts of Land Use/Land Cover Changes on Groundwater …

155

The Distributed Area Expansion of Built-up Structure

Resulting

LCCLUA (Areal aspects)

Area in decreasing rate

Area in increasing rate Built-up structure

Non Built-up structure

More heat consumption and store

Atmospheric pollution

Evapotranspiration at maximum rate

Impervious surface increasing rate Surface runoff (SRO) increasing

Rainfall variability is maximum

Low infiltration and percolation of water/groundwater store

Temperature increasing

+

Hydrological circulation is being disturbed

Increasing requirement of groundwater at household level

Fastest falling of groundwater table

Planning for healthy and sustainable land use

Fig. 7.2 Showing natural environment is affected by the increasing rate of built-up structure

7.2.2 Methodology Georectification of satellite imageries has been done into a World Geodetic System 1984 and UTM projections using GIS software. For the classification of LULC data has been used of GIS software with a Google earth image has been used as a referenced image. However, a variety of change detection methods has been introduced to LULC studies. Figure 7.2 shows the schematic representation of methodology

156

S. Halder et al. Satellite Remote Sensing Data acquired from USGS [Landsat (MSS, TM, ETM+ and OLI), LISS-III and Google Earth] Pre processing

Land use/ Land cover Mapping with ground base observation

LULC Change Detection Methods

Unsupervised Classification

Various land use/land cover classes in an area

Image Differencing

Supervised Classification

Decreased Increased

Discriminant Function

Different types land use/land cover features

Additive Subtractive

Some increase Combined Some decrease Unchanged

Impact of LULCC on groundwater table fluctuation

Decision policy

Fig. 7.3 Schematic representation of the methodology applied to the present LULCC and its implication on GWT

adopted in this research work. For the land use/land cover studies different techniques were used as a powerful tool to trace land use/land cover features based on satellite images.

7.2.2.1

Detection Techniques for Land use and Land cover

For the land use/land cover studies different techniques were used as a powerful tool to trace land use/land cover features based on satellite images.

7 Studies on Impacts of Land Use/Land Cover Changes on Groundwater …

157

Unsupervised Classification Land use/land cover (LULC) changes play a significant role in agricultural policymaking, and land and water resources management framework. Generally, LULC alterations have occurred for the purpose of regional planning in most cases. Changes in LULC features are located by the various Spatio-temporal satellite images. LULC changes in an area are analyzed based on various satellite sensors such as Landsat MSS, Landsat TM, Landsat ETM + , LISS-III, and Google earth images. The pixel density around the mean spectrum depends on the spectral variability of the land cover and the area’s extent of the land cover. LULC analysis is carried out based on the unsupervised classification in which the Iterative Self-Organizing Data Analysis Technique (ISO-DATA) is used (Sahoo et al. 2016). Trace of Land use/land cover alteration is performed by unsupervised classification in which k means classifier algorithm. In this case outcomes of clustering derive from the following equation (Patraet al. 2018): Jj =



x − z j (l + 1)2

(7.1)

xes j (l)

and Zj(l + 1) =

1  x N j xes(l)

(7.2)

In this equation jj is indicate the clustering criteria of the error sum of squares and where Nj indicates the remained of sample number and Zj is the clustering class, j = 1, 2, 3, 4, 5 … k. Liaqat et al. (2021) have studied in Al Ain region of the United Arab Emirates, they are classified as raw images under unsupervised classification. The overall classification was successfully prepared based on a clustering algorithm (Iterative). Then the new statistics of the cluster were recalculated. In this classification to collect the appropriate number of spectral classes for the classification of the image, a number of trials were used.

Supervised Classification In an area, various land use/land cover types are determined by themeasure of pixel classification. A classifier iteratively is generated by the past arrangement outcomes through oneself preparing calculation. Alshari and Gawal (2021) have used a supervised classification method to develop of a classification system for LULC using remote sensing and GIS. In supervised classification, the primary idea on all land use/land covers types that to be designed interior part of the characterized scene is accepted. This data is used to characterize marks of the classes of interest, to be

158

S. Halder et al.

applied to the study area. Tirupati, India was studied to land use/land cover changes detection by Mallupattu et al. (2013). This study was used supervised classification with an algorithm to categorize LULC mapping bases of IRS 1D georeferenced and merged LISS-III and PAN. The classification programs as well as for signature creation has been used to collect accurate location points of data for every LULC class.

Image Differencing Image differencing is a digital technique of image processing. This technique is performed to calculate changes between two images. The dissimilation between two images obtained from times t1 and t2 of the same area is statistically defined by finding the dissimilation between every pixel in each image and preparing an image based on the outcomes (Sahoo et al. 2016). The different images are. Id (x, y) = I1 (x, y) − I2 (x, y)

(7.3)

where I1 and I2 are the images collected from t1 and t2 . Also x, y is the co-ordinate of the pixels and Id is the resulting image. Amin (2017) was studied change detection using Image Differencing surrounding Kumta in India. They are shown Image differencing is one type of special technique to change the detection of land use/land cover. This method is calculated by two images (I1 and I2) that are derived from two different times. These two images are incorporated for the same area, same pixel, and same band. In this case, the output final image is drawn from the differentiation of the Digital Number (DN) value of two images that are collected from times t1 and t2. This technique has provided significant change detection outcomes under the homogenous climatic situation.

Discriminant Function Discriminant function change detection isa technique that calculates the quantity of change per pixel that collects from two images for the same area. Sahoo et al. 2016 was representing of change detection method as a discriminant function in the Hirakud area, Odisha. Discriminant Function refers to a natural allocation of spectral clusters in the dataset. The alteration between two same spatial images is traced by the Discriminant Function Change algorithm. An unsupervised pattern classified technique with spectral classes 64–128 is applied to this algorithm. To eliminate a set of multivariate signatures (covariance matrix, mean vector) from the alternative image was used unsupervised classification of the base image. Every pixel is computed by the Mahalanobis Distance (MD) metric based on the signature corresponding to the class as result generates final images. The pixel range of the final image varies from 0.0 to 1.0. These values represent changes in data in a significant

7 Studies on Impacts of Land Use/Land Cover Changes on Groundwater …

159

level. Singh et al. (2013) have used the change detection techniques as Discriminant Function for Allahabad city. Discriminant Function is a typical method to change the detection of land use land cover. It is performed based on two images that are drawn in the same spatial area with various views in time. This method is used to calculate the possibility of change of every pixel using two images. To prepare any resulting files have used the discriminant function technique based on the possibility of alteration between the inputs after the image. The resulting images are drawn up with continuous data, a single band, and a range of pixel values from 0 to 1.0. These values indicate the possibility of alteration of every pixel. The conceptual procedure that algorithms have is controlled by the base image (that image is changed against another image).The base image algorithm will yield various outcomes. Thereafter an unsupervised pattern classified technique is being used on the base image through the measurable number of spectral classes. This generated thematic image as a zonal mask that is used to eliminate a set of multivariate signatures (mean vector and covariance matrix) from the alternative image (change image). In a change image, every pixel is calculated by the Mahalanobis Distance method based on the signature corresponding to the class. Every pixel is composed of a resulting image based on the Mahalanobis distance metric.

7.2.2.2

Temperature and Rainfall Data Analysis

The impacts of land use/land cover changes on climatic parameters (spatial distribution of temperature and rainfall) are examined by various methods. Variability at spatial and temporal scales could have a critical impact on groundwater resources. Normally, the decreasing trend of precipitation can improve events of drought in an area over time. Deb and Tarafdar (2019) have observed the trend analysis of rainfall and temperature patterns in the Mid Himalayan catchment. To find out variability trends of climatic parameters i.e. diurnal temperature and rainfall data for the study area was downloaded from the Indian Meteorological Department (IMD). The annual and seasonal time series data of rainfall was analyzed based on gridded 0.5° × 0.5° (IMD). This dataset has led to the detection trends of climatic parameters using the non-parametric rank-based Mann–Kendall method (Mann 1945; Kendall 1975). Also Hirsch et al. (1982) have proposed a nonparametric median-based slope method. This method has been used to estimate the probability of magnitude trend. As given below.

Inverse Distance Weighting (IDW) Method Toblers first law (1970) is used in the IDW method. The Inverse Distance Weighting is a type of deterministic method for multivariate interpolation with a known scattered set of points. Also, it is a mapping technique of convex interpolation which fits the continuous models of spatial variation. Spatial temperature and rainfall data are

160

S. Halder et al.

interpolated by the IDW method (Patra et al. 2018). The IDW derives the values of a variable at some new location using data obtained from other locations (Childs 2004).

7.2.2.3

Groundwater Level Fluctuation

It is found that the probable effects of land use land cover changes (LULCC) and variation of local climatic conditions on the spatial variability of groundwater levels over the years (pre-monsoon and post-monsoon). Ultimately, in the present context fluctuation of water level can be from a human intervention such as recharge, groundwater withdrawals, and effects of climatic parameters and also reflect the number of negative pressure put on the natural resources. Generally, several years are selected to quantify the level of ground water variability. In an area, a maximum number of the well and its related part are selected through the systematic sampling technique for better outcomes. Normally, in the aquifer water level are a balancing through recharge, storage, and discharge. To manage groundwater are different methods exist here based on the fluctuation of groundwater level. The method incorporates like SVF-method (Saturation Volume Fluctuation Method) and CRD-method (Cumulative Rainfall Departure Method) that is mostly depended on the recharge which is ruled from rainfall. The Water Level Fluctuation is a method that assesses water recharge and store to an aquifer. Also it related the increment of water level to water extending the water table through the soil (Poeter et al. 2020).

7.2.2.4

Kendall’s Tau Test

The Kendall’s Tau is a rank correlation coefficient measure as non- parametric .Dhar et al. (2014), was used Kendall’s Tau measure method to examine the relationship between hydro-meteorological components like rainfall, temperature and hydrological parameters like depth to groundwater level. The Tau value range 0–1, where 0 indicate is no relationship and 1 means is perfect relationship. Also this correlation coefficient test will take values –1 and + 1. Where –1 (negative values) have identified that as one variables rank increases and other one decreases. On the other hand + 1 (positive values) means the both variables rank increase. In this case Kandells Tau test has measured by the following formulaKendalls Tau = (C − D/C + D)

(7.4)

where C indicate to number of concordant pairs and D indicate is number of discordant pairs.

7 Studies on Impacts of Land Use/Land Cover Changes on Groundwater …

161

7.3 Results In the present context, the land use/land cover (LULC) rapidly changing by anthropogenic activities and Spatio-temporal changes of climatic parameters that affectgroundwater quantity. So the LULC and climatic parameter of an area is integral to the prediction of groundwater recharge. It is observed that assessment of different parameters which are relatedto the detection of LULC changes and groundwater table fluctuation using different methods and techniques.

7.3.1 Assessment of Spatio-Temporal Changes of Land Use/ land Cover (LULC) It is common that different land use/land cover classes in a particular area continue to changes by enormous anthropogenic intervention. Detection of land use/land cover changes isanalyzed mainly based on unsupervised classification, supervised classification, image differencing, and discriminant function (pixel-based). These methods are applied by using different sensors of satellite and Google Earth. Land use/land cover patterns like cultivation land, built-up construction, vegetation, water bodies, wastelands/barren land etc. are traced under different techniques for several decadal years. In order to examine that, the area under different land use/ land cover considerably changes over time. Most of the cases in Land use classes like built-up areas have grown up at the fastest rate over the area. Patra et al. (2018) have observed that land use/land cover changed with a spatial scale in the Howrah Municipality Corporation region from 1975-to 2015. LULC changes continue expansion at the high rate against of vegetation, wetland, and cultivated lands. Urban built-up area has expanded by approximately 30 km2 at an average rate of 0.75 km2 over the past 40 years (built-up area covered more than half of the total area). Wang et al. have observed that in the oasis of the dried-up river in the Tarim basin the area of cropland, forest, and urban land gradually growing up over times of view. In this case, urban areas had quantitatively the highest percentage increase at the rate of 68.1% with dramatic changes from 2010 to 2018. While the area of grassland, bare land and water bodies continuously decreased over time. Furthermore, the major LULC type like cropland over the study period covered more than half of the oasis area as a result of the high water demand required. Nath et al. (2020) has also observed the loss of vegetation and waterbodies including wetland to urbanization in Guwahati city. It is suggesting that the trees which are belong on the surface of the earth that is destroying for human use. While vegetation to fallow land indicates that there was a destruction of vegetation for urban activities. Overall there is an increase of 103% in urban areas from 1990 to 2020.

162

S. Halder et al.

7.3.2 Spatial and Temporal Distribution of Temperature Spatial and temporal variations of an average temperature are observed in an area at various times of points. It is examined that, high and low-temperature zones are fluctuated in an area by the various purpose of uses of the land surface. This examination is supported by the land use/land cover and NDBI maps. Normalized Different Builtup Index (NDBI) indicates the built-up area and its index. Generally, the temperature gradient is observed in more urbanized (covered by asphalt and concrete that carry a high radiant temperature) areas to rural areas. Patra et al. 2018 have observed that significant expansion of the high-temperature zone times going on compared to before times in the Howrah Municipality Corporation(HMC) region. In this region temperature gradient was observed between northern and north-eastern parts of the southern and south-western parts of the city from 2000-to 2014.

7.3.3 Spatial and Temporal Distribution of Rainfall The average rainfall distribution of an area very sharply changes over different times of periods at a spatial scale. Rainfall distribution is mainly controlled based on temperature distribution patterns. It is observed that the direction of declining rainfall and high temperature has followed the way of rapid urbanization and industrialization. To rainfall distribution of an HMC area Patra et al. (2018) have observed that the changing land use/land cover pattern indicates the increasing trend of an average temperature. As a result, may be declining in rainfall with temporal and spatial context from 2008 to 2014. In this city declining rainfall gradient is most probably distributed from the western and south-western parts to the north and north-eastern parts.

7.3.4 Spatio-Temporal Variations of Groundwater Generally, different seasons like spring, summer, autumn, and winter (pre-monsoon, monsoon, post-monsoon) are the major controller of the spatial distribution pattern of depth to groundwater level. Also, land use/land cover changes in an area direct influence groundwater saturation level. Additionally, depth to groundwater level fluctuation also depends on underlying surface conditions, hydro-meteorological conditions, and human activities. In most cases, the groundwater table is recharged through infiltration and percolation processes due to monsoonal rainfall. Patra et al. (2018) have observed that in pre-monsoon period the depth of groundwater level has altered at the fastest rate. In this case, groundwater level has decreased at a significant level

7 Studies on Impacts of Land Use/Land Cover Changes on Groundwater …

163

mainly to the growth of urban built-up as well as impervious nature. In the postmonsoon period, the depth to groundwater level has also controlled a declining trend with as time goes on. The spatial distribution of the range of groundwater level shave more declining in 2015 may be largely due to high accumulated population, expansion of built-up areas of non-impervious nature, and lesser infiltration capacity. Wang et al. have observed that the spatial distribution pattern of DGL (depth to groundwater level) and DM (degree of mineralization) mainly depend upon four seasons. Chen et al. haves observed that the continuous water-carrying to the lower part of Tarim River has increased the recharge to groundwater level in the downstream area. As earlier information, most of the studies reveal that alteration of LULC causes severe impacts on groundwater resources due to change in recharge rate and initiation of new abstraction regimes (Foster et al. 1988). It is found that the above scholarly principle gives the reason for low recharge of groundwater as well as decreasing of groundwater level and increasing the variability of groundwater level over the times mainly depend on impermeabilization of land surface due to rapid growth of urbanization.

7.4 Discussion 7.4.1 Built-Up Environment Influence Spatio-Temporal Alteration in LULC Land use/land cover change is a system in which underlying surface like agricultural landscape, forest land, wetland, water bodies and barren/wasteland as well as all the land use types is continue transformed to built-up landscape. In order to examine the maximum portion of the colonized global is always altered by the fastest growth of built-up structure and population growth. Present time over 55% of global pupils living in urban areas (https://ourworldindata.org/). For the maximum benefits acquired peoples are now more attracted to a modern life. In developed countries LULC changes intensity is very low but in developing countries the rate of LULC changes due to urbanization are more extensive (Dewan and Yamaguchi 2009). Moreover, the implication of LULC changes on natural environment is very complex and spatial variance due to high standard living temptation (Dewan and Yamaguchi 2009; Carlson and Traci Arthur 2000). Patra et al. 2018 have found that LULC (land use/ land cover) changes in the Howrah Municipality Corporation (HMC) region shows a built-up area increased during 1975–2000. Several land use/land cover classes like agriculture, vegetation, wetland and water bodies have recorded decreasing trend at the cost of built-up area expansion. Regression analysis with very high R2 = 0.9839 values shows trends of the alteration in land use land cover class like built-up area over the years.

164

S. Halder et al.

7.4.2 Climate Parameters and Land Cover Changes Influence on Groundwater Table Various climatic parameters interact on groundwater resources. Generally depth to ground level water is significantly correlated to precipitation while reverse condition related to air temperature. Maximum dry air (high temperature) could help to decrease groundwater table by high rate of evapotranspiration (Fu et al. 2019). Additionally high rate of rainfall (precipitation) during rainy season could improve several cultivation types which are more growth by progressive soil moisture content (Riley et al. 2019). Furthermore, actual evapotranspiration in an area is one of the dominant essential factors which influence to ground water resources. Moreover, groundwater table could increase by high intensity stream flow through groundwater recharge (Guo et al. 2019; Wang et al. 2014; Gu et al. 2016). Zhang et al. have observed that positive correlation between groundwater level and stream flow in autumn and winter due to greater flood during 2002–2003. Land use/land cover like vegetation coverage may great influence on groundwater table fluctuation. LULC change in an area controls all over demand and supply of water (water balance). Also water resources are reallocation through LULC changes by 8modifying recharge rate; recharge location on geological and different surface features (Han et al. 2017; Berihun et al. 2019). The cultivation land is linked with to depth of groundwater level. In several cases cultivated lands could utilize the huge amount of water to the better agricultural production that lead to groundwater table fluctuation. This agricultural water consumption also influence to loss of surface water through transpiration and evaporation (Shukla et al. 2018). Thereby groundwater has exploitation in greater rate and leads to lower groundwater level. Moreover, various types of vegetation also control to groundwater level. Vegetation categories could controlled evaporation, transpiration also its intercepting rainwater influence fluctuation of depth to groundwater level (Levia and Frost 2003; Jobbagy and Jackson 2004; Berihun et al. 2019). Elmahdy et al. (2020) was observed in United Arab Emirates that urbanization and groundwater table obviously linked because groundwater table has declining rate at the cost of built-up features and irrigated cultivated land expansion. Cultivated sector was considered to be main water consumer which contains about 60% at an average rate. Resulting negative impact have observed on groundwater table with in an areas high concentration NO3 . Land use/land cover (LULC) like vegetation leads to negative and positive impacts on groundwater resources. In that case positive impact is considered by the infiltration of surface runoff and negative impact is success by the transpiration from the rooted soil profile. Sherif and Singh (1999), Elmahdy and Mohamed (2016) was found it that groundwater table and quality at decline rate by the rapid urbanization, rainfall scarcity and the high growth rate in temperature anomaly.

7 Studies on Impacts of Land Use/Land Cover Changes on Groundwater …

165

Table 7.3 Temperature, rainfall and groundwater levels have correlated by the Kendall’s correlation coefficient measure (Patra et al. 2018) Kendalls Tau (ó)

Temperature

Rainfall

Groundwater level

−0.7711

–0.7536

Rainfall

−0.7711

1.0000

0.6976

Groundwater level

−0.7536

0.6976

1.0000

Temperature

1.0000

7.4.3 Correlation Analysis in Different Variables by the Outcomes of Kendall’s Tau Test (R) The correlations between meteorological and hydrological parameters like temperature, rainfall and groundwater resources are better analyzed by Kendall’s Tau test results. Patra et al. 2018 for Howrah Municipality Corporation reported that the annual average rainfall (−0.7711) and annual depth to groundwater level (−0.7536) have significantly negative correlation with the annual temperature, which is indicated by table no. 3. Additionally, this correlation indicates that amount of rainfall and ground water depth is very low with respect to increase in temperature. Although the test result shown annual rainfall and depth to groundwater level (0.6976) highly positive correlated. This positive relation refers to increase rainfall lead to rising up of groundwater level. Finally, it is clear that all the parameters are linked with one another.

7.5 Recommendation for Sustainable Land Use Planning • Making integrated planning: Integrated planning is an essential instrument to promote a proper and scientific way to land use/land cover changes in an area. Additionally, the major role of spatial planning is the integration of different land use land cover patterns with the systematic development of infrastructure regarding built-up construction. These types of efforts are mostly necessary for the sustainable use of land and water resources for the basis of a long time. • Increase of afforestation: vegetation is considered a carbon sink and natural cooler. Also, it does encourage evapotranspiration and more energy dissipated through latent heating. Moreover, afforestation helps in maximum recharge of water in the ground part. Also, it does conserve soil from erosional agents. However, in this case, the types of vegetation are very important. It is expected that various types of vegetables to be cultivated and different types of inputs to be used for the improvement of land-use strategies by the proper guidance of local agencies. • Conservation of reservoir and wetland: Water is a key resource of an area for sustainable planning. Present times a large amount of water misuse by anthropogenic intervention as well as rapid urban growing up. On the other hand, vegetation, reservoir, and wetlands promote to decreasing surrounding air temperature through the absorption of heat. Hence water sensitive land use planning is mostly

166

S. Halder et al.

required to sustain of the local climate. Proper structure should be developed in an area for the restoration and conservation of wetlands and reservoirs. • Spatio-temporal groundwater management plan: Sustainable planning should be required to mitigate an alarming stage of groundwater condition. The scientific land use will been suring sustainable groundwater recharge with careful consideration of socioeconomic characteristics. Sustainable groundwater planning should emphasize on rational use of groundwater and promote surface water conservation. Also, this plan promotes awareness among the people regarding the sustainable use of groundwater. However, scientific planning on water distribution, irrigation, and domestic water-saving practice will ensure an increasing groundwater table. • Awareness and well training: Pupils should be made aware of land use/land cover change as well as environmental changes over times. Misuse of natural resources like land, soil, and water due to unplanned, unsystematic growth of the built-up area should be prevented. Proper training of peoples is to be provided to mitigate land and water stress. Well awareness programs should be initiated by the state government, local bodies, and NGOs to provide and spread information about the alarming effects of rapid changes in underlying water resources (Patra et al. 2018).

7.6 Conclusions The present study focus on alteration in land use/land cover pattern and the dynamic expansion of built-up features on underlying surface over the times based on remote sensing and GIS technique. It is observed that in present time most of the area has experienced fastest alteration in LULC regarding built-up environment. In this case expansion of built-up features has responsible to substantial declining of an area under different types land use/land cover features like agricultural land, forest land and water bodies etc. The large distribution of built-up structure as well as construction surfaces absorb and deposit incoming solar radiation. Therefore, average temperature is increased and rainfall is decreased in area over the times. The spatio-temporal distribution of precipitation and temperature has changing rate with respect of builtup area. Also this built-up structure antithetical affects on depth to groundwater level during both pre and post monsoon season. However, unscientific, unplanned and unsystematic land use threatens the sustainability of environment component like climatic parameters, groundwater level etc. To solve this problem systematic and intelligible planning is necessary with spatio-temporal scale. For sustainable improvement and healthy environment of an area systematic and scientific use of land is necessary. Generally, an integrated approach will be most necessary to ensure preservation of water and sustain of suitable climatic condition at local level. Moreover, sustainable natural resources and peoples livelihoods in an area mainly depend on protective integrated planning. However a perfect and systematic approach with a significant responsibility of the local body to sustainable development of an area is required.

7 Studies on Impacts of Land Use/Land Cover Changes on Groundwater …

167

Finally, to form appropriate policies for sustainable development of an area a regulation of systematic built-up construction considering proper use of land and water is required to be developed. Acknowledgements The authors are deeply acknowledged to United States Geological Survey (USGS) and National Remote Sensing Centre (NRSC) for providing the satellite data of an area. Also the authors would like to thanks of Jadavpur University for providing essential technological support to continue this research.

References Alam MS, Rahman MS, Hosen MI, Mubin KA, Hossen S, Mridha MF (2019). Delineation of groundwater potential zones using geospatial techniques and analytical hierarchy process in Dumka district, Jharkhand, India. Groundwater for Sustainable Development, 9(October). https://doi. org/10.1016/j.gsd.2019.100239 Alshari AE, Gawali WB (2021) Development of classification system for LULC using remote sensing and GIS. Global Transitions Proceedings 2(2021):8–17 Amin K, Shivakumar BR (2017) Change detection using image differencing: a study over area surrounding Kumta, India. In: IEEE International conference on electrical,computer and communication technologies, 978–1–5090–3239–6. Andualem TG, Demeke GG (2019) Groundwater potential assessment using gis and remote sensing: a case study of guna tana landscape, Upper Blue Nile Basin, Ethiopia. J Hydrol: Reg Stud 24:100610. https://linkinghub.elsevier.com/retrieve/pii/S2214581818302428 Berihun LM, Tsunekawa A, Haregeweyn N, Meshesha TD, Adgo E, Tsubo M, Masunaga T, Fenta AA, Sultan D, Yibeltal M (2019) Exploring land use/land cover changes, drivers and their implications in contrasting agro-ecological environments of ethiopia. Land Use Policy 87:104052. https://linkinghub.elsevier.com/retrieve/pii/S0264837719304089. Bhattacharya KR, Chatterjee DN, Das K (2020) An integrated GIS approach to analyze the impact of land use change and land cover alteration on ground water potential level: Astudy in Kangsabati Basin, India. Groundwater for Sustainable Development, PII: S2352–801X(18)30097–3 CGWB (2013) Ground water information booklet of Kamrup & Kamrup metro district, Assam.Central Ground Water Board, North Eastern Region, Ministry of Water Resources, Guwahati Carlson TN, Traci Arthur S (2000) The impact of land use-land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective. Global Planetary Change 25:49–65 Chemura A, Rwasoka D, Mushore T (2020) The impact of land use/land cover changes on water balance of the heterogeneous Buzi sub-catchment, Zimbabwe. Environ Sci 213601853 Chen S, Li WB, Du YD, Mao CY, Zhang L (2015) Urbanization effect on precipitation over the Pearl River Delta based on CMORPH data. Adv Clim Chang Res 6(1):16–22 Chowdhury M, Mamun A (2020) Land use/land cover change assessment of Halda watershed using remote sensing and GIS. The Egyptian Journal of Remote Sensing and Space Science 23(1):63–75 Das, S., Nayek, M., Das, S., Dutta, P., &Mazumdar, A. (2014). Impact on water quality in Piyali River, Sundarbans due to saline water intrusion.Indian journal of environmental protection, 34(12), 1010–1019. Dewan AM, Yamaguchi Y (2009) Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Appl Geogr 29:390–401 Dhar A, Sahoo S, Dey S, Sahoo M (2014a) Evaluation of recharge and groundwater dynamics of a shallow alluvial aquifer in central Ganga basin, Kanpur (India). Nat Resour Res 23(4):409–422

168

S. Halder et al.

Dhar A, Sahoo S, Sahoo M (2014b) Evaluation of recharge and groundwater dynamics of a shallow alluvial aquifer in central Ganga basin, Kanpur (India). Nat Resour Res 23(4):409–422 Dhar A, Sahoo S, Sahoo M (2015) Identification of groundwater potential zones considering water quality aspects. Environ Earth Sci 74(7):5663–5675 Elmahdy IS, Mohamed MM (2016) Land use/land cover change impact on groundwater quantity and quality: a case study of Ajman Emirate, the United Arab Emirates, using remote sensing and GIS. Arab J Geosci 9:722. https://doi.org/10.1007/s12517-016-2725-y Elmahdy SI, Mohamed MM (2017) Monitoring and analyzing the Emirate of Dubais land use/land cover changes: An integrated low-cost remote sensing approach. Int. J. Digit. Earth 11:1132– 1150 Foster SSD, Hirata R (1988) Groundwater pollution risk assessment. A methodology using available data. Lima: Pan Ame. Cent. For Sanit. Engin. And Envir. Scien. (cepis) Fu Q, Shi R, Li T, Sun Y, Liu D, Cui S, Hou R (2019) Effects of land-use change and climate variability on streamflow in the woken river basin in Northeast China. River Res Appl 35(2):121– 32. https://onlinelibrary.wiley.com/doi/10.1002/rra.3397 Gregorio A (2016) Land cover classification system: Advanced database gateway. Land and Water Div. eng 187275 Gu W, Guo J, Fan K, Chan WHE (2016) Dynamic land use change and sustainable urban development in a third-tier city within Yangtze Delta. Procedia Environ Sci 36:98–105. https://linkin ghub.elsevier.com/retrieve/pii/S1878029616302213. Guo A, Zhang Y, Hao Q (2019) Monitoring and simulation of dynamic spatiotemporal land use/ cover changes. Complexity 2020:1–12. https://www.hindawi.com/journals/complexity/2020/ 3547323/ Han D, Curell JM, Cao G, Hall B (2017) Cultural differences in preferences for facial coloration. Evol Human Behav 39(2):154–59. https://linkinghub.elsevier.com/retrieve/pii/S10905138173 01988. Hassan Z, Shabbir R, Ahmad SS, Malik AH, Aziz N, Butt A, Erum S (2016) Dynamics of land use and land cover change (LULCC) using geospatial techniques: a case study of Islamabad Pakistan. SpringerPlus, 5(1). https://doi.org/10.1186/s40064-016-2414-z Jobbágy GE, Jackson BR (2004) Groundwater use and salinization with grassland afforestation. Glob Change Biol 10(8):1299–1312. https://onlinelibrary.wiley.com/doi/10.1111/j.1365-2486. 2004.00806.x. Khan R, Jhariya D (2018) Assessment of land-use and land-cover change and its impact on groundwater quality using remote sensing and GIS techniques in Raipur City Chhattisgarh, India. J Geol Soc India 92(1):59–66. https://doi.org/10.1007/s12594-018-0953-3 Kayet, N., Chakrabarty, A., Pathak, K., Sahoo, S., Mandal S., Fatema, S., Tripathy, S., Garai, U., & Das, T. (2018). Spatiotemporal LULC change impacts on groundwater table in Jhargram, West Bengal, India. Sustainable water resources management. https://doi.org/10.1007/s40899-0180294-9. Lamichhane S, Shakya MN (2012) Land Use Land Cover (LULC) change projection in Kathmandu valley using the CLUE-S model. J Adv College Eng Manag 6:202 Levia, F. D. and Frost, E. E. (2003). A review and evaluation of stemflow literature in the hydrologic and biogeochemical cycles of forested and agricultural ecosystems. J Hydrol 274:1–29. Liaqat, U. M., Mohamed, M. M., Chowdhury, R., Elmahdy, I. S., Khan, Q., Ansari R. (2021).Impact of land use/land cover changes on groundwater resources in Al region of the United Arab Emirates using remote sensing and GIS techniques.Groundwater for Sustainable Development, 14, 100587. Liu F, Zhang Z, Zaho X, Wang X, Zuo L, Wen Q, Yi L, Xu J, Hu S, Liu B (2019) Chinese cropland losses due to urban expansion in the past four decades. Sci Total Environ 650:847–857 Mallupattu KP, Reddy J, Reddy S (2013) Analysis of Land Use/Land Cover Changes Using Remote Sensing Data and GIS at an Urban Area, Tirupati, India. The Scientific World Journal, volume 2013, Article ID 268623

7 Studies on Impacts of Land Use/Land Cover Changes on Groundwater …

169

McGrane SJ (2016) Impacts of urbanisation on hydrological and water quality dynamics, and urban water management: a review. Hydrol Sci J 61(13):2295–2311. https://doi.org/10.1080/ 02626667.2015.1128084 Mngube MF, Anyona ND, Abuom OP, Matano A (2019) Land cover change and its socio-economic impact on the residents of the Mara River. Kenya. American Journal of Climate Change 08(03):404–438 Molla, H. R. (2011). Embankment and changing micro-topography of lower Ajoy basin in eastern India.Ethiopian journal of environmental studies and management, 4–4. Mondal B (2015) Anthropogenic impact on river of Sundarbans deltaic settlement area; with special references to the Piyali River, south 24 Parganas, West Bengal, India. International Journal of Multidisciplinary Research and Development 2(3):465–469 Naschen K, Diekkruger B, Evers M, Hollermann B, Steinbaach S, Thonfeld F (2019) The Impact of Land Use/Land Cover Change (LULCC) on Water Resources in a Tropical Catchment in Tanzania under Different Climate Change Scenarios. Sustainability 2019(11):7083 Nath B, Meister NW, Choudhury R (2020) Impact of urbanization on land use and land cover change in Guwahati city. India and Its Implication on Declining Groundwater Level. https://doi.org/10. 1016/j.gsd.2020.100500 Ozdemir A (2011) GIS-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparison. J Hydrol 411(3–4):290–308. https://doi.org/10.1016/j.jhydrol.2011.10.010 Patra, S., Sahoo, S., Mishra, P., & Mahapatra, C. S. (2018). Impacts of urbanization on land use/ cover changes and its probable implications on local climate and groundwater level. Journal of Urban Management 7 (2018) 70–84 Porter, R. J., Challinor, J. A., Henriksen, B. C., Howden, M. S., Marttre, P. & Smith, P. (2020). Invited review: intergovernmental panel on climate change, agriculture, and food—a case of shifting cultivation and history. Glob Change Biol 25(8) 2518–29. https://onlinelibrary.wiley. com/doi/10.1111/gcb.14700 Riley D, Mieno T, Schoengold K, Borozovic N (2019) The impact of land cover on groundwater recharge in the high plains: An application to the Conservation Reserve Program. Sci Total Environ 696:133871 Samal RD, Gedam S (2021) Assesing the impacts of land use and land cover change on water resources in the Upper Bhima river basin. India. Environmental Challenges 5(2021):100251 Sen PK (1968) Estimates of the regression coefficient based on Kendalls tau. J Am Stat Assoc 63(324):1379–1389 Sharp JM (2010) The impacts of urbanization on groundwater systems and recharge. Aqua Mundi. https://doi.org/10.4409/Am-004-10-0008 Sherif MM, Singh VP (1999) Effect of climate change on sea water intrusion in coastal aquifers. Hydrol Process 13:1277–1287 Shukla AK, Ojha CSP, Mijic A, Buytaert W, Pathak S, Garg RD, Shukla S (2018) Population growth, land use and land cover transformation, and water quality nexus in the Upper Ganga River basin. Hydrol. Earth Syst. Sci. 22 (9) Singh KS, Singh KC, Mukherjee S (2010) Impact of land-use and land- cover change on groundwater quality in the Lower Shiwalik hills: a remote sensing and GIS-based approach. Central European Journal of Geosciences 2(2):124–131 Singh A, Singh S, Garg KP, Khanduri K (2013) Land Use and Land Cover Change Detection: A Comparative Approach Using Post Classification Change Matrix and Discriminate Function Change Detection Methodology of Allahabad City. International Journal of Current Engineering and Technology, ISSN 2277–4106

170

S. Halder et al.

Sinha M (2016) Dwarekeshwar River basin and anthropogenic intervention as sand mines. International Journal of Research in Geography 2(2):48–56 Wang G, Yang H, Wang L, Xu Z Xue B (2014) Using the SWAT model to assess impacts of land use changes on runoff generation in headwaters. Hydrol Process 28, 1032–1042 (2014). https:// doi.org/10.1002/hyp.9645 Wang W, Chen Y, Wang W, Jiang J, Cai M, Xu Y (2020) Evolution characteristics of groundwater and its response to climate and land-cover changes in the Oasis of dried-up river in Tarim Basin. J Hydrol 594(2021):125644

Part II

Water Resources

Chapter 8

Crowdsourcing as a Tool for Spatial Planning in Water Resource Management Gouri Sankar Bhunia, Soumen Bramha, Manju Pandey, and Pravat Kumar Shit

Abstract Citizen participation has recently become more common in water resource management and prevention, led to advances omnipresent and interactive technology. This scenario has provided a once-in-a-lifetime chance to capitalise on the efforts of large groups of volunteers. The importance of crowdsourcing in knowledge acquisition for water resource applications is the subject of this research. Our study compares crowdsourcing techniques created for water resource management and conservation, which can be used to optimise surface water and sustainable management procedures, using a systematic literature review. We present a road map for future water research that draws together fine-grained findings from previous crowdsourcing research to produce a high-level, macro-perspective of the crowdsourcing phenomena and its geopolitical significance, directed by our analysis. Keywords Water resource · Crowd sourcing · GIS sustainable management · Participatory mapping

G. S. Bhunia (B) Independent Researcher, Paschim Medinipur, West Bengal, India e-mail: [email protected] S. Bramha Department of Geography, Nalini Prabha Dev Roy College, Bilaspur, Chhattisgarh, India M. Pandey Department of Geography, Government Mata Sabri Navin Girls PG College, Bilaspur, Chattishgarh, India P. K. Shit Department of Geography, Khan Women’s College (Autonomous), Raja N. LMidnapore, West Bengal, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_8

173

174

G. S. Bhunia et al.

8.1 Introduction The majority of countries are getting ready to put severe strain on their freshwater resources (FAO 2017). The population of the planet is quickly growing, and estimates show that by 2030, the entire world would confront a 40% shortfall between forecast demand and available water supply if existing systems continue (Boretti and Rosa 2019). Water scarcity, hydrological uncertainty, and severe storms (floods and droughts) are all considered as major threats of climate change economy and prosperity. Feeding 9 billion people by 2050 will need a 60% increase in agricultural production (which presently consumes 70% of the resource) and a 15% rise in the water demands (Fakhrul and Karim 2019). Besides from growing demands, the commodity is already in short supply throughout many regions of the universe. According to statistics, 40% of the world’s population lives in water-scarce areas, and this dilemma marks about 14% of worldwide gross domestic product (GDP). By 2025, approximately 1.8 billion people will be living in water-scarce regions or countries (World Bank 2017). For several countries today, water security is a crucial—and often rising—issue. Climate change will worsen the situation by disturbing hydrological cycles, increasing the frequency and intensity of floods and droughts, and creating water more unpredictable. The 500 million people who live in floodplains and the 1 billion people who live in tropical monsoon rainfall regions are very susceptible (Islam 2016). Droughts put constraints on the rural poor, who rely heavily on monsoon rainfall for survival. Flood reparations are projected to be $120 billion per year (only from property damage), and droughts enact constraints on the rural poor, who are strongly reliant on precipitation patterns for sustenance. Water security is also hindered by the decentralisation of this resource (Dastagir 2015). There are 276 intergovernmental basins held by 148 countries that contribute for 60% of global freshwater flow. Furthermore, 300 aquifer systems are transnational in nature in character, implying that 2 billion people rely on groundwater around the planet (Fienen et al. 2016). Concerns about fragmentation are regularly echoed at the national level, demanding coordination in order to provide effective water resource management and development policies for all riparian countries. In the face of increased demand, water shortages, rising ambiguity, greater fluctuations, and fragmentation difficulties, users will need to partake in institutional development, information management, and (natural and man-made) infrastructure enhancement (Gleeson et al. 2012). Administrative processes such as rules and regulations, water price, and incentives are expected to better allocate, control, and safeguard water supplies. Howe (2006) first coined the word “crowdsourcing” in his paper “The Rise of Crowdsourcing.“ Crowdsourcing has gained popularity in recent years among businesses, institutions, and colleges as a crowd-centered modern “tool” for issue resolution. The need to meld prior evidence from several professional and academic disciplines with the “wisdom of the crowd” in the problem-solving method prompted the development of crowdsourcing. Surowiecki (2014) stated that “Under the appropriate circumstances, groups are extremely brilliant, and are often smarter

8 Crowdsourcing as a Tool for Spatial Planning in Water Resource …

175

than the smartest individuals among them”. “The reality for advanced design nowadays is dominated by three notions,” according to Mau (2004). “Crowdsourcing tries to mobilise competence and expertise that is distributed across the population,” according to Zhao and Zu (2012). The goal of crowdsourcing is to collect and synthesise decentralised knowledge to solve problems. Primary information gathering, data refinement, information generation, and eventually evaluation of the information created are all part of the knowledge implementation. The extraordinary advancements in digital communication, as well as the widespread usage of e-tools that sturdily promote crowdsourcing, have generated novel prospects for knowledge dissemination and issue explaining in spatial planning over the previous few decades (Papadopoulou and Giaoutzi 2014). Numerous scientists and professionals from a variety of sectors have employed similar technology to improve and accelerate decision-making. Information systems are required for resource monitoring, unpredictability-based decision-making, computational modeling, and hydro-meteorological prediction and warning. Investment portfolios in technological developments for improving efficiency, conserving and protecting resources, repackaging storm water and wastewater, and progressing non-conventional water sources should be evaluated in addition to looking for opportunities for enhanced water storage, including aquifer recharge and recovery. Based on the above discussion, present work aims to find out the role of crowdsourcing in knowledge acquisition for water resources management and conservation. The beginnings of crowdsourcing in knowledge development are discussed in this chapter. The combination of crowdsourcing with particular online technologies and GIS (Geographic Information Systems) for spatial planning in water resource management is also discussed.

8.2 Methods A systematic literature review was conducted instead of a narrative review since it is less prone to bias. The literature was evaluated using a process based on Bryman’s (2014) dimensions: (1) year of conduct, (2) geography, (3) sample size, (4) data collection approaches, and (5) key findings. The usefulness and authenticity of all of the items identified were assessed. Scientific publications with peer review, conference papers, and reports were all considered. The following advanced search in Web of Science was used as the starting point for each search: “participatory monitoring” OR “participatory sensing” OR crowdsensing OR “crowdsourcing” OR “Water Resources” OR “Remote Sensing” OR “GIS”. Following that, relevant publications were thoroughly examined and evaluated based on the methodologies employed. At beginning, the only consideration for deletion was duplicate publishing, which resulted in a total of 122 articles. Backward snowball sampling, a technique used to supplement the typical systematic evaluation process by discovering references that have mentioned the articles obtained in the search phase, was used to add several research. Only articles with a clear technique explanation and unique results

176

G. S. Bhunia et al.

were considered, and only success aspects and impetuses specified in the section of the report were counted separately to eliminate duplication. Articles with non-peer reviewed literature (e.g., theses and dissertations) as well as editorials, conference reports, supplementary abstracts, panel discussions, and special issue openers were eliminated. Book chapters, tutorials, keynotes, and technical documents were also rejected, enabling about 45 articles were chosen.

8.3 Spatial Crowdsourcing for Water Resource Boulos (2005) coined the expression ‘Wikification of GIS (Geographic Information Systems) by the masses’ in 2005, two years before Goodchild (2007) popularised the term ‘Volunteered Geographic Information (VGI)’. Six years later (2005–2011), Google Earth (GE) has evolved into a full-fledged, crowdsourced ‘Wikipedia of the Earth,’ with millions of consumers contributing their own material, tagging photographs, videos, comments, and even 3-D (three-dimensional) models to places in GE. Through spatial crowdsourcing, individuals, groups, and communities contribute to the collection, evaluation, and dissemination of environmental, social, and other spatio-temporal data (To et al. 2014). VGI is progressively being used as a bottom-up approach in geographic applications, including map generation or elaboration by multiple users. Data is created “by the consumers for the public” in a decentralised system. As a result, one of the web 2.0 challenges is to incorporate intelligence into the way users generate, distribute, and smear data on their own (Hudson-Smith et al., 2009). Mobile crowd sensing (MCS), which involves citizen science and the use of mobile devices for monitoring, has a lot of potential (Kaku 2012). Mobile apps are a fascinating, engaging method to get people involved in water monitoring and education in their communities. Sensors for inertia, quickening, sound (microphone), position (GPS), ambient light, and immediacy are all standard features in smartphones, as a compass, camera, gyroscope, and light (Lane et al. 2010). Many classic scientific instruments, includingoutdated compasses and GPS systems, could be replaced with such sensors (Fig. 8.1).The CrowdWater project’s (http://www.crowdwater.ch/en/ welcome-to-crowdwater/) purpose is to enhance hydrologic forecasting using crowdsourced data such as water level, streamflow, soil moisture, and interrupted stream flow conditions. This Swiss National Science Foundation-funded project also evaluates the data’s correctness, the usefulness of quality control procedures, and the utility of citizen science data in calibrating or improving hydrologic models. Crowdsourcing has been combined with a number of online machineries (e.g., web GIS) and digital services to allow for the rapid creation and manipulation of data and information, including the dissemination of knowledge. The generation and manipulation of geographical data (e.g., paper maps, geographic latitudes and longitudes, satellite sensor data) is becoming increasingly popular with web users who want to discover locations or routes on a map, analyse geographical data, and, in several cases, generate spatial data and distribute it “for free” on the web. Geo-tagged,

8 Crowdsourcing as a Tool for Spatial Planning in Water Resource …

177

Fig. 8.1 Overview of crowdsourcing in water resource monitoring

street-level audio samples uploaded on YouTube by pedestrians using their locationaware smartphones can be gathered to establish region-wise information for distinct periods of the day and week, while commuters’ GPS (Global Positioning System) data is used to produce regular updates of real-time data using their Internet-enabled mobile phones or apps (Kamel Boulos et al. 2011). GIS has aided the development of applications that allow for the gathering, storage, modification, dissemination, and reproduction of geographic information. As a result, crowdsourcing has been implemented into other tools, and it is now a technology that may enable geographical and collective planning, cartography, and a wide range of other spatial programs that require the “knowledge of the crowd” (Papadopoulou and Giaoutzi 2014). It is now obvious that crowdsourcing, along with GIS technology, has laid the groundwork for a new strategy to spatial planning, in which the progression of spatial data creation (maps, etc.) takes place over the internet, with volunteers contributing to the overall project. Simultaneously, participants in a dilemma process can use maps and geographical data for a variety of purposes, facilitating and enriching the entire process.

178

G. S. Bhunia et al.

8.4 Citizen Engagement for Water Resource Monitoring The crowdsourcing framework creates a mechanism for citizens to participate in calculating and determining about their own drinking water quality (Rutten et al. 2017). As a result of citizens involvement provide utilities and water supply agencies with cost-effective water quality data in near-real time (Table 8.1). Consumers utilise their mobile phones to record and monitor the water quality information to a central service, following a standard crowdsourcing paradigm (Rotman et al. 2012). That service collects data, repackages it, and distributes it via text messages, websites, dashboards, and social media. Citizen engagement has been identified as a cornerstone to better natural resource revenue management in studies of water resource governance (Epremian et al. 2016). Citizen participation in water resource credit policies can assist citizens in forming or amending their opinions, debating matters, and voicing their anxieties about resource ascendency (Epremian et al. 2016; Lujala and Epremian 2017). OpenStreetMap (OSM) is a recent geospatial development that is widely used around the world and places a strong emphasis on community participation (Neis and Zielstra 2014). Its goal is to create an openly editable world map in order to address the lack of geo-information in many parts of the world (Haklay et al., 2014). GeoChat (http://instedd.org/technologies/geochat/http://instedd.org/tec hnologies/geochat/) and Ushahidi are two open source platforms that make it simple to develop crowdsourced interactive mapping apps using Web forms/e-mail, SMS (Short Message Service), and Twitter (http://twitter.com/http://twitter.com/). They can be publicly downloaded from the internet on one’s own server by anyone with the necessary technical skills, or they can be used as digital services provided by the platform providers (e.g., Crowdmap is the hosted version of Ushahidi). Ushahidi can be accessed via mobile apps for smartphones and tablets, such as the Android platform. Several organisations, ranging from the commercial sector to academics to non-profits, have recently expressed interest in developing mWASH apps (mobile phone applications for the water, sanitation, and hygiene WASH sector) (Hutchings et al. 2012). Recent academic research looked at how mobile phones could help water suppliers and public health authorities in Africa exchange water quality data (Kumpel et al. 2015). In Tanzania, USAID is piloting a smartphone application (https://www. mwater.co/) to assist consumers in testing their water for E. coli.

8.5 Challenges and Prospects The accessibility of a participating ‘crowd,’ according to Lichten et al. (2018) and Gummidi et al. (2019), is crucial to the growth of any crowdsourcing initiative, particularly spatial ones. It can be challenging to persuade members of the public to participate in a spatial crowdsourcing water resource initiative. The general public may be ignorant of a crowdsourcing initiative that requests their participation, or they may be reluctant to join if they do not consider the situation to be essential to

Water quality, biodiversity

USA

USA

Surface waterbody monitoring (lake, stream, estuary, wetland)

Rainfall estimation

Water quality (chemical/biological/ visual) Water monitoring Water quality monitoring Water quality parameters

Developing data standards New Jersey, USA and protocols at the state level for interoperability

Minnesota, USA

Wisconsin, USA

Florida, USA

Citizen lake and stream monitoring

Water quality, flow and biological health

Water quality monitoring

Rain Snow, Hail

Water quality

River reaches

San Pedro, USA

Mapping of a spatial non-contiguous perennial river

Water quality observation, Global education, awareness

Water quality parameters

Nicaraguan-Honduran

Prototype technique for conflict resolution

Data types

Geography

Goal

Table 8.1 Crowdsourcing in water resource management

Distributed intelligence

Distributed intelligence

Distributed intelligence

Distributed intelligence

Disseminated intelligence

Distributed intelligence

Distributed intelligence

Distributed intelligence

Collaborative / participatory

Crowdsource approach

Overdevest and Orr (2004)

Minnesota Pollution Control Agency (2014)

New Jersey Department of Environmental Protection (2014)

Community Collaborative Rain, Hail and Snow Network (2014)

US Environment Protection Agency (2014)

World Monitor Challenge (2014)

Turner and Richter (2011)

Macknick and Enders (2012)

References

(continued)

Framework of monitoring, Canfield et al. (2002) Labelling, Data interpretation

Design of monitoring, Labelling, Data interpretation

Design data protocol, Capacity building

Design data protocol

Framework, data dissemination, training

Monitoring Design, Training

Framework, monitoring, labelling, dissemination

Monitoring strategy, labelling, data analysis, and evaluation

Training, Data analysis, interpretation

Principle methods

8 Crowdsourcing as a Tool for Spatial Planning in Water Resource … 179

Pathogen (coliforms)

New York – New Jersey

Ontario, Canada

Citizen water quality testing

Watershed action toward environmental sustainability

Observation of pollution occurrences

Pollution monitoring

Maryland and Virginia, USA

Turbidity, electrical conductivity, pH, Phosphorous

Water quality Assessment Victoria, Australia

Pathogen (coliforms)

Data types

Geography

Goal

Table 8.1 (continued)

Crowdsourcing

Distributed intelligence

Distributed intelligence

Distributed intelligence

Crowdsource approach

New York City water Trail Association (2014)

References

Design data exchange mechanism

Water Reporter (2014)

Framework of monitoring, Nicholson et al. (2002) Labelling, Data interpretation

Architecture of observing, Au et al. (2000) Labelling, Data evaluation

Design of monitoring, Training, Data analysis, dissemination

Principle methods

180 G. S. Bhunia et al.

8 Crowdsourcing as a Tool for Spatial Planning in Water Resource …

181

them, or if they have other, more pressing demands. Furthermore, persons who are conscious of the crowdsourcing initiative and want to join may lack the necessary equipment (e.g., a smart phone or Internet connection), a communication channel (e.g., an email or social media account), or the necessary expertise (e.g., an capacity to read and write). It is critical to contact a significant portion of the public in order to rise the number of people who participate in water resource studies employing geographical crowdsourcing. As a result, attracting citizens’ consideration to the platform is critical. This entails comprehending how members of the target audience often stay updated about problems that concern them. Cooperation through a newspaper, radio, and/or television campaigns could be advantageous in this aspect (Lujala et al. 2020). Because Internet connections in several chunks of developing nations are inadequate or inconsistent, finding a solution to make the spatial crowdsourcing water resource platform work offline is critical. One option is to employ an interface that enables survey participants to view the survey, save their comments offline, and resubmit them whenever they have internet connectivity. Furthermore, such platforms must be able to grab and keep the appropriate location in order to verify that the waterbody location is appropriately documented (i.e., not the area where the participant accesses the Internet). Furthermore, in order to download and install the software as well as upload responses, users must have need Internet connectivity. Voluntary engagement, privacy, and the security of study participants are not simply ethical concerns, but also necessary for obtaining high-quality data (Robinson et al. 2018). Encouraging participants of their anonymity will make them feel more comfortable, allowing them to react more freely and effectively. The GIS-based Survey App can be customised to capture participants’ position at a predefined offset to maintain anonymity. We may use a 50–100 m offset. On a more technical level, the layout of the survey’s interface and navigation are critical. To complete the questionnaires, the participants had to navigate from the top of their phone’s screen to the bottom.

8.6 Conclusion Crowdsourced water quality monitoring has the allure of size, spatial resolution, cost effectiveness, and local participation, but it’s no easy undertaking. A participatory citizen science approach encourages participants and instils a sense of responsibility in them. People join projects for a variety of reasons, including curiosity or personal gain. In this conceptual framework, a number of research findings have been presented, each illustrating how crowdsourcing can intensify the entire process by leveraging the prospective input/knowledge engendered by a decentralised network of people interested in contributing to the planning process by offering suggestions. Such applications have suggested a new approach to spatial planning, including tools and approaches that improve public participation and the use of crowdsourced data.

182

G. S. Bhunia et al.

Despite being an integral aspect of scientific advancement and information development, citizen science as a concept and potential has only recently gotten more attention from the scientific community. New technology advancements are allowing for the development of new and more efficient data collecting, processing, visualisation, and transmission systems. Reflecting on the problems and potential of citizen science, particularly in the context of managing natural resources and utilising them for human well-being, is pertinent and important because of these opportunities. This is particularly true when it comes to water resources, which are often one of the most basic ecological functions and a major barrier for long-term eradicating poverty. In consequence, these systems, together with a smart water distribution network, indicate that WebGIS might become the Digital Twin for the entire world. In this sense, a system like this would make environmental costs more transparent and minimize financial externalities by using real-time data from monitoring devices all the way down to the aquafarm/hydrologist, allowing for more data-driven policymaking. To summarise, crowdsourcing facilitates the integration of analysis provided and serves as the foundation for a multidisciplinary approach to solving issue. The final result develops as a consequence of a collaborative effort in which the expertise of academics, professionals, and the “public” is creatively synthesised, leading to a final remedy to the subject under investigation. Crowdsourcing boosts creativity and, in the case of spatial analysis, lays the groundwork for a more in-depth connection with the “crowd” as well as the formation of a constructive collaboration between planners and the community at large. Government should strengthen citizens to address their concerns and act to them effectively. To do so, administrations must make a concerted effort to inform citizens about the benefits of participating in the management of water resource revenue.

References Au J, Bagchi P, Chen B, Martinez R, Dudley S, Sorger G (2000) Methodology for public monitoring of total coliforms, escherichia coli and toxicity in waterways by Canadian high school students. J Environ Manag 58:213–230. https://doi.org/10.1006/jema.2000.0323 Boretti A, Rosa L (2019) Reassessing the projections of the World Water Development Report. npj Clean Water 2, 15. https://doi.org/10.1038/s41545-019-0039-9 Bryman A (2012) Social Research Methods, 4th edn. Oxford University Press, Oxford, United Kingdom Canfield DE, Brown CD, Bachmann RW, Hoyer MV (2002) Volunteer lake monitoring: testing the reliability of data collected by the floridalakewatch program. Lake Reserv. Manag. 18:1–9. https://doi.org/10.1080/07438140209353924 Community Collaborative Rain, Hail and Snow Network (2014) Community Collaborative Rain, Hail and Snow Network. http://www.cocorahs.org/ (Accessed July 25 2014) Dastagir MR (2015) Modeling recent climate change induced extreme events in Bangladesh: A review. Weather and Climate Extremes 7:49–60 Epremian L, Lujala P, Bruch C (2016) High-Value Natural Resources and Transparency: Accounting for Revenues and Governance. Oxford Research Encyclopedia of Politics. https://doi.org/10. 1093/acrefore/9780190228637.013.2

8 Crowdsourcing as a Tool for Spatial Planning in Water Resource …

183

Fakhrul SM, Karim Z (2019) World’s Demand for Food and Water: The Consequences of Climate Change. In M. H. Davood, V. Vatanpour, & A. H. Taheri (Eds.), Desalination - Challenges and Opportunities. IntechOpen. https://doi.org/10.5772/intechopen.85919 FAO. 2017. The future of food and agriculture – Trends and challenges. Rome. Available at: https:// www.fao.org/3/i6583e/i6583e.pdf Fienen MN, Arshad M (2016) The International Scale of the Groundwater Issue. In: Jakeman A.J., Barreteau O., Hunt R.J., Rinaudo JD., Ross A. (eds) Integrated Groundwater Management. Springer, Cham. https://doi.org/10.1007/978-3-319-23576-9_2 Gleeson T, Wada Y, Bierkens M et al (2012) Water balance of global aquifers revealed by groundwater footprint. Nature 488:197–200. https://doi.org/10.1038/nature11295 Goodchild MF (2007) Citizens as sensors: The world of volunteered geography. GeoJournal 69:211– 221 Google Earth. http://earth.google.com/http://earth.google.com/ Haklay M, Antoniou V, Basiouka S, Soden R, Mooney P (2014) Crowdsourced geographic information use in government, report to GFDRR. World Bank, London Howe J (2006) The Rise of Crowdsourcing. Wired. June 2006. Available online: http://www.wired. com/wired/archive/14.06/crowds.html Hudson-Smith A, Batty M, Crooks A, Milton R (2009) Mapping for the masses: Accessing Web 2.0 through crowdsourcing. Soc. Sci. Comput. Rev. 2009, 27, 524–538 Hutchings M, Dev A, Palaniappan M, Srinivasan V, Ramanathan N, and Taylor J. 2012. mWASH: Mobile Phone Applications for the Water, Sanitation, and Hygiene Sector. Available at: https://pacinst.org/publication/mwash-mobile-phone-applications-for-the-water-sanita tion-and-hygiene-sector/ Islam SN (2016) Deltaic floodplains development and wetland ecosystems management in the Ganges–Brahmaputra–Meghna Rivers Delta in Bangladesh. Sustain. Water Resour. Manag. 2:237–256. https://doi.org/10.1007/s40899-016-0047-6 Kaku M (2012) Physics of the Future: How Science Will Shape Human Destiny and Our Daily Lives by the Year 2100. Westminster: Random House LLC Kamel Boulos MN: Web GIS in practice III: creating a simple interactive map of England’s Strategic Health Authorities using Google Maps API, Google Earth KML, and MSN Virtual Earth Map Control. Int J Health Geogr. https://doi.org/10.1186/1476-072X-4-22 Kamel Boulos MN, Resch B, Crowley DN et al (2011) Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples. Int J Health Geogr 10:67. https://doi.org/10. 1186/1476-072X-10-67 Kumpel E, Peletz R, Bonham M, Fay A, Cock-Esteb A, Khush R (2015) When Are Mobile Phones Useful for Water Quality Data Collection? An Analysis of Data Flows and ICT Applications among Regulated Monitoring Institutions in Sub-Saharan Africa. Int J Environ Res Public Health 12(9):10846–10860. https://doi.org/10.3390/ijerph120910846.PMID:26404343; PMCID:PMC4586647 Lane ND, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell AT (2010) A survey of mobile phone sensing. IEEE Trans Commun Mag 48:140–150 Lujala P, Epremian L (2017) Transparency and natural resource revenue management: empowering the public with information? In: Williams A, Billon PL (eds) Corruption, Natural Resources and Development. Edward Elgar Publishing, From Resource Curse to Political Ecology, pp 58–68 Lujala P, Brunnschweiler C, Edjekumhene I (2020) Transparent for whom? Dissemination of information on Ghana’s petroleum and mining revenue management. J. Dev. Stud., 56 (12): 2135–2153. https://doi.org/10.1080/00220388.2020.1746276 Macknick JE, Enders SK (2012) Transboundary forestry and water management in Nicaragua and Honduras: from conflicts to opportunities for cooperation. J. Sustain. Forest. 31:376–395. https:// doi.org/10.1080/10549811.2011.588473 Mau B, Leonard J (2004) The Institute Without Boundaries. In Massive Change, 6th ed.; Phaidon:London, UK

184

G. S. Bhunia et al.

Minnesota Pollution Control Agency (2014) Citizen Water Monitoring. Available online at: http:// www.pca.state.mn.us/index.php/water/water-monitoring-and-reporting/volunteer-water-mon itoring/volunteer-surface-water-monitoring.html(Accessed July 25 2014 Neis P, Zielstra D (2014) Recent developments and future trends in volunteered geographic information research: The case of OpenStreetMap. Future Internet 6:76–106 New Jersey Department of Environmental Protection (2014) Volunteer Monitoring. http://www. state.nj.us/dep/wms/bwqsa/vm/ New York City Water Trail Association. (2014). 2014 Citizens’ Water Quality Testing Program. http://www.nycwatertrail.org/waterquality.html Nicholson, E., Ryan, J., and Hodgkin, D. (2002). Community data–where does the value lie? assessing confidence limits of community collected water quality data. Water Sci. Technol. 45, 193–200. http://www.ncbi.nlm.nih.gov/pubmed/12171352 Overdevest, C., and Orr, C. H. (2004). Volunteer stream monitoring and local participation in natural resource issues. Hum. Ecol. Rev. 11, 177–185. http://www.humanecologyreview.org/pastissues/ her112/overdevestorrstepenuck.pdf Papadopoulou C-A, Giaoutzi M (2014) Crowdsourcing as a Tool for Knowledge Acquisition in Spatial Planning. Future Internet. 6(1):109–125. https://doi.org/10.3390/fi6010109 Robinson L.D., Cawthray J.L., West S.E., Bonn A.Ten principles of citizen science. S. Hecker, M. Haklay, A. Bowser, Z. Makuch, J. Vogel, A. Bonn (Eds.), Citizen Science: Innovation in Open Science, Society and Policy, UCL Press, London (2018), pp. 27–40 Rotman, D., Preece, J., Hammock, J., Procita, K., Hansen, D., Parr, C., Lewis, D., Jacobs, D. Dynamic Changes in Motivation in Collaborative Citizen-Science Projects. Proceedings of the 2012 ACM Conference on Computer Supported Cooperative Work. pgs. 217–226 Rutten M, Minkman E, van der Sanden M (2017) How to get and keep citizensinvolved in mobile crowd sensingfor water management? A reviewof key success factors andmotivational aspects. Wires Water 4:e1218. https://doi.org/10.1002/wat2.1218 Surowiecki, J (2004) The Wisdom of Crowds; Anchor Books: New York, NY, USA To H., Ghinita G., Shahabi C.A framework for protecting worker location privacy in spatial crowdsourcing. Proc. VLDB Endowment, 7 (10) (2014), pp. 919–930. https://doi.org/10.14778/273 2951.2732966 Turner D, Richter H (2011) Wet/dry mapping: using citizen scientists to monitor the extent of perennial surface flow in dryland regions. Environ Manag 47:497–505. https://doi.org/10.1007/ s00267-010-9607-y US Environment Protection Agency (2014) Monitoring and Assessing water Quality - Volunteer Monitoring. http://water.epa.gov/type/rsl/monitoring/ Water Reporter. (2014). Water Reporter. http://waterreporter.org/ World Water Monitoring Challenge (2014) World water monitoring challenge. http://www.monito rwater.org/ World Bank (2017) Water Resource Management. Available at: https://www.worldbank.org/en/ topic/waterresourcesmanagement#1 Zhao Y Zhu, Q (2012) Evaluation on crowdsourcing research: Current status and future direction.Inform. Syst. Front. 1–18. https://doi.org/10.1007/s10796-012-9350-4.

Chapter 9

Spatio-Seasonal Runoff and Discharge Variability in the Ganga River Basin, India: A Hydrometeorological Perspective Raghunath Pal

Abstract The study focuses to understand spatial and seasonal variability of runoff and discharge across the Ganga River basin. To execute the study runoff and discharge data of 13 stations (5 stations on the Ganga River and other 8 are on the major tributaries) across the river basin had been used. The classification of season: monsoon (June–September), post monsoon (October-December), winter (January-March) and summer (April–May) had been considered as the base of seasonal variability analysis. The average runoff distribution reflectall major tributaries of the Ganga River have experienced high runoffwith respect to the stations on the Ganga River because the tributaries receive more direct runoff than the Ganga River and there is a decreasing trend in the amount of runoff from the up-stream to the down-stream of the Ganga River. In the post monsoon period Sasaram receives very lowrunoff probably because of low rainfall in the Son River basin during this time. Seasonal variability of runoff of all stations reflect that the tributaries receive more runoff than the Ganga. The construction of Farakka barrage play a significant role in the variability of runoff and discharge in downstream of the basin especially of Rajmahal, Jangipur and Nabadwip stations. The stations which are on the Ganga River receive high discharge and the stations on the tributaries get relativelylow discharge. This scenario is somehow reciprocal with the seasonal variability of average runoff of the basin. Keywords Ganga River · Runoff · Discharge · Monsoon · Seasonal variability

9.1 Introduction The hydrology of the Ganga River is characterised by itstropical-monsoon duality, high seasonal variability and monsoon season flood throughout its course (Singh 2007; Singh et al. 2007; Pal and Pani 2016a, b).The river experiences very high flows R. Pal (B) Department of Geography/Vidya Bhavana, Visva-Bharati, Santiniketan 731235, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_9

185

186

R. Pal

during heavy monsoon rainfall and low flows during pre- and post-monsoon periods (Singh et al. 2007). The excessive rainfall during the entire monsoon season and the pre- (July) and post-monsoon (September) cyclonic rainstorms are the prime reasons of flood in the basin. Furthermore, all rivers in the Ganga River basin experience annual flooding events (Singh et al. 2007). Along with seasonal fluctuations, the river basin also hasa high degree of spatial variability in rainfall, runoff and discharge. With regard to the spatial variability of monsoon rainfall, Kolkata receives 1200 mm, Patna 1050 mm, Allahabad 760 mm and Delhi 560 mm (Singh et al. 2007). This distribution shows a decreasing trend of rainfall from the east to the west in the basin. The estimated mean monsoon flow of the river is 5860 m3 s−1 at Haridwar, 6317 m3 s−1 at Kanpur, 24,131 m3 s−1 at Allahabad, 37,424 m3 s−1 at Patna and 55,776 m3 s−1 at Farakka (Singh 2007).The estimated mean total flow of the river is 21393 × 106 m3 at Haridwar, 37,330 × 106 m3 at Kanpur, 130,116 × 106 m3 at Allahabad, 240,498 × 106 m3 at Patna and 320,380 × 106 m3 at Azamabad.The mean monsoon flow and the mean total flow of the river demonstrate a decreasing trend from the west to the east. The river experiences a drastic increase in mean monsoon flow to downstream after Allahabad. The reason is the contribution of various tributaries of the river draining different areas of the basin. The northern tributaries contribute more discharge to the Ganga than the southern tributaries (Singh 2007).Therainfall and discharge distribution across the basin,however,reflect a reverse spatiality in general. The study aims to understand the spatial and seasonal variability of runoff and discharge across the Ganga River basin.

9.2 The Ganga River Basin The Ganga Riveris an international river as well as the most important river of India, which flows 2525 km distance. The basin of the river is about 10, 60,000 km2 that is 26.2% of India’s total surface area (Singh 2007).The basin area is shared by India (79%), Nepal (14%), Bangladesh (4%) and China (3%) (Mirza 1997). The river system lies between the southern slopes of The Himalayas and the northern portion of the Indian Peninsula. The convergence tectonics handles the Ganga system. The existing sediment load and very high discharge of the river are responsible factors for the formation of the great tectonic plain, the Ganga–Brahmaputra Delta and the largest submarine fan (Singh 1971). The Ganga starts as Bhagirathi from the Himalaya’s Gangotri Glacier at Gaumuch (elevation 3800 m). The Bhagirathi and the Alaknandameet at Devprayag, and the combined flow is known as Ganga. The river reaches to the alluvial surface at Haridwar after flowing about 310 km from its origin in the south-east direction. The Yamuna, the principal tributary of the Ganga meets the river at Allahabad at a distance of 720 km from Haridwar. Between Allahabad and Farakka, many important tributaries join the Ganga: Tons, Son, Punpun and Phalgu from the south and Gomti, Ghaghara, Gandak, BurhiGandak and Kosi from the north. After passing the Rajmahal trap, the river enters the delta. At Farakka, the river splits into two distributaries (Singh 1971, 2007). The main distributary

9 Spatio-Seasonal Runoff and Discharge Variability in the Ganga River …

187

Fig. 9.1 The map of the Ganga River and its major tributaries. The red colour stars indicate the stations that had been selected for the study, a Karnal on the Yamuna River, b Bijnor on the Ramganga River, c, d and e Fakhrpur, Rudauli and Gosainganj on the Ghaghra River, g Bettiah (GFDS 198) on the Gandak River, h Sasaram (GFDS 2,015) on the Son River, j Saharsa (GFDS 52) on the Kosi River, f Mirzapur, i Begusarai, k Rajmahal, lJangipur and m- Nabadwip on the Ganga River

of Ganga, the Padma enters Bangladesh and meets the Brahmputra (Fig. 9.1). The combined flow is then known as the Yamuna, which joins the Meghna, and this high volume of water and sediment load finally drains into the Bay of Bengal. The Bhagirathi, also known as Hugli, the other distributary of the Ganga, flows south through West Bengal (Bandyopadhyay et al. 2014). The river collects discharge from several tributaries off the north-east extension of the Indian Plateau: Damodar, Ajay, Mayurakshi, Rupnarayan and Haldi and finally drains into the Bay of Bengal (Singh 2007).

9.3 Database and Methodology To fulfil the requirements of the study, runoff and discharge data of 13 stations across the river basin were used. Out of these 13 stations, 5 are on the Ganga River, namely Nabadwip (GFDS1 site no. 2,016), Jangipur (GFDS 193), Rajmahal (GFDS 51), 1

GFDS–Global Flood Detection System-Version 2.

188

R. Pal

Begusarai (GFDS 195), Mirzapur (GFDS 205) and other 8 stations are on the major tributaries of the Ganga River. The stations are: Saharsa (GFDS 52) on the Kosi River, Bettiah (GFDS 198) on the Gandak River, Fakhrpur (GFDS 200), Rudauli (GFDS 199) and Gosainganj (GFDS 203) on the Ghaghra River, Bijnor (GFDS 208) on the Ramganga River, Sasaram (GFDS 2,015) on the Son River and Karnal (GFDS 211) on the Yamuna River (Fig. 9.1). In this study, satellite-based runoff and discharge data of 23 years (1998–2020) wereincorporated as per the data availability. On this river, previous studies regarding hydrologymainly dealt with the field measured discharge data at different gauging stations on the Ganga River. But the present study focuses not only on the Ganga River but also on its tributaries in a holistic way incorporating recent satellite-based data (http://floodobservatory.colorado.edu/SiteDisplays.htm). The climate of the basin is humid subtropical, and there are four seasons that characterise the basin climate. The seasons aremonsoon (June–September), postmonsoon (October-December), winter (January–March) and summer (April–May) (Singh et al. 2007). This classification was considered in this study in order to analyse the spatio-seasonal variability of runoff and discharge of the mentioned stations. The hydrology of the Ganga River exhibits very strong seasonal fluctuations that are controlled by heavy monsoon rainfall and the least rainfall during the other seasons (Singh et al. 2007). The river’s flowsthroughout the year are often considered as winter, summer, monsoon and post-monsoon (Singh et al. 2007). However, the data was then analysed in three phases: (1) the calculation of average runoff and average discharge of every mentioned station in the basin considering the dataset of 23 years (1998–2020), (2) the calculation of average runoff and average discharge of every mentioned station of the four different seasons (winter, summer, monsoon and post-monsoon) considering the dataset of 18 years (1998–2015).

9.4 Result and Discussion 9.4.1 Spatial Variability of Runoff and Discharge in the Basin Figure 9.2a, b reflects the average runoff and average discharge of each station in the basin. Considering 23 years data (1998–2020), each station has been given a single value for runoff and discharge individually.Seasonal fluctuations did not consider here, firstly, the averagerunoff and discharge of every monthwere calculated and then the total average was calculated and obtained as a single value for each station. However, Fig. 9.2a shows the average runoff of 13 stations of the Ganga River and its tributaries. The station Bettiah(120.04 mm) on the Gandak River has the highest runoff throughout the year, whereas Mirzapur (22.79 mm) receives the lowest runoff. Other stations with high runoff are Karnal (62.70 mm) on the Yamuna River, Bijnor (88.34 mm) on the Ramganga River, Fakhrpur (62.06 mm), Rudauli (53.37 mm) and Gosainganj (43.06 mm) on the Ghaghra River and Saharsa (94,87 mm) on the Kosi River. The values of average runoff indicatethat all major tributaries of the

9 Spatio-Seasonal Runoff and Discharge Variability in the Ganga River …

189

Fig. 9.2 a average runoff versus stations (Karnal, Bijnor, Fakhrpur, Rudauli, Gosainganj, Mirzapur, Bettiah, Sasaram, Saharsa, Begusarai, Rajmahal, Jangipur and Nabadwip respectively), b average discharge vs stations, c the relationship between average runoff and average discharge

Ganga River have experienced high runoff than the stations on the Ganga River because the tributaries receive direct runoff more than the Ganga River (Rudra 2014). Furthermore, there is a decreasing trend in the amount of runoff from the up-stream to the down-stream of the Ganga River. The reason probably the number of major tributaries is less in the downstream of the Ganga River, which is the reason forless supply of runoff to the Ganga. Figure 2b displays the distribution of average discharge in the basin. The station Rajmahal(13,108.35 m3 /s) receives the highest discharge, whereas Nabadwip (239.13 m3 /s) receives the lowest one. There is an increasing trend of discharge from up-stream to the down-stream of the basin because of the addition of discharge of a station to the next station. The station Nabadwipon the Bhagirathi River, the prime distributary of the Ganga, is in the down-stream of the basin, and the station Jangipur (11,905.50 m3 /s) on the Ganga (the Padma River) is also in the down-stream of the basin, and both receive less discharge with respect their previous station Rajmahal (13,108.35 m3 /s) (Rudra 2010). The reason could be the construction of Farakka Barrage (commenced in 1975) that distributes discharge to the Bhagirathi and the Padma (Fig. 9.3). Hence, the amount of discharge is less in the down-stream after Rajmahal. Figure 9.2c shows a negative but not significant linear relationship between average runoff and average discharge considering the 13 stations that suggest the need forfurther query considering the monthly individual value of runoff and discharge of each station (Pal and Pani 2016a, b).

190

R. Pal

Fig. 9.3 Location of Rajmahal, Jangipur, Nabadwip stations and Farakka barrage. The map is not to scale

9.5 Seasonal Variability of Runoff in the Basin Figure 9.4 shows theseason-wise distribution of average runoff of the 13 stations considering the data of 1998–2020. The figure reflects high runoff during the monsoon and post-monsoon periods and low runoff during winter and summer. The station Bettiah on the Gandak River receives the highest runoff for all most all seasons. Figure 9.4a shows the winter season variability in the runoff. Three stations: Bijnor (48.71 mm), Bettiah (48.20 mm), and Saharsa(47.56 mm),receive high winter runoff with respect tootherstations, i.e., Gosainganj (12.3 mm), Sasaram (11.28 mm), Mirzapur (14.51 mm) and Begusarai (19.00 mm). Figure 9.4b displays all stations receive rainfall below 55 mm during summer except Bettiah (71.9 mm). The station Karnal and Bijnor get almost similarsummer runoff, Bijnor has no change with the winter runoff, and Karnal has a high difference with the winter runoff. Figure 9.4c shows monsoon season fluctuations of average runoff with respect to the stations. All stations receive high runoff during this season except some stations (Mirzapur (33.12 mm), Rajmahal (28.72 mm) and Nabadwip (38.22 mm)), which are on the Ganga River (Pal 2015). The station Bijnor (154.39 mm), Bettiah (167.62 mm) and Saharsa (153.23 mm) receive very high runoff during this season. Figure 9.4d stands for the post-monsoon runoffdistribution that says all stations receive above 40 mm runoff except Sasaram (13.75 mm) and Mirzapur (27.01 mm).The station Fakhrpur, Rudauli and Gosainganj on the Ghaghra River show a down falling trend of runoff for all the seasons (Fig. 9.4). In the post-monsoon period, Sasaram receives very low runoff, probably because of low rainfall in the Son River basin during this time.Seasonal variability of runoff of all stations reflects that the tributaries receive more runoff than the Ganga. The range of average runoff is very high for the monsoon (138.90 mm) and post-monsoon (146.77 mm) period with respect to winter (37.43 mm) and summer (63.66 mm) season (Pal 2015).

9 Spatio-Seasonal Runoff and Discharge Variability in the Ganga River …

191

Fig. 9.4 Average runoff versus stations (Karnal, Bijnor, Fakhrpur, Rudauli, Gosainganj, Mirzapur, Bettiah, Sasaram, Saharsa, Begusarai, Rajmahal, Jangipur and Nabadwip respectively), a winter season, b summer season, c monsoon season and d post-monsoon season

9.5.1 Seasonal Variability of Discharge in the Basin Figure 9.5 exhibits the variability of average discharge of the 13 stations considering the dataset of 1998–2020on the basis ofseasonal classification. Figure 9.5a shows station wise fluctuations of discharge during the winter season. The station Rajmahal receives the highest discharge (14,966.31 m3 /s), whereasNabadwip stands for the lowest discharge (261.33 m3 /s). Figure 9.5b displays all most a uniform pattern with the winter season, but the received the highest discharge is less compared to the winter season. In the case of summer,Rajmahal receivesthe highest discharge (9417.68 m3 /s),and Nabadwip receivesthe least (100.97 m3 /s). Nabadwip receives very low discharge during summer mainly because of less release of discharge from Farakka barrage to the Bhagirathi River (Pal and Pani 2016a, b). Figure 9.5c reflects monsoon season discharge variability with a high discharge of all stations with respect to the other seasons due to high monsoon rainfall in all parts of the basin. The station Begusarai (13,520.25 m3 /s) and Jangipur (13,306. m3 /s) receive a high discharge in comparison to Rajmahal (10,001.21 m3 /s). Jangipur station receives more discharge than Rajmahal probably because of the extra release of discharge from Farakka barrage during monsoon season, whereas Rajmahal stands for low discharge because of the release of the discharge from the upstream of the barrage. Figure 5 (D) exhibits the discharge variability of the post-monsoon period with all most the same pattern as it is in the monsoon period except the station Rajmahal. The station Begusarai

192

R. Pal

(16,271.81 m3 /s), Rajmahal(17,853.7 m3 /s) and Jangipur(17,744.41 m3 /s) receive a very high discharge that ismore than the monsoon season discharge. The postmonsoon season down falling trend of discharge supports the falling stage of a rating curve (Pal and Pani 2016a, b). Figure 9.5 shows a distributional pattern that is some stations receive low discharge for all seasons whereas some stations get a high discharge. Furthermore, the stations which are on the Ganga River receive a high discharge, and the stations on the tributaries get a relatively low discharge. This scenario is somehow reciprocal with the seasonal variability of average runoff of the basin in general (Figs. 9.4 and 9.5). Among all stations which are on the tributaries,Saharsa on the Kosi River receives high discharge and Sasaram on the Son River gets the least discharge for all the season.

Fig. 9.5 Average discharge versus stations (Karnal, Bijnor, Fakhrpur, Rudauli, Gosainganj, Mirzapur, Bettiah, Sasaram, Saharsa, Begusarai, Rajmahal, Jangipur and Nabadwip respectively), a winter season, b summer season, c monsoon season and d post-monsoon season

9 Spatio-Seasonal Runoff and Discharge Variability in the Ganga River …

193

9.6 Conclusion i. The study reflectsthat the season-wise distribution and the distribution without considering seasons, a uniform type distribution with some distortions led by seasonal characteristics for runoff and discharge both, but seasonal uniqueness also exists by means of the amount of runoff and discharge. ii. The study had been executed without considering rainfall data. The analysis would have been more concrete if rainfall data were incorporated because there is a positive relationship between rainfall and discharge in general. iii. The study results that there is a reverse relationship between runoff and discharge if the Ganga River and its tributaries factor areconcerned, but the relationship is not significant. In order to establish the relationship between these two the data analysis of individual stations is needed. Acknowledgements The author would like to acknowledge the Flood Observatory authority for providing runoff and discharge data.

References Bandyopadhyay S, Kar NS, Das S, Sen J (2014) River systems and water resources of West Bengal: a review. Geological Society of India, pp 63–84 Mirza MMQ (1997) Hydrological changes in the Ganges system in Bangladesh in the post-Farakka period. Hydrol Sci J 42(5):613–631 Pal R (2015) Fluvial Planform Dynamics and Adjoining Floodplain Morphology: A Study from the Middle-Lower Part of the River Ganga, India, New Delhi: Unpublished M.Phil. dissertation (JNU) Pal R, Pani P (2016) Recent Changes in Braided Planform of the Tista River in the Eastern Lobe of the Tista Megafan, India. Earth Science India, pp 104-113 Pal R, Pani P (2016b) Seasonality, barrage (Farakka) regulated hydrology and flood scenarios of the Ganga River: a study based on MNDWI and simple Gumbel model. Modeling Earth Systems and Environment 2(2):1–11 Rudra K (2010) Dynamics of the Ganga in West Bengal, india (1764–2007): Implications for science policy interaction. Quatern Int 227:161–169 Rudra K (2014) Changing river courses in the western part of the Ganga-Brahmaputra delta. Geomorphology 227:87–100 Singh IB (2007) The Ganga River: Geomorphology and Management. In: Gupta A (ed) Large Rivers. John Wiley and Sons, London, pp 347–371 Singh M, Singh IB, Muller G (2007) Sediment characteristics and transportation dynamics of the Ganga River. Geomorphology 86:144–175 Singh R (1971) India: A Regional Geography. National Geographical Society of India, Varanasi

Chapter 10

Appraisal of Drinking Water Quality of Kalahandi District Using Geospatial Technique M. Patnaik, C. Tudu, M. Priyadarshini, and C. Dalai

Abstract The primary objective of the present research is to assess the effectiveness of geospatial studies in the spatial variation of groundwater quality. QGIS (version 3.14) software is used for geospatial analysis of water quality parameters. Spatial distribution maps of water quality parameters are extracted. Inverse distance weighted (IDW) technique is utilized for the spatial interpolation. Visual interpretation of these maps implies that most of the water quality parameters are well within the permissible limit as per Indian Standard 10,500 (2012): Drinking Water Standard. Weighted overlay map is created by overlaying spatial distribution layers of 13 water quality parameters. Calculated values of groundwater quality index (GWQI) at all stations are similarly interpolated and spatial distribution map of the same is derived. Major portions of the block of Golamunda, Koksara, Dharmagarh, Junagarh and Bhawanipatna the groundwater quality are very poor. The overall water quality ranges within good to medium for most of the regions of the district whereas some of the extreme northern parts of Bhawanipatna and central region of the Junagarh block exhibit excellent groundwater quality. Surprisingly close resemblance between water quality index map and weighted overlay map has been detected. The effectiveness of IDW interpolation is assessed by validating with root mean square error estimates and the value of RMSE is determined very close to 0 indicating that the IDW method has less estimation error. However, IDW over estimates the overall water quality for the present range of data sets. Keywords QGIS · GWQI · IDW · Weighted overlay · RMSE

M. Patnaik · M. Priyadarshini Department of Civil Engineering, Government College of Engineering, Kalahandi, Odisha, India C. Tudu Department of Civil Engineering, College of Engineering & Technology, Bhubaneswar, Odisha, India C. Dalai (B) Department of Civil Engineering, Odisha University of Technology and Research, Bhubaneswar, Odisha, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_10

195

196

M. Patnaik et al.

10.1 Introduction India, a country with 1.3 billion populations has an acute scarcity of potable water of acceptable quality. Though, the climatic condition of the country is neither dry nor having a dearth of surface and groundwater resources; extremely poor water management practices have laid to this crisis. In such a scenario where the dependence on surface sources are curtailed due to the above mentioned causes, the groundwater could be imbibed as next major source of water either for domestic, industrial or irrigation purposes. This necessitates a detailed understanding of hydro-chemical, geo chemical aspects of groundwater, its movement under the ground and the changes both quality and quantity terms. Statistical analyses such as correlation studies, factor and principal component analysis, multi variate analyses etc. attempts to analyze the various factors governing the groundwater quality (Yang et al. 2020; Lescešsen et al. 2015; Sinha et al. 2006; Mahato et al. 2004). Geographic information system is a powerful soft technology in managing, processing, integrating, modelling, identifying vulnerable ground water zones and also assists in making decision regarding suitability of water quality (Basheer et al. 2019; Babiker et al. 2007; Devatha et al. 2015). It maps a large set of geo referenced data and precisely handles the spatial data. In addition to this it addresses many problems related to water such as water availability, proposal for recharge sites, preventing flooding and degradation, managing water resources, flow modelling solute transport, etc. (Gidey 2018). Even in the understanding and monitoring of water from reservoirs affected by diversion, the spatial variation maps created using GIS are considered (Oseke et al. 2021). In the recent years application of geospatial studies in groundwater management practices such as detection of source of pollution, protection of groundwater etc. have been immensely reported in the literatures. (Dawoud et al. 2005) developed a GIS-based model to simulate the water resources in the western Nile delta. Owing to its effectiveness and easy handling the application of geospatial studies in mitigating various water issues is gaining momentum in recent times. The availability of high speed computers to store huge generated data makes the application of GIS in these fields more suitable. Groundwater quality relies on several physio-chemical and biological factors. The application of geospatial analysis has hugely simplified the assessment of ground water quality by integrating all the factors affecting its quality. In the analysis generally spatial distribution maps are generated of an entire region taking attribute values at few points in the study area. The technique known as spatial interpolation is a procedure of forecasting the value of an attribute at ungauged points from the measurements made at the point location within the same area (Basheer et al. 2019; Wipki et al. 2017). Groundwater stress zone maps are produced by weighted overlay analysis. It is a multi-criteria analysis tool in allocating areas on the basis of weights of various attributes that the selected areas should possess (Pande Chaitanya et al. 2018; Raikar et al. 2012). The weights on thematic layers are applied to generate suitable maps. The deduced maps are re classified to divide the entire study area into regions of relative suitability of ground water for potable use. This

10 Appraisal of Drinking Water Quality of Kalahandi District Using …

197

spatial variation map of each individual parameter will help the decision makers in implementing groundwater management policies specific to a location taking into account the associated vulnerability in a particular block. However the study of temporal variation maps of these quality parameters will indicate the development of anthropogenic activities in the groundwater environment over period of time. Thus the objective of this present work primarily focuses on. a. Development of an index for groundwater quality (GWQI) to scale the quality of groundwater of the entire district. b. Zonation of Kalahandi district according to the ground water quality by preparing spatial distribution maps of individual water quality parameters. c. Performing weighted overlay analysis of the thematic layers of water quality parameters, to identify the stress zones in the present study area.

10.2 Study Area and Data Collection Kalahandi district falls between North latitudes 19°03’ and 20°45’ and East longitudes 82°18’ and 83°48’, could be marked in Survey of India topo sheet nos. 64 L, 64P, 65 I and 65 M. The entire district has been mapped by Geological Survey of India and Central Ground Water Board (C.G.W.B) on 1: 50,000 scale. The climate of the region is sub-tropical with average relative humidity in the district varies from 27 to 80% throughout the year. The district experiences ample rainfall from June to September and the average annual rainfall differs within the range from 1111.8 to 2712.9 mm. At present, in the district 54 dug wells and 4 piezometer wells have been drilled and maintained by C.G.W.B. Ground water quality parameter from these wells are monitored over four seasons in a year. Figure 10.1 represents the block boundary map of the study area showing the spreadability of the sampling stations of CGWB over the entire district. Groundwater quality data are collected from Central Ground Water Board, Bhubaneswar (Odisha) for Kalahandi district consisting of 13 blocks over 63 stations. The sampling stations are either dug wells or bore wells. 13 physiochemical parameters pH, EC, TDS, Total Alkalinity, Hardness, Ca2+ , Mg2+ , Na+ , K + , Cl − , SO4 2 − , CO3 2 − , HCO3 − pertaining to 63 stations distributed over the district for pre monsoon season are collected from the year 2000 to 2017. For the present research the data has been sorted as time averaged spatial values. The ground water quality data collected over 63 stations is averaged over a time period of 18 years. The time averaged spatial data is necessitated in the preparation of spatial distributes map of individual water quality parameters. The data is imported as attribute values in QGIS for further processing of maps. Further log transformation is implemented where the data is asymmetrical (i.e., not normally distributed) and the parameters are checked for the presence of seasonal trend, if any.

198

M. Patnaik et al.

Fig. 10.1 Block boundary map showing CGWB gauged stations of Kalahandi district

10.3 Materials and Method 10.3.1 Water Quality Index (WQI) To conceive an extensive view of overall quality of groundwater, the WQI was used. The Indian standard for drinking water and standard values of some parameters as per WHO guidelines is used in the calculation of WQI. The methodology laid by Ramakrishaniah et al. (2009) is adopted for obtaining water quality index for all the stations. The computation of WQI is accomplished in three steps. First, each of the 13 parameters is allocated a weight (wi ) according to their relative importance in the overall quality of water for drinking purposes (Honarbakhsh et al. 2019; Krishan et al. 2017; Kalra et al. 2012). The weights are proposed for each parameter which is listed in the following Table 10.1. Wi Wi = ∑n i=1

where, W i is the relative weightage. wi is the weight of parameter. n is the number of parameters.

wi

(10.1)

10 Appraisal of Drinking Water Quality of Kalahandi District Using …

199

Table 10.1 Assignments of weights to groundwater quality parameters Sl. no

Parameters

Weightage

Relative weight

1

pH

5

0.15

2

Electrical conductivity

2

0.06

3

Total dissolved solids

4

0.12

4

Total alkalinity

1

0.03

5

Hardness

2

0.06

6

Ca2+

3

0.09

7

Mg2+

2

0.06

8

Na+

2

0.06

9

K+

1

0.03

10

Cl−

3

0.09

11

SO4

2−

2

0.06

12

CO3 2 −

2

0.06

13

HCO3 −

4

0.12

A quality rating scale (qi ) of each parameter is assigned by dividing its concentration value by its respective standard according to guidelines (BIS, 2012), and further multiplied by 100: qi = Ci /Si × 100

(10.2)

where, qi is the quality rating, C i is the concentration of parameter in mg/L, and S i is the standard as per BIS 10200 for each parameter in mg/L Sli = Wi × qi W QI =

∑

Sli=n

(10.3) (10.4)

For computing WQI, the sub index (SI) is first determined for each chemical parameter, as given below: where, SI i is the sub index of ith parameter; W i is relative weight of ith parameter; qi is the concentration based rating of ith parameter, and n is the number of physio-chemical parameters.

200

M. Patnaik et al.

10.3.2 Inverse Distance Weighted IDW (Inverse Distance Weighted) method assumes that each known point has an effect that decreases with distance. For this method the effect of a known data point is inversely related to the distance from the unknown location that is being estimated. This technique works by specifying a certain search radius and the interpolation will only use the number of known points in that search radius (Arianti et al. 2018). The weighting general function is the inverse of the distance square, and this equation is used in the inverse distance weighted method which is formulated in the following formula Z=

n ∑

Wi Z i

(10.5)

i=1

where Z i (i = 1,2,3,…..,n) is the data height value will be interpolated by a number of N points and weights wi which are expressed as follows −p

h Wi ∑n i

j=1

−p

hj

(10.6)

p is called the power parameter (its value usually 2) and hj is the distance from the point distribution to the interpolation point which described as follows hi =

√

(x − xi )2 + (y − yi )2

(10.7)

(x, y) are coordinates of the interpolation point and (x i , yi ) are coordinates for each spread point. Inverse distance weighted technique is an efficient interpolation tool. Local gradients are best captured by this technique as it functions with evenly spread input points and densely populated sample. This technique results in introduced errors when executed with uneven distributed sample points. Other demerit includes its inability to estimate points above and below maximum and minimum value respectively of the sample points (Pramono 2008).

10.3.3 Overlay Weighted Analysis Overlay weighted analysis in GIS is the multi criteria analysis tool for water quality and suitability analysis as the scale of measurement of each ground water quality parameter is not similar (Konkey et al. 2014). Prior to the integration of thematic layers, each input layer with different ranges are reclassified based on the preference values in a relative scale. This process is known as normalization. The preference

10 Appraisal of Drinking Water Quality of Kalahandi District Using …

201

values are not only assigned within the layer but should have the same meaning between the layers. The respective weightages are attached to every thematic layers and the summation of these layers yield the overall groundwater quality map of the district. The sequence of the process involved in the preparation of spatial distribution map and overlay weighted analysis is represented in the flow chart shown in the Fig. 10.2.

Fig. 10.2 Flow process in geospatial analysis

202

M. Patnaik et al.

10.4 Result and Discussion 10.4.1 Spatial Distribution Maps Spatial distribution study is a crucial tool to understand the spread of ionic concentration of ground water in an area. It provides a pictorial representation of levels of concentration of parameters classified in different classes. The spatial (latitude, longitude information of station points) and non-spatial (attribute) databases are generated and merged to yield the thematic layers representing the ground water quality parameters including groundwater quality index (GWQI) map of the entire region. Each station points (spatial database) is allotted with an attribute value and interpolated using inverse weighted distance (IDW) technique to generate various layers of thematic maps. Thematic maps of 13 physio chemical parameters are processed in QGIS platform. The resultant maps are produced in Figs. 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9, 10.10, 10.11, 10.12, 10.13, 10.14 and 10.15 and individual groundwater parameter is discussed for its compliance with drinking water standard (IS 10500, 2012), in the following section.

Fig. 10.3 Spatial distribution map of pH

10 Appraisal of Drinking Water Quality of Kalahandi District Using …

203

Fig. 10.4 Spatial distribution map of electrical conductivity

10.4.1.1

Spatial Distribution Map of pH

pH is an indicative parameter to estimate acidity and alkalinity of water. A good value of pH ranges from 7.5 to 7.9. However according to the drinking water Indian Standard code IS 10500:2012, the acceptable limit prescribed is in the range 6.5-8.5. The maximum and minimum values of pH for the present study area are observed to be 8.27 and 7.43 respectively. This shows that the ground water of the study area is mostly alkaline but the maximum value is well within the maximum permissible limit of 8.5 as prescribed by BIS. For most of the regions of the district the pH falls in the range between 7.71 and 7.99. The alkalinity may pose problems if the groundwater is harnessed for irrigation but as far as for drinking or domestic utility is not a major concern.

10.4.1.2

Spatial Distribution Map of Electrical Conductivity

Electrical conductivity refers to the capability of water to conduct electric current, usually expressed in micro-Siemen’s/cm (µS/cm). However, the unit of expression is

204

Fig. 10.5 Spatial distribution map of total dissolved solids

Fig. 10.6 Spatial distribution map of Alkalinity

M. Patnaik et al.

10 Appraisal of Drinking Water Quality of Kalahandi District Using …

205

Fig. 10.7 Spatial distribution map of Hardness

micro-mhos/cm @ 25°C. A direct relationship can be established between conductivity and the concentration of total dissolved salts in the groundwater. Most blocks of the district EC value ranges in between 523.2 to 726.3 µ-mhos/cm. Fewer higher values (EC > 1132) is detected in north and southern parts of Golamunda and northern parts of Junagarh. As far as for drinking water quality the electrical conductivity in groundwater is well below the permissible limit of 1400 µ-mhos/cm for the entire district.

10.4.1.3

Spatial Distribution Map of Total Dissolved Solids

Presence of mineral dissolved in water forms the total dissolved solids. The water with TDS more than 500 mg/L renders unfit for drinking and also for other utilities. TDS represents an indicative parameter for overall suitability of water for many types of uses. For most of the regions of the district this value falls in a range of 400 to 544 mg/L. Blocks of the district such as northern part of Junagarh, south eastern parts of Golamunda and some of the western parts of Bhawanipatna have TDS concentrations more than desirable limit but less than the permissible limit (500–2000 mg/L). Higher concentration of TDS in these regions of the blocks can be attributed to the presence of

206

M. Patnaik et al.

Fig. 10.8 Spatial distribution map of Calcium

sodium, chlorides, sulphates, carbonates and bi carbonates. Prior to use the ground water of these places must be treated well by reverse osmosis to produce potable water.

10.4.1.4

Spatial Distribution Map of Alkalinity

Alkalinity is property of water that is dependent on presence of certain radicals such as carbonates, bicarbonates and hydroxides in water. It is reported as equivalent amount of calcium carbonate in mg/L. Major contaminant sources of alkalinity include leaching from landfills and other sites where alkaline or basic chemicals have been dumped. Alkalinity in higher concentrations may lead to objectionable taste, or incrustation in pipes and containers. Alkalinity in the groundwater is not a major concern for the district as alkalinity for most of the parts is observed in the range of 169–219 mg/L.

10 Appraisal of Drinking Water Quality of Kalahandi District Using …

207

Fig. 10.9 Spatial distribution map of Magnesium

10.4.1.5

Spatial Distribution Map of Hardness

Hardness in water is attributed to the presence of carbonates and bi carbonates of calcium and magnesium (temporary), and chlorides, nitrates and sulphates of calcium and magnesium produces permanent hardness. Spatial distribution map of hardness reveals some regions of northern Junagarh where the concentrations are above 600 ppm. Major values of the hardness in the groundwater of the district lies in between 237.24 to 358.42 ppm. As hardness is observed to be more than the acceptable limit of 200 ppm in major places of the study area, softening of water is necessary to impart palatability to water.

10.4.1.6

Spatial Distribution Map of Calcium

Calcium is one of the major cation present in the groundwater. Basically calcium is derived from earth crust formed of limestone, gypsum and gypsiferrous. The maximum and minimum values of calcium detected in groundwater of the research area are 147.6 and 24 ppm respectively. Most of the places of the district have

208

M. Patnaik et al.

Fig. 10.10 Spatial distribution map of Sodium

a calcium concentration ranging in between 73.37 and 98.06 ppm which is in compliance with the BIS range of 75–200 ppm.

10.4.1.7

Spatial Distribution Map of Magnesium

The main source of magnesium in the groundwater is the derived sub surface flow within vertisols which are the medium black soil found along the course of Tel river and its tributaries. These soils contain high amount of magnesium. The desirable limit of magnesium in water as per BIS is 30 ppm. The excess presence of magnesium in groundwater imparts hardness which makes the water undesirable for drinking utility. There are regions like northern parts of Junagarh, western parts of Bhawanipatna where elevated levels are marked and some parts of Koksara and Dharmagarh, the magnesium concentrations ranges from 30.26 to 56.6 mg/L.

10 Appraisal of Drinking Water Quality of Kalahandi District Using …

209

Fig. 10.11 Spatial distribution map of Potassium

10.4.1.8

Spatial Distribution Map of Sodium

The observation of sodium concentration in groundwater of the study area ranges in between 61.58 and 5.8 ppm. Sodium is one of the major cation present in groundwater. The higher values of this parameter is an indicative of increased TDS and hardness in the groundwater. Spatial distribution map of sodium shows that there are certain areas in the district such as northern parts of Bhawanipatna, Koksara blocks, eastern parts of Lanjigarh and Dharmagarh and some western regions of Golamunda, where the concentration of sodium is found on higher side.

10.4.1.9

Spatial Distribution Map of Potassium

Potassium in groundwater is released due to weathering of igneous rocks. Histosols are generally the black soils occupying some of the blocks of Bhawanipatna, Narla, Kesinga, Dharmagarh, Koksara and Golamunda are the soil rich in potassium. One of the potential source of potassium may be seeping irrigation water to join the groundwater table from the fertilizer laden agricultural fields. The observation of potassium concentration in groundwater of the district ranges in between 29.35 to

210

M. Patnaik et al.

Fig. 10.12 Spatial distribution map of Chloride

0.16 ppm. Spatial distribution map of potassium reveals its higher values towards western locations of Lanjigarh and central regions of Bhawanipatna block.

10.4.1.10

Spatial Distribution Map of Chloride

Chloride is very mobile in ground water and is not readily removed by inorganic and biological processes. Concentration of chloride in small amount is required for normal cell functioning in plants and animals. If present in excess, it imparts salty taste to drinking water, and may even corrode metal pipes and valves. Chloride concentration in the study area ranges from 306.28 to 11.34 ppm. In most parts of the district a chloride concentration of value less than 70.62 ppm prevails, which is well below the acceptable limit as prescribed in BIS.

10.4.1.11

Spatial Distribution Map of Sulphate

Sulphate is the minor anion in groundwater. Its value may range from 1 to 200 ppm depending upon the topography and geologic conditions of the area. Primary source of sulphate include atmospheric deposition, gypsum-bearing bedrock and sulphide

10 Appraisal of Drinking Water Quality of Kalahandi District Using …

211

Fig. 10.13 Spatial distribution map of Sulphate

mineral oxidation. Anthropogenic sources include coal mines, power plants, rain water in the area having high atmospheric pollution and refineries. Sulphate distribution map indicates that the concentration of sulphate is well below the acceptable limits of 200 ppm as recommended by BIS.

10.4.1.12

Spatial Distribution Map of Carbonate

Carbonate in the groundwater ranges from 0 to 17.9 mg/L. Some places of the present study area such as northern part of Lanjigarh, Golamunda and Koksara blocks, where the carbonate concentration is as high as 14 mg/L. There are areas where these values are observed in lower concentrations such as Jayapatna, Kesinga, Western regions of Bhawanipatna, Karlamunda blocks. Spatial distribution maps of carbonate depict that most of the areas of the district falls in between 3.57 and 7.15 mg/L.

212

M. Patnaik et al.

Fig. 10.14 Spatial distribution map of Carbonate

10.4.1.13

Spatial Distribution Map of Bicarbonate

Groundwater contains bicarbonate ions as major anion followed by chloride and sulphate. In the present study area, the bicarbonate value ranges between 390.4 and 74.59 ppm. Lower values of bicarbonate in groundwater have been reported in eastern parts of Bhawanipatna, western places of Lanjigarh and northern regions of Karlamunda. However, the higher values of bicarbonate concentration are marked towards northern parts of Junagarh and Kesinga, western regions of Golamunda and central parts of Narla.

10.4.2 Ground Water Quality Index Map (GWQI) The GWQI map of the Kalahandi district is presented in the Fig. 10.16. It is quite inferred from the GWQI map that the overall quality of groundwater in the district varies substantially from place to place. The WQI of entire area is classified into 5 classes as per the quality rating index of Rawat et al. (2017a). WQI values are rated

10 Appraisal of Drinking Water Quality of Kalahandi District Using …

213

Fig. 10.15 Spatial distribution map of Bicarbonate

as excellent (WQI < 25), good (25 < WQI > 50), medium (50 < WQI > 75), poor (75 < WQI > 100) and unfit for human consumption (WQI > 100). The same is presented in the Table 10.2. Ground water quality index map is used to indicate the stress zones prevail in the district. From the spatial distribution map of GWQI, it is marked that the groundwater of the entire district falls in the range of good to medium. Some portions of Narla, Kesinga, north eastern regions of Bhawanipatna, Lanjigarh, Thuamulrampur, Kalampur, Junagarh and Jayapatna blocks of the district shows excellent groundwater quality. Golamunda block exhibit very poor quality of groundwater rendering almost unfit for drinking purpose. Very poor water quality can be depicted towards extreme south eastern parts of Bhawanipatna, Western portions of Lanjigarh, Dharmagarh and Koksara, extreme southern part of Thuamulrampur. The controlling parameters of groundwater quality index must be identified and accordingly treatment processes must be adopted to treat groundwater of these regions before consumption.

214

M. Patnaik et al.

Fig. 10.16 Map showing groundwater stress zones of Kalahandi District

10.4.3 Weighted Overlay Analysis Weighted overlay is a technique for applying a common scale of measurement of values to dissimilar variables to create an integrated analysis. The raster maps of individual water quality parameter are normalized and assigned a weight according to their relative importance in overall water quality. Further the weighted layers are overlaid and the values in the raster’s are reclassified to a common suitability case as is indicated in Fig. 10.17.

10.4.4 Root Mean Square Estimation Normalized values of resultant water quality values at 270 random sampling stations are extracted from GWQI map and Weighted overlay map. The extracted points from GWQI map are regarded as measured values as such the points from weighted overlay map are the software generated predicted values. One of the cross validation

10 Appraisal of Drinking Water Quality of Kalahandi District Using …

215

Fig. 10.17 Weighted Overlay Map of Groundwater Quality of Kalahandi District

Fig. 10.18 Normalized Measured & Predicted Overall Water Quality values

technique i.e., root mean square error is calculated to validate the predicted values. The formula for calculation of RMSE is given by / RMSE =

∑N i=1

_

Ai = Ai N

(10.8)

216 Table 10.2 Groundwater quality categorization based on GWQI value

M. Patnaik et al.

GWQI value

Water quality

100

Very poor

where Ai is the measured value, whereas A¯ i is the predicted value of the variables and N is the number of observations. Where, qi is the quality rating, S i is the standard as per BIS 10200 for each parameter in mg/L. The RMSE value is observed quite close to 0 (i.e., 0.0000463) which indicates that the IDW method has less estimation error. The predicted values fall above the measured values which can be well depicted from the Fig. 10.18. Therefore, IDW technique in QGIS over estimates the overall water quality for the present range of data set.

10.5 Conclusions This paper aims to assess the groundwater quality in the locality and ensure the presence of contaminants if any. As the contamination of groundwater is increasingly threatened by the agricultural and industrial waste that leaches into the ground finding its way up to the groundwater reservoirs, modern GIS techniques are acquired to confirm its suitability for potable utility. Geospatial studies in association with ground water quality index (GWQI) in determining the overall drinking water quality is one of the most advanced techniques available to understand the vulnerabilities in groundwater quality and provide current status of quality estimate over a spread of area. Groundwater quality stress zones could be identified from the spatial distribution maps which will help the policy makers, water quality managers and researcher’s in tackling the quality issues in stress areas appropriately. The concluding remarks of the present research have been summarized in the following lines. 1. Inferences about the overall groundwater quality of the district complying to drinking water standards (IS 10500:2012) can be made by visual interpretation of either Weighted Overlay map or Water Quality Index map. As both these maps yields similar results. 2. For most of the regions of the district such as Golamunda, Koksara, Dharmagard, Junagarh and Bhawanipatna the groundwater quality is very poor. This may be attributed to increased concentration of TDS and some major ions like Ca++ ,

10 Appraisal of Drinking Water Quality of Kalahandi District Using …

3.

4.

5.

6.

217

Mg++ and Na+ . The groundwater of these locations requires standard treatment operations before consumption. For most of the parts of the district the overall water quality ranges within good to medium. Whereas some of the extreme northern parts of Bhawanipatna and central region of the Junagarh block exhibit excellent groundwater quality. Hardness in the groundwater is a common issue of the present study area. The hardness for most of the parts of the district falls within a range of 116 - 722 mg/ L. Some of the north western regions of Golamunda block and northern parts of Junagarh where the concentration levels of hardness are intense are of values higher than 600 mg/L. The overall ground water quality map resulted from overlay weighted analysis reproduce similar results with the groundwater quality index GWQI map. Even though in the absence of any promising indexing technique, overlay weighted analysis in GIS could be conceived as effective. Though Root Mean Square Error (RMSE) of the measured and predicted values is too small i.e., in the order of 10–5 , the predicted values are observed to be on higher side. Therefore, from the results it could well depicted that IDW technique over estimates the overall water quality.

References Ahmed J, Wong Li P, Chua Yan P, Channa N (2020) Drinking Water Quality Mapping Using Water, Quality Index and Geospatial Analysis in Primary Schools of Pakistan. Water 12:12, 3382 Arianti I, Soemarno HAW, Sulistyono R (2018) Rainfall estimation by using Thiessen polygons, inverse distance weighted, spline, and kriging methods: a case study in Pontianak, west Kalimantan. International Journal of Education and Research. 6(11):301–310 Babiker IS, Mohamed MAA, Hiyama T (2007) Assessing groundwater quality using GIS. Water Resource Management 21:699–715 Balakrishnan P, Saleem A, Mallikarjun ND (2011) Groundwater quality mapping using geographic information system (GIS): A case study of Gulbarga City, Karnataka, India. Afr J Environ Sci Technol 5(12):1069–1084. https://doi.org/10.5897/AJEST11.134 Bangar KS, Tiwari SC, Verma SK, Khandkar UR (2008) Quality of groundwater used for irrigation in Ujjain district of Madhya Pradesh, India. Journal of Environ. Science & Engg. 50(3):179–186 Basheer A, Elubid A et al (2019) Geospatial distribution of groundwater in Gedaref state using GIS and Drinking Water Quality Index (DWQI). Int J Environ Res Public Health 16:731 Batabyal AK, Chakraborty S (2015) Hydro geochemistry and water quality index in the Assessment of groundwater quality for drinking uses, West Bengal. Water Environment Research, 87:7, 607–617.https://doi.org/10.2175/106143015x14212658613956 Bureau of Indian Standards (2012) Drinking Water Specification (Second Revision). ManakBhavan, 9 Bahadur Shah Zafar Marg New Delhi, India Das S, Roy PK, Mazumdar A (2013) Development of Water Quality Index for Groundwater in Kolkata City, West Bengal, India. ARPN Journal of Engineering and Applied Sciences. 8:12, ISSN 1819–6608 Dawoud MA, Darwish MM, El-Kady Mona M (2005) GIS-Based Groundwater Management Model for Western Nile Delta. Water Resources Management, 19(5): 585–604. https://doi.org/10.1007/ s11269-005-5603

218

M. Patnaik et al.

Devatha CP, Singha SS, Singha S, Verma MK (2015) Assessing groundwater quality using GIS,” International Journal of Engineering Research & Technology (IJERT), 4:11. https://doi.org/10. 17577/IJERTV4IS110604 George GK, Arundev R, Parvathy M, Seethal PV, Sanjith P (2017) Water quality analysis of Edappally canal using GIS mapping. International Research Journal of Engineering and Technology 4(4):1664–1669 Gidey A (2018) Geospatial distribution modelling and determining suitability of groundwater quality for irrigation purpose using geospatial methods and water quality index (WQI) in Northern Ethiopia. Appl Water Sci 8:82. https://doi.org/10.1007/s13201-018-0722-x Gong D, Gao X, Ntakirutimana T, Guo J, Li K (2013) Water Quality Status along the Liangtan River and Control Planning Alternatives for Pollution Reduction. Pol. J. Environ. Stud., 22, :4, 1061 Honarbakhsh A, Tahmoures M, Tashayo B, Mousazadeh M, Ingram B, Ostovari Y (2019) GISbased assessment of groundwater quality for drinking purpose in northern part of Fars province, Marvdasht. Journal of Water Supply: Research and Technology-Aqua, 68 (3): 187–196.https:// doi.org/10.2166/aqua.2019.119 Judeh T, Bian H, Shahrour I (2021) GIS- based spatio-temporal mapping of groundwater potability and palatability indices in arid and semi-arid areas. Water 13:1323. https://doi.org/10.3390/w13 091323 Kalra N, Kumar R, Yadav SS, Singh RT (2012) Water quality index assessment of ground water in Koilwar block of Bhojpur (Bihar). J Chem Pharm Res 4(3):1782–1786 Karthikeyan S, Prabhakar D, Alagu Raja RA (1996) Groundwater Quality Monitoring System using GIS – A Case Study for Madurai District. International Journal of Engineering Research and Technology, RTICCT-2017 Conference Proceedings, 5: 17 Konkey S, Chitranshic UB, Garg RD (2014) Groundwater quality analysis and mapping using GIS techniques. Int J Eng Sci Technol 6:8 Krishan G, Singh S, Kumar CP, Gurjar S, Ghosh NC (2017) Assessment of Water Quality Index (WQI) of Groundwater in Rajkot District, Gujarat, India. Journal of Earth Science and Climate Change 7:3. https://doi.org/10.4172/2157-7617.1000341 Lescešsen I, Pantelic M, Dolinaj D, Stojanovic V, Milosevic D (2015) Statistical Analysis of Water Quality Parameters of the Drina River (West Serbia), 2004–11. Pol. J. Environ. Stud 24:2, 555–561. https://doi.org/10.15244/pjoes/29684 Machiwal D, Clotier V, Guler C, Kazakis N (2018) A review of GIS-integrated statistical techniques for groundwater quality evaluation and protection. Environmental Earth Sciences, Springer Link, 77:681 Mahananda MR, Mohanty BP, Behera NR (2010) Physico-chemical analysis of surface and ground water of Bargarh district, Orissa, India. Int J Res Rev Appl Sci 2(3) Mahato LL, Pathak AK, Kapoor D, Patel N, Murthy MSR (2004) Surface water monitoring and evaluation of IndravatiReservoir using the application of principal component analysis using satellite remote sensing technology. Map Asia Conference, Beijing, China Megahed HA (2020) GIS-based assessment of groundwater quality and suitability for drinking and irrigation purposes in the outlet and central parts of Wadi El-Assiuti, Assiut Governorate, Egypt, Spinger Open, Bull Natl Res Cent (2020) 44:187. https://doi.org/10.1186/s42269-02000428-3 Nagalakshmi R, Prasanna K, Chandar SP (2016) Water quality analysis using GIS interpolation method in Serthalaikadu lagoon, East Coast of India. Rasayan J Chem, Volume 9, No. 4, 634–640 Anornu OFI G K, Adjei KA, Eduvie MO (2021) Assessment of Water quality using GIS techniques and water quality index in reservoirs affected by water diversion. KeAi, Chinese Roots Global Impact, Water Energy Nexus 4:25–34. https://doi.org/10.1016/j.wen.2020.12.002 Pande CB, Moharir K (2018) Spatial analysis of groundwater quality mapping in hard rock area in the Akola and Buldhana districts of Maharashtra, India. Appl Water Sci 8:106 Praveena SM, Aris AZ (2010) Groundwater resources assessment using numerical model: A case study in low-lying coastal area. International Journal of Environmental Science 7(1):135–146

10 Appraisal of Drinking Water Quality of Kalahandi District Using …

219

Raikar RV, Sneha MK (2012) Water quality analysis of Bhadravati taluk using GIS- a case study. International Journal of Environmental Sciences, ISSN: 0976–4402, Volume 2, No 4, 2443–2453 Ramakrishaniah CR, Sadashivaiah C (2009) Assessment of Water Quality Index for the Groundwater in Tumkur Taluk, Karnataka State, India. E-Journal of Chemistry 6(2):523–530 Ravikumar P, Somashekar R, Prakash K (2015) A comparative study on usage of Durov and Piper diagrams to interpret hydro chemical processes in groundwater from SRLIS river basin, Karnataka, India. Elixir Earth Sci. 80:31073–31077 Rawat KS, Singh SK (2018) Water Quality Indices and GIS- based evaluation of a decadal groundwater quality. Geology, Ecology and Landscapes, Taylor & Francis Group, 2, No. 4, 240–255. https://doi.org/10.1080/24749508.2018.1452462 Remesan R, Panda RK (2007) Groundwater quality mapping using GIS: A study from India’s Kapgari watershed. Environmental Quality Management, Wiley Inter Science, Volume 16, 3. https://doi.org/10.1002/tqem.20130 Sharma KD (2009) Remote Sensing and Watershed Modelling: Towards a Hydrological Interface Model. Indo-US Symposium Workshop on Remote Sensing and its Applications, Mumbai (India) Sinha DK, Saxena R (2006) Statistical assessment of underground drinking water contamination and effect of monsoon at Hasanpur, J. P. Nagar (Uttar Pradesh, India). J Environ Sci Eng 48(3):157–164 Venkataraman T, Manikumari N (2019) Spatial distribution of water quality parameters with using QGIS. International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278 – 3075, 9(2). https://doi.org/10.35940/ijitee.B6863.129219 Venkateswaran S, Deepa S (2015) Assessment of groundwater quality using GIS techniques in Vaniyar watershed, Ponnaiyar river, Tamilnadu, International Conference on Water Resources, Coastal and Ocean Engineering (ICWRCOE), Aquatic Procedia 4, 1283–1290 Wipki M, Germann K, Schwarz T (2017) Alunitickaolins of the Gedaref region (NE-Sudan). GeoscientificResearch in Northeast Africa, CRC Press: Berlin, Germany, 509–514 Yang W, Zhao Y, Wang D, Wu H, Lin A, He Li (2020) Using Principal Components Analysis and IDW Interpolation to Determine Spatial and Temporal Changes of Surface Water Quality of Xin’anjiang River in Huangshan, China. Int J Environ Res Public Health, 17:8, 2942

Chapter 11

Allocation of Potential Tourism Gradient Sites at Maithon Dam of Damodar Valley Corporation (DVC), India: A Geospatial Approach Manika Saha and Susmita Sengupta

Abstract Through a “geospatial strategy” for promoting Nature-Based Tourism (NBT) in the target region, the current article investigates the potential for tourism development at the site of the Maithon dam. The potential for creating leisure tourism at the dam site is tremendous. Since Damodar Valley Corporation’s (DVC) primary focus is on flood control, irrigation, and water management, the site’s recreational value is still underappreciated. The geospatial approach is used in the present study to determine the gradient suitability for scenic beauty and adventure depending on the tourist interest. The potential tourist places is generated in a GIS environment assigning weights to the existing environment based on detailed ground investigation. Topographical maps, ASTER data, LISS III satellite image, Google Earth image, and thematic maps are employed in the GIS environment to prepare the final layout. Keywords Scenic beauty tourism · Geospatial approach · Nature-Based Tourism (NBT) · Adventure tourism · Sustainable tourism · Pleasure tourism

11.1 Introduction One of the industries with the fastest recent growth rates, tourism makes a significant contribution to global economies (Aminu et al. 2013; TIES 2009). In the 1980s, the national and regional economies of the country placed a great premium on developing the tourism industry. The world’s most popular tourist destinations now include a sizeable amount of the natural environment (Buckley and Coghlan 2012; Buckley 2009). According to the 2016 UN-WTO Report, nature-based tourism (NBT) accounts for M. Saha Department of Geography, Asansol Girls’ College, Burdwan, West Bengal, India S. Sengupta (B) Department of Geography, Rabindra Mahavidyalaya, Champadanga, Dist. Hooghly, West Bengal, India e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_11

221

222

M. Saha and S. Sengupta

around 20% of all international travel and is rapidly expanding. It has a huge potential to increase income, create jobs, develop underdeveloped areas, and lessen regional disparities. The dam sites spreading worldwide hold huge promises and prospects for the development of pleasure tourism. Over the years, DVC has given thrust on flood control, irrigation, and water management only, but due to the inter-state buffer location of Maithon Dam, both the Government of West Bengal and Jharkhand have neglected the dam’s recreational importance. Using a geospatial method, the study determined which gradients were suitable for scenic beauty and adventure depending on the interests of tourists. The study has applied the geospatial approach to finding the gradient suitability for adventurous and scenic beauty depending upon the tourist interest. The spare time, desire, and interest that people have in wildlife and forests are key drivers of tourism. According to Healy (1988), the nature tourism idea is most prevalent in relatively impoverished countries and focuses on the use of the environment, particularly its scenic, topographical, water, and wildlife resources. According to Eagles (1995), there are at least two distinct submarkets for nature-based tourism, including scenic beauty and adventurous/wilderness tourism, that can be distinguished based on the reasons why people choose to travel there. According to Fung and Marafa (2002), Ikonos satellite photos, spectral data, and textural data used in Geographic Information Systems (GIS) can have significant potential for the growth of tourism. In order to identify possible areas for nature-based tourism using socioeconomic and environmental characteristics, Armstrong (1994) also used remote sensing and GIS. Use of natural resources is the foundation of the nature-based tourism idea. Tourism site allocation refers to an area-specific tourism potential development considering the natural environment resources, the existing infrastructure, and the tourists’ flow for a certain amount of time (Ciobotaru et al. 2018). Geographic Information Systems (GIS) tools are used in tourism research as a decision-supporting tool for planning sustainable tourism, impact evaluation, tourist flow management, and tourism site selection (Rahman 2010; Giles 2003; Bahaire and Elliott-White 1999).GIS application in tourism planning at the regional level is enlightened by Culberston et al. (1994), posing the excellent potential for GIS tools (Bahaire & Elliot-White 1999) as an extension of environmental analysis. In the planning of tourism, site selection is another crucial GIS use. With the help of suitable location identification tools and topology, potential areas for future tourism development can be diagnosed. These tools are also helpful to delineate conservation and recreation areas, facility monitoring, and visitor management (Boyd and Butler 1996; Williams et al. 1996). Additionally, according to the literature, IKONOS satellite photos, including both spectral and textual metadata, have significant promise for the growth of tourism when used with Geographic Information Systems (GIS) (Fung and Marafa 2002). Based on environmental and socioeconomic characteristics, Armstrong (1994) used remote sensing, geographic information systems (GIS), and multi-criteria decision making (MCDM) to identify viable areas for nature-based tourism. Land use categories are important in the planning industry, according to Sharma et al. (2010).

11 Allocation of Potential Tourism Gradient Sites at Maithon Dam …

223

According to Andrew and Shaw (2007), GIS can be used to store, manage, analyse, and visualise both geographical and non-spatial data related to the various resources and activities in the parks. The nature-based tourism comes into the limelight and is given the most priority due to its ecological importance. Tourism depends on the tourists’ leisure time, desire, and interest in wildlife in forests.According to Eagles (1995), there are two sub-types of nature-based tourism, depending on the reasons why people visit, such as to experience the adventure and aesthetic beauty of the destination. A desire to spend time in locations and settings that offer significant, transforming, unplanned, and extraordinary experiences is referred to as naturebased tourism (Bastian et al., 2015; Curtin, 2013). Natural tourist attractions highlight certain geographic areas with picturesque appeal (Buckley & Coghlan, 2012; Chhetri & Arrowsmith, 2008). These natural places frequently offer diverse landscapes with intriguing geographical features for a wide range of recreational activities, including hiking trails, bird watching, camping, and enjoying a gorgeous location (Bell et al., 2007). The tourism industry of India is carrying a vibrant look and becoming a ‘major global destination.‘ The progressive numbers of domestic and foreign tourists have spurred on the Indian travel and hospitality sector. West Bengal refers to one of the first destinations in a tourist’s itinerary visiting the eastern part of India. The Annual Final Report of Tourism Survey for the State of West Bengal (2020) declares that between 2001 and 2017, the total tourist footfall in the state has increased from 5.2 million to a staggering 81.2 million at a Compound Annual Growth Rate (CAGR) of 18.74%. The same period experiences the number of foreign tourists in the state growing from 2.8 lacs to 15.7 lacs at a CAGR of 1 1.37%. In addition, historic structures, jungles, mountains, tea plantations, and a few dams in the Rarh Bengal and Chota Nagpur Plateau edge in the western portion of the state have become popular weekend and holiday tourist destinations. Maithon Dam on the Barakar River has developed as a pleasure tourism center in recent days and barely needs proper planning and management to develop as a potential tourism site. With this aim, the study has employed a geospatial approach to identify the adventure tourism spots and scenic beauty spots in the destination. The study combines GIS and overlay methods to identify the most suitable spots based on their scenic beauty and adventurous importance.

11.2 Objectives 1. exploring Maithon dam and its surroundings as a pleasure tourist spot; 2. using geospatial methods to determine the gradient suitability around Maithon Dam for tourist and adventure gradient locations; 3. to determine whether Maithon Dam could serve as a viable tourism destination.

224

M. Saha and S. Sengupta

11.3 Area Identity The West Bengal and Jharkhand borders of India are home to the concrete and earthen Maithon dam on the Barakar river (Fig. 11.1). The reservoir covers 65 km2 in total. The dam is 50 m tall and 4,789 m long. The dam’s objectives include flood control, irrigation plan development and operation, domestic and industrial water supply, navigation and drainage, and the production, transmission, and distribution of electrical energy. The region connects Dhanbad, the main mining agglomeration in the Chota Nagpur plateau in Jharkhand, and Asansol, the largest mining town in West Bengal (25 km), through road (52 km). The potential for the growth of leisure tourism in the dam area is enormous given all the factors. Its cultural worth has also been harmed by a few old temples that are located along the route to Maithon Dam. Since flood control, irrigation, and water management are the main thrust areas of DVC and its inter-state buffer location, the dam’s recreational relevance has been overlooked. Due to lack of planning and infrastructure, the dam site has become a place of extreme pleasure tourism for picnic purposes mainly. At present, the pleasure tourism at the Maithon dam site characterizes extreme seasonality. At present, tourist activity developed at Maithon and its surroundings center on the dam, its scenic beauty, and religious purposes, out of which Maithon dam is the central attraction. After the Dam, another significant location that draws a sizable number of tourists is the Kalwaneswari Temple, where people congregate throughout various festive

Fig. 11.1 Location of the study area and its surroundings

11 Allocation of Potential Tourism Gradient Sites at Maithon Dam …

225

seasons separately with the primary intention of visiting the temple and the dam as a secondary choice. A thorough field investigation showed that same-day visitors make up a significant portion of domestic tourists, accounting for 85% of all visitors and mostly coming to the location for leisure. These visitors are typically “excursionists,” not “tourists,” as they do not stay for more than 24 h. The remaining 15% of visitors are overnight travelers who have Maithon Dam as their secondary destination after travelling there for religious reasons as their primary one. Religious travelers also favour winter travel to the area. So, urgency is needed to develop potential tourism sites to increase domestic and international tourists. The present study attempts to develop tourism potential gradient sites using geospatial techniques at the dam site.

11.4 Materials and Methods The methods adopted in the present study consist of two different but inseparable approaches, viz., geospatial approach and field-based approach. The geospatial approach analyses relative relief, land use pattern, existing transportation system, location of tourist attractions spots, and density-distribution of flora-fauna in the dam site. LISS III satellite image downloaded from National Remote Sensing Centre (NRSC) Open Data EO Archive has been used to prepare NDVI (Normalized difference vegetation index) map and making Standard False Colour Composite of Maithon dam site. The Google Earth images have prepared the transportation maps, historical tourist site maps, and wildlife maps. Normalized Difference Vegetation Index map better discriminates forest and other land use classes. ASTER data generates Relative Relief Map for identifying different nature-based tourism potential zones; as higher relative relief area is favorable for adventurous sports like Trekking and ropeway climbing; middle relative relief area gives the site seeing, Eco-Park, and botanical gardens; and low area for residential and service centers (Kanga et al., 2011). The topographical maps (73I/9, 73I/10, 73I/ 13, and 73I/14 of India, scale: 1:50,000) extract current physical features to identify potential tourism sites of the Maithon dam site.

11.5 Preparation of Relative Relief Map The altitudinal difference between the highest and the lowest point in a unit area is relative relief. It is a crucial morphometric variable to examine the morphological properties of terrain as a whole. The relative relief was calculated using the Shuttle Radar Topography Mission (SRTM) data. SRTM 1 Arc-Second Global Elevation Data Provides open access of this high-resolution global data set and provides global coverage of void-filled data with a resolution of 1 arc-second (30 m). SRTM data were downloaded from https://earthexplorer.usgs.gov/.

226 Table 11.1 Relative relief categories

M. Saha and S. Sengupta

Relative relief (meters)

Area km2

0–25

1583

55.3

26–50

1119

39.1

Percentage

51–100

80

2.8

101–200

58

2.0

201–442

20

0.7

The study region was divided into one sq km grids, with center points created using the fishnet tool (ArcGIS 10.5). The difference between each grid’s highest and lowest point was calculated using SRTM elevation data and based on range computation in zonal statistics as a table tool. Label points were linked to relative relief values from statistics tables. For constructing a raster of relative relief, the Inverse Distance Weighted (IDW) approach was applied to the points vector (Senapati and Das 2020). The study area’s relative relief values range from 0 to 442 m. The relative relief value was split into five categories. In each category, the area and percentage were also estimated (Table 11.1).

11.6 Preparation of NDVI Map The present study derives vegetation indices from the LISS III (IRS-P6) satellite images of 23.5 m spatial resolution for 2019 (January 1, 2019). Six tiles, namely F45C10, F45C09, F45C14, F45C13, G45U12, G45U16, were downloaded from https://bhuvan-app3.nrsc.gov.in/data/download/ (access on January 18, 2021). Four spectral bands—bands 2, 3, 4, and 5—represent the green, red, near-infrared, and shortwave infrared portions of the electromagnetic spectrum, respectively, in the LISS III photographs (ISRO 2003). Band 3 (Red) and band 4 (near-infrared) were applied to calculate the NDVI. The NDVI is a vital vegetation index that can track the health and growth of vegetation in a region over time and distinguish other land uses from vegetation (Jensen 2009; Palchaudhuri and Biswas 2020). A healthy plant’s chlorophyll pigment absorbs the most visible red light but reflects the most near-infrared light. The following equation computes the NDVI. (NIR − Red)/NIR + Red)

(11.1)

The wavelength bands of near-infrared and red light, respectively, have reflectance known as NIR and Red. The results of the NDVI calculation range from –1 to 1. Negative and near-zero values correspond to areas with water surfaces, clouds, snow, rocks, artificial structures; bare soil usually falls within the 0.1– 0.2 range; plants always have positive values between 0.2 and 1 (Zaitunah and Sahara 2021).

11 Allocation of Potential Tourism Gradient Sites at Maithon Dam … Table 11.2 Distribution of NDVI values in Maithon Dam and surroundings

NDVI values

Area km2

227

Percentage of area

–0.4–0

108.8

3.8

0–0.1

119.2

4.2

0.101–0.2

1352.8

47.5

0.201–0.3

1034.0

36.3

0.301–0.4

187.6

6.6

0.401–0.7

46.3

1.6

The NDVI ranges from –0.4 to + 0.7 in the Maithon Dam site and its surroundings. NDVI values were categorized into six classes to distinguish dense vegetation, other vegetations, bare soil, and waterbody area. The classified NDVI raster was used to determine the area and area percentages (Table 11.2). The field-based approach in the study includes intense field observation and questionnaire survey at hotels, boating centers, amusement parks, and picnic spots with tourist parties, local people, and also the group of individuals who either directly or indirectly engage in tourism at the dam site. Firstly, a semi-structured questionnaire survey was administered to the tourists to gain information of current tourist attractions, especial pull factors of the Maithon dam site, the existing conditions of the tourist spots, and the tourism-potentialities of the site. Additionally, open-ended interview was conducted to the local people along with the auto-drivers, mobile vendors, tiny booth proprietors, and the gateman at the picnic area’s ferry ghat are all actively involved in the tourism industry during peak season. The survey measured the level of infrastructure development at the tourist destination for accommodations, infrastructure, and attractions. participation and employment opportunities of local people at the dam site, and problems and prospects of the Maithon as a ‘potential tourism site.’ To have a qualitative flavour, taking photographs of different tourist activities at dam site remained an important part of the study. Besides, the key informant interviews and focus group discussions with local aged people, people engaged with tourism activities, hotel managers, tourist parties, to unfold the prospects and potentialities of the Maithon dam through the lens of local people. Finally, the layout of adventure tourism and nature-based scenic beauty tourism has been prepared based on their adventurous importance and scenic value for suitable planning and proper management of the Maithon dam site to grow “ecologically and economically viable” tourism. The geospatial methodology summarizes in detail in Fig. 11.2.

228

M. Saha and S. Sengupta

Fig. 11.2 Methodology adopted for potential tourist spots through geospatial techniques

11.7 Results and Discussion 11.7.1 Present Status of Tourism Activity at Maithon Dam Attractions, lodging, seasonality, and entertainment make up the tourism business. Traveling to and from a place is part of tourism, making it a complex activity (Briassoulis 2002). Current Tourist Attractions: At present, tourist activity developed at Maithon and its surroundings center on the dam, its scenic beauty, and religious purposes, out of which the Maithon dam is the central attraction. After the Dam, Kalwaneswari Temple is a significant site that draws a lot of tourists. The transit path to get to the end objective, which is Maithon dam, is where most people congregate during various celebratory seasons separately. Other places to visit include Millennium Park (in the state of Jharkhand), the West Bengali border, and Jharkhand’s Bhander Pahar (Figs. 11.3 and 11.4). Flow of tourists: The tourist arrival data of Maithon was obtained from the boating point ticket counter to gain specific information regarding the flow of tourists at the tourist spot. The detailed field observation has shown that the massive influx of tourists occurs mainly in winter. Most of them are of domestic origin. The number of national (within India) and foreign tourists is negligible compared to domestic tourists. A big amount of domestic tourists is the same-day visitors (85% of total

11 Allocation of Potential Tourism Gradient Sites at Maithon Dam …

229

Fig. 11.3 Current tourist attractions of Maithon dam site

tourists), they mainly visit the tourist spot for recreation. These travelers are called ‘excursionists’ who do not stay 24 h or more. It makes no such profit because such excursionists spend little on the destinations. The remaining 15% stay at night, they usually come for religious purposes as their primary destination, then Maithon dam as a secondary one though religious tourist also prefer winter season to visit the place. A significant variation also exists in the arrivals of same-day visitors. The majority is from neighboring Burdwan, Hugli, Bankura districts of West Bengal, and Dhanbad district of the state of Jharkhand. Asansol topped the list with an average of 28% contribution, followed by Chittaranjan-Rupnarayanpur (22%), Barakar (15%), Dhanbad (14%), and Durgapur (11%). The remaining 10% are far of places of both states. Mode of Transport: The domestic tourists travel by bus, rented car, and private car while the same-day visitors coming for picnic purpose travel by bus mainly. The tourists coming from Burdwan district and its surroundings have to follow the bypass road from Durgapur. Every day approximately 28 buses flow from Asansol to Chittaranjan via Dendera. This junction is the nearest to the Maithon Dam site. Out of these buses, only eight buses flow to Maithon. The nearest rail station of Maithon dam for tourists from Jharkhand state is Kalubathan Rail station. Few rented cars and buses are available from this rail station to reach the dam. A little tourist from

230

M. Saha and S. Sengupta

Fig. 11.4 a to i. Tourist attractions at Maithon Dam Site and its surroundings. a Reservoir of Maithon Dam; b Scenic beauty of Maithon dam; c Power Plant; d Sabuj Deep; e Shooting Point; f Boating Point; g Kalyaneswari Temple; h Bhandar Pahar and i Millennium Park

Kumardhubi and Chirkunda of Jharkhand state avail private car to visit the dam. Very poor accessibility and connectivity are the negative tourism aspects of the Maithon dam site (Fig. 11.5). Accommodation Scenario: One of the basic facilities of any tourist spot is its accommodation. Several hotels and lodges are present at the study site. The bulge of hotels and lodges, namely Maa Kalwaneswari Hotel, Asha Resort, and Kalwaneswari Lodge, are located surrounding the Kalyaneswari temple. In contrast, three Govt. sponsored hotels (Majumdar Nibas, Forest Department Tourist Lodge, and Maithon Tourist Lodge) and a few newly built-up private hotels (Barsha Hotel, Santi Nivas) are found at the dam site. The pilgrim tourists are attracted by the hotels near the temple due to its nearness, whereas dam-side hotels accommodate the leisure tourists spending at weekends and holidays at the dam site. Each hotel provides different categories of rooms like AC. Non-AC, Deluxe Suite, etc. However, there is no star category hotel in the area. Thus comfort-seeking tourists, tremendous businesspeople, and foreign tourists are absent. Another essential feature is that there is no quality restaurant to provide traditional food to tourists.

11 Allocation of Potential Tourism Gradient Sites at Maithon Dam …

231

Fig. 11.5 Accessibility and location of hotels at Maithon dam site

Seasonality: Another striking feature of the Maithon Dam site tourism is profound seasonality, as seen in the case of tourist arrival. Seasonality principally depends on a combination of climatic factors (traveling during the winter season is more comfortable, and travel during the hot, dry summer months is impossible), the occurrence of public holidays (Christmas Holidays, New Year Day, Puja Holidays, and Republic Day) and finally the seasonality of the attractions in Maithon Dam itself. Most tourists come in the Winter season every year. The rest of the months receive very few tourists except a mild upswing in the monsoon period to feel the beautiful scene of discharge of voluminous water from the Maithon dam. However, different visitors exhibit various seasonal characteristics. Villagers have little opportunity to trade with such guests. Due to the increased demand, a number of hotels and lodges are springing up near the Kalyaneswari temple and dam site, creating economic opportunities. Villagers take advantage of low-paying jobs like boat drivers, cooks, water suppliers, sweepers, ticket salesmen, street vendors, and small-business owners during the busiest season. There is very little chance for a village home to rely only on tourism throughout the year due to the extreme seasonality of visitor arrivals (Fig. 11.6). The tourists are overwhelmingly from within the country. Same-day visitors make up a sizable portion of the domestic tourist population (85% of all visitors), who primarily come for leisure. Because they don’t stay in the places for at least 24 h, they can better be described as “excursionists” rather than “tourists.“ The others are day visitors who primarily came for religious reasons. Bus, rental cars, and private

232

M. Saha and S. Sengupta

Fig. 11.6 Seasonality of sale of ticket of a boating place at Maithon

cars are among the means of transportation utilized by domestic tourists. Tourism in the study area has enormous potential thanks to good connection from all directions of the neighbouring districts.

11.8 Maithon Dam Site: Potentiality as a Sustainable Tourist Spot The physical environment considers a critical element of tourism (Theobald 1998). The flourishing of the tourism industry requires natural resources to facilitate its expansion. Resources like water and land at the Maithon Dam site are the two natural resources. The study’s primary objective lies in generating the tourism potential sites based on thematic maps that are vital for generating final layouts. These potential sites were identified based on a few criteria like a relative relief map, accessibility map, NDVI map, and lastly, land use map from the extraction of a topographical map. Weights of different criteria were given depending on knowledge-based prior information. Hilly areas, including islands having moderate to steep slopes, are ideal for adventure tourism (Fig. 11.7) that offers enough opportunities for aero, aqua, and terrestrial adventure tourism activities. The reservoir is suitable for boating, kayaking, speed boat cruises, and power sailing. It is also noteworthy that the Sports’ Hostel built up during the 34th National Games of Jharkhand, now abandoned, can be used

11 Allocation of Potential Tourism Gradient Sites at Maithon Dam …

233

for this purpose. Trekking may be possible along the hillocks in the dam. Besides, the dam site is enriched with the blessings of nature. The presence of hillocks, dense forests, and wildlife adds color to nature’s splendor. So, the study area bears the immense potential to be a scenic beauty tourist spot (Fig. 11.8), too, e.g., Eco Park, Medicinal Plant Garden, and Butterfly Park.

Fig. 11.7 Suitable places for Adventure tourism at Maithon dam site

Fig. 11.8 Suitable places for Scenic Beauty tourism spots at Maithon Dam site

234

M. Saha and S. Sengupta

The new locations that the new breed of visitors are eager to see because many tourists are looking for pure air, water, and environment include the area surrounding the dam, hillocks, forest-covered islands, and walking on the nature trail. They prefer to visit places with little pollution, a natural setting, and appropriate amenities. The potential tourism products of the Maithon dam site to make it a sustainable one is as follows: Adventure Tourism: The dam site offers great aero, aqua, and terrestrial adventure tourism opportunities. Additonally, it provides a broader outlook of life. The reservoir is ideal for boating, kayaking, speed boat cruises, and power sailing. The Sport’s Hostel built up during the 34th National Games of Jharkhand, now abandoned, can be used for this purpose. All the ingredients like different types of boats, digital display board, night guard, solar panel for electricity, room for changing dresses are present in the hostels. Besides, trekking along the hillocks possesses another potentiality of adventure tourism in the dam site. The Relative Relief map of Maithon Dam and its surroundings (Fig. 11.9) depicts that the hillocks’ peak in isolated patches bears comparatively high relative relief. The ropeway track connecting these isolated hillocks would attract adventure lovers to this site. Thus, adventure tourism encourages teamwork and reduces stress. It is desirable as it is very close to nature.

Fig. 11.9 Relative relief map of Maithon Dam and surroundings

11 Allocation of Potential Tourism Gradient Sites at Maithon Dam …

235

Ecotourism: Since many tourists are seeking for clean air, water, and environments, there is a new breed of tourists who are eager to explore the areas surrounding the Maithon dam, hillocks, and islands covered in forest, as well as going on the nature trail. Travelers like to go to places with low pollution levels, a natural setting, and suitable amenities. Ecotourism and adventure tourism may grow together. Film Tourism: Both the state of West Bengal and the state of Jharkhand can actively promote film tourism in the study region since film tourism refers to luring tourists to the locations depicted in movies. The government can take advantage of the natural resources at the dam site to support the Indian film industry and use these productions as “brand ambassadors” for dam site tourism, which will boost the number of visitors to Maithon. Leisure Tourism: The study area has immense potential to be a leisure tourist spot. Recreation is a must for every human being. In our modern competitive world, the monotony and drudgeries of daily work chores and stresses and strain need a respite through various recreational means. For the above-said purpose, the Maithon dam and its surroundings are ideal. Pilgrimage Tourism: Tourist movements are due to various motivations. A significant portion of the population moves for religious purposes. On the way to the Maithon dam site, two important Hindu temples, namely ‘Kalyaneswari Mandir’ and ‘Ghaghorburi Mandir,’ indirectly boost tourism development at Maithon dam and further bears the potentiality of religious tourism surrounding two temples. Education Tourism: The study area bears large educational values also. DVC is one of the first-ever multipurpose river valley projects of independent India, having a network of four dams - Tilaiya and Maithon on river Barakar, Panchet on river Damodar, and Konar on river Konar. Besides, the Durgapur barrage and the canal network are a part of the total water management system. An underground power station at the Maithon dam site is the first in Southeast Asia. It is an enormous reservoir in DVC. All these above mentioned will grow interested to the minds of school and college goers of different disciplines. If any museum can be built-up having models depicting the projects of DVC, water management system, the process of generation of Hydel power, and mechanisms of the turbine. It will quench the thirst for knowledge of the students. Some display boards or posters along the roadside are necessary to flourish educational tourism surrounding the Maithon dam. Eco-Park: The region has a collection of numerous flora and fauna species. These species are left unguarded and unprotected. It is necessary to protect and preserve the local bio-diversity. The establishment of an eco-park serves the purpose. The main objective of the eco- Park is to preserve the variety of flora and fauna. Compared to National Park and Sanctuary, this area is micro in the areal framework, but the objective is the same as protecting local biodiversity. This region’s climatic and edaphic conditions have supported a variety of floral species. The NDVI map of the Maithon Dam site (Fig. 11.10) shows that the north, northwest, and north-east part of the dam are rich in vegetation density. The plant species found in this region have both

236

M. Saha and S. Sengupta

commercial and medicinal value. Some of the plant species are Arjun (Terminalia arjuna), Palash (Butea monosperma), Sal (Shorea robusta), Mahua (Madhuca Indica), Sirish (Albizia lebbeck), Kurchi (Hollarrhoena antidysentirica), Kuchle (Strychmos nux-vomica), Neem, Bonkhejur, Dhutra, Babla, Sonajhuri, and Chaim. Apart from this, different insects, reptiles, birds are found here. This eco-park can contribute hugely to the tourism sector. Thus, local flora and fauna have plenty of space to live in harmony with human uses. For the tourist, it will be a place for discovery about local bio-diversity. Medicinal plant Garden: On the way to Maithon dam vast track of land is remained in unutilized condition. Those parts of the land can be used to cultivate medicinal plants in proper planning and managed way. In West Bengal, 145 species of medicinal plants are found. Among them, 32 species are significant in respect of their commercial value in the national and international arena. This study area is the habitat of some of the important medicinal plants. These are Haritakin (Terminalia chebula), Behera (Terminalia bellerica), Asok (Saraca asoca), Arjun (Terminalia arjuna), Kurchi (Hollarrhoena antidysentirica), Kuchle (Strychmos nux-vomica), Tulsi (Ocimum sanctum), Basok (Adhatoda vasica), Nayantara (Catharanthus roseus)

Fig. 11.10 NDVI analysis of Maithon dam and surroundings

11 Allocation of Potential Tourism Gradient Sites at Maithon Dam …

237

which include within 32 species. Besides, the region is also favorable for other medicinal plants: Ayapana, Sarpoghandha, Aswogandha, Ghritakumari, Sonapata, Safedmusli, and Nisindha. So, this type of medicinal garden can be a tourist attraction in the study area. It will attract the students of Botany and encourage them to visit nature. Butterfly Park: Another project, such as the set-up of Butterfly Park, also attract tourist, and it also acts as a catalyst to conserve some species like birds and lizards. Some of the butterfly garden projects have already been initiated in different parts of eastern India by Forest Department. This area also can become a rich haven for butterflies. These pretty colorful fluttering creatures give tourists pleasure, but they are the most critical biodiversity indicator. Already a park is present in the study area, but it is not adequately utilized. If proper planning should be undertaken to grow up some nectar and host plants for the growing life cycles of butterflies, then the butterfly park offers a live preview of what tourists and visitors can expect to see in the nearby forest. It creates interest in ordinary people and attracts tourists. Angling tourism: Maithon dam is abundant in water resources and is favorable for the growth of angling tourism. It is a recreational fishing activity with a rod and line in which recreational fishers catch fish and either release it into the water or eat it. From the Relative Relief Map (Fig. 11.9), the dark blue patches may be the ideal site for angling tourism in the Maithon Dam site. Angling tourism may attract many tourists in this region. Angling tourism is a complex industry. It can ensure income growth and employment opportunity and conserve fish and their habitat. The study has observed that DVC has demarcated some water areas by the net system for fish growth. The authority should have taken proper planning and management to promote this type of tourism. Dam Triangle concept: To improve the tourism potentiality of the border area of both of the states, a dam triangle concept may be proposed in this regard. The triangle will be a tourist circuit that includes three dams: Maithon, Panchet, and Tilaiya. The trip usually lasts for 2 to three days, and the circuit starts and ends at Maithon. It covers the vast geographical area of the state of Jharkhand and West Bengal. It will be favorable to make the trip by local small tour organizers or travel agencies. Some sight-seeing places like Gar Panchakot hills and nearby temple, Hazaribagh National Park, Konar Dam may include in the triangle plan. It will enhance the potentiality of the study area as a tourist spot, but local people can also augment their livelihood through employment generation.

11.9 Conclusion and Recommendations If tourist development is to be sustainable, it must change from its conventional growth-oriented paradigm to one concerned with a sustainable set of aims and principles. Achieving such a goal is challenging; however, sustainably developing tourism

238

M. Saha and S. Sengupta

must be essential goal in the developing process. The present study employs a geospatial approach to develop tourism at the Maithon Dam site. The study area holds enormous potential for domestic tourist attraction. The scenic beauty, along with religious importance, scope of conserving the natural environment, and last but not least, an ample opportunity of introducing adventure tourism maintain the substantial pull factors that may be strengthened by the involvement of local people in the tourism sector to increase the livelihood options for them making the tourism industry ‘socio-economically viable.‘ It may be concluded that the recreational tourism at the Maithon Dam site has not reached its commercial level. The surrounding villages are poverty-stricken and live a poor quality of life. Thus, proper planning and management must be undertaken to develop ‘responsible’ tourism that enables local people to gain better access to tourists to improve their livelihood by getting employment and small-scale enterprise development. Based on the study, some important areaspecific planning proposals coming from the field investigation are recommended as follows: a. Joint management within all probable governmental and non-governmental agencies (Forest, Tourism, Environment and Local) required for developing Maithon Dam site a potential tourist spot; b. Launching the adventure tourism along with the nature tourism in the dam site with the aid of Government and simultaneously non-Government institutions; c. Improving infrastructure and safety of the current tourist spots at the dam site; d. Both the Jharkhand and West Bengal Government can extend opportunities for tour operators’ organization through providing easy license, releasing loan with low-interest, offering training and guidelines; e. Local people’s interest must be involved in planning to augment their employment opportunities from the tourism; f. Protection of ecosystem must gain priority in the planning of ecotourism development in Maithon dam site; and g. Launching environmental awareness programs.

References Aminu M, Matori AN, Wan Yusof K, Zainol R (2013) A framework for sustainable tourism planning in Johor ramsar sites, Malaysia: A geographic information system (GIS) based analytic network process (ANP) approach. Res J Appl Sci Eng Technol 6(3):417–422 Andrew SD, Shaw SL (2007) A GIS-based spatial decision support system for tourists of grest smoky mountains national park. J. Retailing Consumer Services 14:269–278 Armstrong M (1994) Requirements for the Development of GIS-based Group Decision-Support Systems. American Society for Information Science 45(9):669–677 Bahaire T, Elliott-White M (1999) The application of geographical information systems (GIS) in sustainable tourism planning: A review. J Sustain Tour 7(2):159–174 Bastian O, Stein C, Lupp G, Behrens J, Renner C, Grunewald K (2015) The appreciation of nature and landscape by tourism service providers and visitors in the Ore Mountains (Germany). Landscape Online 41:1–23

11 Allocation of Potential Tourism Gradient Sites at Maithon Dam …

239

Bell S, Tyrväinen L, Sievänen T, Pröbstl U, Simpson M (2007) Outdoor recreation and nature tourism: A European perspective. Living Reviews in Landscape Research 1(2):1–46 Boyd SW, Butler RW (1996) Seeing the forest through the trees: Using GIS to identify potential ecotourism sites in Northern Ontario, in: L.C. Harrison &W. Husbands (Eds) Practising responsible tourism: International case studies in tourism planning, policy & development, pp. 380–403 (New York: Wiley & Sons) Briassoulis H (2002) Sustainable Tourism and the question of the commons. Ann Tour Res 29(4):1065–1085 Buckley R, Coghlan A (2012) Nature-based tourism in breadth and depth. Critical Debates inTourism 57:304–307 Buckley R (2009) Ecotourism: Principles and Practices. CAB International, Wallingford Chhetri P, Arrowsmith C (2008) GIS-based modelling of recreational potential of nature-based tourist destinations. Tour Geogr 10(2):233–257 Ciobotaru N, Lupei T, Laslo L, Matei M, Boboc M, Velcea A-M, Deak G (2018) A GIS approach regarding tourism suitability of wetland areas of Romania. RevCAD Journal of Geodesy and Cadastre. 23:69–78 Culbertson K, Hershberger B, Jackson S, Mullen S, Olson H (1994) GIS as a tool for regional planning in mountain regions: Case studies from Canada, Brazil, Japan, and the USA, in: M.F. Price & D.I. Heywood (Eds) Mountain Environments and GIS, pp. 99–118 (London: Taylor & Francis) Curtin, S. (2013). The intrinsic motivations and psychological benefits of eco and wildlife tourism experiences. In International handbook on ecotourism (pp. 203–216). Edvard Elgar Publishing Eagles PFJ (1995) Linking tourism, the environment and sustainability. Understanding the market for sustainable tourism. Issue INT 323:25–33 Fung. T and Marafa. LM. (2002). Landscape ecology of Feng Shui woodlands and the potential for ecotourism using IKONOS images and GIS. Geoscience Remote Sensing Symposium 6 Gile W (2003) GIS applications in tourism planning (online), Retrieved 4 January 2013 from URL: www. cnc.bc.ca/gis/documents/340TourismTermPaper. pdf Government of India (2020) Annual Final Report of Tourism Survey for the State of West Bengal (April 2014-March 2015). Ministry of Tourism (Market Research Decision) Healy RG (1988) Economic Considerations in Nature-Oriented Tourism: The Case of Tropical Forest Tourism. FPEI Working Paper no. 39. Research Triangle Park, North Carolina: Forest Private Enterprise Initiative Holden A (2000) Environment and Tourism. Routledge, London, England ISRO (2003) IRS-P6 Data Users’ Handbook, NRSA Report No. IRS P6/NRSA/NDC/HB-10/03, October 2003, Hyderabad, India. https://bhuvan.nrsc.gov.in/bhuvan/PDF/Resourcesat-1_Hand book.pdf (Access on January 18, 2021) Jensen JR (2009) Remote sensing of the environment: An earth resource perspective 2/e. Pearson Education India Kanga S (2011) Geospatial approach for allocation of potential tourism gradient sites I a part of Shimla District in Himachal Pradesh. India. Journal of GIS Trends. 2(1):1–6 Palchaudhuri M, Biswas S (2020) Application of LISS III and MODIS-derived vegetation indices for assessment of micro-level agricultural drought. The Egyptian Journal of Remote Sensing and Space Science 23(2):221–229. https://doi.org/10.1016/j.ejrs.2019.12.004 Rahman M (2010) Application of GIS in ecotourism development: a case study in Sundarbans, Bangladesh Senapati U, Das TK (2020) Assessment of Potential Land Degradation in Akarsa Watershed, West Bengal, Using GIS and Multi-influencing Factor Technique. In Gully Erosion Studies from India and Surrounding Regions (pp. 187–205). Springer, Cham Sharma LK, Pandey PC, Nathawat MS (2010) Assessment of land consumption rate with urban dynamics change using geospatial techniques. Journal of Land Use Science, Taylor and Francis. https://doi.org/10.1080/1747423x.2010.537790

240

M. Saha and S. Sengupta

TIES (2009) TIES global ecotourism fact sheet: The international ecotourism society. Retrieved from http://www.ecotourism.org Theobald FW (1998) Global Tourism, 2nd edn. Reed Educational and Professional Publishing Ltd., UK UN-WTO, Conservation International, Rainforest Alliance, UNEP. (2016). A practical guide to good practice for tropical forest-based tours. Retrieved from http://www.rainforest-alliance.org/ sites/default/files/publication/pdf/good_practice.pdf Williams PW, Paul J, Hainsworth D (1996) Keeping track of what really counts: Tourism resource inventory systems in British Columbia, Canada. In L.C. Harrison and W. Husbands (eds)_ Practising Responsible Tourism: International Case Studies in Tourism Planning, Policy & Development, 404–421 Zaitunah A, Sahara F (2021) Mapping and assessment of vegetation cover change and species variation in Medan. North Sumatra. Heliyon 7(7):e07637. https://doi.org/10.1016/j.heliyon.2021. e07637

Chapter 12

Watershed Management Process Under MGNREGA: An Approach to Natural Resource Management Through People’s Participation Soumik Halder, Sumit Panja, and Sayani Mukhopadhyay

Abstract The Watershed management project is dedicated to soil and water conservation as well as livelihood development. This paper summarizes the importance of MGNREGA in watershed management. This is an observational study based on the review of papers and field level experience. Various watershed management structures can be set up under Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), which are extensively discussed here, considering the ridge to valley treatment method. So many natural resource management (NRM) schemes have been included in the guidelines of permissible work for MGNREGA, but some have a less significant impact on watershed management. This paper discusses those schemes which have a high impact on watershed management. The participatory approach of planning and the preparation of a detailed planning report (DPR) for micro watersheds have been conferred here. Impact of “Ushar Mukti Project” in watershed management has been analyzed as a case study. It is identified that the main obstacles to watershed management planning are communication gaps, insufficient Participatory Rural Appraisal (PRA) material, selection of schemes irrespective of land types, etc. The possible solutions to those problems are active participation of the community in the planning process and cooperation between the government and civil society organizations. Keywords Watershed management · MGNREGA · Natural resource management · Water harvesting

S. Halder · S. Panja Research Scholar, Department of Geography, Asutosh College, University of Calcutta, Calcutta, India S. Mukhopadhyay (B) Associate Professor, Department of Geography, Asutosh College, University of Calcutta, Calcutta, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_12

241

242

S. Halder et al.

12.1 Introduction Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) is a wage employment programme, launched to ensure social security, livelihood security for vulnerable people, drought prevention and flood management, etc. (Sameeksha 2012). Soil and Water conservation and plantation schemes have been widely implemented since the inception of the programme. Under MGNREGA, at least 65 percent of the cost is spent to ensure natural resource management (NRM) work. MGNREGA has benefited more beneficiaries than any other major Indian social security scheme (World Bank 2011). In the annual muster circular, 2020–21 of MGNREGA, the priority has been given on watershed management. So, the questions are that what should be the ideal area of watershed for effective treatment and what types of schemes of MGNREGA are suitable for watershed management. The natural resource management process requires integration among the activities and the topography. According to Tidemann (1996), a watershed is a planning unit where water, soil and vegetation resource can be managed simultaneously, effectively and collectively (Freie Universitat, ¨ Berlin). A watershed is defined as any surface area from which runoff resulting from rainfall is collected and drained through a single outlet (Wani and Garg 2009). The planning unit should not be too large and should not be too small (Chand and Puri 1983); therefore, the micro watershed (100 hectares to 1000 hectares) is the perfect unit for the watershed management process. The use of soil and water resource in the upstream and the downstream areas, the natural resource management, agricultural productivity and the standard of living of people within the watershed is properly managed in micro watershed management (Chang et al. 2009). Participatory approaches are necessary for the proper implementation of the watershed management process (German et al. 2007). Integrated watershed management is the process of creating plans and executing projects to withstand and boost watershed functions that provide environmental and economic benefits to the community within a watershed boundary (Wang et al. 2016). Not all NRM activities under MGNREGA have a significant impact on the watershed management process. For example, the road site plantation works have no significant importance in watershed management because the mortality rate of the plant is relatively higher. The impact of such scheme in soil-moisture restoration is significantly low than the block plantation. As the effectiveness of the watershed management is based on water budget and livelihood development, a large number of non-NRM works like livestock shed, vermicompost pit, work shed of Self-Help Groups, etc., are adopted in watershed management project as a livelihood promotion scheme. The aim of this research paper is to highlight the potential natural resource management schemes of MGNREGA for watershed management with special reference to Ushar Mukti project (a micro watershed management project to facilitated MGNREGA programme).

12 Watershed Management Process Under MGNREGA: An Approach …

243

12.2 Study Area The impact assessment of micro watershed management under MGNREGA has been done on the basis of Ushar Mukti project. This project area has been sub-divided into 2512 numbers of micro watershed, spread over six districts.Out ofthese, one micro-watershed of Birbhum district and two micro-watersheds from Paschim Bardhaman have been selected randomly to identify the importance of MGNREGA in micro watershed management and overall, four districts namely Birbhum, Bankura, Paschim Bardhaman and Purulia are taken for the impact assessment of the Ushar Mukti project.The Panchkath-Mahula-Nabagrammicro watershed of Birbhum district (Micro Watershed Code: 2A3C3a5) is located on (23°56' 36'' N to 23°58' 33'' N and 87°25' 01'' E to 87°28' 35'' E) the southern part of the Mayurakshi river basin and the Kendulia-Gopedangamicro watershed (MW Code: 2A3B3k3) which is located on the southern part of the Ajay river basin of Paschim Bardhaman (23°36' 46'' N to 23°38' 46'' N and 87°20' 56'' E to 87°24' 19'' E). The area of PanchkathMahula-Nabagram micro watershed is 1095 hactare and Kendulia-Gopedanga micro watershed is 1063 hectare (Maps 12.1, 12.2 and 12.3).

Map 12.1 Location map of the Ushar Mukti project area and study area

244

S. Halder et al.

Map 12.2 Terrain map of Panchkath-Mahula-Nabagram micro watershed of Birbhum District

Map 12.3 Terrain map of Kendula-Gopedanga Micro Watershed, Paschim Bardhaman

12 Watershed Management Process Under MGNREGA: An Approach …

245

12.3 Methodology The paper is an empirical study based on both qualitative and quantitative data. This study used the semi-structure interview method to identify the potential of MGNREGA programme in watershed management. The process and the problems of Participatory Rural Appraisal (PRA) adopted by the planners for community involvement in watershed planning have been extensively discussed on the basis of field level experience. The estimated cost of the watershed management structure isconferred here for cost assessment in watershed planning. The MGNREGA activities which have no substantial impact on watershed managementis categorized on the basis of field survey. This paper illustrates the most successful natural resource management schemes of MGNREGA in respect of topography for better implementation of the MGNREGA schemes on other watershed. The expenditure related to the structures is calculated on the basis of 2021–22 wages, i.e., Rs. 213 per 54 cft of soft soil cutting which can vary according to the hardness of soil. In order to determine the effects of micro watershed management planning in the MGNREGA programme, 103 respondents from the districts of Birbhum, Paschim Bardhaman, Bankura, and Purulia participated in the study. These respondents included government and Civil Society Organisation (CSO) personnel. Both the Kendulia-Gopedanga micro watershed in Paschim Bardhaman and the PanchkathMahula-Nabagram micro watershed in Birbhum have undergone a micro level study on structures.

12.4 Result and Discussion 12.4.1 Potential Schemes of MGNREGA for Integrated Watershed Management The schemes of MGNREGA, potential for watershed management is discussed below (Table 12.1).

12.4.2 Participatory Approach in Integrated Watershed Management The watershed management plans should be organized on the ridge to valley principle. Typically, errors occur when soil and water conservation work begin at the bottom of the watershed, resulting in rapid siltation in the water harvesting structure and reduced economic viability of structures (Sahayog 2006). To avoid such problems, treatment of watershed should be done from ridge to valley by analyzing

246

S. Halder et al.

Table 12.1 Schemes under NRM and Non-NRM works. Source Table generated on the basis of MGNREGA permissible work list (Schedule 1 of MGNREGA Act 2005) and primary survey 2021 Scheme

Category of work

Type of work (MGNREGA)

Potential to watershed management (Yes/No)

Plantation-all kinds of horticulture and social forestry and nursery

Drought proofing

NRM

Yes

Maintenance of plantation

Drought proofing

NRM

Yes

Excavation or Construction of Hapa/ Farm Pond/Bundh/5% model/Tank/Percolation Tank/Fishery Pond

Water conservation and harvesting

NRM

Yes

Re-Excavation or Water conservation renovation of community and harvesting pond/farm pond/Bundh

NRM

Yes

Elephant proof trench across forest area/cattle proof trench/water absorption trench

Water conservation

NRM

Yes

Staggered trench, 30–40 model, contour trench, box trench, cattle proof trench

Water conservation

NRM

Yes

Vetiver plantation

Soil conservation

NRM

Yes

Irrigation well

Water harvesting

NRM

Yes

Rock Check/Gully Plug/ LBS/boulder check/ Gabion structure/RCD (Rock Check Dam)

Soil moisture conservation

NRM

Yes

All kinds of Canal work Soil moisture in irrigation canal sector/ conservation water grid project

NRM

Yes

Check dam/earthen dam/ Water conservation stop dam and harvesting

NRM

Yes

Land levelling in case of common land

NRM

Yes

NRM

No

Soil moisture conservation

Plantation at school, Drought Proofing ICDS, College, Crematorium, Road Side Kitchen garden

Livelihood promotion NRM NA

No

Rain Water harvesting at roof top

Water harvesting

NRM NA

No

Soak Pit

Waste water management

NRM NA

No (continued)

12 Watershed Management Process Under MGNREGA: An Approach …

247

Table 12.1 (continued) Scheme

Category of work

Type of work (MGNREGA)

Potential to watershed management (Yes/No)

Playground

Construction

NRM NA

No

Earthen stadium

Construction

NRM NA

No

Crematorium

Construction

NRM NA

No

Rural Hut

Construction

NRM NA

No

Drain in IC sector

Flood protection

NRM

No

De-siltation of River bed Flood protection

NRM

Yes

Land Levelling in case of Livelihood promotion NRM private Land (IBS)

No

Livestock shed

Livelihood promotion Non NRM

Potential for livelihood development

Cattle shed, Goat Shelter, Livelihood promotion Non NRM Piggery Shed, Poultry Shed, Mushroom Shed

Potential for livelihood development

Nadep/Vermi compost/ Livelihood promotion NRM-allied Other compost/Azolla Pit activity

Potential for livelihood development

Workshed for SHG

Livelihood promotion Non NRM

Potential for livelihood development

PCC ROAD Construction

Construction

Non NRM

No

PMAY-G House for Individuals Construction

Construction

Non NRM

No

Rajiv Gandhi Building

Construction

Non NRM

No

the terrain, considering the climate and morphological history of the area (Swami 2021). A participatory approach for integrated watershed management meansplanning and implementation ofsoil and water conservation workusing the knowledge of local people onlocation-specific natural resources through active participation of the community in the planning process (Narendra et al. 2021). This planning process has been conducted from grass root level with the aim of benefiting the entire population of the watershed. Participatory Rural Appraisal (PRA) and comprehensive baseline surveyare significant approaches for development planning in rural areas (Aggarwal 2001). The plans that will come out after the PRA have been compiled in Details Project Report (DPR) format. The steps for DPR formation are.

12.4.2.1

Informal Meeting with Some Villagers

In such meetings, the planners are visiting the villages of the respective watershed to spread awareness among the villagers and for relationship building.

248

12.4.2.2

S. Halder et al.

Formal Meeting or PRA with Villagers

This is a general meeting with the villagers in which planners are moving to villages with delineated satellite maps or watershed delineated cadastral maps.Information about the watershed, existing resources, problems of that area, the potential of the area etc.are identified and planned according to the topography in the PRA. Resource Map Generation The resource map shows the assets of the area that has the most significant impact on the population. As such, ponds, wells, earthen dams, springs etc. Planners are mapping theresourcesduring the PRA and mark them on the watershed map. This type of mapping process is called Participatory Resource Mapping (PRM). The advantage of such a map is that, first of all, it becomes very easy to negotiate with the villagers; second, the plan is very quickly visible tothe villagers; tired, presentation in front of officials becomes easier; fourth, the volume of surface flowof a particular land or area has been calculated in a very short time due to the precise scale of the cadastral maps. Survey on Problems and Potential of the area The problems faced by the people are identified, and the possibilities of solving the problems are also discussed with the people. In the problem map, the hindrances of different areas in the watershed areplotted. In the prospect map or opportunity map, suitable plans are proposed. Transect Walk and Planning of schemes After the PRA, planners go to the potential areas with some villagers and plan different types of structure considering the topography, agricultural activity, soil characteristics and source of water.

12.4.2.3

Data Compilation

The schemes, come out from the PRA are compiled and the estimated cost for the schemes are calculated. There after the data is submitted in document format which is called Detail Project Report (DPR).

12.4.2.4

Finalize DPR

The DPR was then presented to government officials and handed over to them.

12 Watershed Management Process Under MGNREGA: An Approach …

12.4.2.5

249

Approval of Schemes in Gram Shabha

The schemes are provided in ‘gram Shabha’ for discussion and approval at village meetings as per priority of implementation.

12.4.2.6

Revisit of DPR

If there is any need to upgrade the former DPR, then planners are doing the PRA with the villagers and the necessary schemes are included in the DPR. This process is call revisit of DPR.

12.4.2.7

Technical Help

The necessary training on watershed management and structure isprovided to the implementing agency. The MGNREGA mates and the RojgarSabak are given field level support regarding layout of structures. Such participation and cooperation lead the watershed management project into success.

12.4.2.8

Assessment Report

The watershed management project has been launched with a determination. So, in the last phase, the report card has to be submitted for evaluation. In this report the achievement has been summarized with proper justification. This report card includes resource maps, problem maps, opportunity maps, village maps and activity maps as well as post activity maps to demonstrate the effectiveness of the watershed project. MGNREGA is focusing on livelihood development through asset creation. There is huge potentiality for integrated watershed management. Usually, MGNREGA follows such process. The plans that came from the village meeting are included in the Annual Action Plan (AAP). In addition to the requirements, the GP is adopting the plan as a Supplementary Action Plan (SAP) for the current financial year. But the concept of DPR is not identically prevailed in MGNREGA.Now the central and state government are trying to implement watershed management project through the memorandum of understanding (MoU) with several Civil Society Organizations (CSOs). CSOs are responsible for developing accurate DPRs and providing technical support to implementing agency. The implementation of the scheme is done by the MGNREGA (Fig. 12.1).

250

S. Halder et al.

Fig. 12.1 DPR planning process. Source Primary survey, 2021

12.4.3 Some Important MGNREGA Permissible Works for Watershed Management Watershed management delivers the concept of Ridge to valley treatment. The cross section from ridge to valley classifies four types of lands i.e. upland, mid upland, mid low land and low land and stream line. The main concept of ridge to valley treatment is conservation and management of soil–water from ridge line to stream line. As maximum uplands are devoid of vegetation and more prone to soil erosion. Thus, soil erosion cannot be significantly checked if the upland area is not fully treated. The treatment of land has been conducted in respect of degree of slope and soil condition. The structures suitable for watershed management are tabulated in Table 12.2.

12 Watershed Management Process Under MGNREGA: An Approach …

251

Table 12.2 Schemes according to land types. Source Primary survey, 2021 Classification

Scheme type

Select activity

Land type

Plantation

Forestry

Social forestry

Upland Upland

Water harvesting

Stream line work

Irrigation

Orchard

Horticulture

Vetiver

Flood protection bund Lowland

Happa and 5% model

WHS

Low land farm pond

Lowland

Harvesting tank

Medium upland

Percolation tank

Up/Medium Land having crack and fracture in the rock

Sunken Pond

River Flow Maintain

2nd and 3rd Order stream

Rock check/Gabion

Rock check/Gabion

2nd order stream

Check dam

Check dam/stop dam

3rd Order Stream

Check dam

Earthen dam

2nd Order Stream

Irrigation well

Micro irrigation

Upland

Irrigation Canal Micro-catchment water conservation

Upland/Medium Upland

30–40 model

30 × 40 in existing plantation

Upland

Recharge pit

Staggered trench

Forest/Upland

Recharge pit

Box Trench

Forest

Recharge pit

Contour Trench

Forest/Upland

Land shaping

Land development

Medium upland

Livelihood

Upland

Field binding Livelihood development

12.4.3.1

Medium upland

Cattle shed/

Medium upland

Goat Shelter

Upland

Piggery shed

Upland

Poultry shed

Upland

Nadep/Vermi compost

Upland

Micro-catchment Water Harvesting

Micro-catchment water harvesting systems are planned to trap and store water flowing from a relatively small catchment area, usually (10–500 m2 ) within the farm boundary. The runoff water is collected intoa hole, pitand bundthatenters into the soil and helps to maintain moisture in the root zone (FreieUniversitat, ¨ Berlin). Micro-catchment water harvesting systems includes somecommon technologies like triangular bunds, semi-circular bunds, eyebrow terraces, cross slope barriers (like

252

S. Halder et al.

Fig. 12.2 Model of typical watershed management planning. Source Based on watershed manual of PRADAN

staggered trench, contour trench), trench cum bund (TCB), waterabsorption trench (WAT) etc. (Fig. 12.2) The mostly practiced structures are discussed below: (a) Continuous Contour Trench and Staggered Trench Continuous Contour Trench (CCT) is the structure for upland treatment. A contour line is the imaginary line that joins the points of same height. Such trenches are dug along the contour to reduce the surface runoff and soil erosion. Typically, CCT constructed with in the slope of 8% to 25%. The width and depth of the trench are 2ft and 1 ft respectively (Fig. 12.3). The main problem with CCT is that if the trenches are not constructed according to the contour lines, then the water flow forms channels which leads extensive gully erosion. Therefore, it has been observed that instead of making continuous trenches, they should be made along the contour but in a staggered and discontinuous manner (Sahayog 2006) (Fig. 12.4). Staggered trench (ST) is the structure to check the surface runoff on the land with 8% to 25% slope which is zcharacterized by thin layer of soil that is not suitable for terrace cultivation. The length, width and depth of the pit will be 6 ft, 2 ft and 1 ft respectively. And the excavated soil will be kept at one feet distance behind the pit. The staggered should be excavated in alternative rows. The distance between the pits along the column would be 12 ft. The volume of the pit would be 12 CFT. The length from the end of a pit to end of another pit along the slope will be 12ft and the distance between the pits will be 6 ft across the slope. Therefore, it is identified that the area in front of the pit would be 72 sqft. If 2-inch rainfall will occur at once, 12 CFT water will store in the pit.

12 Watershed Management Process Under MGNREGA: An Approach …

253

Fig. 12.3 Typical model of continues contour trench. Source Field level experience

It such way the surface runoff will be checked and the soil moisture can be restored. The depth and width can be altered according to the land category. Generally, digging of 54cftof soil counts as a full person-day and one day wage is Rs. 213 in 2021–22. Therefore, it can be estimated that about 1495 numbers of staggered trench can be dug per hectare of land and from these about 17,940 cft of soil will be excavated. It requires about 332.22 person-days and the expected cost of unskilled labour wage cost will be aroundRs. 70,762.86. The semi-skill labourer wage in this case will be approximately Rs. 2122.90 for (332 ÷ 50) 6.64semi-skill person-days because one supervisor has been supervising 50 un-skill labourers. Approximately, Rs. 10,000.00 will be taken as material expenditure excluding semi-skilled labour wage. Therefore, the expected total expenditure will be Rs. 82,884.76. In this land quick growing plant (Acacia Auriculiformis), drought resistant plant (Arjuna) and grasses (vetiver, sabai etc.) can be cultivated. (b) 30 × 40 Model

Fig. 12.4 Typical model of staggered trench. Source Field level experience

254

S. Halder et al.

Photo 1 Staggered and continuous contour trench of selected micro watershed. Source Google Earth Pro, February 2022

It is the method for soil water conservation by checking the surface runoff in the areas of slope from 3 to 8%. In this process the entire land has been divided into the plot of 30ft x 40 ft. The width of the plot 40ft can be found across the slope and the 30ft length will be along the slope. The area of each plot would be 1200 sqft and the pit should be excavated at the lowest part of the plot for better storage of the water. The size of the pit will be 7 × 7 ft at the top, 5 × 5 ft at the bottom and 3ft in depth. The volume of the pit would be 108 CFT. The excavated soil will be used for bunding the side of the plot. The length of the bund (30ft) is smaller than the width (40ft) to protect the bund from the flow of water. The slope of the bund will be 1:1. The top of the bund will be 1ft in width and the width of the base of the bund will be 2 ft. It can be estimated that one hectare of land can be divided in to 90 numbers of 30 X 40 plots. From these about 9720 CFT of soil can be excavated which will generate about 180 number of person-days. The expected total unskilled wage cost will be Rs. 38,340.00. The wage expenditure on semi-skill workers will be Rs. 1150.2. To create 30 × 40 model on one hectare of land about Rs.49490.20 will be needed including wage and material cost. Modification in 30 × 40 Model It is not suitable to cut the proper 30 × 40 pit in the forest lands because forest is the home of several animals and such three fit deep pit may cause of death of the small size animal. In this case the depth of the pit can be alter in to 1.5 feet and the length and width can be alter according to the depth of the pit but 108CFT

12 Watershed Management Process Under MGNREGA: An Approach …

255

should be maintained. In some it is not possible to treat the area from the ridge line through such model. In such case in the upper area 2 to 3 continuous trench or 3 to 4 rows of staggered trench can be excavated consecutively and below that the 30 × 40 model can be developed. It is also observed that the ideal square shape field cannot be available for 30 × 40; so, the pit size can be altered in proportion to the size of remaining plots. The plot size can also be adjusted upto 10% (A. Samal et.al).of the 30 × 40 model on 3% to 8% slope. During the layout of 30 × 40 model it should be taken into a consideration that the plots should be in zig jag type. If the plots are in parallel type the probability of the pits may be dug out parallel to each other due to this the moisture of the soil can retains in a strip along the pits. But if the plots are in zig jag types, then there will be the probability that the pits are also found in zig zag manner. Therefore, the moisture of soil can be spread over the land instead of strips. If the slope will be less than 3% then the plot size can be altered upto 4 times of the 30 × 40 model i.e. the size can be taken as 120 ft along the slope and 160 ft across the slope. But the slope and soil type should be taken into consideration. The reason behind this modification is that, 30 × 40 concept has been initiated in Purulia district of West Bengal on the slope of 3% to 8% to arrest the surface runoff fully and to reduce the plant mortality through water conservation. But now this scheme has been included in the MGNREGA SECURE portal and widely accepted in the other region irrespective of soil and slope. Therefore, to reduce the mortality rate of the plant the Plan Implementation Agencies (PIA) are unanimously accepted this model but some-times this structure is constructed on less than 3% slope. Due to the availability of conserved water grasses are growing significantly in the rainy season but as the grasses have short life span, in the winter it dries up and accidently some orchards were destroyed due to bonfire. It was recommended by him that weeding is necessary onset of monsoon and intercropping should be done. As MGNREGA does not provide agricultural labour wage, the SHG members who are provided the ‘BrikkoPatta’ (ownership of trees) should engage in intercropping practice by the agriculture department and they should use low-cost weeding machine for weeding. And as the slope is low, if the plot will be 4 times of 30ftx 40ft plot, then one pit (4 times of a pit of 30 × 40 model i.e. 432 CFT) can be excavated at the deep part of the plot instead of 4 small pits which give then additional benefits regarding weeding and the soil which will dig out from the pit can spread on the boundary of the plot. Such bunding must be stronger to protect excessive volume of surface runoff. (c) Field Levelling and Bunding It is the process of treatment of undulating land for better utilization. It is an in-situ soil-moisture conservation techniques. Those plots can be chosen for field levelling and bunding which have more than 6 inch of soil remains after cutting the topsoil of the elevated lands of the topography. It should be taken in to the mind before planning that the hard rock strata cannot be exposed. And the excavated soil has to be spread on the depressed part of the plot to make the top of the land uniform. The bund outside the plot has to be developed by using the remaining excavated soil. Now such scheme is taken into consideration if any water harvesting structure is constructed along with it. Because without any water harvesting structure the

256

S. Halder et al.

Fig. 12.5 Typical 30x40 Model. Source Field level experience

potential of the scheme reduces. Besides, to make the bund stable the excavated soil from the water harvesting structure can be used. Such structure has been constructed on the land having less than 3% slope (A. Samal et.al). The bund should be more than 6 inch in height (varies from 6 inches to 1.5 feet). Because, maximum farmers are using traditional process of paddy cultivation that needs 5 to 6 inches of water on the field. Generally, the slope of the bund is 1:1 (A. Samal et.al). (d) Trench Cum Bund It is an important structure to arrest the runoff water which can be constructed at the foothill or at the break of slope area. It can be constructed at the periphery of the extended fallow land area. The length, width and depth of a single unit of a trench cum bund (TCB) is 12 ft, 3 ft and 3 ft respectively. It has been estimated that about 108 CUM soil will be excavated from a unit which will generate tow person-days per unit. The gap between to TCB is 3 feet to protect the structure from channel formation due to flow of water. About 66 TCB will be constructed on 1000 RFT length. The excavated soil should be placed transversely to the slope, in a continuous manner behind the TCB to check the surface runoff. TCB has been constructed on

12 Watershed Management Process Under MGNREGA: An Approach …

257

Photo 2 30 × 40 model of selected micro watershed. Source Google Earth Pro Image, February 2022

higher than equal to 8% slope. The expected total expenditure for 66 pits will be Rs.33960.00 and from it about 132 person-days will be generated. (e) Cattle Proof Trench/Water Absorption Trench This structure has been designed specially to avoid human-animal conflict. Such structure has been constructed in the periphery of the area having plantation to protect the plants from the cattle. It has been widely accepted in MGNREGA. CPT has been constructed in the slope less than 8%. The width of the structure is ranging between 3 to 5ft and the depth of it is 3 ft. The length is considered according to the total periphery of the area. Despite TCB, CPT has been constructed continuously. The width of CPT can be considered 5 ft if the land size is significantly big or costliest plantation has been done or to protect the plants from giant animals. As for example, such large size CPT has been constructed at Bankura district of West Bengal to protect the plant from forest’s elephants which is called Elephant proof Trench. The excavated soils are kept at the side of plantation area as the bund at one feet distance from the trench. The height of the bund will be three ft. It has been estimated that the width of the buffer area created through the CPT will be about seven ft including the width of pit (3 ft), gap between the pit and bund (1ft) and width of bund (3ft) which is impossible to jump a small size cattle. The width of buffer area of EPT will be 11 ft. The CPT and EPT are also known as Water Absorption Trench (WAT).

258

S. Halder et al.

Photo 3 Trench cum bund of Panchkath-Mahula-Nabagram micro watershed. Source Google Earth Pro Image, February 2022

It has been estimated that to create CPT and EPT on 1000 RFT, about 9000 CFT and 15,000 CFT soil will be excavated which will generate approximately 167 person-days and 278 person-days respectively. The estimated total expenditure of CPT and EPT will be Rs. 41650.00 and Rs. 66000.00 per 1000 RFT respectively.

12.4.3.2

Work on Stream Line

(a) Loose Bolder Check and Gully Plugging The narrow canal is called Gully. It is created to check soil erosion by reducing the speed of the surface runoff. Gully plugging has been done on the gully one by one starting from near to the source by locally available loose boulders. The purpose of this structure is to check the surface runoff. The slope of the upstream side will be 1:1 but the downstream side of the water flow will be 3:1. The slope of the streamline should not be more than 20% (Sahayog 2006). To build a loose bolder check in the 1st order stream Approximately, 4 to 7 person-days will be generated which and the expected cost will be Rs. 900.00 (LBS 1.5 ft height) to Rs. 1500.00 (LBS 2 ft height).

12 Watershed Management Process Under MGNREGA: An Approach …

259

Photo 4 Cattle protection trench at Kendulia-Gopedanga micro watershed. Source Google Earth Pro, February 2022

Table 12.3 Difference between trench cum bund and cattle proof trench. Source Field level experience Characteristics

Trench cum bund

Cattle proof trench

Slope

Higher slope (>= 8%)

Lower slope ( 0.75. Kundu (2018), used SPOT-VGT NDVI data to quantify the trend of change in vegetation cover. A lag in vegetation response and the rainfall exits. Rainfall and NDVI relation was used to estimate the areas of natural and anthropogenic vegetation changes. It identified the spatial and temporal dynamics of agricultural, meteorological and hydrological drought and their inter-relationships. The variation in NDVI is related to the crop yield and vegetation growth. It assessed the socio-economic risk of drought using multi-criteria analysis and GIS. The GIS based study with AHP and pair-wise comparison gave excellent results. Kundu et al. (2020) studied meteorological and agricultural drought risk on Bundelkhand, using NDVI, VCI, and SPI. Daily rainfall data from NOAA and Climate Prediction Center integrated with remotely sensed vegetation and ground based crop yield data. The SPOT-VGT NDVI- time series for rainfed crops for 2002– 2013 were used to assess vegetation condition with meteorological drought indices. The satellite-based drought indices and meteorological data were found good to assess the spatiotemporal features of the drought. Kundu (2021) studied recurrent drought events in Bundelkhand caused by the climatic conditions, its unique soil, and physiography, the coping capacity of the local communities. Estimating the socio-economic vulnerability is a must for adopting specific mitigation strategies. The 10-day SPOT-VGTNDVI for 1998–2013 was used for computing the VCI, for finding spatio-temporal dynamics of drought. NDVI maps of each year were assessed for mapping drought frequency. To estimate socioeconomic vulnerability five parameters, viz. population density, marginalized population, cultivators, agricultural labours, and literacy rate, were used. Most of the districts under hot semi-arid agro-climatic zone in UP are highly vulnerable, mainly due to high population density and intense agriculture. The MP part of Bundelkhand, is comparatively less vulnerable to socio-economic drought as a substantial part of this region is forest, and the population engaged in agriculture is comparatively low. It is found that nearly half of the area is vulnerable to high (20.1%) and very high (29.1%) risk of socio-economic drought. On which indices to be used for drought monitoring and DEWS, depends on what data is available to the disaster managers. Many countries do not have proper records of the rainfall data. In such situation they try to compare the rainfall of the current period with one that has good rainfall. If the climate normals are available the Percent of Normal Precipitation is used. For monitoring Meteorological drought the SPI and for Agricultural drought the VCI is most commonly used. But in many cases the SPI does not work well. In fact there is no drought index which works well in all the situations. For selecting a drought index, it is important to select the index based on the impact to be studied.

276

B. Bhushan et al.

13.2 A Review of Drought Indices Drought monitoring and DEWS are helpful to detect drought early and reduce its damaging effects. The indices help to determine onset, cession and severity of a drought. Alahacoonet al. (2022) conducted a bibliometric analysis based on 5000+ studies of some selected drought indices. A higher tendency of focusing on drivers based on meteorological observations and remote sensing was found. Studies on drought impacts are localised. Many studies are on Sub-Saharan Africa (SSA) and Australasia respectively on food security and water security Drought is a creeping process and it impacts persist for years and these impacts may be cumulative for droughts occurring one after another (Van Lanen Henny et al. 2017). The UN Convention to Combat Drought and Desertification defines drought as “when precipitation has been significantly below normal recorded levels, causing serious hydrological imbalances that adversely affect land resource production systems”. Three main types of drought: meteorological, agricultural and hydrological droughts have associated numerical values of hydro-climatic parameters. The temporal propagation of drought is often considered to occur almost linearly (Wilhite and Glantz 1985). This is a simplification of a complex process, where an anomaly or a standardised normalisation of the values of drivers have cascade reaction on other physical parameters leading to some kind of drought. DEWS has objective to monitor the drivers to predict occurrence of drought. They detect drought at an early stage and can reduce damages due to drought. For assessing the severity of a drought, the driver values are used to compute drought indices. Their values and the threshold is used to define the severity of a drought. The impacts, like water and food security, are rarely continuously monitored or even included in DEWS. The indices mentioned were classified according to the categories and their frequency of occurrence. Meteorological drought (MD) indices were used most frequently. It was followed by agricultural drought (AD) indices, and hydrological drought (HD).indices Standardised Precipitation Index (SPI), followed by the Normalised Difference Vegetation Index (NDVI) is most common. HD indices are less used compared to the other two. There are a few studies using HD indices for Australia-Oceania, Middle-East and North Africa (MENA) and SSA. Most areas have similar overall pattern. For Australia-Oceania and SSA, the AD indices are most frequently used. The MD indices are the mostly used, as compared to AD, HD and impacts. MD indices are in 53% of the studies while AD are in 42% and HD in 5%.Precipitation and temperature are mostly used to report drought. There were five times more studies on food-security than to water-security in drought. For SSA the food security related studies are 93%, and for Asia and Europe 84%. Drought-related water security studies are 52% for Australia-Oceania. In SSA it is 6.6%. For the drought-related impacts studies there are two main observations: (i) the repartition of the impacts studies differs from the driver studies and (ii) the impacts

13 Meteorological and Agricultural Drought Monitoring Using Geospatial …

277

on food and water security show different geographic pattern. The studies show unbalanced attention to drought drivers and impacts across the world. The Drought monitoring is influenced by physical, socio-economic, data availability, and scientific interests. There are more number of MD indices than the AD and HD except for SSA and Australia-Oceania. The SPI is the most commonly used. For MD, mainly the precipitation deficiency is analysed. Precipitation deficit is less likely to affect the water scarcity in humid regions, tropical, continental or temperate climates. The drought is studied as a below average situation and it does not reflect water demand. In arid and semi-arid regions the SPI should be used with caution and opt for indices that include ET. In such areas where ET plays larger role, water stress is common. There is an upper and a lower limit for many AD indices. It is independent of the climate of the area. Thus the AD indices are relevant for any climate. The SPI and most MD and HD indices, show a deviation from average and standardised for all climates. The drought monitoring has more challenges in dryer climates compared to that in wet climates. MD and HD indices, in contrast to AD indices are a response of hydro-climatic features e.g. the reflectance for NDVI and Leaf Area Index (LAI). SSA was found to have lowest number of studies on the indices and highest on impacts. The SSA experienced a rise of temperatures and aridity increase through observation and model forecasts the details in the Emergency Database (EM-DAT) are limited (Harrington and Otto 2020). The EM-DAT has the global data on past natural and man made disasters. In SSA, there is economic water scarcity, including lack of human, institutional and finance to meet water demand. The symptoms are associated to economic water scarcity with scanty infrastructure. The populations meet difficulties in getting sufficient water to meet their domestic needs and irrigation. The drought drivers are under-investigated in SSA, resulting in effects of economic water scarcity: and food security. In the low income countries most of the population is exposed to droughtrelated food insecurity. These poorest countries, mostly located near Sahelian region about 30% of GDP is from agriculture. The SPI is the most widely used for studying drought. The SPI is computed for different periods of 1, 2, 3, 6 or 12 months that may include periods of missing-data. It ideally requires 30 years data. Many zero precipitation values at short time scales may result in biased values of the SPI. It is because of that the rainfall may not fit in the gamma distribution. This is more applicable to dry climates, when calculated for periods shorter than 12 months. An index having temperature as parameter to account for ET is more suitable for such regions. Even short-lived dry spells often combined with heatwaves of a few days, during the reproductive stage of crop development can damage yield resulting in food insecurity (Hatfield and Prueger 2015). In the developing countries 67% of the hydrological networks are in bad state. The number of rain gauges across SSA is one eighth of the WMO recommended. So the rainfall reanalysis in these areas are not reliable due to low ground truth data. The availability of data seems to depend on socio-economic condition of these countries. River flow monitoring in SSA have a similar issues. Globally, a little attention seems to be given to monitor HD indices.

278

B. Bhushan et al.

The NDVI is the most commonly used in AD. Only 20% AD indices do not use remote sensing. As for the HD indices, this shows at (i) the lack of hydrometric observing or (ii) if they exist, a lack of access. The most used index is the NDVI. The NDVI datasets are available at high spatial and temporal values. The MD indices are used for different scope to those of AD and HD. The SPI has a temporal focus with a strong statistical perspective. The NDVI has a spatial distribution focus that determines water stress in the vegetation. Thus the NDVI is a measures a drought impact. The areas of HD and WS reported are not same suggesting that the occurrence of the HD is not the only driver of WS. Food systems have three main components: (i) food availability (ii) food access and (iii) food utilisation. When food systems are stressed, food insecurity develops. The food security is vulnerable to the disturbances. There can be disturbances by a number of factors like droughts, conflict, international trade and policies, AIDS etc. (Gregory et al., 2005). Food insecurity is exacerbated if these factors combine. SSA is prone to extreme heat-related impacts and also to other causes. SSA has: (i) more than 95% of agricultural land as rainfed (ii) about 75% of the world AIDS cases (iii) 19 of the 43 economies of highest poverty rate, and also conflict-affected (Corral et al., 2020). Thus, drought related food security studies in SSA may also be related to other implications. Australia, the driest inhabited continent (Hill 2004), has a National Plan for Water Security that comprises a variety of mechanisms. Water security is also aimed to be addressed in an integrated and multi-scale manner by climate change mitigation, using water wisely, securing water supplies and supporting healthy rivers and wetlands. Water and food insecurities result from complex multi-disciplinary interactions including social and physical processes. Thus, for proper monitoring, drought-related water and food insecurities also need multi-disciplinary approach. Drought indices give a incomplete picture of drought severity, if social processes that may trigger and enhance drought impacts, are excluded. Inoubli et al. (2020) has reviewed drought monitoring using remote sensing and data mining methods.

13.3 Meteorological Drought Meteorological drought represents the degree of dryness compared to some long term average and the duration of the dry period over some part of the land that may extend to a country or a continent. Meteorological droughts are area specific as the deficiencies in precipitation vary. Some definitions of meteorological drought identify periods of drought on the basis of the number of days of precipitation below a threshold value.

13 Meteorological and Agricultural Drought Monitoring Using Geospatial …

279

13.3.1 Drought Indicators and Drought Indices Drought indicators are the parameters that are used to describe drought severity. Some of the parameters are rainfall/snowfall, snowpack, temperature, stream flow, groundwater, reservoir water level, soil moisture etc. The drought indices are computed numerical values that are measure of drought severity. The drought indices are drought indicators as well (WMO, 2016) Indices are used for quantitative assessment of the drought severity, affected area, time of occurrence and duration of the droughts. A threshold value for the indices may be set to declare onset and the cession of the drought and the geographic area affected. The duration is determined by the period between the date of onset and the date of cessation of the drought. The impact of the drought is estimated by the interaction of the drought and the exposure of elements like people, crops, forests, reservoirs, water supplies etc. and their vulnerabilities to droughts. Vulnerabilities increases by the earlier droughts, if occurred in recent past. They might have triggered human activities like the sale of their assets to meet their urgent needs. They might sell their land, animals or houses or withdraw children from schools, postpone marriages etc. The timing of drought is important in determining its impacts e.g. a short duration, low severity, intra-season drought, can be more devastating for crops, if it happens during period of high moisture demand of a crop than a longer drought when these crops are not in the field. More severe drought in a less critical time of the agricultural cycle may not have that severe impact. The drought indices combined with details on assets having exposure and their vulnerability of these assets is used for assessing damages due to drought. If there is a forecast to evaluate values of the indices it can be used to anticipate the damages. The datasets on the indices of the past droughts may play critical role in linking historical details. This helps in determining the probability of recurrence of droughts of different intensities. The changing climate may alter historical patterns of the droughts. Information derived from the drought indices is useful in planning and designing risk assessment, developing a Drought Early Warning Systems (DEWS) and decision-support tools for mitigation and management of drought. The traditional drought indices, based on the locally observed data, are also used to validate drought indices based on remotely sensed observations. As many as more than 150 drought indices (Alahacoon et al. 2022) and drought monitoring methods were developed and many of them are in use to identify and to determine the intensity of droughts. The standardised precipitation index (SPI) developed by McKee et al. (1993) is being used extensively. Four different indices namely the relative precipitation index (RPI), the effective drought index (EDI), the SPI and the climatic water balance (CWB) for monitoring and assessing the intensity of meteorological drought (Łab˛edzki et al. 2014) are used in Poland. The RPI, EDI and SPI are computed using at least 30 years precipitation data for many observatories.

280

B. Bhushan et al.

13.3.2 The RPI The RPI is computed as the percentage ration of total rainfall/snowfall for a given duration and long-term average value of the precipitation for this period. The classes of dry periods are as given in Table 13.1.

13.3.3 Effective Drought Index (EDI) The EDI is calculated as a standardised daily difference of weighted accumulated precipitation for preceding period and its multi-year averaged for each day. It is calculated on a daily basis. The 2-category classification is given in Table 13.2.

13.3.4 Standardised Precipitation Index (SPI) The SPI is a standardised deviation of precipitation in a duration from the median value for this period (McKee et al. 1993). SPI is computed for 1, 2, 3, 6, 12, 24, 36 and 48 months using time series of precipitation at many locations. The SPI for periods longer than 1 month are calculated for moving sums of rainfall/snowfall. For each month a new series is made with elements of corresponding moving sum of precipitation e.g. the 3-month SPI computed for June 2021 uses the precipitation total of April 2021 to June 2021. A 12-month SPI for June 2021 uses the precipitation sum for July 2020 to June 2021. The SPI was used to detect mild droughts, especially during short periods, e.g. for a months. Mild drought was considered to be for SPI values −0.50 to −1.00. McKee Table 13.1 Value of RPI and the severity of drought

Table 13.2 EDI values and the severity of drought

S. no.

Value of RPI for

Treated as

Month

Quarter, year

1

0–24.9

0–49.9

Extremely dry

2

25.0–49.9

50.0–74.9

Very dry

3

50.0–74.9

75.0–89.9

Dry

4

75.0–125.9

90.0–110.9

Average

S. no.

EDI

Precipitation conditions

1

(0.7 to −0.7)

Normal

2

[−0.7 to −1.5)

Dry

3

≤−1.5

Very dry

13 Meteorological and Agricultural Drought Monitoring Using Geospatial …

281

Table 13.3 Drought categories by SPI values Drought category

SPI value

Defined by

McKee et al. (1993)

Vermes (1998)

1

Mild drought

−0.50: −1.00

0: −0.99

2

Moderate drought

−1.00: −1.49

−1.00: −1.49

−0.50: 1.49

3

Severe drought

−1.50: −1.99

−1.50: −1.99

−1.50: −1.99

4

Extreme drought

≤−2.00

≤−2.00

≤−2.00

S. no.

Łab˛edzki (2007)

(1993), Vermes (1998) took SPI values 0 to −0.99 for mild drought. Łab˛edzki (2014) considered SPI values −0.50 to −1.49 as moderate drought as given in Table 13.3.

13.3.5 Climatic Water Balance (CWB) Climatic Water Balance (CWB) is used for monitoring and assessing intensity of meteorological drought. It uses moisture conditions computed using precipitation data and evaporation data for loss of water. The standardised climatic water balance (SCWB) is a standard deviation of CWB for a given duration from the median value of that period (Łab˛edzki 2014). The 3-categories are given in Table 13.4.

13.4 Agricultural Drought Agricultural drought results from the impacts of meteorological or hydrological drought on agriculture. The hydrological drought is a condition that specifies lower than usual water level in the water resources. Water demand of crops depends on weather, plants’ characteristics, its growth stage, and the soil characteristics. Soil moisture deficit at sowing/planting time may reduce germination and may result in low plant population and thus in reduced yield. Deficiencies in soil moisture may affect the crops at growth or maturing stage also. The agricultural drought is often defined as loss of soil moisture for individual crop for particular time, that may cause crop yield loss. Table 13.4 The drought category and SCWB values

S. no.

Drought category

SCWB value

1

Normal conditions

0.50: −0.99

2

Moderate

−1.00: −1.49

3

Severe

−1.50: −1.99

4

Extreme

≤−2.00

282 Table 13.5 Agricultural drought categories according to CDI

B. Bhushan et al.

S. no.

CDI

1

0.10–0.19

Agricultural drought category Moderate drought

2

0.20–0.49

Severe drought

3

0.50–1.00

Extreme drought

Soil moisture drought is also termed as agricultural drought. The agricultural drought gives the impression that it concerns only the land for agricultural use and ignores other types of land. It occurs on drying the soil due to evaporation or moisture drains deeper into the ground so that it is not accessed by vegetation roots or due to over extraction of water from aquifers. The drought indices and the soil and the crop parameters are computed through models, e.g. the CROPBALANCE model.

13.4.1 Crop Drought Index (CDI) The CDI is used to quantify intensity of agricultural drought (Brunini et al. 2005). It represents the reduction in ET compared to the PET due to soil moisture deficit and it is computed as: CDI = 1 − (ET/PET) here ET is the actual evapotranspiration in mm, PET is potential evapotranspiration [mm]. The ET and PET are calculated using the methodology of Allen et al. (1998). It measures reduction in ET in relation to PET. The drought classification is as give in Table 13.5.

13.4.2 Soil Moisture Index (SMI) The SMI is used to estimate soil moisture contents. It is used to determine intensity of a soil drought. It is computed following (Hunt et al. 2009) SMI = 10 ASWa /TASW − 5 The ASW a is actual soil moisture in mm, TASW is the total soil water available in mm. The drought category classification with SMI is as in Table 13.6.

13 Meteorological and Agricultural Drought Monitoring Using Geospatial … Table 13.6 Drought category according to SMI

Table 13.7 Agricultural drought categories for YR values

283

S. no.

Value of SMI

Drought category

1

≥5.0

No drought—excessive moisture

2

[0.0; 5.0)

No drought—optimum moisture

3

[−2.0; 0.0)

Moderate drought

4

Severe drought

Severe drought

S. no.

YR (%)

Agricultural drought category

1

[0:10)

No drought

2

[10:20)

Moderate drought

3

[20:50)

Severe drought

4

[50:100]

Extreme drought

13.4.3 Crop Yield Reduction (YR) Crop Yield Reduction (YR) defined as 1-YR/YP where YR is the real yield and YP is the potential yield. YR quantifies the effect of water stress and thus used to measure the intensity of agricultural drought. The categorisation is given in Table 13.7.

13.5 Remote Sensing of Drought Parameters Computations of drought indices use hydrological variables which can be estimated through remote sensing. These remotely sensed data helps in assessing the drought on regions like countries or continents or even the entire globe. Remote Sensing of vegetation condition is an additional support for drought monitoring. These indicators are being monitored as follows.

13.5.1 Precipitation It is not easy to make out spatial distribution of rainfall/snowfall from the location specific rain gauge data. The gridded Mean Arial Precipitation (MAP) is needed in majority of the models. TIROS-1 was launched in 1960. The WMO established the World Weather Watch in 1963 to commence use of Geostationary (GEO) and Low Earth Orbit (LEO) and named it the Global Observing System. Precipitation data can be extracted from the satellite images in IR range, from GEOs, and passive microwave observations of LEOs. The GEOs, give high temporal resolution as they sense the same locations of the Earth all the time. Some authors

284

B. Bhushan et al.

used remote sensing data in models to calculate hydro-meteorological flux like runoff, soil moisture etc. (Zulkafli et al. 2014). Satellite based precipitation missions provided data for many applications e.g. CMAP (Xie and Arkin 1997), SSM/I on board the Defense Meteorological Satellite Program (Ferraro 1997), Global Precipitation Climatology Project (Adler et al. 2003), Climate Predicting Center Morphing Technique (Joyce et al. 2004), TMPA (Huffman et al. 2007), PERSIANN (Ashouri et al. 2015), NASA’s Global Precipitation Measurement (GPM) mission to develop the Integrated Multi-satellite Retrievals for GPM (Huffman et al. 2015). Precipitation estimates based on IR remote sensing depend on the cloud top temperature and albedo (Turk et al. 1999). PMW sensors provide more accurate, but less frequent, rainfall data. The PMW can be supplemented by IR data to improve precipitation data. Some satellite-based precipitation products in operational use are: • • • • • • •

PERSIANN TMPA CMORPH PERSIANN-CDR GPCP GSMaP Multi-satellite Retrievals for GPM (IMERG)

These products offer precipitation data in near real-time based on PERSIANNCloud Classification System (PERSIANN-CCS) at a 0.04° grid.

13.5.2 The Relative Humidity Data on Water vapor contents and RH are available from NASA’s AIRS. The AIRS was not specifically build for drought monitoring. It was found that near-land surface RH provides valuable information on drought onset and drought development. Precipitation is closely linked to the RH. Near-surface RH also affects evaporation. Thus RH provides information for drought monitoring and assessment. RH from AIRS was found to improve the detection of drought onset.

13.5.3 Soil Moisture The Global Climate Observing System, in 2010, added soil moisture in 50 Essential Climate Variables (ECVs) (Bojinski et al. 2014). Ground based soil moisture data are limited to determine the spatial variations. The remotely sensed surface soil moisture provides spatial and temporal distribution of soil moisture. MW(C/X/L bands) can penetrate through the clouds and can be used for remote sensing of soil moisture.

13 Meteorological and Agricultural Drought Monitoring Using Geospatial …

285

Many passive and active MW sensors were developed since 1978. The SMMR, the SSM/I, the microwave imager in the TRMM, the European Remote Sensing Scatterometer, the Advanced Microwave Scanning Radiometer, the ASCAT, the SMOS, the AMSR2, and the SMAP added a new dimension to soil moisture observations. A blended soil moisture product was developed by Liu et al. (2011) by combining passive and active soil moisture data from multiple sensors. The blended soil moisture data of long periods is used to monitor agricultural drought. Many soil moisture-based indicators, e.g. soil moisture percentile and Standardized Soil Moisture Index (SSI), were used for drought prediction and drought monitoring. The remotely sensed soil moisture estimates provide data on the about 5 cm to player of the soil. Root zone soil moisture is required for agricultural drought monitoring. The satellite observations do not directly provide the Root zone soil moisture data. This can be assimilated in models to get the root zone soil moisture simulations (Reichle et al. 2004). The long-term time series of remotely sensed soil moisture were obtained from the WACMOS was used for monitoring the Horn of Africa drought (Ambaw 2013). The USDA estimated surface and root zone soil moisture with a two-layer modified Palmer soil moisture (Palmer and Havens 1958). Near-surface air temperature was used to find PET. It had limitations in finding ET (McVicar et al. 2012). Soil moisture data from AMSR-E was used in the USDA IPAD soil model for drought prediction and drought monitoring. NASA’s SMAP mission provided top soil moisture data in near real-time. This data was merged with other data products for monitoring agricultural drought. SMAP data has many applications in drought-related studies.

13.5.4 Evapotranspiration (ET) The ET impacts water demand of the crops, and so the crop yield. ET depends on net radiation, temperature, wind speed, and the RH. Getting ground data on ET is difficult, at regional to global scales. Remote sensing helps in estimating ET at these scales. Air temperature, radiation and RH affect the ET (Yin et al. 2014). The satellite data for these parameters are used in ET estimation and so the drought. Sensors e.g. Terra/Aqua-MODIS, Terra-Aster, NOAA-AVHRR, Meteosat-MVIRI, Landsat, and ATSR satellites provide LST (Westermann et al. 2011). These data were found good measure of ET with a little positive bias in some cases (Sima et al. 2013).

13.5.5 Terrestrial Water Storage The terrestrial water storage (TWS), the water on and below the earth surface, monitored by the Gravity Recovery and Climate Experiment (GRACE) on monthly basis

286

B. Bhushan et al.

have been and is being observed since 2002. The changes in TWS values include variations in surface water, soil moisture, and groundwater. The GRACE uses traces of global variations in Earth’s gravity field to estimate TWS. It uses the principle of gravimetry is to estimate TWS. This is used to estimate the amount of water. The GRACE data is used for estimating water for the monitoring of drought and the regional climate variability. Velicogna et al. (2015)determined the impacts of variations in water on the vegetation. Syed et al. (2005) estimated basin discharge using GRACE data. The GRACE, data helps to find drought, regionally and globally. Rodell et al. (2004) used GRACE data to find basin level ET. The GRACE data has limitation of spatial resolution with more 150,000 km2 per grid.

13.5.6 Snow Cover Snow deposits on the land give continuous runoff throughout the year in many parts of the world. A decrease in the snow may result in hydrological or agricultural drought. Snow measurements include the Snow Water Equivalent (SWE), Snow Depth (SD), Snow Cover and Snow Albedo (SCA). SWE is used to estimate amount of water in the snowpack. Optical, MW, and the combinations thereof are used for Snow remote sensing. Clouds interfere in the optical-based snow measurements. MW is helpful in monitoring the snow. MW on-board MODIS and AMSR-E, produce low number of observations as compared to the optical sensors onboard GEOs. A number of optically observed snow related data from MODIS (Hall et al. 2002), and procedures for AVHRR data (Simpson et al. 1998) were developed that has different spatial and temporal resolutions. The MW penetrates through snow allowing the estimation of SWE and SD. Many algorithms were developed to estimate SCA using MW observations. There is time lag of a few weeks to few months between snowfall and changes in the stream flow and soil moisture. It provides opportunity to develop EDWS. Estimating time lag between snowfall, snowmelt, and runoff requires data on seasonal temperatures and the time of occurrence of snowfall.

13.5.7 Remote Sensing of Vegetation Remote sensing of vegetation helps in monitoring the agricultural drought. Vegetation health and its abundance are directly related to rainfall and thus it is used for drought assessments. The time series of remotely sensed data for drought monitoring started in 1980s after of Normalized Difference Vegetation Index (NDVI) was developed and the NDVI data could become available for AVHRR data. Wang et al. (2001) got a good correlation among NDVI, soil moisture and rainfall. The NDVI can haveinter-antualvariations due to the variations in weather or ecological conditions.

13 Meteorological and Agricultural Drought Monitoring Using Geospatial …

287

13.6 Remote Sensing Based Drought Indices Since the development of remote sensing, remotely sensed data is being used in the computations of drought indices. Remote sensing data and the hydro-meteorological data from assimilation systems provide input for computing the drought indices. It also provides a basis for drought monitoring, analysis and prediction.

13.6.1 ET Based Drought Indices Many drought indices e.g. Crop Water Stress Index (CWSI) (Idso et al. 1981), Water Deficit Index (WDI) (Moran et al. 1994), Evaporative Stress Index (ESI) (Anderson et al. 2011), Evaporative Drought Index (EDI) (Yao et al. 2010), Drought Severity Index (DSI) (Mu et al. 2013), and Reconnaissance Drought Index (RDI) (Tsakiris and Vangelis 2005), use ET data. PDSI (Palmer 1965), Standardized Precipitation ET Index (SPEI) (Vicente-Serrano et al. 2010), Evaporative Stress Index (ESI) (Anderson et al. 2013) used ET data. Nu´~nez et al. (2013) developed the Green Water Scarcity Index (GWSI), and Wada (2013)developed the Green Water Stress Index (GrWSI) using ET data. A technique to use MODIS data to compute ET/PET dataset for the globe was developed for DSI, using ET/PET data and vegetation status (Mu et al. 2013).

13.6.2 Vegetation Remote Sensing Based Indices The Normalised Difference Vegetation Index (NDVI) (Kriegler et al. 1969) was defined as: NDVI = (NIR − VIS)/(NIR + VIS) where VIS is the spectral reflectance values in the red and NIR is in the nearinfrared range. The chlorophyll, in the leaves, strongly absorbs radiations in the visible range(0.4–0.7 µm) and the cell structure of leaves strongly reflects the nearinfrared range (0.7–1.1 µm).The more greenness the more these wavelengths are affected and higher is the value of the NDVI. The NDVI was used to detect effect of drought on vegetation. It is also used as a base for many other remote sensing based drought indices. The NDVI and temperature were used for computing following drought indices: (i) VHI (Kogan 1995a, b). (ii) TVI (McVicar and Jupp 1998), (iii) VSWI (McVicar and Jupp 1998), (iv) TVDI (Sandholt et al. 2002), (v) VTCI (Wan et al. 2004) etc. The NDVI is used to compute Vegetation Condition Index (VCI) developed in Kogan (1990), the AVI by Weiying et al. (1994), SVI in Peters et al. (2002), NDWIAin

288 Table 13.8 Widely used remote sensing based drought indices

B. Bhushan et al.

S. no.

Index

Parameters used

1

SPI

Precipitation

2

SPEI

Precipitation, potential evaporation

3

SDSI-ETDI

Evapotranspiration

4

SVCI

Vegetation

5

STWSI

Terrestrial water storage

Gu et al. (2007), and PASG by Brown et al. (2008). The VHI was developed from the VCI and TCI, and it is a good tool to monitor stress in vegetation during drought. The VHI data for the globe is available free from the NOAA at 16 km resolution on weekly basis. The TCI is computed using thermal infrared characteristic the vegetation (Kogan, 1995a, b). The TCI outperforms NDVI and VCI, in situations of soil moisture excess due to heavy rains or cloudiness because the low values of NDVI and VCI make erroneous assessment of drought (Kogan 1995a, b). Other NDVI based drought indices include: SAVI, Huete (1988), NDII, Hunt and Rock (1989), NRVI, Baret and Guyot (1991), NDWI, Gao (1996), GVMI, Ceccato et al. (2002) SIWSI, Fensholt and Sandholt (2003), NDDI, Gu et al. (2007), PDI, Ghulam et al. (2007a, b), NMDI, Wang and Qu (2007), SDCI, Rhee et al. (2010). Remote sensing based vegetation drought indices are obtained from the data generated by AVHRR, SPOT VEGETATION, and MODIS. The AVHRR has generated the datasets for longest period. It has somewhat coarser resolution. The AVHRR data has a thermal channel. MODIS data is having a finer resolution. The Sentinel-3 was designed for obtaining data for environmental monitoring and to understand the effects of climate change. A few widely used indices that use remote sensing data and hydro-meteorological data are given in Table 13.8.

13.7 Ecosystem and Vegetation Health Models NDVI is the most widely used vegetation index for drought monitoring through remote sensing. NDVI values are in the range of −1.0: +1.0. Barren land, rocks, sands, and snow produce low values for NDVI e.g. 0.1 or even less. Sparse vegetation like grassland, shrubs, or senesce crops may produce NDVI in the range of 0.2: 0.5. Tropical forests and dense crops at peak growth show NDVI in the range of 0.6:0.9. By computing NDVI one can create products that can estimate vegetation type, amount, and its condition. The NDVI is used to monitor continent to globe scale vegetation health. The NDVI links ecosystem response to water stress. There is a good correlation among precipitation, soil moisture and NDVI (Adegoke et al. 2002). NDVI values averaged for time establishes normal growing conditions in a region for a given time

13 Meteorological and Agricultural Drought Monitoring Using Geospatial … Table 13.9 Drought Indices based on the NDVI concept

S. no.

Index

References

1

CTVI

Perry et al. (1984)

2

DDI

Qin et al. (2010)

3

ETM

Jackson et al. (2004)

4

EVI

Huete et al. (2002a, b)

5

MPDI

Ghulam et al. (2007b)

6

NDDI

Gu et al. (2007)

7

NDII

Hunt Jr and Rock (1989)

8

NDTI

McVicar and Jupp (2002)

9

NDWI

Gao (1996)

10

NMDI

Wang and Qu (2007)

11

NRVI

Baret and Guyot (1991)

12

PDI

Ghulam et al. (2007a, b)

13

PVI

Wiegand et al. (1991)

14

VSDI

Zhang et al. (2013)

15

SAVI

Huete (1988)

16

SVI

Peters et al. (2002)

17

TCI

Kogan (1995a, b)

18

TVI

Deering et al. (1975)

19

VCI

Kogan and Sullivan (1993)

20

VTCI

Wan et al. (2004)

289

during the year. The NDVI values can reveal location for which vegetation is thriving or it under water stress, and also changes in vegetation due to deforestation, wild fires, plants’ phonological stage etc. On the basis of the concept of NDVI, many drought indices were developed that use remote sensing data in different wavelengths. Some well known indices are given in Table 13.9.

13.7.1 The VCI VCI is defined as VCI = 100 (NDVI − NDVImin)/( NDVImax − NDVImin) where NDVI is the smoothed weekly NDVI, NDVImax is the multi-year maximum of NDVI and and NDVIminis multi-year minimum NDVI, for each grid point. VCI ranges from 0 to 100. The VHI drought classification after Kogan (1995a, b) is as in Table 13.10.

290

B. Bhushan et al.

Table 13.10 VHI drought classification after Kogan (1995a, b) S. no.

VHI value

Vegetative drought class

Drought class number

1

5.82. The higher values of drainage density have been accorded higher susceptibility rating. The drainage density map of the study area is given in Fig. 14.7.

318

K. Moharir et al.

Fig. 14.7 Map of the drainage density of the study area

14.4.1.6

Geomorphology

The geomorphological characteristics of the map was prepared by processing the data of Bhu-Kosh in ArcgGIS. The study area is classified into Flood Plain, Water Body, Alluvial Plain, Habitation mask, Pedi plain, Denudation Hills and Structural Hill. The denudation hills and structural hills are generally not inundated during

14 GIS Based Delineation of Flood Susceptibility Mapping Using Analytic …

319

monsoon season and hence, they have been accorded low susceptibility rating. On the contrary, Flood plain and water bodies are generally susceptible to floods during monsoon season and hence, they have been accorded highest susceptibility rating. The map showing the geomorphological characteristics is depicted in Fig. 14.8.

Fig. 14.8 Map of the geomorphological characteristics of the study area

320

14.4.1.7

K. Moharir et al.

Topographic Wetness Index (TWI)

TWI Index is an important flood influencing factor which is used to predict the spatial distribution of saturated areas and wetness areas susceptible to overland flow (Samanta et al. 2018). TWI helps in prediction of areas capable of generating runoff in a basin by virtue of the effect of topography on their size and location. TWI is considered as an important geo-hydrological parameter for delineation of flood plain development (Paul et al. 2019; Das 2020; Quesada-Román et al. 2022). TWI has been categorized into 5 categories i.e., −18.28 to −13.86, −13.86 to −10.78, − 10.78 to −3.83, −3.83 to 3.55, 3.55–9.84. The class of 3.55–9.84 has been accorded the highest susceptibility rating as higher values of TWI indicates the saturated areas susceptible to overland flow. The TWI map of the study area is given in Fig. 14.9.

14.4.2 Flood Susceptibility Map Compared to traditional hydrological modelling approaches, GIS based quantification of flood risk mapping is gaining more attention from researchers as these techniques can delineate the flood susceptibility risk maps with great accuracy and high precision (Das 2020). The relative weightage of each flood causative factor and rating of different susceptibility classes within each factor is given in Table 14.3. East Vidarbha region is source to numerous rivers such as Wardha river, Wainganga River, Pranhita River and inundation in these rivers during Monsoon season creates catastrophic destruction to the people, their property and, livelihoods. Many small towns and cities are located on the banks of these rivers and the inhabitants of these places face huge risk of floods every year. Hence flood susceptibility mapping of the study area is both timely and relevant. One of the fundamental purpose of FSM is to delineate the spatial mapping of high flood prone areas and distinguishing these areas from other less susceptible areas by systematic investigation of all the flood causative factors. After investigating all the flood causative factors, using multi-decision criteria analysis, the flood susceptibility map of Gadchiroli sub watershed is prepared and is given in Fig. 14.10. In order to delineate saturated areas which are prone to floods, FSM is prepared by assigning the relative weigh of each flood causative factor and by further processing in ArcGIS. The final FSM is computed from the given formula: F S M = 0.38 × Drainage Densit y + 0.19 × T W I + 0.12 × Elevation + 0.10 × Slope + 0.8 × Soil + 0.066 × LU LC + 0.064 × Geomor phology The final map hence prepared is further classified into three categories i.e., Low, Medium and High (Fig. 14.11). The area (in ha) with high, medium and low flood susceptibility is 234.36 ha, 66,078.21 ha, 6077.72 ha respectively. In the Flood susceptible map, most of the area falls under moderate risk i.e., 91.28% followed

14 GIS Based Delineation of Flood Susceptibility Mapping Using Analytic …

321

Fig. 14.9 TWI map of the study area

by low susceptible area 8.40%. The high flood susceptible area is 0.32%. The high flood susceptible areas are mostly confined to either western parts of the study area or clustered in central part of the study area. In addition, there is strong overlap of high flood susceptible areas and urban cities which indicates that, these urban cities are especially at higher risk from the flood occurrences. Hence, these areas deserve urgent attention from decision makers and analysts so that an effective planning to

322

K. Moharir et al.

Table 14.3 Classes of each flood causative factor and its relative weightage Flood causative criterion

Unit

Topographic wetness index (TWI)

Level

Elevation

Slope

LULC

M

%

Level

Drainage density Km/ Km2

Soil type

Geomorphology

Level

Level

Classes

Susceptibility classes scale

Susceptibility classes rating

Weightage (%) 19

(−18.28)–(−13.86)

Very low

1

(−13.86)–(−10.78)

Low

2

(−10.78)–(−3.83)

Moderate

3

(−3.83)–(3.55)

High

4

3.55–9.84

Very high

5

116–152

Very high

5

152–169

High

4

169–184

Moderate

3

184–202

Low

2

202–308

Very low

1

0–1

Very high

5

1–3

High

4

3–5

Moderate

3

5–10

Low

2

10–15

Very low

1

Water body

Very high

5

Agriculture land

High

4

Settlement

Moderate

3

Barren land

Low

2

Forest land

Very low

1

0.54–1.43

Very low

1

1.43–2.23

Low

2

2.23–3.17

Moderate

3

3.17–5.82

high

4

>5.82

Very high

5

Clay

Very high

5

Clay loam

High

4

Sandy clay

Moderate

3

Loam

Low

2

Habitation mask

Very low

1

Flood plain

Very high

5

Water body

Very high

5

Alluvial plain

Moderate

4

Habitation mask

Moderate

4

Pedi plain

Low

3

Denudation hills

Very low

2

Structural hill

Very low

1

12

10

6.6

38

8

6.4

14 GIS Based Delineation of Flood Susceptibility Mapping Using Analytic …

323

Fig. 14.10 Flood Susceptibility map of the study area

install the structures and necessary management interventions to mitigate the effects of floods can be implemented in these areas.

324

K. Moharir et al.

Fig. 14.11 Percentage of flood susceptibility classes in the study area

14.5 Conclusions The present study aimed at preparing a high-resolution flood susceptibility map of the Gadchiroli sub-watershed. Seven flood causative factors were finalized to prepare the map i.e., Slope, Elevation, Geomorphology, LULC, TWI, Soil Texture and, Drainage Density. The results of the flood susceptibility map indicate that majority of the area is under moderate flood susceptibility with 91.28% area falling under this class. East Vidarbha region had faced one of the most devastating floods in 2020. Although this study is confined to 4 sub-watersheds in East Vidarbha region however future studies can be undertaken with high resolution mapping of other watersheds in this region so that effective planning and decision making to alleviate the flood risk could be devised. This study will be helpful to the local residents for preparing them to take suitable actions to minimize the risk of future floods. This study can also be utilized to mark high flood susceptible areas and installation of necessary structures to prevent the damage from future floods. Further, incorporation of socio-economic factors along with geo-hydrological, anthropogenic and environmental factors can help in deciphering the vulnerability of people to floods in much more precise way. Hence, future studies in this region can be aimed at incorporation of vulnerability assessment along with flood hazard mapping. As, the damage of moderate flood susceptible areas to local people can still be very high if they have higher vulnerability to floods arising from socio-economic factors.

14 GIS Based Delineation of Flood Susceptibility Mapping Using Analytic …

325

References Abu El-Magd SA, Orabi HO, Ali SA, Parvin F, Pham QB (2021) An integrated approach for evaluating the flash flood risk and potential erosion using the hydrologic indices and morphotectonic parameters. Environ Earth Sci 80(20):1–17 Ajay Kumar V, Mondal NC, Ahmed S (2020) Identification of groundwater potential zones using RS, GIS and AHP techniques: a case study in a part of Deccan volcanic province (DVP), Maharashtra, India. J Indian Soc Rem Sensing 48(3):497–511 Akay AE, Sahin ¸ H (2019) Forest fire risk mapping by using GIS techniques and AHP method: a case study in Bodrum (Turkey). Eur J Forest Eng 5(1):25–35 Akay H (2021) Flood hazards susceptibility mapping using statistical, fuzzy logic, and MCDM methods. Soft Comput 25(14):9325–9346 Alderman K, Turner LR, Tong S (2012) Floods and human health: a systematic review. Environ Int 47:37–47 Al-Hinai H, Abdalla R (2021) Mapping coastal flood susceptible areas using Shannon’s entropy model: the case of Muscat governorate, Oman. ISPRS Int J Geo-Inf 10(4):252 Ali SA, Khatun R, Ahmad A, Ahmad SN (2019) Application of GIS-based analytic hierarchy process and frequency ratio model to flood vulnerable mapping and risk area estimation at Sundarban region, India. Model Earth Syst Environ 5(3):1083–1102 Ali SA, Parvin F, Pham QB, Vojtek M, Vojteková J, Costache R, Ghorbani MA (2020) GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decisionmaking approach, naïve Bayes tree, bivariate statistics and logistic regression: a case of Topˇla basin, Slovakia. Ecol Indicators 117:106620 Al-Juaidi AE, Nassar AM, Al-Juaidi OE (2018) Evaluation of flood susceptibility mapping using logistic regression and GIS conditioning factors. Arab J Geosci 11(24):1–10 Arora A, Pandey M, Siddiqui MA, Hong H, Mishra VN (2021) Spatial flood susceptibility prediction in Middle Ganga Plain: comparison of frequency ratio and Shannon’s entropy models. Geocarto Int 36(18):2085–2116 Arulbalaji P, Padmalal D, Sreelash K (2019) GIS and AHP techniques based delineation of groundwater potential zones: a case study from southern Western Ghats, India. Sci Rep 9(1):1–17 Banerjee P, Ghose MK, Pradhan R (2018) Analytic hierarchy process and information value methodbased landslide susceptibility mapping and vehicle vulnerability assessment along a highway in Sikkim Himalaya. Arab J Geosci 11(7):1–18 Baron N, Petersen LK (2015) Climate change or variable weather: rethinking Danish homeowners’ perceptions of floods and climate. Reg Environ Change 15(6):1145–1155 Bera A, Mukhopadhyay BP, Barua S (2020) Delineation of groundwater potential zones in Karha river basin, Maharashtra, India, using AHP and geospatial techniques. Arab J Geosci 13(15):1–21 Bera S, Das A, Mazumder T (2022) Evaluation of machine learning, information theory and multicriteria decision analysis methods for flood susceptibility mapping under varying spatial scale of analyses. Rem Sensing Appl Soc Environ 25:100686 Bhardwaj C (2021) Disaster-induced internal displacement in India in 2020: a review Bouamrane A, Derdous O, Dahri N, Tachi SE, Boutebba K, Bouziane MT (2020) A comparison of the analytical hierarchy process and the fuzzy logic approach for flood susceptibility mapping in a semi-arid ungauged basin (Biskra basin: Algeria). Int J River Basin Manage 1–11 Chakraborty S, Mukhopadhyay S (2019) Assessing flood risk using analytical hierarchy process (AHP) and geographical information system (GIS): application in Coochbehar district of West Bengal, India. Nat Hazards 99(1):247–274 Coban HO, Erdin C (2020) Forest fire risk assessment using GIS and AHP integration in Bucak forest enterprise, Turkey. Appl Ecol Environ Res 18(1):1567–1583 Costache R, Zaharia L (2017) Flash-flood potential assessment and mapping by integrating the weights-of-evidence and frequency ratio statistical methods in GIS environment–case study: Bâsca Chiojdului River catchment (Romania). J Earth Syst Sci 126(4):1–19

326

K. Moharir et al.

Costache R, T, încu R, Elkhrachy I, Pham QB, Popa MC, Diaconu DC, Bui DT (2020) New neural fuzzy-based machine learning ensemble for enhancing the prediction accuracy of flood susceptibility mapping. Hydrol Sci J 65(16):2816–2837 Dancause KN, Laplante DP, Hart KJ, O’Hara MW, Elgbeili G, Brunet A, King S (2015) Prenatal stress due to a natural disaster predicts adiposity in childhood: the Iowa Flood Study. J Obesity Dankwa P, Cudjoe E, Amuah EEY, Kazapoe RW, Agyemang EP (2021) Analyzing and forecasting rainfall patterns in the Manga-Bawku area, northeastern Ghana: possible implication of climate change. Environ Challenges 5:100354 Das S (2020) Flood susceptibility mapping of the Western Ghat coastal belt using multi-source geospatial data and analytical hierarchy process (AHP). Rem Sensing Appl Soc Environ 20:100379 Didovets I, Krysanova V, Bürger G, Snizhko S, Balabukh V, Bronstert A (2019) Climate change impact on regional floods in the Carpathian region. J Hydrol Region Stud 22:100590 El Jazouli A, Barakat A, Khellouk R (2019) GIS-multicriteria evaluation using AHP for landslide susceptibility mapping in Oum Er Rbia high basin (Morocco). Geoenviron Disast 6(1):1–12 Ghosh A, Kar SK (2018) Application of analytical hierarchy process (AHP) for flood risk assessment: a case study in Malda district of West Bengal, India. Nat Hazards 94(1):349–368 Haghizadeh A, Siahkamari S, Haghiabi AH, Rahmati O (2017) Forecasting flood-prone areas using Shannon’s entropy model. J Earth Syst Sci 126(3):1–11 Hammami S, Zouhri L, Souissi D, Souei A, Zghibi A, Marzougui A, Dlala M (2019) Application of the GIS based multi-criteria decision analysis and analytical hierarchy process (AHP) in the flood susceptibility mapping (Tunisia). Arab J Geosci 12(21):1–16 Harrison S, Kargel JS, Huggel C, Reynolds J, Shugar DH, Betts RA, Vilímek V (2018) Climate change and the global pattern of moraine-dammed glacial lake outburst floods. Cryosphere 12(4):1195–1209 Hunt KM, Menon A (2020) The 2018 Kerala floods: a climate change perspective. Clim Dyn 54(3):2433–2446 Islam S, Tahir M, Parveen S (2021) GIS-based flood susceptibility mapping of the lower Bagmati basin in Bihar, using Shannon’s entropy model. Model Earth Syst Environ 1–15 Janizadeh S, Avand M, Jaafari A, Phong TV, Bayat M, Ahmadisharaf E, Lee S (2019) Prediction success of machine learning methods for flash flood susceptibility mapping in the Tafresh watershed, Iran. Sustainability 11(19):5426 Khosravi K, Nohani E, Maroufinia E, Pourghasemi HR (2016) A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-ofevidence bivariate statistical models with multi-criteria decision-making technique. Nat Hazards 83(2):947–987 Kia MB, Pirasteh S, Pradhan B, Mahmud AR, Sulaiman WNA, Moradi A (2012) An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environ Earth Sci 67(1):251–264 Kittipongvises S, Phetrak A, Rattanapun P, Brundiers K, Buizer JL, Melnick R (2020) AHP-GIS analysis for flood hazard assessment of the communities nearby the world heritage site on Ayutthaya Island, Thailand. Int J Disast Risk Reduct 48:101612 Kumari B, Pandey AC (2020) Geo-informatics based multi-criteria decision analysis (MCDA) through analytic hierarchy process (AHP) for forest fire risk mapping in Palamau Tiger Reserve, Jharkhand state, India. J Earth Syst Sci 129(1):1–16 Lamat R, Kumar M, Kundu A, Lal D (2021) Forest fire risk mapping using analytical hierarchy process (AHP) and earth observation datasets: a case study in the mountainous terrain of Northeast India. SN Appl Sci 3(4):1–15 Lappas I, Kallioras A (2019) Flood susceptibility assessment through GIS-based multi-criteria approach and analytical hierarchy process (AHP) in a river basin in Central Greece. Parameters (Malczewski, 1999) 6(3), 738–751

14 GIS Based Delineation of Flood Susceptibility Mapping Using Analytic …

327

Liu J, Wang J, Xiong J, Cheng W, Li Y, Cao Y, Yang G (2022) Assessment of flood susceptibility mapping using support vector machine, logistic regression and their ensemble techniques in the Belt and Road region. Geocarto Int (just-accepted) 1–40 Liuzzo L, Sammartano V, Freni G (2019) Comparison between different distributed methods for flood susceptibility mapping. Water Resour Manage 33(9):3155–3173 Mahmood S, Ullah S (2016) Assessment of 2010 flash flood causes and associated damages in Dir Valley, Khyber Pakhtunkhwa Pakistan. Int J Disast Risk Reduct 16:215–223 Mahmoud SH, Gan TY (2018) Urbanization and climate change implications in flood risk management: Developing an efficient decision support system for flood susceptibility mapping. Sci Total Environ 636:152–167 Msabi MM, Makonyo M (2021) Flood susceptibility mapping using GIS and multi-criteria decision analysis: a case of Dodoma region, central Tanzania. Rem Sensing Appl Soc Environ 21:100445 Nachappa TG, Piralilou ST, Gholamnia K, Ghorbanzadeh O, Rahmati O, Blaschke T (2020) Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory. J Hydrol 590:125275 Nahayo L, Kalisa E, Maniragaba A, Nshimiyimana FX (2019) Comparison of analytical hierarchy process and certain factor models in landslide susceptibility mapping in Rwanda. Model Earth Syst Environ 5(3):885–895 Nikhil S, Danumah JH, Saha S, Prasad MK, Rajaneesh A, Mammen PC, Kuriakose SL (2021) Application of GIS and AHP method in forest fire risk zone mapping: a study of the Parambikulam tiger reserve, Kerala, India. J Geovisual Spat Anal 5(1):1–14 Nohani E, Darabi F, Maroofinia E, Khosravi K (2017) Evaluation of Shannon entropy in order to flood probability and susceptibility mapping at Haraz catchment. J Nat Environ Hazards 5(10):99–116 Nuthammachot N, Stratoulias D (2021) A GIS-and AHP-based approach to map fire risk: a case study of Kuan Kreng peat swamp forest, Thailand. Geocarto Int 36(2):212–225 Oh EH, Deshmukh A, Hastak M (2010) Disaster impact analysis based on inter-relationship of critical infrastructure and associated industries: a winter flood disaster event. Int J Disast Resil Built Environ Opella JMA, Hernandez AA (2019) Developing a flood risk assessment using support vector machine and convolutional neural network: a conceptual framework. In: 2019 IEEE 15th international colloquium on signal processing & its applications (CSPA). IEEE, pp 260–265 Paul GC, Saha S, Hembram TK (2019) Application of the GIS-based probabilistic models for mapping the flood susceptibility in Bansloi sub-basin of Ganga-Bhagirathi river and their comparison. Rem Sensing Earth Syst Sci 2(2):120–146 Pham BT, Jaafari A, Van Phong T, Yen HPH, Tuyen TT, Van Luong V, Foong LK (2021) Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques. Geosci Front 12(3):101105 Quesada-Román A, Ballesteros-Cánovas JA, Granados-Bolaños S, Birkel C, Stoffel M (2022) Improving regional flood risk assessment using flood frequency and dendrogeomorphic analyses in mountain catchments impacted by tropical cyclones. Geomorphology 396:108000 Radwan F, Alazba AA, Mossad A (2019) Flood risk assessment and mapping using AHP in arid and semiarid regions. Acta Geophys 67(1):215–229 Rahim I, Ali SM, Aslam M (2018) GIS Based landslide susceptibility mapping with application of analytical hierarchy process in District Ghizer, Gilgit Baltistan Pakistan. J Geosci Environ Protect 6(2):34–49 Rahmati O, Pourghasemi HR, Zeinivand H (2016) Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto Int 31(1):42–70 Rajasekhar M, Raju GS, Sreenivasulu Y, Raju RS (2019) Delineation of groundwater potential zones in semi-arid region of Jilledubanderu river basin, Anantapur District, Andhra Pradesh, India using fuzzy logic, AHP and integrated fuzzy-AHP approaches. HydroResearch 2:97–108 Razavi Termeh SV, Pourghasemi HR, Alidadganfard F (2018) Flood inundation susceptibility mapping using analytical hierarchy process (AHP) and TOPSIS decision making methods and

328

K. Moharir et al.

weight of evidence statistical model (case study: jahrom township, fars province). J Watershed Manage Res 9(17):67–81 Roy S, Bose R, Sarddar D (2016) Self-servicing energy efficient routing strategy for smart forest. Braz J Sci Technol 3(1):1–21 Saaty TL (2004) Decision making—the analytic hierarchy and network processes (AHP/ANP). J Syst Sci Syst Eng 13(1):1–35 Sachdeva S, Bhatia T, Verma AK (2017) Flood susceptibility mapping using GIS-based support vector machine and particle swarm optimization: a case study in Uttarakhand (India). In: 2017 8th International conference on computing, communication and networking technologies (ICCCNT). IEEE, pp 1–7 Saha AK, Agrawal S (2020) Mapping and assessment of flood risk in Prayagraj district, India: a GIS and remote sensing study. Nanotechnol Environ Engi 5(2):1–18 Samanta S, Koloa C, Kumar Pal D, Palsamanta B (2016) Flood risk analysis in lower part of Markham river based on multi-criteria decision approach (MCDA). Hydrology 3(3):29 Samanta S, Pal DK, Palsamanta B (2018) Flood susceptibility analysis through remote sensing, GIS and frequency ratio model. Appl Water Sci 8(2):1–14 Saranya T, Saravanan S (2020) Groundwater potential zone mapping using analytical hierarchy process (AHP) and GIS for Kancheepuram District, Tamilnadu, India. Model Earth Syst Environ 6(2):1105–1122 Sarkar D, Mondal P (2020) Flood vulnerability mapping using frequency ratio (FR) model: a case study on Kulik river basin, Indo-Bangladesh Barind Region. Appl Water Sci 10(1):1–13 Shafapour Tehrany M, Kumar L, Neamah M, Shabani F (2019) Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods. Geomat Nat Haz Risk 10(1):79–101 Shafapour Tehrany M, Shabani F, Neamah M, Hong H, Chen W, Xie X (2017) GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques. Geomat Nat Haz Risk 8(2):1538–1561 Shafizadeh-Moghadam H, Valavi R, Shahabi H, Chapi K, Shirzadi A (2018) Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping. J Environ Manage 217:1–11 Shahabi H, Shirzadi A, Ronoud S, Asadi S, Pham BT, Mansouripour F, Bui DT (2021) Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm. Geosci Front 12(3):101100 Singh S, Kanhaiya S, Singh A, Chaubey K (2019) Drainage network characteristics of the Ghaghghar river basin (GRB), Son valley, India. Geology, Ecology, and Landscapes 3(3):159–167 Swain KC, Singha C, Nayak L (2020) Flood susceptibility mapping through the GIS-AHP technique using the cloud. ISPRS Int J Geo Inf 9(12):720 Tehrany MS, Pradhan B, Jebur MN (2014) Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol 512:332–343 Tehrany MS, Pradhan B, Jebur MN (2015a) Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stoch Env Res Risk Assess 29(4):1149–1165 Tehrany MS, Pradhan B, Mansor S, Ahmad N (2015b) Flood susceptibility assessment using GISbased support vector machine model with different kernel types. CATENA 125:91–101 Termeh SVR, Kornejady A, Pourghasemi HR, Keesstra S (2018) Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Sci Total Environ 615:438–451 Vafakhah M, Loor MH, Pourghasemi S, Pourghasemi H (2020) Comparing performance of random forest and adaptive neuro-fuzzy inference system data mining models for flood susceptibility mapping. Arab J Geosci 13(11):1–16 Vojtek M, Vojteková J (2019) Flood susceptibility mapping on a national scale in Slovakia using the analytical hierarchy process. Water 11(2):364

14 GIS Based Delineation of Flood Susceptibility Mapping Using Analytic …

329

Wang Y, Fang Z, Hong H, Peng L (2020) Flood susceptibility mapping using convolutional neural network frameworks. J Hydrol 582:124482 Wang Y, Hong H, Chen W, Li S, Pamuˇcar D, Gigovi´c L, Duan H (2019) A hybrid GIS multi-criteria decision-making method for flood susceptibility mapping at Shangyou, China. Rem Sensing 11(1):62

Chapter 15

Fluoride Contamination in Groundwater—A Review Riddha Chaudhuri, Satiprasad Sahoo, Anupam Debsarkar, and Sugata Hazra

Abstract Fluoride contamination in groundwater is a major environmental issue, mostly in the developing world, due to the extensive negative impact that it has on human health. Excessive consumption of fluoride-contaminated water causes dental fluorosis and, in extreme cases, skeletal fluorosis. An estimated 200 million people all over the world, 62 million from India in almost 17 out of 29 states, which includes 6 million children, suffer from fluorosis due to consumption of water with high fluoride concentration. Natural sources such as weathering of rocks bearing fluoride rich minerals, volcanic eruptions are the major sources of fluoride, although anthropogenic sources are also present. Major chemical controlling factors responsible for fluoride contamination of groundwater are dissolution, hydrolysis, precipitation, adsorption, ion exchange and biochemical reactions. Five major fluoride affected belts have been delineated globally. In India, many districts of states such as Rajasthan, Tamil Nadu, Andhra Pradesh, Telengana and West Bengal show high fluoride in groundwater. Determining factors causing fluoride contamination in groundwater are Rock type, Tectonics, Physico-Chemical properties of water, Soil type and weathering. Keywords Fluoride contamination · Fluoride controlling factors · Fluoride affected belts · Determining factors

R. Chaudhuri (B) Assistant Professor, Calcutta Institute of Engineering and Management, Tollygunge, Kolkata, India e-mail: [email protected] S. Sahoo GeoAgro, International Center for Agricultural Research in the Dry Areas (ICARDA), Cairo, Egypt A. Debsarkar Department of Civil Engineering, Jadavpur University, Kolkata, India S. Hazra School of Oceanographic Studies, Jadavpur University, Kolkata, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_15

331

332

R. Chaudhuri et al.

15.1 Introduction Fluoride is a highly toxic element. Even at very low concentrations, groundwater fluoride contamination causes serious health problems, making it a major environmental issue for the developing world (Mandal and Suzuki 2002). The presence of fluoride in drinking water, although necessary in controlled amounts to prevent dental caries, causes endemic dental and skeletal fluorosis when it exceeds the limit (Grandjean et al. 1992). Ingestion of fluoride over an extended period affects tissues, organs, and organ systems. Crippling skeletal deformity is one of the severe manifestations of fluorosis. Fluoride in drinking water cannot be detected unless tested chemically. The victims of fluorosis suffer without their disease being diagnosed, undergo various treatments, including surgical interventions, with no sign of relief to the pain or disability (Susheela et al. 2005). An estimated 200 million people all over the world, 62 million from India in almost 17 out of 29 states suffer from fluorosis as they consume water with high fluoride content (Susheela 1999). Mostly, countries from South and Southeast Asia experience the problem of fluoride contaminated groundwater (Rasool et al. 2015). Detailed reviews of fluoride contamination have been conducted by Mukherjee and Singh (2018), Rasool et al. (2018), Chowdhury et al. (2019) among a plethora of other bodies of work. Five major fluoride belts have been delineated across the world, corresponding to particular lithological and tectonic characters, along which groundwater with high fluoride concentration are found exclusively (Fawell et al. 2006). India has a major problem of undesirably high fluoride contaminated groundwater which seriously affects large parts of its arid and semiarid regions. The relatively high-fluoride-contaminated states in India are Andhra Pradesh, Rajasthan, Gujarat, Telengana, Tamil Nadu and West Bengal (Mukherjee and Singh 2018). There are several natural factors that play a vital role in determining fluoride concentration in groundwater. These include geological, microorganisms, geothermal, physicochemical properties of water and even type of soil. However, certain anthropogenic factors do play a vital role. It is important to correlate all of these factors in order to facilitate a clearer perception of fluoride contamination, with special regard to the Indian circumstances. The current review intends to concretely correlate the major natural and anthropogenic causes of contamination of fluoride in groundwater and conclusively investigate the role played by geological factors, especially tectonics, in controlling and exacerbating fluoride contamination in groundwater.

15.2 Materials and Methods The following methods were followed by various authors whose works have been reviewed:

15 Fluoride Contamination in Groundwater—A Review

333

1. Water sample collection and hydro-chemical analysis: (Gupta et al. 2012; Datta et al. 2014; Mondal et al. 2014; Ghosh et al. 2016): In this process, groundwater samples have been collected and analysed for water quality parameters, including fluoride. Correlations have been drawn between fluoride contamination and other parameters such as pH, various cation and anion concentrations. 2. Geophysical Exploration (including electrical conductivity): (Misra et al. 2006; Mondal et al. 2014): In this method, various Geophysical Exploration methods, such as Vertical Electrical Sounding (VES), Spontaneous Potential (SP) Log etc. were carried out to understand the subsurface hydrogeology of the fluoride affected belts. 3. XRF analysis of whole rock: Rango et al. (2009) conducted X-Ray Fluorescence (XRF) analysis for selected whole rock samples. This enabled to understand the geochemistry of constituent mineral phases. A wavelength dispersive automated ARL Advant’X spectrometer was used for this purpose. 4. Remote Sensing and GIS: Extensive use of Remote Sensing and GIS has been done by various authors such as Thapa et al. (2017), Batabyal and Gupta (2017). Using RS and GIS, Lineament maps, Lineament Density maps, Land cover, soil use maps have been created. Base Geological maps have been prepared upon which fluoride affected regions were marked. These have helped to correlate various factors such as Lithogy, Lineaments with fluoride contamination.

15.3 Results 15.3.1 Sources and Control of Fluoride Mobilisation in Groundwater Environmental existence of fluoride is as fluoride compounds formed due to combination with several elements and are observed naturally in several food products, water and soil. Fluorite, fluorapatite, apatite, sellaite, several amphiboles, cryolite, mica and topaz are the major fluoride-bearing minerals that are abundant in a variety of rock types as well as sediments (Chae et al. 2007; Ghosh et al. 2013; Banerjee 2015). Fluoride is a mostly naturally occurring element constituting approximately 0.32% of the Earth’s crust and is extensively dispersed in the environment (WHO 1984). The first major natural source of inorganic fluorides in the groundwater is the weathering of fluoride-bearing minerals such as fluorite, apatite, fluor-apatite; volcanic eruptions being the second major natural source. Excess agricultural application of phosphatic fertilizers, landfill sites cement manufacture, aluminium smelting, coal combustion are major anthropogenic activities contributing to environmental fluoride (Camargo 2003; Ozsvath 2009; Dey et al. 2012).

334

15.3.1.1

R. Chaudhuri et al.

Geological Sources

Major natural sources of fluoride are geological, mostly products of weathering of minerals enriched in fluoride. Geological sources may be enlisted as follows. Rocks and minerals Rocks containing fluoride rich minerals are the major reserves of fluoride (WHO 1984). According to (Srivastava and Lohani 2015) there are almost 150 minerals bearing fluoride, most of which are silicates and a few phosphates. The composition of the rocks determines the value of fluoride contained in them. In ultramafic rocks fluoride content is around 100 mg/kg, increasing to 1000 mg/ kg in alkaline igneous rocks and in marine shales as 1300 mg/kg (Ozsvath 2009). The crustal average of fluoride is 625 mg/kg which varies with varying type of rocks (Vithanage and Bhattacharya 2015). But fluoride concentration in sedimentary rocks maybe higher if they contain fluoride enriched clays or fluorapatite (Ozsvath 2009). Granite group of rocks contain muscovite, amphiboles and hornblende which have a high content of fluoride. These minerals, by weathering, enrich fluoride in groundwater (Vithanage and Bhattacharya 2015). In hydrothermal vein deposits fluorite commonly exists as fluorspar (Ozsvath 2009). Coal contains fluoride, around 295 mg/ kg, which also contributes to environmental fluoride (Churchill et al. 1948).

15.3.1.2

Geothermal Sources

In many regions, a major contributor of fluoride are volcanic and geothermal activities. It is estimated that between 60 and 6000 kilotons of inorganic fluorides released, globally, from volcanoes and volcanic activities (Camargo 2003). Post cessation of volcanic activities, high fluoride concentrations might persist for several years (Araya et al. 1993). Due to similar ionic radius, ionic replacement between OH− and F− takes place during magmatic differentiation (Sivasankar et al. 2016). PreCambrian granitic, amphibolitic rocks elevate fluoride concentrations in hot spring water (Marbaniang et al. 2014). Another major source of fluoride are the volcanic rocks producing around 2000 mg/kg in the subduction zones (Anazawa 2006).

15.3.1.3

Atmospheric Deposition

Atmospheric air contribute to fluoride in the environment from 0.01 to 0.4 µg/m3 whereas contribution of precipitation ranges from almost undetectable quantities to 0.089 mg/L (Gupta et al. 2005). Fluorides in the atmosphere can vary in form, either gaseous or particulate, sources of emission might be natural and or anthropogenic.). Through fallout as particulate matter or by rainfall, atmospheric fluorides reach the surface of the earth. From the surface, percolating rainwater along with fluoride, reaches the groundwater zone (Brindha and Elango 2011). Atmospheric fluoride compounds are transported over large distances by atmospheric circulation process which removes them via dry and wet deposition (Walna et al. 2013) The

15 Fluoride Contamination in Groundwater—A Review

335

Fig. 15.1 Hydrogeochemical cycle (Edmunds and Smedley 2005)

most damaging air pollutants among all air borne fluorides are hydrogen fluoride (HF) and silicon tetrafluoride (Malayeri et al. 2012) (Fig. 15.1).

15.3.1.4

Anthropogenic Sources

Anthropogenic fluoride emission in the environment is mainly caused by thermal power plants, aluminium, iron and steel industries, fertilizer and ceramic industries (Dey et al. 2012). Airborne dust, ashes and fumes rich in fluoride are the major industrial emissions. Soil, water and vegetation near the industries and even at significant distances, may be polluted by emissions through atmospheric deposition (Ranjan and Ranjan 2015).

15.4 Factors Controlling Enrichment and Mechanisms of Movement of Fluoride in Groundwater Certain factors controlling the fluoride enrichment in groundwater are temperature, pH, anion exchange capacity (OH− for F− ), rock-water interaction time, dissolution of minerals rich in fluoride, type of geological formation traversed by water and the absence of precipitating ions and colloids (Farooqi et al. 2007). As arid and semiarid regions receive low rainfall and have high evaporation rates, therefore the hydraulic conductivity of groundwater is low (Su et al. 2006). Consequently, groundwater recharge rate is low which leads to prolonged water rock interaction. This causes elevation of fluoride content in groundwater. It has been observed that

336

R. Chaudhuri et al.

higher concentrations of fluorides in groundwater are present inplains whereas low concentration is reported from highlands. This happens due to the fact that in the plains water is in longer contact with the aquifer and groundwater movement is slow (Dissanayake 1991). The retention time of water in the aquifer determines the interaction with geological materials and hence the concentration of fluoride is proportional to it. (Czarnowski et al. 1996). Precipitation of lower solubility minerals (CaCO3 ) is caused due to high evaporation which, in turn, reduce calcium ions in groundwater, promoting the fluorite minerals’ dissolution (Vithanage and Bhattacharya 2015). The value of pH is a determining factor for fluoride conc in groundwater. The release of Na+ and HCO3 − ions increases the pH which induces fluoride bearing minerals for further dissolution (Rango et al. 2009).When pH is low, fluoride tends to form complexes with aluminium and iron in soil. Below pH 4.0, fluoride is actively adsorbed by clay minerals while above pH 6.5, adsorption decreases. At pH > 7, that is when conditions are alkaline, anion exchanges take place in the form of OH− replacing F− from biotite, muscovite and clay minerals (Li et al. 2014). Handa (1975), Saxena and Ahmed (2003) are of the opinion that the processes controlling fluoride mobilization in groundwater are ambiguous. The process of exchange of anions increases concentration of fluoride in water to the extent of upto 30 mg/L (Rasool et al. 2018). As stated earlier, similar ionic radius facilitates the substitution of F− in OH− positions due to similar atomic sizes and single negative charge (Farooqi et al. 2007; Sivasankar et al. 2016). Similarly, it has been already stated that at low pH fluoride gets adsorbed by clay minerals. Thus, alkaline condition is favourable for the enrichment of fluoride ions in groundwater (Singh et al. 2015a, b). At pH > 7, that is when conditions are alkaline, anion exchange is facilitated wherein F− is replaced by OH− in fluoride-containing minerals (Li et al. 2014). Prolonged water retention promotes replacement of Ca2+ ions by Na+ that leads soft Na+ rich water formation that is favourable for groundwater fluoride leaching (Karro and Uppin 2013). Dissolution rate of fluoride from fluorite (CaF2 ) is promoted by the presence of excessive bicarbonates HCO3 − (Handa 1988). As a result the value of water’s pH, along with other factors like TDS, EC and Eh, gets increased. Elevated levels of fluoride in groundwater is facilitated by Ca2+ and Mg2+ in low concentrations and a Na+ being in higher concentrations (Farooqi et al. 2007). In acidic solutions, clay adsobs fluoride ions whereas in alkaline solutions F− ions are desorbed (Sarma and Rao 1997). However, major factors controlling fluoride contamination are hydrolysis, dissolution, adsorption, precipitation, ion exchange and biochemical reaction (Saxena and Ahmed 2003). Higher fluoride concentration in water from arid and semi- arid region may be explained evapo-transpiration and evaporation leading to soluble components condensation (Jacks et al. 2005).

15 Fluoride Contamination in Groundwater—A Review

337

15.5 Global Perspectives of Fluoride Contamination Recent studies repudiate the previous belief that only within tropical countries fluorosis remains restricted (Dissanayake and Chandrajith 2009). High fluoride containing drinking water can be encountered in extensive geographical belts having specific lithological and tectonic characters. According to the WHO report placed by Fawell et al. (2006) waters having high fluoride concentrations may be encountered extensively along belts defined by geography. Association of these belts are observed with (a) granitic and gneissic rocks, (b) volcanic rocks, (c) tectonic zone and (d) marine sediments in highland areas. Dahi (2009), in his report, demarcated five such global linear belts which connecting several countries (Chowdhury et al. 2019). The first belt or Belt 1 is a southward running linear stretch extending from Turkey to Tanzania. Starting from Turkey, it moves through Syria, passing along Jordan, entering African continent through Egypt, southward on to Sudan, Somalia, Ethiopia, Tanzania, Kenya, South Africa and finally onto Mozambique. Type of rocks and rock-water interactions are the major reasons for higher levels of groundwater fluoride along this belt. This is a well documented area and is associated with volcanic activities along the East African Rift system extending from the Jordan valley to Tanzania. Highly elevated concentration of fluoride is noticed along hot springs found in the area (Fawell et al. 2006). This belt has mostly arid climate, relief is high. Aridisol is the major soil type. The second belt or Belt 2 exists as a stretch running in a linear fashion between Egypt and Mauritania. It starts from Egypt and moves westerly along Libya, Algeria and Morocco. From Morocco it takes a sharp southernly turn and connects Mauritiana with Western Sahara along the edges of western Africa. One of the major sources of fluoride in the ground waters of this belt is the presence of granites and Pre Cambrian basement gneisses. Granites and basement gneisses are the significant source of fluoride in the ground waters of Algeria and Libya whereas meta-rhyolites play the same role in Mauritania. Tectonically, this belt has been found to be running parallel to the West African mobile belt. Western Sahara, Morocco are one of the largest phosphate exporters of the world, several authors have attributed phosphate mining to elevated occurences of fluorosis in these countries (Maadid et al. 2017). The soil type of this belt is Aridisol and the climate desert temperate type. The third belt or Belt 3 is a linear stretch extending from China till Turkey. The belt starts from Turkey. It moves eastward from Turkey till India where it takes a sharp northward turn. In Iraq and Iran, the presence of volcanic and calc alkaline rocks, along with their dissolution, may be the cause of high fluoride content in groundwater. Areas adjoining metamorphic rocks have also been observed to have high fluoride levels in Iran. Presence of alkaline granites and pegmatitic veins in Afghanistan may be attributed to the high levels of fluoride in groundwater. Dry and arid climate, along with geology, further exacerbate fluoride contamination in Pakistan and Afghanistan by enhancing the rate of weathering of minerals bearing fluoride. In India, granitic aquifers, hydrogeological control exerted by fractures and phosphatic fertilizers may be the major contributors of F− in groundwater. In

338

R. Chaudhuri et al.

China and northern Thailand, high fluoride is observed around hot springs. However geological factors, including granitic aquifers, also play a vital role in increasing groundwater fluoride in China. The climate of the belt is varied, albeit dominated by temperate desert, but significant areas have tropical climate with soil type varying from Aridisols to Endisols. Belt 4 exists as a linear stretch extending from the Sierra Nevada in North America to the Andes mountains of South America. This starts from Sierra Nevada in the North followed by the Rockies in the USA, through several South American countries, finally terminating in the Andes mountains. This belt can be called a “Volcanic fluoride belt” since it connects a number of volcanoes, both active and dormant. Active volcanic action, associated ash and acid rain act as major reasons for increased fluoride content in groundwater of the region. Although hot springs and high groundwater temperature is a determining factor contributing to fluoride dissolution in Colombia and Sierra Nevada, vital role is also played by water pH. Andisol is the major soil type here with the climate being desert tropical. Belt 5 is a south-westerly linear stretch running from Japan through Philippines and Indonesia. Itmay be classified as a volcanic fluoride belt since it connects a plethora of active and dormant volcanoes. Major causes of high fluoride in the groundwaters of Japan and Indonesia are the volcanic rocks and ash of the active subduction belts in the region. However, in Philippines hot springs also contribute significantly. Lakes of Indonesia have very high fluoride concentration (>1266 ppm). Temperate humid climate is observed in this belt while the dominant soil type is Andisols.

15.6 Indian Perspective of Fluoride Contamination In 21th century, India more than 35 million populations of 19 states is consuming fluoride above permissible limit through drinking water. This is further exacerbated by the fact that almost 85% of drinking water supply in India comes from groundwater, which increases to 90% in the case of rural water supply (Shankar et al. 2011). According to (Teotia et al. 1998), 12 million ton fluoride deposit is found in India, out of the total 85 million ton found in the earth’s crust. In 1991, 13 of India’s 32 states and territories were reported to have naturally high concentrations of fluoride in water, but this had risen to 17 by 1999 (UNICEF 1999). In India, around 66.62 million people have been affected by fluorosis, with more than five hundred thousand cripples. Majority of the states in India, numbering 20, are fluoride affected. Among these worst affected are Andhra Pradesh, Rajasthan, Gujarat followed by Bihar, Punjab, Haryana, Karnataka, Maharashtra, Madhya Pradesh, Tamil Nadu, Uttar Pradesh and some parts of Delhi. Drinking water in atleast twenty five (25) districts of Gujarat were found to contain excessive fluoride, with reference to WHO limits. Fluoride, therefore has become another public health problem related to water after Arsenic (Susheela 2007). The geological and climatic conditions of India is mostly responsible for the high risk of groundwater contaminated by fluoride (Brindha et al. 2016).

15 Fluoride Contamination in Groundwater—A Review

339

Fig. 15.2 Fluoride in Rajasthan, a spatial distribution (Mukherjee and Singh 2018)

Rajasthan, Andhra Pradesh, Telengana, Tamil Nadu, Gujarat and West Bengal are the most affected states in India (Thivya et al. 2017; Ali et al. 2016). In almost entire cases, geological conditions are mostly responsible for fluoride contamination (Fig. 15.2). Rajasthan is the largest state of India, area wise. Around 18 districts of Rajasthan, with 11 million people are affected by groundwater that has fluoride above permissible limits (Ali et al. 2016). All 32 districts of Rajasthan have been declared as fluoride affected. The worst affected districts are Nagaur, Jaipur, Sikar, Jodhpur, Barmer etc. Geologically, a large part of Rajasthan is made up of Pre-Cambrian Aravalli supergroup, Delhi Supergroup, Bhilwara supergroup, Marwar supergroup and parts also include Deccan traps and other Phanerozoic formations (Sundaram and Pareek 1995). High fluoride contamination has the most serious effect in the state of Rajasthan (Handa 1975). The northern part of the state shows very high fluoride levels (Suthar et al. 2008) on account of host rocks such as quartzite, mica schist, sandstone, phyllites that bear extensive amounts of fluoride. Extended interaction between water and rock perpetuated by arid climate, has been identified as the primary cause behind excessive fluoride contamination. The worst affected district is Nagaur, where fluoride level may reach as high as 5.91 mg/L, with toxic water levels affecting almost the entire district (Hussain et al. 2012) (Fig. 15.3). Gujarat is a severely fluoride affected state. People of 18 districts of the state are vulnerable to fluorosis on account of the presence of high levels of fluoride contamination in groundwater (Barot 1998). Geologically, Gujarat is varied. Rocks of Delhi supergroup, Aravalli supergroup, form the Precambrian rocks whereas rocks of Mesozoic Kutch basin and Cretaceous to Cenozoic Deccan traps form the bulk of

340

R. Chaudhuri et al.

Fig. 15.3 Fluoride in Gujarat, a spatial distribution (Mukherjee and Singh 2018)

Phanerozoic rocks. A large part of the state is covered by the Deccan traps. Unconsolidated alluvium covers the north-eastern whereas fluvial sediments cover the central and western parts (Chopra and Choudhury 2011). The principal cause that may be attributed to contamination of fluoride in groundwater is fluoride rich mineral dissolution in granitic rocks. In Mehsana groundwater fluoride was observed owing to the presence of highly weathered granitic gneiss (Dhiman and Keshari 2006). In Bharuch, high fluoride levels in groundwater is caused by leaching of fluoride rich minerals (Nayak et al. 2009). Geologically, Cambay basin are composed of granites invaded by amphibolitic and pegmatitic dykes. This on leaching contaminates the groundwater making it highly fluoride rich (Gupta et al. 2005) (Figs. 15.4 and 15.5). Andhra Pradesh and Telangana used to be single state till 2014, when Telengana came into existence. Geological makeup of Andhra Pradesh consists of rocks from the most ancient to the recent having igneous, metamorphic as well as sedimentary rocks. The Archean Peninsular Gneissic complex, is covered by a complex group of gneisses and schists. Glacial Talchir formation forms the bulk of Gondwanan deposits composed of grey shale and limestone band. Rocks of Pre Cambrian Cuddapah and Kurnool supergroups are found in the districts of Kurnool, Kadapa, Chittoor, Guntur, Prakasam and Krishna districts. Tertiary formations are observed in East and West Godavari and Vishakhapatnam districts whereas Quarternary deposits occur as thick Alluvium in the river valleys and Deltas in the coastal regions of the east. Nalgonda district is a highly fluoride affected district. Granites and gneisses in Nalgonda are

15 Fluoride Contamination in Groundwater—A Review

341

Fig. 15.4 Fluoride in Andhra Pradesh, a spatial distribution (Mukherjee and Singh 2018)

enriched with fluorides caused by interaction of rock and water (Mondal et al. 2009). Weathered and fractured granites and granitic gneisses, consisting of fluoride rich minerals like fluorite, cover different parts of Nalgonda (Brindha et al. 2016). The high fluoride in Prakasam district is due to the rock water interaction leading to dissolution of rocks of Charnockite and Khondalite group, along with unclassified metamorphics and intrusives like pink granites, pegmatites, gabbros and anorthosites (Reddy et al. 2016). In Medak district of Telengana, high fluoride content of groundwater is also due to rock water interaction leading to fluoride rich mineral dissolution such as apatite, biotite, hornblende present in the granitic and granite gneissic country rock (Narsimha and Sudarshan 2017) (Fig. 15.6). Tamil Nadu is the southernmost state of India. Geologically, Pre-Cambrian rocks belonging to Dharwar craton and Southern Granulite Terrain, make up Tamil Nadu. These are made up ofminerals enriched in fluoride like pyrrhotite, pyrite, chalcopyrite and biotite (Manikandan et al. 2014). In Thoothukudi district, fluoride concentration is high because of charnockites, alluvial sediments and gneisses rich in hornblende-biotite (Singaraja et al. 2014). Rocks bearing fluoride, like peninsular gneiss and charnockite containing apatite, hornblende and biotite majorly contribute to high fluoride content in Dindigul district through weathering and dissolution

342

R. Chaudhuri et al.

Fig. 15.5 Fluoride in Telangana, a spatial distribution (Mukherjee and Singh 2018)

(Chidambaram et al. 2013). Interconnection of fracture zones, along with fluoride rich host rocks and rock water interaction contribute to elevated groundwater fluoride of Erode district (Karthikeyan et al. 2010). Selvam (2015) is of the opinion that semi arid climate with low precipitation and high evaporation leads to salinization of the groundwaters of Tuticorin (Thoothkudi district) which promotes dissolution of fluoride. According to Thivya et al. (2015) Madurai has high groundwater fluoride concentration due to the presence of granitic and gneissic country rocks minerals apatite, and fluorapatite (Fig. 15.7). West Bengal, especially the western districts, is strongly affected by fluoride contamination in ground water. Almost, 60 blocks of several districts in West Bengal are fluoride affected. A recent study conducted by Gupta et al. (2012) found significant fluoride content in groundwater in Ranganj coalfield (Gondwana coal) area, with almost 10% of the surveyed water samples having fluoride exceeding WHO and BIS standards. (Misra and Mishra 2007) reported escalation of fluoride in groundwater from gangetic alluvial plains. According to Datta et al. (2014), the districts of West Bengal mostly reported concentration of fluoride within tolerable limits except, the western part (Purulia, Bankura, Birbhum) which shows a very high concentration of

15 Fluoride Contamination in Groundwater—A Review

343

Fig. 15.6 Fluoride in Tamil Nadu, a spatial distribution (Mukherjee and Singh 2018)

fluoride in groundwater. In Purulia District, Hura, Purulia I, Purulia II blocks, fluoride levels in groundwater were observed to be as high as 10.75 mg/L (Bhattacharya and Chakrabarti 2011). Pre Cambrian rocks underlaying the district consists of fluoride rich minerals such as fluorite, apatite and hornblende which release fluoride in the ground water upon weathering. Bankura, too has similar geology composed mostly of The first report of fluorosis and fluoride contaminated groundwater in West Bengal came from Nasipur village in Nalhati block of Birbhum in 1996 (Patra et al. 2010). In Birbhum district (4545 km2 ) about 52,563 population distributed over 78 villages/ hamlets in 7 blocks are affected by fluorosis. (Mondal et al. 2014). Presence of fluoride has been reported from Granitic, Basaltic terrains as well as alluvial aquifers of Birbhum (Fig. 15.8).

344

R. Chaudhuri et al.

Fig. 15.7 Fluoride in West Bengal, a spatial distribution (Mukherjee and Singh 2018)

15.7 Discussions The determining natural factors controlling contamination of fluoride in groundwater are listed hereby. Rock Type: Throughout the world, lithological causes are the most dominant in effecting the fluoride concentration in groundwater. In most cases, the dominant

15 Fluoride Contamination in Groundwater—A Review

345

Fig. 15.8 Spatial distribution of Fluoride in India (Digitisation of GSI Maps) (Mukherjee and Singh 2018)

rock type have been found to be igneous whereas sedimentary are the second most common rock type observed (Chowdhury et al. 2019). From the preceding discussions regarding various fluoride belts of the world, similar conclusion can be drawn. Analysis of various fluoride affected Indian states also point in the same direction. For igneous rocks, Roelandts et al. (1987) observed a positive association between F− and P2 O5 , which led them to suggest that a considerable buffering on the concentration of F− is exerted by the mineral apatite. They found that amphibole and biotite are other important minerals, beside apatite, with high fluoride content. Tectonics A major influence is played by the tectonics of an area in enhancing the fluoride content in groundwater. It has been observed that certain tectonic zones are always

346

R. Chaudhuri et al.

associated areas having elevated fluoride in groundwater. The geographical belts of fluoride contamination, discussed previously, conform to specific tectonic zones. Belt 1 is parallel to convergent boundary whereas belt 2 is parallel to transform faults. In subduction zone tectonic setting, Belts 3, 4 and 5 are observed (Chowdhury et al. 2019). High fluoride concentrations can be observed along the Circum Pacific ring of fire belt, the major global volcanic belts. Therefore, positive correlation high fluoride concentration in groundwater with volcanic belts may be concluded. Maximum reported cases of fluorosis, are usually reported from intra- continental hotspots, aborted rifts and Andean type magmatic belts (Chowdhury et al. 2019). The East African Rift System (EARS) is of particular importance in understanding the relationship between tectonics and fluoride contamination. EARS is an active continental rift system which extends from Jordan valley till Tanzania. A lot of volcanism has also been observed in the region in the past and currently many volcanoes, both dormant and active, such as Mount Meru, Mount Kenya, Mount Kilimanjaro and Crater Highlands of Kenya (Saemundsson 2010). Here fluoride contamination may reach upto 70 mg/L. Ghiglieri et al. (2020) are of the opinion that volcano-tectonic processes strongly condition fluoride circulation in the groundwater of the EARS region which can be explained in terms of interaction of faults and lithology, volcanism and degree of tectonic segmentation. In the EARS, highest levels of fluoride contamination has been observed in the hot springs (Kloos and Haimanot 1999). Relatively fluorine-rich lavas are produced by calc-alkaline volcanoes in the EARS continental rift (Gasparon et al. 1993), continental arcs such as Andes and island arcs such as Japan (Rosi et al. 2003). In such aquifers, regional thermal fields along with felsic volcanic tocks within high geothermal zones are responsible for high fluoride contamination (Alemayehu et al. 2006; Furi et al. 2011). Waters of geothermal springs show presence of Fluoride much in excess of WHO standards. Thermal springs are surface manifestations of presence of geothermal energy. The heat of such springs partly emanate from the radioactive decay of elements such as U, Th and K in high heat producing granites (Karakus 2015). In India, geothermal provinces are associated with either deep seated rifts or subduction tectonics. In the Himalayan geothermal belt subduction related provinces are observed whereas in the Indian continent rift related provinces are observed. Different geothermal provinces observed in India are the Himalayan, West, Son-Narmada-Tapti (SONATA), Cambay and Godavari rift geothermal provinces. The collision of Indian lithospheric plate with the Eurasian plate has created the Himalayan Geothermal Belt (HGB). The Main Boundary Thrust (MBT) and the Main Central Thrust (MCT) are major tectonic features of HGB. Along MCT, leucogranitic intrusions are observed (Chandrasekhar and Chandrasekharam 2011). Shear heating of the subducting slabs along with radioactive decay heat generates granite melts (Harris et al. 2000). These might be the source of high fluoride in the geothermal waters. Chatterjee et al. (2016) analysed the waters from various hot springs in the Uttarakhand geothermal field, part of the HGB. They found that samples from Badrinath and Gari village show very high levels of fluoride, upto 6.7 ppm. The SONATA geothermal province is controlled by deep mid continental rifts extending upto mantle depth (Kaila 1988). In the eastern parts, the thermal waters flowing through granites of the Chotanagpur

15 Fluoride Contamination in Groundwater—A Review

347

Granite Gneiss Complex (CGGC) show high levels of fluoride contamination, which is much higher than the permissible limit (Minissale et al. 2000; Chandrasekhar and Chandrasekharam 2008; Singh et al. 2015a, b). Waters of the Taptapani hot springs of Odhisa and the Bakreshwar hot springs of West Bengal show very high fluoride content. Most significant hotsprings in the Cambay Geothermal Provinceoccur in Tuwa within Godhra granite whose fluoride content varies from 2.5 to 4 mg/L (Singh and Chandrasekharam 2010). Neotectonic events and later lineament tectonic events may also contribute to excessive fluoride contamination. In the Highland and Vijayan rocks of Sri Lanka, numerous deep lineaments and faults caused by PreCambrian and later lineament tectonic events could be the foci of fluorine outgassing (Vithanage 1989). Physico-Chemical Properties of Water Different parameters of water quality like pH, temperature, rock water interaction affect fluoride concentration in water (Kitalika et al. 2018). Positive correlation could be established between fluoride ions and chloride, bicarbonate, sodium, boron (Warren et al. 2005; Su et al. 2013) whereas Handa (1975) reported that high fluoride groundwater has a negative correlation with Ca2+ content. Total dissolved solids (TDS), pH, Electrical Conductivity(EC) also show a relationship with F− content. Types of Soil in the Area Aridisols and andisols are the dominant soil types in the fluoride belts (Chowdhury et al. 2019). Aridisols are dry soil found in arid regions characterized by low water and organic content. Fluoride desorption and mobility is promoted due to the low organic content (D’Alessandro et al. 2012). Rate of dissolution of F− gets enhanced due to the presence of carbonate and sodium. Andisols are formed downwind of volcanic activity and are marked by the manifestation of volcanic glass. Weathering It has been observed that countries in the fluoride belt have usually very weathered and degraded soil. Some workers have tried to establish a correlation between groundwater fluoride contamination and weathering. From a study in the Polonnaruwa area of Sri Lanka, which shows fluoride level of 5.25 mg/L, Dharmagunawardhane et al. (2016) established that loss of fluoride, from whole rocks as well as individual minerals, was as a result of weathering. Another study conducted in fluoride affected central Iran by Dehbandi et al. (2017) concluded in a similar note that one of the guiding factor of fluoride behaiviour in soil is weathering or alteration of parent rock.

15.8 Mitigation Measures Fluoride contamination can be mitigated by: 1. Sourcing water from alternate sources

348

R. Chaudhuri et al.

2. Removal of excess fluoride through various technologies. Alternative Source of Water Several alternative sources of water are available, such as rainwater, surface water and groundwaters having low fluoride concentrations. Rainwater harvesting(RWH)is a much safer water source, compared to surface water, and has the ability to provide a simple cost effective solution. Uneven distribution of rainwater and the unavailability of storage infrastructure at the community level is a serious challenge posed in this method. RWH is primarily viable in inhabited areas. This may be conducted on different scales, such as on the rooftop, or in large catchment areas. Water Blending Water blending is an option where no treatment is required. Herem polluted and non polluted water are mixed so as to provide a balance of safe drinking water. Here non polluted water, that is water having fluoride levels less than 1.5 mg/L, are mixed with polluted water (F− > 1.5 mg/L) in such a proportion that the resulting water has fluoride content less than 1.5 mg/L. De-Fluoridation Technologies De-fluoridation involves removal of excess fluoride from drinking water through various methods. Through extensive research, several methods have been put into practice. Ion Exchange A anion-exchange resin containing quaternary ammonium functional groups is able to remove fluoride from water. The chloride ions of the resin get replaced by the fluoride ions. The process is continued till all the chloride sites on the resin are occupied by fluoride ions. The resin is then backwashed with supersaturated salt (sodium chloride) water, which leads to the fresh chloride ions replacing fluoride ions, recharging the resin, hence restarting the process. The stronger electronegativity of the fluoride ions is driving force behind this process. This process removes fluoride upto 90–95%. Coagulation and Precipitation The Nalgonda technique was developed by the National Environmental Engineering Research Institute, Nagpur, India. In this process, lime and alumina are added in calculated quantities. Lime aids in forming dense flocks for rapid settling of insoluble fluoride salts. As a rule of thumb, the dose of lime is 1/20th of that of the dose of aluminium salt. Coagulation and sedimentation removes the fluoride. A pH of 6–7 is achieved at which aluminium is completely removed. For disinfection bleaching powder is added to the raw water.

15 Fluoride Contamination in Groundwater—A Review

349

Membrane Process Reverse Osmosis (RO) membrane process is a preferred process providing safe drinking water as it is free from hassles associated with conventional removal methods (Hu and Dickinson 2006). As the name suggests, this physical process is the opposite of natural osmosis. Here, pressurised water is fed through the concentrated side of a semi- permeable membrane in order to overcome the osmotic pressure. On the basis of ionic size and charge, membrane rejects ions. Although, there are a plethora of fluoride removal methods available, most of these are not applicable at the local community level. This is because of the fact that majority of the processes are expensive and requires handling of advanced machinery and or chemicals. So, development of alternative, fluoride free water sources, is the most effective strategy that can be used at the grassroot level.

15.9 Conclusions This review has attempted to summarise various sources and occurrences of groundwater fluoride. It has been established that arid and semi arid regions are most prone to fluoride contamination. The presence of minerals bearing fluoride in the bed rock increases the susceptibility of fluoride contamination in groundwater, as opposed to surface water. Areas with highly contaminated groundwater have bedrock containing fluoride bearing minerals. especially of granitic composition. Rock weathering, low precipitation, and evaporation enhance fluorides in the groundwater of arid and semiarid regions. It has been observed that arid and semi arid regions of India, especially underlain by Archean Granitic rocks, are susceptible to Fluoride contamination in Groundwater. Fluoride concentration in groundwater is inversely proportional to the calcium concentration in groundwater, that is, higher the calcium concentration in groundwater, lower the fluoride concentration. Mineral solubility factor mainly restricts fluoride concentration in groundwater. It is important to understand the occurrence, distribution and mechanism of fluoride in groundwater so as to manage fluoride contamination in regionally high fluoride areas. It can also be concluded that Tectonics, along with lithology, play a vital role in concentrating fluoride in groundwater. Geothermal provinces associated with deep seated rifts or subduction zones, show high levels of fluoride contamination. It has been observed that most de-fluoridation processes are expensive and technology intensive. Therefore, they are not viable at the grassroot community level. Development of alternate water sources, mostly through surface water management and rain water harvesting is the most effective strategy.

350

R. Chaudhuri et al.

References Alemayehu T, Ayenew T, Kebede S (2006) Hydrogeochemical and lake level changes in the Ethiopian Rift. J Hydrol 316(1–4):290–300 Ali S, Thakur SK, Sarkar A, Shekhar S (2016) Worldwide contamination of water by fluoride. Environ Chem Lett 14(3):291–315 Anazawa K (2006) Fluorine and coexisting volatiles in the geosphere: the role in Japanese volcanic rocks. In: Tressaud A (ed) Fluorine and the environment: atmospheric chemistry, emissions, and lithosphere, vol 1 (Chapter 6), Elsevier, Amsterdam Araya O, Wittwer F, Villa A (1993) Evolution of fluoride concentration in cattle and grass following a volcanic eruption. Vet Hum Toxicol 35:437–440 Banerjee A (2015) Groundwater fluoride contamination: a reappraisal. Geosci Front 6:277–284 Barot VV (1998) Occurrence of endemic fluorosis in human population of North Gujarat, India: human health risk. Bull Environ Contam Toxicol 61:303–310. https://doi.org/10.1007/s00128 9900763 Batabyal AK, Gupta S (2017) Fluoride-contaminated groundwater of Birbhum district, West Bengal, India: interpretation of drinking and irrigation suitability and major geochemical processes using principal component analysis. Environ Monit Assess 189(8):369. https://doi.org/10.1007/s10 661-017-6041-0. Epub 2017 Jul 4 Bhattacharya HN, Chakrabarti S (2011) Incidence of fluoride in the groundwater of Purulia District, West Bengal: a geo-environmental appraisal. Curr Sci 101:152–155 Brindha K, Elango L (2011) Fluoride in groundwater: causes, implications and mitigation measures. In: Monroy SD (ed) Fluoride properties, applications and environmental management. Nova Publishers, New York, pp 111–136 Brindha K, Jagadeshan G, Kalpana L, Elango L (2016) Fluoride in weathered rock aquifers of southern India: managed aquifer recharge for mitigation. Environ Sci Pollut Res 23(9):8302– 8316 Camargo JA (2003) Fluoride toxicity to aquatic organisms: a review. Chemosphere 50:251–264 Chae GT, Yun ST, Mayer B, Kim KH, Kim SY, Kwon JS et al (2007) Fluorine geochemistry in bedrock groundwater of South Korea. Sci Total Environ Monitor Assess 187:497. https://doi. org/10.1007/s10661-015-4723-z Chandrasekhar V, Chandrasekharam D (2008) Enhanced geothermal resources in NE Deccan Province, India. Geothermal Res Council Trans 32:71–75 Chandrasekhar V, Chandrasekharam D (2011) Geothermal systems in the Himalayas. GRC Trans 35:1165–1168 Chatterjee S, Sharma S, Ansari MA, Deodhar AS, Low U, Sinha UK, Dash A (2016) Characterization of subsurface processes estimation of reservoir temperature in Tural Rajwadi geothermal fields, Maharashtra, India. Geothermics 59:77–89 Chidambaram S, Bala Krishna Prasad M, Manivannan R, Karmegam U, Anandhan P, Prasanna MV, Manikandan S (2013) Environmental hydrogeochemistry and genesis of fluoride in groundwaters of Dindigul district, Tamilnadu (India). Environ Earth Sci 68(2):333–342 Chopra S, Choudhury P (2011) A study of response spectra for different geological conditions in Gujarat, India. Soil Dyn Earthq Eng 31(11):1551–1564 Chowdhury A, Adak MK, Mukherjee A, Dhak P, Khatun J, Dhak D (2019) A critical review on geochemical and geological aspects of fluoride belts, fluorosis and natural materials and other sources for alternatives to fluoride exposure. J Hydrol 574:333–359 Churchill HV, Rowley RJ, Martin LN (1948) Fluorine content of certain vegetation in a western pennsylvania area. Anal Chem 20:69–71 Czarnowski W, Wrzes´niowska K, Krechniak J (1996) Fluoride in drinking water and human urine in northern and central Poland. Sci Total Environ 191:177–184 D’Alessandro W, Bellomo S, Parello F (2012) Fluorine adsorption by volcanic soils at Mt, Etna, Italy. Appl Geochem 27(6):1179–1188 Dahi E (2009) Fluoride belts of the world. http://www.de-fluoride.net/occurrence.html

15 Fluoride Contamination in Groundwater—A Review

351

Datta AS, Chakrabortty A, Dalal SS, Lahiri SC (2014) Fluoride contamination of underground water in West Bengal, India. Fluoride 47:241–248 Dehbandi R, Moore F, Keshavarzi B, Abbasnejad A (2017) Fluoride hydrogeochemistry and bioavailability in groundwater and soil of an endemic fluorosis belt, central Iran. Environ Earth Sci 76(4):1–15 Dey RK, Swain SK, Mishra S, Sharma P, Patnaik T, Singh VK et al (2012) Hydrogeochemical processes controlling the high fluoride concentration in groundwater: a case study at the Boden block area, Orissa, India. Environ Monit Assess 184:3279–3291 Dharmagunawardhane HA, Malaviarachchi SPK, Burgess W (2016) Fluoride content of minerals in gneissic rocks at an area of endemic dental fluorosis in Sri Lanka: estimates from combined petrographic and electron microprobe analysis. Ceylon J Sci 45(1) Dhiman SD, Keshari AK (2006) Hydrogeochemical evaluation of high-fluoride groundwaters: a case study from Mehsana district, Gujarat, India. Hydrol Sci J 51(6):1149–1162 Dissanayake CB (1991) The fluoride problem in the groundwater of Sri Lanka-environmental management and health. Int J Environ Stud 38:137–156 Dissanayake CB, Chandrajith R (2009) Introduction to medical geology. Springer Science & Business Media Edmunds M, Smedley P (2005) Essentials of medical geology, Chapter 12. Elseveiser Inc., BGS, NERC, Oxford Farooqi A, Masuda H, Firdous N (2007) Toxic fluoride and arsenic contaminated groundwater in the Lahore and Kasur districts, Punjab, Pakistan and possible contaminant sources. Environ Pollut 145:839–849 Fawell J, Bailey K, Chilton J, Dahi E, Fewtrell L, Magara Y (2006) Fluoride in drinking water. World Health Organization (WHO), IWA Publishing, London, pp 97–117 Furi W, Razack M, Abiye TA, Ayenew T, Legesse D (2011) Fluoride enrichment mechanism and geospatial distribution in the volcanic aquifers of the Middle Awash basin, Northern Main Ethiopian Rift. J Afr Earth Sc 60(5):315–327 Gasparon M, Innocenti F, Manetti P, Peccerillo A, Tsegaye A (1993) Genesis of the Pliocene to recent bimodal mafic-felsic volcanism in the Debre Zeyt area, central Ethiopia: volcanological and geochemical constraints. J African Earth Sci (and the Middle East) 17(2):145–165 Ghiglieri G, Pistis M, Abebe B, Azagegn T, Engidasew TA, Pittalis D, ... Haile T (2020) Threedimensional hydrostratigraphical modelling supporting the evaluation of fluoride enrichment in groundwater: Lakes basin (Central Ethiopia). J Hydrol: Regional Stud 32:100756 Ghosh A, Mukherjee K, Ghosh SK, Saha B (2013) Sources and toxicity of fluoride in the environment. Res Chem Intermed 39:2881–2915. https://doi.org/10.1007/s11164-012-0841-1 Ghosh SB, Bhaumik R, Mondal NK (2016) Optimization study of adsorption parameters for removal of fluoride using aluminium-impregnated potato plant ash by response surface methodology. Clean Technol Environ Policy 18:1069–1083 Grandjean P, Olsen JH, Jensen OM, Juel K (1992) Cancer incidence and mortality in workers exposed to fluoride. JNCI: J Nat Cancer Inst 84(24):1903–1909 Gupta SK, Deshpande RD, Agarwal M, Raval BR (2005) Origin of high fluoride in groundwater in the north Gujarat-Cambay region, India. Hydrogeol J 13:596–605 Gupta S, Mondal D, Bardhan A (2012) Geochemical provenance and spatial distribution of fluoride in groundwater in parts of Raniganj coal field, West Bengal, India. Arch Appl Sci Res 4(1):292– 306 Handa B (1988) Fluoride occurrence in natural waters in India and its significance. Bhu-Jal News 3:31–37 Handa BK (1975) Geochemistry and genesis of Fluoride-Containing ground waters in India. Groundwater 13(3):275–281 Harris N, Vance D, Ayres M (2000) From sediment to granite: timescales of anatexis in the upper crust. Chem Geol 162(2):155–167 Hussain I, Arif M, Hussain J (2012) Fluoride contamination in drinking water in rural habitations of Central Rajasthan, India. Environ Monitor Assess 184(8):5151–5158

352

R. Chaudhuri et al.

Jacks G, Bhattacharya P, Chaudhary V, Singh KP (2005) Controls on the genesis of some highfluoride groundwaters in India. Appl Geochem 20(2):221–228 Kaila KL (1988) Mapping the thickness of Deccan Trapp flows in India from DSS studies and inference about a hidden Mesozoic Basin in the Narmada-Tapti region. Memoir-Geological Society of India 10:91–116 Karaku¸s H (2015) Helium and carbon isotope composition of gas discharges in the Simav Geothermal Field, Turkey: Implications for the heat source. Geothermics 57:213–223 Karro E, Uppin M (2013) The occurrence and hydrochemistry of fluoride and boron in carbonate aquifer system, central and western Estonia. Environ Monit Assess 185:3735–3748 Karthikeyan K, Nanthakumar K, Velmurugan P, Tamilarasi S, Lakshmanaperumalsamy P (2010) Prevalence of certain inorganic constituents in groundwater samples of Erode district, Tamilnadu, India, with special emphasis on fluoride, fluorosis and its remedial measures. Environ Monit Assess 160(1):141–155 Kitalika AJ, Machunda RL, Komakech HC, Njau KN (2018) Fluoride variations in rivers on the slopes of mount meru in Tanzania. J Chem Kloos H, Haimanot RT (1999) Distribution of fluoride and fluorosis in Ethiopia and prospects for control. Tropical Med Int Health 4(5):355–364 Li P, Qian H, Wu J, Chen J, Zhang Y, Zhang H (2014) Occurrence and hydrogeochemistry of fluoride in alluvial aquifer of Weihe River, China. Environ Earth Sci 71:3133–3145 Maadid H, El Mzouri EH, Mabrouk A, Koulali Y (2017) Fluoride content in well waters for human and animal consumption with reported high incidence levels of endemic fluorosis in Beni Meskine (Morocco). Euro-Mediterr J Environ Integr 2:11 Malayeri BE, Noori M, Jafari M (2012) Using the pollenviability and morphology for fluoride pollution biomonitoring. Biol Trace Element Res 147:315–319. https://doi.org/10.1007/s12011011-9290-8 Mandal BK, Suzuki KT (2002) Arsenic round the world: a review. Talanta 58:201–235 Manikandan S, Chidambaram S, Ramanathan AL, Prasanna MV, Karmegam U, Paramaguru P, Jainab I (2014) A study on the high fluoride concentration in the magnesium-rich waters of hard rock aquifer in Krishnagiri district, Tamilnadu, India. Arab J Geosci 7(1):273–285 Marbaniang DG, Diengdoh DF, Nongpiur CL (2014) Preliminary investigation of the physicochemical properties of a geothermal spring (hot spring) located at Jakrem, Meghalaya, India. Keanean J Sci 3:35–42 Minissale A, Vaselli O, Chandrasekharam D, Magro G, Tassi F, Casiglia A (2000) Origin and evolution of ‘intracratonic’ thermal fluids from central-western peninsular India. Earth Planet Sci Lett 181(3):377–394 Misra AK, Mishra A (2007) Study of quaternary aquifers in Ganga Plain, India: focus on groundwater salinity, fluoride and fluorosis. J Hazard Mater 144(1):438–448. https://doi.org/10.1016/ j.jhazmat.2006.10.057 Misra AK, Mishra A, Premraj (2006) Escalation of groundwater fluoride in the Ganga alluvial plain of India. Fluoride 39(1):35–38 Mondal D, Gupta S, Reddy DV, Nagabhushanam P (2014) Geochemical controls on fluoride concentrations in groundwater from alluvial aquifers of the Birbhum district, West Bengal, India. J Geochem Explor 145:190–206 Mondal NC, Prasad RK, Saxena VK, Singh Y, Singh VS (2009) Appraisal of highly fluoride zones in groundwater of Kurmapalli watershed, Nalgonda district, Andhra Pradesh (India). Environ Earth Sci 59(1):63–73 Mukherjee I, Singh UK (2018) Groundwater fluoride contamination, probable release, and containment mechanisms: a review on Indian context. Environ Geochem Health 40(6):2259–2301 Narsimha A, Sudarshan V (2017) Assessment of fluoride contamination in groundwater from Basara, Adilabad district, Telangana state, India. Appl Water Sci 7(6):2717–2725 Nayak AK, Chinchmalatpure AR, Rao GG, Jha SK, Khandelwal MK (2009) An assessment of fluoride in the coastal aquifer of the Bara tract in Bharuch district, Gujarat (India). Environ Monit Assess 158(1):315–317

15 Fluoride Contamination in Groundwater—A Review

353

Ozsvath DL (2009) Fluoride and environmental health: a review. Rev Environ Sci Biotechnol 8:59–79 Patra PK, Mandal B, Chakraborty S (2010) Hydrogeochemistry of fluoride rich groundwater in Birbhum district of West Bengal, India. The Ecoscan 4(2&3):209–211 Rango T, Bianchini G, Beccaluva L, Ayenew T, Colombani N (2009) A hydrogeochemical study in the Main Ethiopian Rift: New insights into the source and enrichment mechanism of fluoride. Environ Geol 58:109–118 Ranjan R, Ranjan A (2015) Fluoride toxicity in animals SpringerBriefs in animal sciences, 1st edn. Springer, Berlin Rasool A, Xiao T, Baig ZT, Masood S, Mostafa KMG, Iqbal M (2015) Co-occurrence of arsenic and fluoride in the groundwater of Punjab, Pakistan: source discrimination and health risk assessment. Environ Sci Pollut Res 22:9729–19746 Rasool A, Farooqi A, Xiao T, Ali W, Noor S, Abiola O, Nasim W et al (2018) A review of global outlook on fluoride contamination in groundwater with prominence on the Pakistan current situation. Environ Geochem Health 40:1265–1281 Reddy AGS, Reddy DV, Kumar MS (2016) Hydrogeochemical processes of fluoride enrichment in Chimakurthy pluton, Prakasam district, Andhra Pradesh, India. Environ Earth Sci 75(8):1–17 Roelandts I, Robaye G, Weber G, Delbrouck J, Duchesne J (1987) Determination of fluorine by proton-induced gamma-ray emission (PIGE) spectrometry in igneous and metamorphic charnockitic rocks from Rogaland (SW Norway). J Radioanal Nucl Chem 112(2):453–460. https://doi.org/10.1007/BF02132377 Rosi M, Papale P, Lupi L (2003) Volcanoes. Firefly Books Saemundsson K (2010) East African rift system—An overview. Short Course V on Exploration for Geothermal Resources, organized by UNU-GTP, GDC and KenGen, at Lake Bogoria and Lake Naivasha, Kenya, 29 Sarma D, Rao S (1997) Fluoride concentrations in ground waters of Visakhapatnam, India. Bull Environ Contam Toxicol 58:241–247 Saxena V, Ahmed S (2003) Inferring the chemical parameters for the dissolution of fluoride in groundwater. Environ Geol 43:731–736 Selvam S (2015) A preliminary investigation of lithogenic and anthropogenic influence over fluoride ion chemistry in the groundwater of the southern coastal city, Tamilnadu, India. Environ Monitor Assess 187(3):1–20 Shankar PV, Kulkarni H, Krishnan S (2011) India’s groundwater challenge and the way forward. Economic and political Weekly, pp 37–45 Singaraja C, Chidambaram S, Anandhan P, Prasanna MV, Thivya C, Thilagavathi R, Sarathidasan J (2014) Geochemical evaluation of fluoride contamination of groundwater in the Thoothukudi district of ,Tamilnadu India. Appl Water Sci 4(3):241–250 Singh HK, Chandrasekharam D (2010) Evaluation of Tuwa geothermal system through waterrock interaction experiment. In: Water-Rock Interact-13. Taylor and Francis Group, London, pp 181–183 Singh HK, Chandrasekharam D, Vaselli O, Trupti G, Singh B, Lashin A, Arifi NA (2015a) Physicochemical characteristics of Jharkhand and West Bengal thermal springs along SONATA mega lineament, India. J Earth Syst Sci 124(2):419–430 Singh R, Singh S, Parihar P, Singh VP, Prasad SM (2015b) Arsenic contamination, consequences and remediation techniques: a review. Ecotoxicol Environ Saf 112:247–270 Sivasankar V, Darchen A, Omine K, Sakthivel R (2016) Fluoride: A world ubiquitous compound, its chemistry, and ways of contamination. In: Sivasankar V (ed) Surface modified carbons as scavengers for fluoride from water. Springer, Cham, pp 5–32 Srivastava A, Lohani M (2015) Fluorine, a Dreaded element: a review on occurrence of fluorine in environment and its standard methods of analysis. Int J Environ Res Dev 5:7–21 Su C, Wang Y, Xie X, Li J (2006) Aqueous geochemistry of high-fluoride groundwater in Datong basin, Northern China. J Geochem Explor 135:79–92

354

R. Chaudhuri et al.

Su C, Wang Y, Xie X, Li J (2013) Aqueous geochemistry of high-fluoride groundwater in Datong basin, Northern China. J Geochem Explor 135:79–92 Sundaram RM, Pareek S (1995) Quaternary facies and palaeo environment in north and east of Sambhar lake, Rajasthan. J Geol Soc India (Online archive from Vol 1 to Vol 78) 46(4):385–392 Susheela AK (1999) Fluorosis management programme in India. Curr Sci 77(10):25 Susheela AK (2007) The triumphs in fluoride and fluorosis mitigation in India: strategies for prevention and control. On-Farm Land Water Manage 123 Susheela AK, Bhatnagar M, Vig K (2005) Excess fluoride ingestion and thyroid hormone derangements in children living in Delhi, India. Fluoride 38(2):98–108 Suthar S, Garg VK, Jangir S, Kaur S, Goswami N, Singh S (2008) Fluoride contamination in drinking water in rural habitations of Northern Rajasthan, India. Environ Monitor Assess 145(1):1–6 Thapa R, Gupta S, Guin S, Kaur H (2017) Assessment of groundwater potential zones using multiinfluencing factor (MIF) and GIS: a case study from Birbhum District, West Bengal. Appl Water Sci 7:4117–4131 Teotia M, Teotia SPS, Singh KP (1998) Endemic chronic fluoride toxicity and dietary calcium deficiency interaction syndromes of metabolic bone disease and deformities in India: year 2000. Indian J Pediatr 65:371–381 Thivya C, Chidambaram S, Rao MS, Thilagavathi R, Prasanna MV, Manikandan SJAWS (2017) Assessment of fluoride contaminations in groundwater of hard rock aquifers in Madurai district, Tamil Nadu (India). Appl Water Sci 7(2):1011–1023 UNICEF (1999) State of the art report on the extent of fluoride in drinking water and the Resulting endemicity in India. Report by Fluorosis Research and Rural Development Foundation for UNICEF, New Delhi Vithanage M, Bhattacharya P (2015) Fluoride in the environment: sources, distribution and defluoridation. Environ Chem Lett 13:131–147 Walna B, Kurzyca I, Bednorz E, Kolendowicz L (2013) Fluoride pollution of atmospheric precipitation and its relationship with air circulation and weather patterns (Wielkopolski National Park, Poland). Environ Monit Assess 185(7):5497–5514 Warren C, Burgess WG, Garcia MG (2005) Hydrochemical associations and depth profiles of arsenic and fluoride in Quaternary loess aquifers of northern Argentina. Mineral Mag 69(5):877–886 World Health Organization (WHO) (1984) Fluorine and fluoride, vol 36. Environmental Health Criteria, Geneva

Chapter 16

Analysis of Basin Morphometry for the Prioritization Using Geo-Spatial Techniques: A Case Study of Debnala River Basin, Jharkhand, India Abdur Rahman, Jaidul Islam, and Partha Pratim Sarkar

Abstract A crucial step in understanding the issue of land resource management is to perform the morphometric analysis of a river basin in order to determine changing pattern of the river and its basin characteristics. Geospatial techniques (RS and GIS) have recently arisen as vital tools for evaluating environmental issues and ensuring the complete advancement of a river basin area. Both these tools are combinedly used to determine the numerical account of river basin morphometry in an undistinguishable articulate manner. In this venture, basin morphometric methods have been employed to comprehend the type of soil erosion impending in the study area. The present study considers the river basin of Debnala (located in Purba Singbhum District, Jharkhand). River Debnala is a south bank non-perennial tributary of Dulung River which was invented in Jharkhand at an elevation of over 134 m. In the study, different morphometric parameters (Linear, Areal, and Relief) along with DEM alignment have been derived at grid level (1 km2 × 1 km2 ) over the basin. Finally, all the morphometric parameters have been combined and a weighted score has also been assigned to find the precedence of soil dispassion rate in the study area. The result shows that 1st order stream shows that the fourteen (14) Sub-basin are very high soil erosion potential zone. As a whole in the study area, forty (40) sub-basins recorded very remarkable erosion potential capacity to produce sediments which needs special attention. The result of morphometric analysis-based prioritization shows that soil erosion and sediment production rate are inversely proportional to each other. Keywords DEM · Basin morphometry · Erosion potential · Prioritization · Debnala A. Rahman Department of Geography, Dr. Kanailal Bhattacharyya College, Howrah, India J. Islam (B) Assistant Professor, Department of Geography, PRMS Mahavidyalaya, Bankura, India e-mail: [email protected] P. P. Sarkar Research Scholar, Dept. of Geography, Bankura University, Bankura, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_16

355

356

A. Rahman et al.

16.1 Introduction Soil is one of the most valuable natural resources on earth. Without soil, the existence of life is quite impossible, and human being is likely to become more dependent on the soil in the near future (Bardy and Weil 2002). Though it takes thousands of years for the formation of soil, the rate of soil erosion is very speedy about to with concerning soil formation. Presently, soil erosion is a big problem not only in India but also all over the world. In the past 150 years, the topsoil on earth is thought to have worsened by half (FAO and ITPS 2015). If soil erosion continues at such an alarming rate, then the formation of new soil would be very difficult. Though India shares 2.42% of the global land, the share of the world population in India is about 16% (Kumar et al. 2011) and over-population leads to the rapid run out of natural resources in the country. Many natural phenomena like wind, water, rill, gully erosion etc., play a significant role in soil erosion. Besides these natural phenomena, many anthropogenic activities are also responsible for soil erosion. Approximately 53% of India’s entire geographical area is affected negatively by soil erosion, the annual rate of soil erosion is around 5334 Mt (16.4 t ha−1 ) (Jain et al. 2001; Srinivas et al. 2002; Pandey et al. 2009). Priority-wise adoption of soil conservation measures is essential, as it not only reduces soil erosion but also increases the water availability at the surface and the underground, which ultimately can minimize the frequency of droughts as well as floods (Ahiwar et al. 2019; Makwana and Tiwari 2016). The rapid advancement in remote sensing and GIS has led to the innovation of new techniques for facilitating the mapping of the erosion-prone areas and degradable lands (Skidmore et al. 1997). Prioritizing erosion-prone catchment areas is important when there aren’t enough financial resources to carry out a conservation plan. A location that will likely generate a significant amount of sediment, be highly prone to erosion and be considered for greater priority treatment (Gajbhiye et al. 2014). The measurement of sediment production rate also helps to assess the vulnerability of a watershed to soil erosion (Sunil et al. 2010). The present study mainly focuses on soil erosion and sediment production rate in the Debnala river basin. This study employed a morphometric prioritization method for measuring sub-basins wise soil erosion and sedimentation production rate of the Debnala river basin with the help of Geo-spatial techniques.

16.2 Study Area Debnala River is one of the important tributaries of the Dulung River (Bhattacharya et al. 2019). The origin place of Debnala is in Jharkhand at 100 m height on the western divide. Since its origin this river flows in the south-eastern direction and crosses the border between Jharkhand to West Bengal. In the lower course, it joins with the Dulung at a height of about 55 m. The geographical extension of the Debnala basin is bounded by 86° 43' E to 86° 53' 25'' E and 22° 18' N to 22° 27' N (Fig. 16.1).

16 Analysis of Basin Morphometry for the Prioritization Using Geo-Spatial …

357

Fig. 16.1 Location map of the study area

The entire catchment area covers an area of 103.680 km2 . The basin area falls in the tropical savannah type (Aw) climate as per Koppen’s climatic classification, and accordingly climatic characteristics of this basin are hot summer and moderate rainfalls (75 cm) during the monsoon season and dry winter. The banks of the Debnala are associated with tremendous gully erosion whereas valley-side widening is an implied feature of the basin. One characteristic feature of the Debnala is the origin of numerous short tributaries from the basin, a little larger than gullies as a matter of fact that joins at a high angle throughout its course.

358

A. Rahman et al.

16.3 Methodology The study area of the Debnala River basin is delineated from topographical sheets no.73 J/11 and 73 J/15 (scale 1: 50,000) of the 1975 edition published by Survey of India (SOI) and ASTER DEM (Advance Space borne Thermal Emission and Reflection Radiometer Digital Elevation Model) from USGS earth explorer. After the demarcation of the basin area, the drainage network has been organized as per the Strahler method, and then the drainage network has been divided into Sub-basin. 1 km2 × 1 km2 morphometric grids have been created on the basin, and then DEM, stream network, and slope have been overlaid on the grids for generating a morphometric database (Fig. 16.2) with the help of ArcGIS & MapInfo platform. After that, various morphometric parameters such as Maximum Elevation, Relative Relief, Average Slope, Drainage Density, Stream Frequency, Drainage Texture, Ruggedness Index, Dissection Index, Cumulative Relative Massiveness Index, and Roughness Index have been derived grid-wise as done by other researchers (Saha et al. 2020). After the computation of morphometric parameters, a statistical analyses such as descriptive statistics (mean, standard deviation, skewness) and inferential statistics i.e., correlation matrix and factor loading have been used. Morphometric analysis of every sub-basin has been done and then priority-wise ranking has been given for each sub-basin. This study calculates compound parameter (CP) based on the priority –wise ranking of sub basin and ranks as per the weight of CP. Based on the value of the compound parameter (CP), priority levels have been given ranks. The basin(s) with a lower average score of CP is assigned the highest priority and the basin with the highest average score of CP is assigned the least priority status, it is depicted from the analysis that around forty sub-basins has a high erosion rates which have high capacity to produce sediment. Compound Parameter Constant =

∑

(Pri ∗ Fwi )

where, Pri is ‘Preliminary ranking’ of ‘ith’ parameter for sub- watershed, Fwi is ‘Final weightages’, of same ‘ith’ parameter. The same morphometric parameter used to calculate the sedimentation production rate based on Josh and Dash (1982) model, sediment production rate has been calculated. The SPR mathematical method can be expressed as follows (Table 16.1; Fig. 16.3): Log (SPR) = 419.80 + 48.64 log (100 + FF ) −1337.77 log (100 + CR ) −1165.65 log (100 + CC ) where, SPR is the sediment production rate in ha−m/100 km2 /year, FF is the form factor, CR is the circularity ratio, and CC is the Compactness coefficient.

16 Analysis of Basin Morphometry for the Prioritization Using Geo-Spatial …

359

Fig. 16.2 Using the SOI toposheet and DEM morphometric grid overlay on a contour and b stream network of Debnala river basin

16.4 Morphometric Analysis Measurement and mathematical study of the configuration of the earth’s surface, as well as the size and shape of its landforms, is known as morphometry (Clarke 1966; Rai et al. 2017; Makwana and Tiwari 2016; Dikpal et al. 2017). On the other hand, It can be defined as linear, areal and relief aspects of any basin (Nag and Chakraborty 2003). The development of morphometric technique can be correlated with the path breaking advancement of quantitative explanation of drainage basin geometry and its tributaries or distributaries, which assists to differentiate one drainage basin from another. “This advancement also plays a major role to observe the effect of lithology, rock structure, rainfall etc. on drainage basin” (Esper 2008; Magesh et al. 2011; Bali et al. 2012). For the study different morphometric aspects like linear, areal, and relief features (Bhattacharya et al. 2019) have been considered to prioritize the micro watershed which plays an important role to demarcate erosion susceptible risk assessment zone (Rao et al. 2020). In the morphometric analysis, linear and relief parameters have a positive link with erosion susceptibility but aerial characteristics have an inverse association with erodibility (Nooka et al. 2005; Chauhan et al. 2016; Makwana and Tiwari 2016; Bhattacharya et al. 2019). The key significant aspects of drainage basin (linear, aerial, and relief aspects) are discussed below.

16.5 Linear Aspect Linear parameter of the river basin is one-dimensional approach and it includes several linear aspects such as the number of streams, stream order, basin parameter, basin length, bifurcation ratio, overland flow and length of channels etc. Knowledge

360

A. Rahman et al.

Table 16.1 List of variables Parameters

Description

Stream Order (u)

Strahler (1952)

Bifurcation ratio (Rb)

Horton (1945) Nu/Nu + 1 (Nu, total Number of Stream, Nu + 1, Number of stream next higher order)

Mean Bifuraction ratio (Mb)

Strahler (1952)

Mean Stream Length (Lu)

Strahler (1952) (Lu/Nu), Lu Total Length of the Stream, Nu, Total number of stream)

Mean length Ratio (Lu)

Horton (1945) “(Lu/Lu−1), (Lu Total stream Length of the order, Lu−1, Total length of stream next lower order)”

Mean Area Ratio (Au)

Horton (1945) “(Lu/Lu−1), (Lu Total stream Length of the order, Lu−1, Total length of stream next lower order)”

Mean Relief Ratio (Hu)

Horton (1945) “(Hu/Hu−1), (Hu Total basin relief of order, Lu−1, Total length of stream next lower order)”

Circularity Ratio (Cr)

Miller (1953) (Rc = 4π Ab/Pb2 ), π = 3.14, Ab = Area of basin, Pb = Basin perimeter

Elongation Ration (Er)

Schumm (1956) (Er = 2 * [(Ab)0.5/(Dl*(π)0.5)], Ab = Area of basin, Dl = Maximum Length of the basin

From Factor (Ff)

Horton (1932), (Ff = Ab/Dl2), Ab = Area of basin, Dl = Maximum Length of the basin

Compactnes Cofficent (Cc)

Richardson (1961) (Cc = Pb/(2 (π Ab)0.5) Pb = Basin perimeter, Ab = Basin area

Length of Over land flow (LOF)

Chorley (1969), (Basin Ccm/2, Ccm = Channel constant maintenance

Relative Relief (RR)

Smith (1935) (Maximum elevation−Minimum elevation)

Average Slope (As)

Horton (1945), As = (tan−1 ((Total length of contours × Contour Interval)/Area)

Drainage Density (DD)

Horton (1945), (DD = Total length of streams within a basin/Area)

Stream Frequncy (SF)

Horton (1945), (SF = Total number of streams within a basin/Area)

Drainage Texture (DT)

Smith (1950), (DT = Basin SF x BASIN DD)

Ruggedness Index (RI)

Horton (1945), RI = (Relative relief of basin × Drainage density of basin)/1000)

Diseectin Index (DI)

De Smet (1951), DI = (Realtive relief/Maximum elevatio of basin) (continued)

16 Analysis of Basin Morphometry for the Prioritization Using Geo-Spatial …

361

Table 16.1 (continued) Parameters

Description

Roughness Index (RI)

Hook (1955), RI = (N x (M/4)/4) × 10),where, N = entire number of intersections of contour lines with two sets of perpendicular grids and M = is the distance between grid lines

Co-efficient of Relative Massiveness (CRM) CRM = (Mean Elevation−Minimum Elevation)/ Relative Relief Co-efficient of Relative Massiveness (CRM) Merline (1965), CRM = (Mean elevation−Minimum elevation/Realtive relief of basin) Source Computed by author

Flow chart

Topo Map (73 J/11, 73 J/15)

Morphometric Mapping Maximum Relative Relief

Geo-referencing

Stream Frequency

Digitization Stream

Drainage Density

Stream Network Morphometric Analysis

Drainage Texture Ratio

Liner Parameter

Dissection

Ruggedness Index

Areal Parameter Relief Parameter

Erosion and Sedimentation

Fig. 16.3 Flow chart of methodology

362

A. Rahman et al.

about these parameters gives the idea about the channel pattern of the drainage system indicates the control of the structural pattern on the same. The parameters which are considered for prioritization of sub-watersheds, are stream number (Nu), Stream order (Su), bifurcation ratio (Rb), stream length (Lu), and length of overland flow (Lg). All these parameters are calculated using the formula given by different scholars. Stream Order: This is the first technique of morphometric analysis which was first invented by Horton in (1945) and modified by Strahler in (1952). “According to Strahler (1952), the smallest fingerprint is numbered as 1st order, the 2nd order of stream forms where two 1st order streams join and a 3rd order stream forms where two 2nd order streams join and so on”. “The stream order depends on the shape, size and relief of basin characteristics of such basin” (Haghipour and Burg 2014; Mahala 2020). The study found up to 4th order stream in Debnala river basin. After analysis, it is found that the number of stream segments is 63 for the 1st order stream and for the 2nd, the 3rd, and the 4th, they are found to be 14, 02, and 01 respectively (Table 16.2; Fig. 16.4). Bifurcation Ratio: “It is the ratio of the number of streams of a given order (μ) to its next higher order (μ + 1)” (Horton 1945). According to Strahler (1952), the bifurcation ratio exhibits relatively small regional variation, the exception is found in the case of strong control of geological formation on the basin (Kanhaiya et al. 2019). It is regarded as a crucial factor in determining any basin’s water carrying capacity and associated risk of flooding (Mahala 2020; Kanhaiya et al. 2019). The value of the bifurcation ratio varies from 2 to 5. The study shows that the river basin is dissected in nature. In this study area, 3rd order stream shows the highest bifurcation value (7) and the 4th order stream shows the lowest value which is 02. The mean bifurcation ratio of the Debnala river basin is 4.50 which indicate the river basin mountainous, hilly dissected basin (Table 16.3). Based on mean bifurcation ratio, it is inferred that the basin soil erosion is much more active. Table 16.2 Drainage network properties of the Debnala river basin Order (u)

No. of stream Segment (Nu)

Total length (Lu km)

Mean length (Lu Km)

Mean basin area (Au km2 )

Mean basin relief (Hu M)

Mean CCM (km2 /km)

1

63

58.04

0.92

0.39

16

0.35

2

14

36.64

2.62

3.57

34

0.7

3

2

5.94

2.97

17.56

53

0.89

4

1

15.29

15.29

103.68

87

1.1

Source Computed by author

16 Analysis of Basin Morphometry for the Prioritization Using Geo-Spatial …

363

Fig. 16.4 Drainage network of Debnala basin. Source Computed by the authors using SOI Toposheet and DEM

Table 16.3 Magnitude ratios of the drainage network of the Debnala River Basin Order (u)

Nu

Rb

RL

RA

Rr

Rc

1

63

–

–

–

–

–

2

14

4.5

2.84

9.1

2.12

1.99

3

2

7

1.13

4.92

1.55

1.27

4

1

2

5.15

5.91

1.64

1.23

Total

13.5

9.12

19.93

5.31

4.49

Average

4.5

3.04

6.64

1.77

1.5

Source Computed by author using SOI Toposheet and DEM

16.6 Laws of Drainage Composition Drainage composition means the organizational structure of the drainage networks (Horton proposed the concept of laws of drainage composition). Major elements of the drainage composition are stream number, total length, mean length, mean basin

364

A. Rahman et al.

area, and mean basin relief, mean channel constant maintenance and investigated relation with stream order (Fig. 16.5). On analyzing, it is found that stream order is inversely proportional to stream with number segment(s). At the same time, the study also reveals that other factors like mean length, mean basin area, mean basin relief, and mean CCM are directly proportional to stream order. The following equations have been usedLaw of stream Numbers: Nu = Rb (s−u) (1 ≤ u ≤ = s) Horton (1945); Law of stream lengths: Lu = L1 · RL (u−1) Horton (1945); Law of Basin Areas: Au = A1 · Ra (u−1) Horton (1945);

Fig. 16.5 Stream order and its relation of different morphometry aspect

16 Analysis of Basin Morphometry for the Prioritization Using Geo-Spatial …

365

Law of stream Gradients: Su = S1 · Rs (s−u) ) Horton (1945), Morisawa (1962); Law of Basin Relief: Hu = H1 · RR (u−1) Morisawa (1962); Law of Allometric growth: A = a1b Woldenberg (1966), Strahler (1969); where, Nu = number of stream of order ‘u’ in a basin of order ‘s’, Rb = bifurcation ratio, Lu = mean length of streams of order ‘u’, R1 = stream length ratio, Au = mean area drained by streams of order ‘u’, Ra = basin area ratio, Su = average channel slope of streams of order ‘u’, Rs = gradient ratio ∑ or slope ratio, Hu = average relief of basins of order ‘u’, Rr = basin relief ratio, Lu = cumulative stream length, b = an exponent (Patel et al. 2013). Stream Lengths: As the stream ordering increase, the mean length of the stream segment of each order also increases (Chorley 1969). The analysis depicts that in the case of 1st order stream, the stream length is 58.04 km and the mean length is 0.92 km, while in the case of 2nd order stream, the total length is 36.64 km and the mean length is 2.62; in the case of 3rd order stream, the total length is 5.940 km and mean length is 2.97; and in the 4th order stream, the total length is 15.290 and mean length is 15.29. The result shows that the relation between stream order and total length is inversely proportional whereas the relation between stream order and mean length is proportional.

16.7 Areal Aspect The areal aspect which depends on the spatial scale variation of the drainage basin is the two-dimensional approach and it includes several attributes. For prioritization purposes, each of the attributes acts as factor which indicates basin shape. An important factors of Arial Aspects is elongation ratio, drainage texture, circularity ratio, stream frequency etc. (Fig. 16.6). The Arial Aspects which are important for assessing the erosion of soil and the rate of sediment production of the Debnala basin are discussed in the following way; Elongation Ratio: “Elongation ratio is the ratio of the diameter of a circle having the same area as of basin to the maximum basin length” (Schumm 1956). “It is also considered as a significant index to determine basin shape” (Gayen et al. 2013; Rai et al. 2017). The range of the Elongation Ratio varies from ‘0’ which indicates maximum elongation to near ‘1’, which indicates maximum circularity (Mahala 2020). “If the elongation value is close to 1, it means that there are minimum geomorphological controls over the river basin” (Strahler 1964). The regions with low elongation ratio values are susceptible to more erosion whereas regions with high values corresponds to high infiltration capacity and low runoff (Govindaraj and Lakshumanan 2019; Asfaw and Workineh 2019). The 4th order Debnala basin elongation ratio shows 0.63 which indicates the basin is moderately elongated.

366

A. Rahman et al.

Fig. 16.6 Magnitude of different parameter of Debnala basin. Source Computed by author using SOI Toposheet and DEM

Form Factor: As per Horton (1932), the Form Factor is the ratio of the area of the basin to the square of basin length (Mahala 2020; Makwana and Tiwari 2016; Magesh et al 2013; Soni 2017; Dikpal et al. 2017; Ismail et al. 2022; Asfaw and Workineh 2019). This Form Factor indicates the flow characteristics of any basin (Castillo et al. 1988). A higher value of form factor indicates the circular shape of the basin which indicates high peak flow and the low form factor suggests an elongated basin, which represents stumpy peak flow with lengthier duration (Bali et al 2011). In the study area of the Debnala sub-basin, the value of the form factor in the 4th order sub-basin is 0.32 which means the sub-basin is elongated. Circularity Ratio: “Circularity ratio is defined as the ratio between the areas of the basin to the area of a circle, having the same perimeter on a circumference” (Strahler 1964). The value of the circularity ratio varies from 0 to 1, the higher the value of CR the more circular shape of basin ‘0’ (Makwana and Tiwari 2016; Ismail et al. 2022; Asfaw and Workineh 2019). The study area found that 4th order Basin of Debanala River shows the value of circularity ratio is 0.34 which indicates the basin is elongated rather than circular, whereas the Sub-basin wise mean circularity ratio is 1.5.

16.8 Relief Aspect The Relief Aspect deals with three-dimensional features of any river basin. It involves the area, volume and altitude and vertical dimension of landforms. For the prioritization of the river basin, a few parameters of Relief Aspect like relative relief, an important morphometric variable is used for the assessment of morphological characteristics of any topography (Gayen et al. 2013; Kanhaiya et al. 2019). The

16 Analysis of Basin Morphometry for the Prioritization Using Geo-Spatial …

367

significant relief aspects which are considered for the study are Maximum Elevation, Relative Relief, Average Slope, Drainage Density, Stream Frequency, Drainage Texture, Dissection Index, Ruggedness Index, Coefficient Relative Massiveness, and Roughness Index (Saha et al. 2020; Kanhaiya et al. 2019). This technique is used for all the assessment of morphological characteristics of terrain and degree of dissection (Kanhaiya et al. 2019).

16.9 Maximum Elevation Maximum elevation which means the actual value of each grid is used to draw isoline with the help of the interpolation technique. The isoline helps us to understand the terrain characteristics. It also indicates the nature of the relief before the erosion starts. In the Debnala basin, it is found that the upper part and middle parts of the basin have high elevation, and the rest of the basin has low elevation (Fig. 16.7a).

16.10 Relative Relief The difference between base level, which is an area’s lowest altitude, and summit level, which is an area’s highest altitude, is known as Relative Relief (Ray et al. 2006). Relative Relief is an important morphometric variable used for the assessment of morphological characteristics of any topography (Gayen et al. 2013; Saha et al. 2020; Kanhaiya et al. 2019). It is the important parameter of the relief characteristics of an area computed without considering sea level (Singh 1992) and this technique is used for all the assessment of morphological characteristics of terrain and degree of dissection. In the Debnala basin, relative relief value shows higher (0.638 km2 ) in the upper portion of the sub-basin and the rest of the portion shows moderate to low relative relief. In this basin maximum part of the basin is covered by a moderate type of relative relief Fig. 16.7b). It’s computed as following equation: Rr = (Maximum elevation−Minimum elevation).

16.11 Average Slope “Average slope, defined as the angular inclinations of the terrain between hilltops and valley bottoms, is a significant morphometric attribute in the study of landforms of a drainage basin. It is caused by the interaction of many causative factors, including geological structure, absolute and relative relief, climate, vegetation cover, drainage texture and frequency, dissection index, etc.” (Singh and Srivastava 1975).

368

A. Rahman et al.

Fig. 16.7 a Maximum elevation, b Relative relief, c Average slope, d Drainage density, e Stream frequency, f Drainage texture

The Debnala basin has a maximum average slope of 7 and lowest slope 2 degree (Fig. 16.7c). It is calculated as; As = (tan − 1((Total length of contours × Contour Interval)/Area)

16 Analysis of Basin Morphometry for the Prioritization Using Geo-Spatial …

369

16.12 Drainage Density According to Horton (1945), the drainage density is defined as the total length of streams per unit of drainage basin area and the hydrological utility of drainage density was suggested as early as 1900 by Neumann (Gardiner and park 1978; Soni 2017; Harini et al. 2018). Drainage density is related to several of landscape dissection features, including valley density, channel head source area, relief, climate and vegetation, soil and rock qualities, and landscape evaluation procedure (Moglen et al. 1998; Magesh et al. 2013; Harini et al. 2018), soil and rock properties (Kelson and Wells 1989; Soni 2017; Dikpal et al. 2017), and the landscape evaluation process etc. Drainage density is an excellent indicator of the permeability of the surface of drainage, (Horton 1932; Makwana and Tiwari 2016; Harini et al. 2018) and determines the amount and types of precipitation. The drainage density also gives an idea about the soil characteristics. A higher drainage density results a high bifurcation ratio. The high value of drainage density also indicates high relief, impermeable surface materials, and sparse vegetation whereas low value indicates vice versa. In this basin, the high drainage density is strongly present in 10.99 km2 area and the maximum area of the river basin is covered by low to moderate drainage density Fig. 16.7d). It is calculated as Drainage Density (Dd) = Total Length of Stream of order/Grid area

16.13 Stream Frequency “The entire number of stream segments for all orders are counted as one unit of stream frequency” (Horton 1932). Reddy et al. (2004) stated that low stream frequency readings point to the presence of a permeable underlying material, low relief, and an inadequate drainage system (Thomas et al. 2010; Saha et al. 2020). Stream frequency has a positive correlation with relative relief. A lower value of relative relief indicates low stream frequency and vice versa. Drainage density and stream frequency also have positive relation. With the increase of the stream frequency drainage density also increases and vice versa. Channel frequency and channel density is a methods for determining the erosional processes taking place in a region. In the Debnala river basin, high stream frequency is distributed over a minimum basin area. The total basin area with very high stream frequency is 5.072 km2 and the maximum part of the river basin is characterized by low to moderate types of stream frequency Fig. 16.7e). High stream frequency reveals that the river has much more erodible capacity and vice versa. Stream Frequency (SF) = Number of Stream channel flowing a Grid/Grid area

370

A. Rahman et al.

16.14 Drainage Texture Drainage texture is the product of stream frequency and drainage density (Horton 1945; Magesh et al. 2013). “Horton recognized infiltration capacity as the single important factor influencing texture ratio” (Soni 2017). “Drainage texture depends upon several natural factors like the amount of rainfall, density of vegetation, soil types, infiltration capacity, stages of geomorphic developments and relief” (Smith 1950). The value of drainage texture < 2 indicates very coarse texture, whereas 2–4 indicates coarse texture, 4–6 moderate texture, 6–8 fine texture and > 8 very fine texture (Smith 1950). Basins with higher texture tend to have more erosion-prone. Drainage Texture (DT) = Drainage Density × Stream Frequency In the study area, higher drainage texture is found in the central part of the basin, which is a very small portion of the basin i.e. about 0.59 (km/km2 ) and the rest of the part shows a moderate to lower value of the same. And basically above 50% of the area is covered by a very small drainage texture Fig. 16.7f).

16.15 Dissection Index The ratio of relative reliefs to absolute relief is known as the Dissection Index. It depicts a basin’s dissected features and rate of vertical erosion (Haghipour and Burg 2014). “Dissection Index value ranges between 0 to 1, where 0 implies presence of flat terrain, less degree of dissection, absence of vertical erosion and old stage of the basin while 1 indicates undulated terrain, hill slope escarpments, cliffs or at seashore The value of dissection ranges between ‘0’, indicates an absence of vertical dissection to 1, indicates the presence of vertical dissection” (Gaurav Singh and Singh 2022). “Mountain basins have relatively higher dissection index values in comparison to plateau – plain river basins” (Waiker and Nilawar 2014). The Dissection Index is also used as a determinant of the stage of the cycle of erosion. In the young stage all the erosional activity is much more active and in the mature stage the erosion rate is less as compared to the young stage and the rest of the old stage erosion rate is low due to the decline of a slope. In the Debnala river basin, higher DI is found in a very small portion (1.885 km2 ) and the entire basin is covered by a low to a moderate value of dissection index Fig. 16.8d). Dissection Index (DI) = Relative Relief/Maximum elevation

16 Analysis of Basin Morphometry for the Prioritization Using Geo-Spatial …

371

Fig. 16.8 a Ruggedness index, b Relative massiveness index, c Roughness index, and d Dissection index

16.16 Ruggedness Index Drainage density and Relative Relief are the two factors that make up the Ruggedness Index. If both drainage density and Relative Relief are recorded high, the Ruggedness Index’s value will be high (Ansari et al. 2012). Any location with a higher Ruggedness Index score is either undergoing denudation activity or is in the early stages of geomorphic development. Different work witnesses that the mountain environment basin has a higher Ruggedness Index concerning the plateau river basin. In the Debnala river basin, high Ruggedness value shows in a very small portion of the river basin and the area is 1.97 km2 and maximum part of the river basin is covered by a low to moderate type of Ruggedness value Fig. 16.8a). Ruggedness Index (RI) = (Relative Relief × Drainage Density)/1000)

372

A. Rahman et al.

16.17 Coefficient Relative Massiveness Initially, this index was worked out by De Martonne (1940). This analysis of CRM gives a similar value to the hypsometry integral. The value of CRM ranges from 0 to 1. CRM value close to 01 indicates a profile in which most of the flat land is located on divides and the valleys are narrow while a value close to 0 indicates a profile of open valleys and widely spaced isolated hills (Pal 1972). In the Debnala basin above 50% of the area is covered between 0.4 to 0.5 CRM value (Fig. 16.8b). CRM = (Mean Elevation − Minimum Elevation)/Relative Relief

16.18 Roughness Index The concept of the Roughness Index (RI) has been devised by Hook (1955). This index helps to determine the terrain characteristics whether the terrain is much rougher or not. The greater value of Roughness Index indicates surface terrain condition is going to be rough. Debnala river basin has more than 50% area which has high Roughness Index which helps to conclude that the maximum part of the river basin belongs to rough surface condition. On the other side, the remaining part of the basin is characterized by a low value of RI Fig. 16.8c). Roughness Index RI = (N × (M/4)/4) × 10) where, N = total number of intersections of contour lines with two sets of perpendicular grids and M = is the distance between grid lines.

16.19 Sub-Basin Wise Soil Erosion Potentiality For assessing sub-basin-wise soil erosion potentiality, some statistical techniques have been used such as descriptive statistics for describing and summarising the basin characteristics. To measure the soil erosion potentiality, multivariate analysis especially factor analysis has been done.

16 Analysis of Basin Morphometry for the Prioritization Using Geo-Spatial …

373

Table 16.4 Descriptive statistics of the morphometric parametrs Variable

Parameters Maximum

Minimum

Mean

Sd

Kurtosis

Skewness

X1

133.998

61.009

97.935

18.057

−0.974

0.071

X2

119.007

51.001

79.798

16.587

−0.959

0.224

X3

126.785

54.339

88.020

17.887

−0.983

0.117 −0.157

X4

31.009

7.998

18.137

3.409

0.784

X5

6.000

0.000

0.658

1.255

3.621

2.054

X6

3.093

0.000

0.324

0.643

4.134

2.161

X7

18.558

0.000

0.959

2.518

15.299

3.661

X8

0.071

0.000

0.006

0.012

5.152

2.284

X9

0.282

0.105

0.188

0.034

0.196

0.139

X10

0.648

0.166

0.449

0.098

0.271

−0.495

X11

5.976

2.523

3.890

0.537

0.058

0.009

Source Computed by authors-based SOI Toposheet and ASTER DEM

16.20 Descriptive Measures In this study area computed descriptive statistics for 11 morphometry parameters such as Maximum Elevation, Minimum Elevation, Relative Relief, Average Slope, Stream Frequency, Drainage Density, Drainage Texture, Ruggedness Number, Dissection Index, Coefficient of Relative Massiveness, and Roughness Index has been taken. The distribution of X1, X2, X3, X4 and X7 recorded high unevenness though X5 is moderate variability and X8, X9, X10, and X11 recorded very low variability. In the study area, maximum and minimum elevation, mean, standard deviation, kurtosis and skewness of 11 morphometric parameters have been calculated. High standard deviation values are shown in X1, X2, and X3 parameters and low standard deviation values are shown in X6, X8, X9, and X10 parameters. Moderate standard deviation is present in X4, X5, X6, and X7 parameters. As far as skewness is concerned, it shows that positive skewness is present in all except X4 and X10. High skewness value is shown from X7 parameters and the value is 3.660920634 (Table 16.4).

16.21 Multivariate Analysis of Morphometric Variable Various morphometric factors are interrelated with each other. So, here multivariate analysis is suitable for the analysis of basin morphometry. Factor analysis has been employed for creating a composite score (Table 16.5). The correlation matrix has been done for understanding the correlation of different morphometric variables. The correlation matrix brings out the nature of statistical relationships between and among variables considered for the study. It shows that

1

0.423

X11

0.495

0.366

0.524

0.231

0.46

0.231 0.076 −0.095

0.055 −0.035

−0.003 −0.095

−0.01

0.98

0.921

1

X5

−0.035

0.916

0.907

0.926

1

X4

−0.128

0.046

−0.128

0.024

0.906

1

X6

−0.079

0.111

−0.079

0.037

1

X7

−0.158

0.053

−0.158

1

X8

1

−0.064

1

X9

−0.064

1

X10

1

X11

N.B. (X1—Maximum Elevation, X2—Mean Elevation, X3—Relative Relief, X4—Stream Frequency, X5—Drainage Density, X6—Drainage Texture, X7— Ruggedness Index, X8—Disection Index, X9—Relative Massiveness Index, X10—Mean Slope, X11—Roughness Index.) Source Computed by Authors Based SOI Topo Sheet and ASTER DEM

0.418

X10

0.495

−0.527

−0.45

0.423

X8

X9

0.063

0.086

X7

0.14

0.054

0.051

0.022

−0.009

0.037

0.076

0.009

0.059

X3

X5

0.071

X4

0.423

1

X2

X6

0.992

0.508

X2

X3

1

X1

X1

Table 16.5 Correlation matrix of morphometric analysis

374 A. Rahman et al.

16 Analysis of Basin Morphometry for the Prioritization Using Geo-Spatial …

375

X1 is strongly correlated with (X2, X3, X9, X10, X11), X2 with (X1, X3, X9, X10, X11), X3 with (X1, X2, X9, X10, X11), X4 with (X5, X6, X7), X5 with (X4, X6, X7), X6 with (X4, X5, X7), X7 with (X4, X5, X6), X8 with shows negative values, X9 with (X1, X2, X3, X9, X10, X11), X10 with (X1, X2, X3, X9, X11), and X11 with (X1, X2, X3, X9,) shows positive value (Table 16.6).

16.22 Factor Analysis To express the relationships among variables, factor analysis is used. In factor analysis, the “factor loadings” matrix is used to identify specific “fundamental” or “abstract” variables and to quantify numerically the strength of the correlation between these and the original variables. Factor analysis offers a streamlined data matrix known as the “factor score” or weightings matrix in addition to illuminating the processes that lead to the observed connections between the selected variables (Sarkar and Patel 2017; Mukherjee and Patel 2022). If the various morphometric variables are viewed as vectors, the angle between any two vectors indicates how similar the various variables are to one another (Shit et al. 2022). Thus, whilst identical variables are represented by two vectors overlaid on one another, two completely different variables might be represented by two vectors pointing in opposing directions (Shit et al. 2022) (Fig. 16.9).

16.23 Major Finding The foregoing study observes that the morphometric technique is an important method for assessing sub-basin-wise soil erosion and sediment production rate. Moreover the river Sub-basins Soil erosion and sediment production rate depend on various parameters, like form factor, elongation ratio, circularity ratio, and compactness coefficient, which have inverse effects on soil erosion, and bifurcation ratio, length of overland flow, Drainage density, Drainage texture, and Stream frequency, have direct effects on soil erosion, (Fig. 16.7d–f) this parameter already has been discussed (Tables 16.1 and 16.2). For the determination of soil erosion susceptibility, it is important to determine the sediment production rate. The increasing rate of soil erosion may be correlated with the decreasing rate of sediment production and vice versa.

Cumulative %

0.000

X11

0.000

0.001

0.118

65.217

100.000

100.000

99.999

99.881

99.737

98.951

97.968

93.577

80.699 1.416

1.703

3.341

3.833

Source Computed by authors based SOI Topo sheet and ASTER DEM

0.000

X10

0.143

0.016

0.013

X8

X9

0.786

0.086

X7

4.392

0.983

0.483

0.108

12.877

X5

1.416

X4

30.373

15.482

X6

3.341

1.703

X2

X3

34.844

12.877

15.482

30.373

34.844

% of Variance

93.577

80.699

65.217

34.844

Cumulative %

Total

34.844

% of Variance

Total

3.833

Extraction sums of squared loadings

Initial eigenvalues

X1

Component

Table 16.6 Total variance explain

1.643

2.361

2.506

3.784

Total

14.934

21.462

22.781

34.400

% of Variance

93.577

78.643

57.181

34.400

Cumulative %

Rotation sums of squared loadings

376 A. Rahman et al.

16 Analysis of Basin Morphometry for the Prioritization Using Geo-Spatial …

377

Fig. 16.9 Factor score based on different morphometry variable

16.24 Identification of the Sub-Basin Wise Soil Erosion Potentiality Zone of Debnala River Basin This study discussed stream order wise soil erosion potentiality, these are following as; 1st order sub basin wise soil erosion potentiality 1st order stream shows that very high erosion potential zone is distributed all over the basin. High soil erosion zones are also found in a greater number in this basin. The number of high erosion potential zone is more in the upper and lower part of the basin. On the other hand, medium erosion potential zone are found in the upper and middle parts of the basin. Basically, in the 1st order sub-basins, the maximum basin high soil erosion rate is recorded in fourteen sub-basin (14), while the maximum sedimentation rate has been observed in the fifteen (15) sub-basins. At the same time, eleven (11) sub-basin are recorded as medium soil erosion zone whereas medium sediment production rate has been observed in eight (8) sub-basins of the study area. However, comparatively low soil erosion and low sediment production rate have been observed among the rest of the sub-basins (Fig. 10a, b). 2nd order sub basin wise soil erosion potentiality The Debnala River 2nd order Sub-basin has been studied which indicates that the bifurcation ratio is low, circularity ratio, elongation ratio, more than (1), moreover the upper and middle portion recorded Maximum Elevation, Drainage Density and Stream Frequency etc. In the 2nd order sub-basins, the maximum soil erosion rate has been observed in seven (7) sub-basins, while the maximum sedimentation rate has been observed in three (3) sub-basins, and three (3) sub-basin witnessed medium soil

378

A. Rahman et al.

Fig. 16.10 a Prioritization in first order basin based on ranking of morphometry variable and b First order sediment production rate based on Jose and Dash (1982)

erosion zone and medium sediment production rate found in three (3) Sub-Basins. However, comparatively low soil erosion and low sediment production rate have been observed in four (4) and six (6) sub-basins respectively (Fig. 11a, b). 3rd order sub basin wise soil erosion potentiality 3rd order stream very high soil erosion potentiality zones are distributed in the north eastern part of the basin area. The Debnala river basin has only two 3rd order Sub-basins located in the upper part of the basin, The maximum soil erosion rate has been observed (Basin id 3.01) and the maximum sediment production rate has been observed (Basin id 3.01) and low erosion rate and the low sediment production rate has been observed (Basin id 3.02) (Fig. 12a, b).

Fig. 16.11 a Prioritization in second order basin based on ranking of morphometry variable and b Second order sediment production rate based on Jose and Dash (1982)

16 Analysis of Basin Morphometry for the Prioritization Using Geo-Spatial …

379

Fig. 16.12 a Prioritization in third order basin based on ranking of morphometry variable and b Third order sediment production rate based on Jose and Dash (1982)

16.25 Sediment Production Rate of Debnala River Basin The sediment production rate of any river depends on various factors such as slope, water discharge, river length, water depth etc. In this study area, stream order-wise sediment production rate has been computed. For the 1st stream order, the highest sediment production rate is found in the lower part of the basin and the value is 1.62. For the 2nd order stream, the highest sediment order value is found in the upper part of the river and the value is 2.13. For the 3rd stream order, the sediment production rate shows a higher value in the upper part of the river and the value is 3.02.

16.26 Conclusion In the evaluation of river basins and the prioritizing of watersheds for soil and water conservation and the micromanagement of natural resources, it is discovered that the quantitative analysis of linear, relief and areal morphometric characteristics using GIS is of enormous benefit. A river basin’s drainage morphometry reveals the hydrogeological maturity of that river. In the current study, 11 morphometric variables were calculated and subjected to a rigorous analysis. Soil erosion and sediment production both are vital for understanding the nature of the river. This study identifies the rate of soil erosion and sediment production in Debnala River varies over different stream orders. The result of morphometric analysis-based prioritization shows that soil erosion and sediment production rate are inversely proportional to each other. The result also shows that 40 sub-basin have a high rate of erosion potentiality capacity to produce sediment in the Debnala river basin. In the sub-watersheds with the highest

380

A. Rahman et al.

priority, soil and water conservation measures may be implemented. Therefore, planners and decision-makers should prepare efficient land and water management plans for the conservation measures to locale-specific planning and development.

References Ahiwar R, Malik MS, Shukla JP (2019) Prioritization of sub-watersheds for soil and water conservation in parts of Narmada river through morphometric analysing using remote sensing and GIS. J Geol Soc India 94:515–524 Ansari ZR, Rao LA, Yusuf A (2012) GIS based morphometric analysis of yamuna drainage network in parts of Fatehabad area of Agra district, Uttar Pradesh. J Geol Soc India 79:505–514 Asfaw D, Workineh G (2019) Quantitative analysis of morphometry on Ribb and Gumara watersheds: implications for soil and water conservation. Int Soil Water Conserv Res 7(2):150–157 Bali R, Agarwal K, Nawaz Ali S, Rastogi S, Krishna K (2012) Drainage morphometry of Himalayan Glacio-fluvial basin, India: hydrologic and neotectonic implications. Environ Earth Sci 66(4):1163–1174. https://doi.org/10.1007/s12665-011-1324-1 Bali R, Agarwal KK, Ali SN, Rastogi SK, Krishna K (2011) Drainage morphometry of Himalayan Glacio-fluvial basin, India: hydrologic and neotectonic implications. Environ Earth Sci 66(4):1163–1174 Bhattacharya RK, Das Chatterjee N, Das K (2019) Multi-criteria-based sub-basin prioritization and its risk assessment of erosion susceptibility in Kansai-Kumari catchment area, India. Appl Water Science 9(4):1–30 Brady NC, Weil RR (2002) The nature and properties of soil, 12th edn. Prentice Hall, Upper Saddle River Castillo V, Diazsegovia A, Alonso SG (1988) Quantitative study of fluvial landscapes, case study in Madrid, Spain. Landsc Urb Plan 16:201–217 Chauhan P, Chauniyal DD, Singh N, Tiwari RK (2016) Quantitative geo-morphometric and land cover-based micro-watershed prioritization in the Tons river basin of the lesser Himalaya. Environ Earth Sci 75(6):498 Chorley RJ (1969) The drainage basin as the fundamental geomorphic unit. In: Chorley RJ (ed) Water, earth and man. Methuen, London, pp 77–100 Clarke JI (1966) Morphometry from Maps. Essays Geomorphol 252:235–274 De Martonne E (1940) Traite de Geographie Physique. Colin, Paris Dikpal RL, Renuka Prasad TJ, Satish K (2017) Evaluation of morphometric parameters derived from Cartosat-1 DEM using remote sensing and GIS techniques for Budigere Amanikere watershed, Dakshina Pinakini Basin, Karnataka, India. Appl Water Sci 7(8):4399–4414 Esper AMY (2008) Morphometric analysis of Colanguil river basin and flash flood hazard, San Juan, Argentina. Environ Geol 55:107–111 FAO & ITPS (2015) Status of the world’s soil resources (SWSR)—main report. Food and agriculture Organization of the United Nations and Intergovernmental Technical Panel on soils, Rome, Italy Gajbhiye S, Mishra SK, Pandey A (2014) Prioritizing erosion-prone area through morphometric analysis: an RS and GIS perspective. Appl Water Sci 4(1):51–61 Gaurav Singh V, Singh SK (2022) Analysis of geo-morphometric and topo-hydrological indices using COP-DEM: a case study of Betwa River Basin, Central India. Geol Ecol Landscapes 1–28 Gayen S, Bhunia GS, Shi PK (2013) Morphometric analysis of Kangshabati-Darkeswar interfluves area in West Bengal, India using ASTER DEM and GIS techniques. Geol Geosci 2(4):1–10 Gardiner V, Park CC (1978) Drainage basin morphometry: review and assessment. Prog Phys Geogr 2(1):1–35 Govindaraj V, Lakshumanan C (2019) Morphometric analysis and characterization for subwatershed delineation in Gomukhi river basin of Villupuram district, Tamilnadu using GIS.

16 Analysis of Basin Morphometry for the Prioritization Using Geo-Spatial …

381

In: Proceedings of international conference on remote sensing for disaster management. Springer, Cham, pp 805–815 Haghipour N, Burg JP (2014) Geomorphological analysis of the drainage system on the growing Makran accretionary wedge. Geomorphology 209:111–132 Harini P, Sahadevan DK, Das IC, Manikyamba C, Durgaprasad M, Nandan MJ (2018) Regional groundwater assessment of Krishna River basin using integrated GIS approach. J Indian Soc Remote Sens 46(9):1365–1377 Hook JC (1955) The relationship between roughness of Terrain and Phenomena related to agriculture in northeastern United States. The University of Iowa Horton RE (1932) Drainage-basin characteristics. Trans Am Geophys Union 13:350–361 Horton RE (1945) Erosional development of streams and their drainage basins: hydrophysical approach to quantitative morphology. Geol Soc Am Bull 56:275–370 Ismail M, Singh H, Farooq I, Yousuf N (2022) Quantitative morphometric analysis of Veshav and Rembi Ara watersheds, India, using quantum GIS. Appl Geomatics 14(2):119–134 Jain SK, Kumar S, Varghese J (2001) Estimation of soil erosion for a Himalayan watershed using GIS technique. Water Resour Manag 15:41–54 Josh CS, Dash DC (1982) Geomorphic prediction models for sediment production rate and intensive priorities of watersheds in Mayurakshi catchment. In: Proceedings of International symposium on hydrological aspects of mountainous watersheds, School of Hydrology, University of Roorkee, Roorkee, 1, pp 15–23 Kanhaiya S, Singh S, Singh CK, Srivastava VK, Patra A (2019) Geomorphic evolution of the Dongar river basin, Son Valley, central India. Geol, Ecol Landsc 3(4):269–281 Kelson KI, Wells SG (1989) Geologic influences on fluvial hydrology and bed load transport in small mountainous watersheds, northern New Mexico, USA. Earth Surf Process Landf 14:671–690 Kumar A, Jayappa KS, Deepika B (2011) Prioritization of sub-basins based on geomorphology and morphometricanalysis using remote sensing and geographic informationsystem (GIS) techniques. Geocarto Internat 26(7):569–592 Magesh N, Chandrasekar N, Soundranayagam J (2011) Morphometric evaluation of Papanasam and Manimuthar watersheds, parts of Western Ghats, Tirunelveli district, Tamil Nadu, India: a GIS approach. Environ Earth Sci 64(2):373–381. https://doi.org/10.1007/s12665-010-0860-4 Magesh NS, Jitheshlal KV, Chandrasekar N, Jini KV (2013) Geographical information system-based morphometric analysis of Bharathapuzha river basin, Kerala, India. Appl Water Sci 3(2):467–477 Mahala A (2020) The significance of morphometric analysis to understand the hydrological and morphological characteristics in two different morpho-climatic settings. Appl Water Sci 10(1):1– 16 Makwana J, Tiwari MK (2016) Prioritization of agricultural sub-watersheds in semi arid middle region of Gujarat using remote sensing and GIS. Environ Earth Sci 75(2):1–12 Merlin P (1965) A propos des méthodes de morphométrie. Actageographica 56:14–20 Morisawa ME (1962) Quantitative geomorphology of some watersheds in the Appalachian plateau. Geol Soc Am Bull 73(9):1025–1046 Moglen GE, Eltahir EA, Bras RL (1998) On the sensitivity of drainage density to climate change. Water Resour Res 34:855–862 Mukherjee J, Patel PP (2022) Landscape characterization using geomorphometric parameters for a small sub-humid river basin of the Chota Nagpur Plateau, Eastern India. In: Drainage basin dynamics. Springer, Cham, pp 127–152 Nooka RK, Srivastava YK, Venkateshwara Rao V, Amminedu E, Murthy KSR (2005) Check dam positioning by prioritization of micro-watersheds using SYI model and morphometric analysis— remote Sensing and GIS perspective. J Indian Soc Remote Sens 33(1):25–38 Nag SK, Chakraborty S (2003) Influence of rock types and structures in the development of drainage network in hard rock area. J Indian Soc Remote Sens 31(1):25–35 Pal SK (1972) Quantitative geomorphology of drainage basins of Himalayas. Geog Rev Ind Calcutta 35:81–101

382

A. Rahman et al.

Pandey A, Mathur A, Mishra SK, Mal BC (2009) Soil erosion modeling of a Himalayan watershed using RS and GIS. Environ Earth Sci 59:399–410 Patel D, Gajjar C, Srivastava P (2013) Prioritization of Malesari mini-watersheds through morphometric analysis: a remote sensing and GIS perspective. Environ Earth Sci 69:2643–2656. https:// doi.org/10.1007/s12665-012-2086-0 Rao JH, Patle D, Dubey S (2020) Implementation of morphometric analysis in prioritizing subwatersheds: a remote sensing and GIS aspect. Indian J Pure Appl Biosci 8(4):318–329 Ray R, Sheth HC, Mallik J (2006) Structure and emplacement of the Nandurbar- Dhule mafc dyke swarm, Deccan Traps, and the tectonomagmatic evolution of food basalts. Bull Volcanol 69:537. https://doi.org/10.1007/s00445-006-0089-y Rai PK, Mohan K, Mishra S, Ahmad A, Mishra VN (2017) A GIS-based approach in drainage morphometric analysis of Kanhar River Basin, India. Appl Water Sci 7(1):217–232 Reddy GPO, Maji AK, Gajbhiye KS (2004) Drainage morphometry and its influence on landforms characteristics in basaltic terrain, central India: a remote sensing and GIS approach. Int J Appl Earth Observ Geoinf 6:1–16 Richardson LF (1961) The problem of contiguity: an appendix to statistics of deadly quarrels. Gen Syst Yearb 6:139–187 Saha S, Paul GC, Hembram TK (2020) Classification of terrain based on geo-environmental parameters and their relationship with land use/land cover in Bansloi River basin Eastern India: RS-GIS approach. Appl Geomat 12(1):55–71 Sarkar A, Patel PP (2017) Land use-terrain correlations in the piedmont tract of Eastern India: a case study of the Dulung river basin. In: Remote sensing techniques and GIS applications in earth and environmental studies. IGI Global, pp 147–192 Schumm SA (1956) Evolution of drainage systems and slopes in badlands at Perth Amboy, New Jersey. Geol Soc Am Bull 67:597–646 Shit PK, Bera B, Islam A, Ghosh S, Bhunia GS (2022) Introduction to drainage basin dynamics: morphology, landscape and modelling. In: Drainage basin dynamics. Springer, Cham, pp 1–9 Singh S, Srivastava R (1975) Morphometric determinants of the stage of cycle of erosion. Geograph Observer 12:29–40 Singh S (1992) Quantitative geomorphology of the drainage basin, reading on remote sensing application. In: Chouhan TS, Joshi KN (eds) Scientific Publishers, Jodhpur, India Skidmore AK, Bijker W, Schmidt K, Kumar L (1997) Use of Remote Sensing and GIS for sustainable land management. ITC J (3/4):302315 Smith G (1935) The relative relief of Ohio. Geogr Rev 272–284 Smith KG (1950) Standards for grading texture of erosional topography. Am J Sci 248:655–668 Soni S (2017) Assessment of morphometric characteristics of Chakrar watershed in Madhya Pradesh India using geospatial technique. Appl Water Sci 7(5):2089–2102 Srinivas CV, Maji AK, Reddy GPO, Chary GR (2002) Assessment of soil erosion using remote sensing and GIS in Nagpur district, Maharashtra for prioritization and delineation of conservation units. J India Soc Remote Sens 30:197–212 Strahler AN (1964) Quantitative geomorphology of drainage basin and channel networks. In: Chow VT (ed) Handbook of applied hydrology. McGraw Hill Book, New York, pp 4–76 Strahler A (1952) Hypsometric (area-altitude) analysis of erosional topography. Bull Geol Soc Am 63:1117–1142 Strahler AN (1969) Physical geography, 3rd ed, pp 13–66. John Willey, New York Sunil L, Nathawat MS, Subudhi AP (2010) Erosion suceptibility zoning and prioritization of miniwatersheds using geomatics approach: Int J Geomat Geosci 1(3). ISSN 0976-4380 Thomas J, Joseph S, Thrivikramaji KP (2010) Morphometric aspects of the small tropical mountain river system, the southern Western Ghats, India. Int J Digital Earth 3(2):135–156 Waikar ML, Nilawar AP (2014) Morphometric analysis of a drainage basin using geographical information system: a case study. Int J Multidiscip Curr Res 2(2014):179–184 Woldenberg MJ (1966) Horton’s laws justified in terms of allometric growth and steady state in open systems. Geol Soc Am Bull 77(4):431–434

Chapter 17

Geo-Spatial Techniques to Analyze Fluvial Morphometry of River Kangshabati and Some Associated Features, in Selected Parts of Bankura District, West Bengal Ayan Das Gupta

Abstract Quantification of drainage network with special reference to climatic conditions, geo-tectonic uniqueness, lithological configuration, geomorphic characteristic features etc. It extends conspicuous evidences of drainage evolution, hydrogeomorphic specialties and denudational features of the area over which the drainage does flow along with its tributaries as well as distributaries. The present paper will throe focus on the satellite-image based analysis of digital elevation model that entails the drainage basin of river Kangshabati in selected portion of the western forested track of Bankura District. Along with the morphometric techniques, the impacts of fluvial Morphometry over the hydro-geomorphicfeatures have also been studied in the current paper. Drainage density, dissection Index, study of slope features and nature of relief have been corroborated with all minute details in this research work. The shape features show that the drainage basin is extended towards north and south and the highest fourth order stream Kangshabati has drained the entire region. Lots of fingertip as well as the First order channels are developed over the area and after their joining with each other, a good number of second order streams have also been originated. Gradually with the increase in order, the occurrence of higher order streams has got reduced gradually. With chromatic variations, the higher-magnitude areas with special regards to drainage density, relief feature, gradient characteristics have been delineated clearly and the result of such high occurrences have been explained as well with respect to their connections to topography. In the analysis with adequate inputs from the Geographical information system, the zones of high, moderate and low regions of drainage density, relative relief, average slope, dissection index etc. have been manifested through the map layouts and some prospective studies are also done over here from the present situation of the drainage and relief.

A. Das Gupta (B) Department of Geography, Chandernagore Government College, Strand Road, Burrabazar, Hooghly, West Bengal 712136, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_17

383

384

A. Das Gupta

Keywords Relief features · Fluvial morphometry · Drainage basin · Slope analysis · Dissection index

17.1 Introduction Flourishing of a drainage system over the geological past depends upon multiple factors encompassing the climatic condition, lithological structure, tectonic uniqueness and geomorphic specialties (Rao et al. 2010) exercised over a spatial unit. Morphometry with special reference to Geology as well as Geomorphology connotes the mathematical searching (Schumm 1956) and minute measurement of the earth’s surface along with its linear, areal, relief features. The morphometric parameter chosen for preparation of map layout over a certain area may help the geologists and geomorphologists to make obvious comments on cycle of erosion. In the normal course of cycle of erosion as postulated by William Morris Davis, the uninterrupted continuation of cyclic relief is quite obvious but their specific features and nature do vary significantly from one particular region to the another region.

17.2 History of Morphometric Studies A good number of learned persons or scholars have tried to investigate the intricacies of fluvial Morphometry uptil date but the pioneering work was done by Horton in the year of 1932 and the modification to his work was done for the second time (Valdiya 2008) in the year of 1945, by the same scientist. Miller and Strahler are also famous for contribution in the scientific domains of Fluvial Morphometry (Valdiya and Rajagopalan 2000). The current scientists are all working upon the basics derived successfully by the previous three geomorphologists in the spectrum of morphometric analysis. Sufficient works have been done globally on the present situation of fluvial Morphometry related to different fluvial systems but only a very few papers have highlighted upon the correlation between climatic condition, lithological configuration, tectonic disturbances etc. with special reference to morphometric studies. This current paper has tried to fulfil the research gap, as far as it is practicable.

17.3 Study Area Here the study area is South-western portion of Bankura District which is comprised of multiple community development blocks and subdivisions but for the studies related to relief analyses, slope study, drainage density, stream ordering, dissection index etc. with special reference to Kangshabati River, the Khatra Subdivision

17 Geo-Spatial Techniques to Analyze Fluvial Morphometry of River …

385

Map 17.1 Study area and surroundings at a Glance

has been chosen. This Khatra Subdivision is made up of four community development blocks along with a well-developed drainage basin unit and those blocks are Ranibandh, Hirabandh, Khatra and Raipur (Map 17.1). Bankura District is well populated and so far the density distribution layout is concerned, the Khatra Community Development Block is pretty dense and the density of population over here is between 90 and 132 persons per square kilometres. Other community development blocks like Sarenga, Simlapal Indpur, Taldangra, Chhatna, Saltora etc. are falling within the zone of moderate population density and here the density is ranging between 38 to 89 persons per square kilometres. The central as well as the Eastern portion of Bankura district are not that much densely populated. Onda, Bishnupur, Patrasayer, Jaypur, Indus, Kotulpur etc. are characterized by low population density. So far the class distribution is concerned; the total number of persons dwelling in the aforementioned community development blocks is between 8 and 37 persons (Maps 17.2 and 17.3).

17.4 Objective and Methodology The focal objective is to throw adequate focus on the fluvial Morphometry through stream ordering and to comment upon the alignment of the fingertip channels along with the further higher order drainages. Here the river Kangshabati along with its different distributaries has been aligned in typical ordering to form a drainage basin in the Khatra Community development block. In addition to the analyses on the fluvial alignments in the drainage basin, separate techniques have been adopted to show the zones in Drainage density, dissection Index, Relative relief, Average slope etc.

386 Map 17.2 Altitude map of the study area

Map 17.3 Forested tracks of the study area

A. Das Gupta

17 Geo-Spatial Techniques to Analyze Fluvial Morphometry of River …

387

with special reference to the Kangshabati drainage basin. Apart from the aforesaid works, thrust has been given to depict upon the elevation of the entire study area with reference to the digital elevation model. The forest patches in the study area and overall variation in the altitude or height has also been portrayed. In the platform of remote sensing and Geographic information system, all the works are done. The objectives will certainly try to meet up the research gap in relation to this type of paper, so far it’s possible. In the methodology-part, through digital image processing with special connection to the unsupervised image classification, the forested areas have been highlighted and in the podium of Arc GIS software, the digital elevation models and the altitude-map are prepared. In the case of morphometric analysis in the drainage basin, the TNT Mips software has been used and through the options provided in the dashboard of Digital image processing, the layouts are prepared. Lastly with the help of concept of zoning depending on the varying intensity, the chromatic variations are applied to delineate the zones of slope, relief etc. with low to high intensities.

17.5 Results and Discussions 17.5.1 Altitude and Occurrence of Forest in the Study-Area The accompanying map shows the altitudinal variations in the study area at Khatra subdivision. The total altitude ranges between 56 and 302 m. Different colour variations are applied to delineate clearly the low, moderate and high altitude regions at the study area. From the map, it is clearly evident that the eastern portion of the Khatra subdivision and specially the southeast corner with the community development block of Raipur actually falls within the spectrum of low altitude and here basically the height varies between 56 and 100 m. The Western side of the Raipur C.D.Block is slightly elevated and therefore a sloped terrain has been formed over here. Khatra community development block is the area of mixed relief and due to occurrence of undulating topography; here the mixed relief has been originated basically. In the northern part of the Khatra Subdivision, which is in Hirabandh block, the major portion is lying within the elevated terrain and here the height is varying between 100 and 150 m. Ranibandh area is characterized by the presence of three types of elevations at a time. In the extreme western portion of Ranibandh block, the altitude reaches from 200 to 302 m and gradually a sloped terrain has been formed from western part to the central and south eastern part of Ranibandh. In the northern and north western section, the relief is low and here the altitude has come down to below 60 m. Due to the existence of hillocks and highly elevated topography in the plateau region of Khatra subdivision, the slope map has shown considerable variation in gradient values.

388

A. Das Gupta

The forested areas are usually dense in different parts of the Khatra community development block. Because of the occurrence of plateau fringe areas in the western side of Bankura, the luxuriant growth of deciduous trees are found over there. Hirabandh C.D.Block is profusely covered by greenery and except some patches at the extreme south, the entire Hirabandh area is forested. In the eastern portion of Khatra community development block, the green cover is dense but the eastern part of the same block is inhabited by tribal people and because of construction of their settlements, they have cleared the forested areas and thus the western part is devoid of vegetation. If the two maps of altitude as well as the forest cover can be correlated with each other, then it will be clearly evident that the areas of high altitude are actually densely forested. Naturally, due to presence of wavy and undulating topography, the western and central part of Ranibandh is not ideal for the construction of human settlements and thus the tribal population of Ranibandh area has chosen the northern and north eastern portion of the community development block for the construction of their housings. Hence the northern part is almost devoid of forest. In comparison with all other three community development blocks, the forest cover or greenery is quite little in Raipur areas and the several scattered green patches are happening in different places of this community development block. The economy of the tribal people in Khatra subdivision is mainly regulated with special reference to forestry and forest based products. The tribal population basically thrives upon fruits, flowers, stems, honey, wood etc. derived from the green resources. Forest based timber products and the non-timber forest products actually help the tribals to earn their regular bread. The tribal population by generation is dependent on the green resources and in order to promote them economically, the Government has introduced social forestry, agro-forestry and participatory management of forest in the tribal inhabited areas of the Khatra subdivision. Not only in this particular sub division but also in almost the whole forested lands of Bankura district, the forest based economy is prevalent and government also earns certain amount of revenues from the forest products (Map 17.4).

Map 17.4 Digital elevation model

17 Geo-Spatial Techniques to Analyze Fluvial Morphometry of River …

389

One Digital elevation model has been prepared above with the help Erdas Imagine software and the elevation model clearly shows that the higher elevation is mainly concentrated in the western, central and northern part of the entire study area and the altitude is gradually getting lowered from western and north western side towards the southern, eastern and the south-eastern part of the study-unit.

17.6 Stream Ordering Within the Kangshabati Drainage Basin Stream ordering basically connotes to the branching or the alignment of streams and channels and their further joining or coalescence with the bigger streams. After the onset of rainfall, the water is received by leaf surface first lading to the interception storage and thereafter the remaining water percolates through the interstices of soil to contribute to the ground water zones. After fulfilling the moisture requirements of soil, the remaining water flows in the form of fingertip channel over the surface of the earth and these forms of streams are regarded as overland flow or surface run off. The initially generated run offs or the fingertip channels do join each other to give rise to the second order drainage and further the two second order drainages connect each other to form the third order drainage. In this way, ultimately the highest order drainage is formed and that highest order drainage stands actually as the main stream or the river of the entire region. This definite alignment or designing of drainage connection was postulated by Strahler in the year of 1952. It is actually a simplest method of categorizing the stream segments based on the counting of total tributaries upstream. Here in the drainage network of river Kangshabati, a big reservoir is situated in the North western part of the drainage basin. It is covering some portions of the three community development blocks namely Hirabandh, Khatra and Ranibandh. In the Hirabandh community development block, the fingertip channels have been originated from the western and north western portion of th block boundary and thereafter the fingertip channels have been connected with each other to give rise to the second order drainages. Some of the second order drainages have been connected with the Kangshabati Reservoir in the west. In the eastern part of the C.D.Block, mainly the third order drainages have intersected the boundary of the community development block but no fourth order drainage has been traced out within the boundary limit of the Hirabandh C.D.Block. So far the extension of the Khatra Community development block is concerned, the western part is almost covered by the presence of Kangshabati reservoir. Several first order channels or the fingertip streams have been originated from the reservoir itself and there is noticeable extension of first order, second order and third order drainages in the eastern and north eastern portion of the boundary of Khatra Community development block. In the southern part of the block boundary, the highest order or the fourth order drainage of Kangshabati has been extended luxuriantly and it has intersected the boundary of Raipur as well.

390

A. Das Gupta

Strahler’s stream ordering is having a good mathematical base and therefore it is so popular amongst the fraternity of the fluvial geomorphologists. Usually the pattern of drainage alignments does have pretty resemblances with the branching of trees and all the catchment areas containing streams from lower order to higher order form graphical construction somehow, while plotted in the Strahler’s network. There is a disadvantage as well of the Strahler’s system and that is the mixing of the main channel somehow with the other inconspicuous drainage network. In Horton’s stream ordering separate importance is assigned to the main stream althrough. In the comparative analysis of fluvial ordering, Strahler’s technique is much more lucidly explained to the interpreters whereas the technique put forth by Horton corroborates the alignment to the lower order streams in connection with the highest order one. Raipur community development block is extended in the eastern portion of the Khatra sub-division. Here the fourth or highest order stream of Kangshabati has been extended in the western and south western part of the block boundary. After intersecting the block boundary at the east, the fourth order drainage is flown towards western direction. Kangshabati fourth order drainage has been extended in the north western part as well. In the eastern and the south eastern part, mainly the first, second and the third order drainages are extended. At the western and south western corner, a bunch of first and second order drainages are found as well. Ranibandh community development block is the biggest community development block of the entire Khatra Subdivision. Kangshabati reservoir is situated at the north of the block boundary and a good number of first and second order drainage patterns are flourished in the western part of the Ranibandh C.D. Block. One third order drainage is also found to be connected with the Kangshabati reservoir in the western extremity of the reservoir. In the northeastern part of the block boundary, multiple numbers of first, second and third order channels are spread and those streams are usually extended towards the southern direction. The persistence of the highest or the fourth order drainage is envisaged at the eastern and south western part of the block boundary. In the central part of the Ranibandh block, mainly the second order and third order drainage patterns are spread and in the overall spreading of the drainages in the block of Ranibandh, a formation like the trellis drainage has been observed. In the overall analysis of the drainage network at the Khatra sub-division, it can be said clearly that the lower order drainages are spreading in quite a good numbers in the entire community development blocks but the highest or the fourth order drainages are seen in only in a few numbers in the entire study area. In the overall drainage basin of river Kangshabati, the reservoir is playing a very conspicuous role and that is perennial by nature. Due to onset of monsoonal rain, the volume of water in that reservoir gets increased leading to the enhancement of the flood susceptibility in the entire subdivision (Map 17.5).

17 Geo-Spatial Techniques to Analyze Fluvial Morphometry of River …

391

Map 17.5 Drainage network and stream ordering in the study-area

17.7 Relative Relief Basin relief is a conspicuous geo-lithological factor to comprehend the erosional as well as the mass wasting related procedures within the basin area. Basin relief may also be defined as the distance or difference between the highest altitude and the lowest altitude within the basin perimeter. Very high or elevated relief within the basin denotes steep gradient where the hill slope processes predominate and low relief indicates the comparatively flat terrain with occurrence of in situ weathering as well as denudational processes. The accompanying figure indicates the relative relief within the Kangshabati river basin and here the relative relief map is divided into five distinct relief zones where the highest relief ranges between 24 and 116 m on an average and the lowest relief actually is ranging between less that 18 m to almost a monotonously flat topography. Between the two extremities, there stand three more zones. So far the relative relief map is concerned, it is clearly depicted that the central

392

A. Das Gupta

part of the Hirabandh community development block is a flat terrain and its southern and the eastern portions are highly elevated. Near Baharamuri and Baliyan gram Panchayats, the highly elevated topography is seen. Raipur community development block is mainly of flat nature and specifically in the south eastern and the eastern part of Raipur community development block, the topography is almost flat but at the northern and the north western part of the Raipur C.D.Block, the topography is considerably elevated. Dundar, Sonagara gram Panchayats are situated in the plateau proper area with significantly high altitude. The Ranibandh community development block and specially its central parts are of high altitude and Rajkata, Haludkanali and Ambikanagar are situated in the high altitude region. Near the region of Kangshabati reservoir, the topography is moderate to flat. Khatra Community development block is basically of moderate relief but its central portion is situated within the moderate to high elevation. From the relative relief map of the Kangshabati Basin, it is clearly manifested that three distinct elevation regions are there like low, moderate and high. The moderate elevation has different occurrences in different places starting from slightly higher altitude to the altitude towards higher elevation. As per the postulations of the Climatic Geomorphologists like Tricart. Peltier, Brudel etc., there stand a definitive connection amongst climatic parameters, lithological uniquenesses, and tectonic distribution and that relation has been deciphered through this academic composition (Map 17.6).

17.8 Slope Study Like relief, the slope is also a significant determinant (Das and Pardeshi 2018) in the evolution of topography and because of the intensive dissection of the terrain by active intervention of the fluvial agency, multiple slope features get originated within a river basin. Slope stands as a geo-lithological aspect (Dornkamp and King 1971) that pleads in favour of the tilting or inclination of a river basin with special reference to flat topography or the horizontal stratum where no underlying control is exercised upon the fluvial agency. Usually it has been observed that within the horizontal topography, the dendritic drainage pattern develops. Proper understanding of slope is of paramount significance to the fluvial geomorphologists because it can make him enlightened regarding the agricultural practices, construction of engineering features, and adoption of multiple regional planning strategies and so on and so forth. Climatic geomorphology actually corroborates the relation between climatic phenomena and evolution of slope. Potential evapotranspiration and thermal efficiency play a pivotal role in the dynamics of gradient in a particular study unit. Occurrences of different petrological elements of varying hardness basically lead to the formation of steep as well as gentle gradient in a particular belt. River by its fluvial pressure as well as erosive potential may modify topography and end up in the creation of terrain with mixture of steep and gentle gradients but here the dominant role is played by the strength or hardness of the rock body. Here the slope map has been prepared in the GIS Environment. The minute observation of the slope map reveals that there are five

17 Geo-Spatial Techniques to Analyze Fluvial Morphometry of River …

393

Map 17.6 Relative relief

distinct zones of gradient in the entire map. The highest gradient is ranging between 10 and 31° approximately whereas the lowest gradient is more or less 0° to 2°. From the overall range of the slope map, it is distinct to the target audience that the study unit is mainly a mixture of flat topography and elevated as well as undulating terrain. Between the two extreme ranges, three other slope regions are found. The Hirabandh community development block is mainly flat towards the western part whereas in the northern, central and eastern portion, the elevated terrains do predominate and there is a small patch of high topography in North astern corner of the community development block and that high topographic expression is found in the Mosiara gram Panchayat. The eastern, south eastern and central portion of the Raipur Community development block is mainly flat by nature with limited occurrences of moderately wavy topography. The western and the south western parts are comparatively elevated

394

A. Das Gupta

and there the gradient is also considerably steeper. Near the gram Panchayat of Shyamsundarpur and Sonagara, the highly elevated terrains are seen. Ranibandh community development block is characterized by frequent occurrences of wavy and high altitude regions. The central and south western part show steep gradient and almost the entire region standing at the south to the Kangshabati reservoir is falling within the zone of plateau proper. Ratuora, Rudra gram Panchayats are mainly situated on the elevated terrain with multiple interventions of high gradient tills. Western side of the Khatra community development block is covered by the presence of Kangshabati reservoir and just at the eastern side of the reservoir; there stand a high gradient region. Khatra-1 and Supur Gram Panchayats are mainly falling within the zone of this steep gradient. Otherwise, the rest of the Khatra block is not of that much steep gradient. Baidyanathpur, Dhanara and surrounding rural areas are situated in the plateau fringe and erosional plain regions.

17.9 Dissection Index Map In the panorama of fluvial Morphometry, the place of dissection index is very special because the fluvial agencies starting from their point of initiation upto their confluence do dissect the topography as a result of which, the polycyclic as well as the multi-cyclic relief (Clerke 1996) features get generated. Dissection index actually shows the intensity of dissection of the fluvial agent (Das 2017) in a particular piece of land. It also defines the roughness of the surface created by the good number of ravines, rivulets and valleys. In the study of terrain morphology and to understand the stage of definite fluvial agency in the entire course of evolution (Costa 1987), the dissection index becomes profusely helpful to the researchers. In the accompanying map, the magnitude of dissection done by the major fluvial agent Kangshabati river has been established with great importance and from the layout, it is quite clear that total five distinct dissection index zones have been delineated. The maximum amount of dissection index is found to be near about 51 m and the maximum intensity zone is ranging between 27 m and >51 m approximately. On the contrary, the lowest region related to dissection index in metres ranges between less than 1 m and 11.5 m. Between the two extremities, stand the three other dissection index zones. Hirabandh community development block is not so much dissected by the river Kangshabati and therefore the northern, western and eastern zones are ranging from less than 1 m to 28 m approximately but the southern part is characterized by the occurrence of high dissection index value and this occurs near Hirabandh and Moshiara gram Panchayats. Khatra community development block is having comparatively low dissection index value near the Kangshabati Reservoir but the north western part is significantly dissected. Northern and north eastern parts are low to moderately dissected but the south eastern and central part of the Khatra community development block are conspicuously dissected by the fluvial hegemony of river Kangshabati. Dahala, Dhanara and Baidyanathpur areas are the places of high dissection Index values. The central portion of the Raipur Community development

17 Geo-Spatial Techniques to Analyze Fluvial Morphometry of River …

395

block is found to be ranging between low to moderate dissection index value but the extreme north eastern part is having quite high value, so far the dissection index is concerned. If seen minutely then it will be found that the Dundar gram Panchayat is mainly the area with such a high value of dissection Index. The central and eastern part of the Ranibandh block is found to be with high values of dissection index and Ranibandh, Rajakata etc. rural areas are falling within the zone of remarkably high dissection index value. In other portions of the Ranighat Community development block, the dissection Index is ranging from moderate to low value. On the other side, especially the southern part of the Kangshabati reservoir, the values of dissection index is raised and it proves that the river has extensively cut down the valleys leading to the increase of roughness over this region (Map 17.7).

Map 17.7 Average slope

396

A. Das Gupta

17.10 Drainage Density According to Horton in the year of 1932, the drainage density corroborates the closeness of spacing of the streams (Boulton 1968) that are flowing in a particular region. It can be expressed as a significant ratio (Bull and McFadden 1977) between the total lengths of the streams in different orderings within a big drainage basin area. Usually it has been observed that the low relief regions are characterized by the low drainage density (Chopra et al. 2005) and the high relief areas are on the other hand found with considerably higher drainage density in kilometres per square kilometres (Verstappen 1983). This relationship was first successfully deciphered by the fluvial geomorphologists Strahler. The accompanying map displays the segments of drainage density zones within the Kangshabati drainage basin. The highest drainage density in the study area is found to be greater than 6 kms per square kilometres and the lowest drainage length in kilometres per square kilometres is less than 1 over here. Between the two extremities, stand the other three moderate drainage density zones. In the Hirabandh Community development block, the drainage density is highest in the western and in the eastern part whereas in the northern segment, the drainage density is considerably low. In the Khatra C.D.Block, the drainage density is extremely high near the Kangshabati reservoir and apart from this particular western portion; the drainage density is significantly high in the central as well as eastern part. The Raipur community development block is featured by the mixed occurrences of varying drainage densities. In the south western and the south eastern part, the drainage density is pretty high whereas in the northern portion, the magnitude of the same is spectacularly low. Lastly in the northern and north western part of the Ranibandh community development block, the drainage lengths per square kilometres is quite high because of the persistence of the big Kangshabati reservoir and similarly in the south western, south eastern and eastern portions, the occurrence of high drainage density is quite remarkable. Apart from these areas, all other parts in Ranibandh C.D.Block are characterized by low to moderate drainage density (Maps 17.8 and 17.9).

17.11 Conclusion In the aforementioned discussion, a totalistic picture of the Kangshabati River Basin within the sub-division of Khatra has come in the fore front. Multiple morphometric analyses are done in remote sensing and GIS platform, in terms of relative relief, average slope, drainage density, dissection index etc. Different zones with varying intensities related to the morphometric values have been deciphered in front of the target audiences, through the detailed oriented discussions. Not only the morphometric analyses along with stream ordering are done in geo-spatial platform but also with the help of different geo-spatial techniques, the elevation mapping, forest mapping etc. are also done over here. Digital elevation model has also been prepared

17 Geo-Spatial Techniques to Analyze Fluvial Morphometry of River …

397

Map 17.8 Spatial distribution of dissection Index

with the help of GIS Software to show the nature of multi-cyclic relief in the study arena. So in a nutshell, the morphometric as well as several associated parameters of the Kangshabati drainage basin within the Khatra sub-division encompassing total four community development blocks namely Ranibandh, Hirabandh, Khatra and Raipur have been portrayed upto an optimum extent in the present paper. Though the approach of dealings of this paper with special reference to the fluvial Morphometry is traditional but at the same time the focus on the study area from a unique perspective is definitely novel by approach and that has made this academic output special.

398

A. Das Gupta

Map 17.9 Spatial distribution of drainage density

References Boulton AG (1968) Morphometric analysis of river basin characteristics. Water Resource Board, UK Bull W, McFadden L (1977) Tectonic geomorphology north and south of the Garlock fault California. In: Doehring DO (ed) Geomorphology in arid regions. State University of New York, Binghamton, pp 115–138 Chopra R, Dhiman RD, Sharma PK (2005) Morphometric analysis of sub watersheds in Gurdaspur district Punjab using remote sensing and GIS techniques. J Indian Soc Remote Sens 33:531–539 Clarke JI (1996) Morphometry from maps. Essays in geomorphology. Elsevier Publ. Co., New York, pp 235–274 Costa JE (1987) Hydraulics and basin morphometry of the largest flash floods in the conterminous United States. J Hydrol 93:313–338

17 Geo-Spatial Techniques to Analyze Fluvial Morphometry of River …

399

Das S (2017) Signatures of morpho-tectonic activities in western upland Maharashtra and Konkan region. Unpublished M.Sc. thesis, Savitribai Phule Pune University Das S, Pardeshi SD (2018) Comparative analysis of lineaments extracted from Cartosat, SRTM and ASTER DEM: a study based on four watersheds in Konkan region India. Spatial Inf Resour 26(1):47–57 Dornkamp JC, King CAM (1971) Numerical analyses in geomorphology, an introduction. St. Martin’s Press, New York, p 372 Horton RE (1932) Drainage basin characteristics. Trans Am Geophys Union 13:350–361 Rao NK, Latha SP, Kumar AP, Krishna HM (2010) Morphometric analysis of Gostani river basin in Andhra Pradesh State, India using spatial information technology. Int J Geomagn Geosci 1:179–187 Schumm SA (1956) Evolution of drainage systems and slopes in badlands at Perth Amboy. Geological Society of America, New Jersey, vol 67, pp 597–646 Valdiya KS (2008) Sinking of ancient Talakad temples on the Kaveri bank, Mysore plateau, Karnataka. Curr Sci 95:1675–1676 Valdiya KS, Rajagopalan G (2000) Large paleolakes in Kaveri Basin in Mysore plateau: late quaternary fault reactivation. Curr Sci 78:1138–1142 Verstappen H (1983) The applied geomorphology. International Institute for Aerial Survey and Earth Science (I.T.C), Enschede

Chapter 18

Morphometric Analysis of Panzara River Basin Watershed, Maharashtra, India Using Geospatial Approach Pranaya Diwate, Firoz Khan, Sanjeev Kumar, Kunal Chinche, Pavankumar Giri, and Varun Narayan Mishra

Abstract This work aims to deal with the morphometric analysis of the Panzara River Basin (PRB) watershed, a tributary of the Tapi River in Maharashtra, India. In this study, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model (DEM) and Survey of India (SOI) toposheets have been used along with various parameters like linear, areal, and relief aspects in Geographical Information System (GIS) environment. Out of total 293 number of streams in the watershed, 226, 54, 12, and 1 are of first, second, third and fourth orders respectively. PRB watershed is of 4th order and less elongated in shape, having lower peak flows of longer duration with dendritic pattern and having coarse drainage texture. The bifurcation ratio lies between 6 and 7, indicating that geological structure doesn’t have more influence on drainage patterns. The area forms a rugged topography having an elevation range from 123 to 1199 m above Mean Sea Level (MSL). Variables like Stream Frequency and Drainage Density shows impermeable surface of PRB watershed which causes higher water discharge volume and speed in the basin so that the probability is maximum for frequent floods. The results indicates that PRB is having high slope in SW part as compare to NE part and having very low P. Diwate Department of Geology, School of Basic and Applied Sciences, MGM University, Chhatrapati Sambhajinagar (Aurangabad), Maharashtra, 431003, India K. Chinche Centre for Climate Change and Water Research, Suresh Gyan Vihar University, Jaipur, Rajasthan 302017, India F. Khan · S. Kumar Department of Geology, H. N. B. Garhwal Central University, Srinagar, Garhwal, Uttarakhand 246174, India P. Giri Shri Shivaji College of Arts, Commerce and Science, Akola, Maharashtra 444001, India V. N. Mishra (B) Amity Institute of Geoinformatics & Remote Sensing (AIGIRS), Amity University, Sector 125, Noida, Uttar Pradesh, 201313, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_18

401

402

P. Diwate et al.

gradient ratio. These variables also shows a temporal variation in the water flowing speed in the stream when the flood reaches its peak. The outcomes of this work can suggest and recommend a better mechanism for proper watershed management in the PRB. Keywords GIS · ASTER DEM · Morphometric analysis · Panzara river basin · Watershed

18.1 Introduction The morphometric analysis provides accurate information of measurable parameters vital for characterising a river basin (Kumar and Chaudhary 2016; Rai et al. 2017). The river basins include a distinct geomorphologic area and are exactly related to the pattern of streams and geomorphology of the area (Strahler 1957). The drainage pattern of the basin also provides information about topography and geological structures (Langbein 1947). Drainage density in the basin varies with the relative age of various rock formations, differing geology, drainage area, etc. and enables comparison of basins and streams (Zaidi 2011). There are three important aspects including linear, areal, and relief that can be used for conducting the morphometric analysis of a basin. The linear aspects provide information about one-dimensional parameters like stream order, stream number, and bifurcation ratio. The areal aspects deal with two-dimensional parameters like drainage density, stream length, stream length ratio, drainage texture, stream frequency, circularity ratio, and form factor. The relief aspects deal with three-dimensional parameters like relief, relief ratio, slope, and gradient ratio. Geospatial technology is a combination of state-of-the-art remote sensing and technology for GIS and Global Navigation Satellite Systems (GNSS) for the mapping and monitoring of Geohydrological properties of drainage basin (Rai et al. 2017). The spatio-temporal variation in planform and characteristics of channel geometry were analyzed using hydraulic geometry equations, decadal variations of unit stream power and its influence on morphological changes of the alluvial river in the Upper Tapi River basin. (Ramani et al. 2021). A quantitative morphometric analysis was done in lower Bhawani basin of Tmil Nadu (Balasubramanian et al. 2017). The drainage characteristics of Upper Tapi River Sub-basin were studied by Munoth and Goyal 2020) to describe and evaluate their hydrological characteristics by using the DEM generating from SRTM data. The morphometric characteristics of various basins have been studied by many scientists (Horton 1945; Strahler 1957; Babar and Kaplay 1998; Kaplay et al. 2004). In many studies the use of using Remote sensing and GIS methods is reported as well for morphometric analysis since 3–4 decades like: Bedi and Bhan (1978) used Landsat imagery for hydrogeological mapping in Cuddapah area of Andhra Pradesh; Palanivel et al. (1996) used an Integrated approach using remote sensing, geophysical and well inventory data for evaluation of Geohydrological properties of upper

18 Morphometric Analysis of Panzara River Basin Watershed, Maharashtra …

403

Agniar and Vellar basins Tamil Nadu. Hydrogeomorphological studies done in Niva River basin Chittoor Andhra Pradesh by using remote sensing application (Rao et al. 1997; Babar 2001, 2002, 2005, 2011). Gives various tecniques and fundamental applications of remote sensing for evaluating Hydrogeomorphological characteristics of watershed and also did various studies in different watershed in India. Sreedevi et al. (2009), Muley et al. (2010a, b) did GPZ study in drought prone areas of Parbhani District, Maharashtra by using geological and RS data. Babar and Shah (2011) demarcate Groundwater Potential Zones in Tawarja River Sub-Basin, Latur District, Maharashtra with the help of RS and GIS. Simultaneously various researchers use RS and GIS techniques to study geomorphometric characteristics of basins in different parts of India (Jadhav and Babar 2014; Kumar 2017; Rai et al. 2017, 2018, 2019; Chaudhary and Kumar 2018; Giri et al. 2020; Kumar and Chaudhary 2021; Rawat et al. 2021). These studies signified that the drainage morphometry is very important in understanding the soil physical properties, landform processes, and erosional characteristics. Morphometric analysis of drainage systems is the main requirement to any hydrological study. A study from the Koshalya-Jhajhara watershed (Kumar and Chaudhary 2016) of northwest India suggested that the drainage patterns show dendritic patterns which indicates the homogeneity in texture and lack of structural control (Kumar and Chaudhary 2016). Rai et al. (2017) described drainage morphometric analysis of Kanhar River Basin, India using Remote sensing data and GIS techniques. The Kanhar River Basin study of all sub-watersheds indicates dendritic to sub-dendritic drainage network which shows homogenous lithology and variations of values of Bifurcation ratio among the sub-watersheds attributed to the difference in topography and geometric development (Rai et al. 2017). The morphometric analysis of Kosi River basin (Bihar) studied by Rai et al. (2018) reveals the hydrological inferences. Giri et al. (2020) studied morphometric analysis of the Tapi River basin from the northern part of the Deccan Plateau. Rawat et al. (2021) determine the soil erosion, flood risk and groundwater potential of the Dhanari River watershed (a tributary of the Bhagirathi River) from Uttarkashi (Uttarakhand) using remote sensing and GIS. The studies of drainage patterns and morphometric analysis also helps to delineate the potential groundwater zones in a watershed (Chaudhary and Kumar 2018). Various hydrologic phenomena can be correlated with physiographic characteristics of drainage basins like size, slope, drainage density, etc. (Rastogi and Sharma 1976). The use of geospatial technologies is very helpful for plans and policies designation for the river basins. Golekar et al. (2016) suggested PRB is presently fall in moderate stage and till now not achieved maturity on the basis of grain size analysis. The main objective of the present study is to carry out the morphometric analysis of the PRB watershed located in the Northern region of Maharashtra state, India. Presently, the Panzara river is at a moderate stage and has not achieved maturity. The present study can be of significance for the development plan of the basin under investigation that can assist the planners and decision makers. It can also be supportive for sustainable development and management of water resources at micro watershed of the PRB.

404

P. Diwate et al.

18.2 Study Area The PRB is a sub-basin of the Tapi River, located in Northern Maharashtra, India. Panzara river originates from Sahyadri mountains at an altitude of 1058 m amsl. The total area of PRB is about 2982.28 km2 . The basin is spread between Survey of India toposheets 46/K/12, 46/K/16, 46/L/9, and 46/L/13 and bounded by latitude 20°54' to 21°13' N and longitude 74°07' to 74°56' E in parts of Dhule districts of Maharashtra, India. The location map of the PRB has shown in Fig. 18.1. The climate of the area is characterized by a hot summer and general dryness throughout the year except during the monsoon season, i.e., June–September. The minimum temperature is 6 °C in December, and the maximum temperature is 45 °C in May (Pawar 2015). The expected annual rainfall ranges from 500 to 655 mm (CGWB 2009).Geologically, the study area belongs to the Deccan trap of the Cretaceous to the Lower Eocene age (Golekar et al. 2017). The study area is almost covered by Deccan basalts except a few patches which are covered by thick alluvium and Quaternary sediments (GSI 1984).

Fig. 18.1 Location map of the study area

18 Morphometric Analysis of Panzara River Basin Watershed, Maharashtra …

405

18.3 Database and Methodology In the present study, ASTER data (30 m) downloaded from the ASTER GDEM Project of Japan-US ASTER Science Team (http://gdem.ersdac.jspa-cesystems. or.jp/) and used to prepare the drainage map of the area. It has also been used to calculate the Relief aspects of the basin area. Length and area of streams in the watershed were calculated by using Arc GIS 9.2. Horton (1945) technique was used for demarcating the stream order in the watershed. Linear and Areal aspects were calculated using standard formulae explained in Table 18.1, Horton (1932, 1945), Strahler (1964), Smith (1950), Schumm (1956). Relief Aspects were calculated using Digital Elevation Model (DEM) from Aster (30 m), and formulae explained in Table 18.1, Hadley and Schumm (1961), Schumm (1963), Mesa (2006). The slope map prepare from DEM data after it transformed date to UTM zone.

18.4 Results and Discussion The parameters determining the nature and characteristics of the watershed have been calculated and described in detail as given below.

18.4.1 Linear Aspects The drainage network in the watershed was analysed to calculate various linear parameters like stream order, stream number, stream length, and bifurcation ratio.

18.4.1.1

Stream Order (Nu)

There are four different techniques for ordering a stream: Horton (1945), and Strahler (1957, 1964). The PRB watershed has been ranked according to Strahler (1964), which is slightly modified by Horton’s system. According to this system, the junction of two 1st order channels produces channel segments of 2nd order, two 2nd order streams join to form a segment of 3rd order, and so on. When two channels of different order join, then the higher order is maintained. Hence PRB watershed exhibits 4th order as the highest inthe basin (Fig. 18.2).

406

P. Diwate et al.

Table 18.1 Formulae used for morphometric analysis Formulae

Parameters

References

Linear aspects Stream order (U)

The smallest permanent streams are called “first Strahler (1964) order”. Two first order streams join to form a larger, second order stream; two second order streams join to form a third order, and so on. Smaller streams entering a higher-ordered stream do not change its order number

Stream length (Lu)

The average length of streams of each of the different Horton (1945) orders in a drainage basin tends closely to approximate a direct geometric ratio

Stream length ratio (RL)

RL = Lu/(Lu−1)

Bifurcation ratio (Rb)

Rb = Nu/(Nu + 1)

Horton (1932)

Stream frequency (Fs)

Fs = ∑Nu/A

Horton (1945)

Drainage density (Dd)

Dd = Lu/A

Horton (1945)

Drainage texture (T)

T = Dd × Fs

Smith (1950)

√ Re = 1.128 A/L

Schumm (1956)

|| Rc = 4 A/P2

Strahler 1964

Ff = A/L2

Horton (1945)

Relief (R)

R = H−h

Hadley and Schumm (1961)

Relief ratio (Rr)

Rr = R/L

Schumm (1963)

Slope (Sb)

Sb = (H−h)/L'

Mesa (2006)

Gradient ratio (Gr)

Gr = (H−h)/L

Areal aspects

Elongation ratio (Re) Circularity ratio (Rc) Form factor (Ff) Relief aspects

18.4.1.2

Stream Number (Nu)

The total number of streams present in each order is the stream number. It is found that as the stream order increases, the stream number decreases in all sub-basins. The first-order streams are maximum in the hilly portion and minimum in the lower part of the basin. There are 226 streams of the first-order, 54 streams of second-order, 12 streams of the third-order, and one of the fourth-order.

18 Morphometric Analysis of Panzara River Basin Watershed, Maharashtra …

407

Fig. 18.2 Stream order map of the Panzara river basin watershed

18.4.1.3

Stream Length (Lu) and Mean/Average Stream Length (Lu1 )

The total length of individual stream segments of each order is the stream length of that order. The stream length in each order increases exponentially with increasing stream order. The total length of first-order streams is 661.41 km, second-order streams have a length of 334.45 km, third-order streams have a length of 119.06 km, and fourth-order streams have a length of about 125.33 km in the PRB watershed. Mean stream length is the ratio of the total length of a particular ordered stream to the total no of streams of the same order and is given in Table 18.2.

18.4.1.4

Stream Length Ratio (RL)

The Stream length ratio is the mean or average length of the order ‘u’ segment to the mean or average length of order ‘u−1’. During the study, the whole watershed has a stream length ratio is 2,041.18 (Table 18.2).

408

P. Diwate et al.

Table 18.2 Linear parameter of Panzara river basin

S. no

Parameter

1

Stream order (Nu)

2

3

Mean stream length (Lsm)

226

2nd order

54

3rd order

12

4th order

1

Total

293

1st order

661,414

2nd order

334,454

3rd order

119,061

4th order

125,335

Total

1,240,265

1st order

2926

2nd order

6193

3rd order

9921

4th order

125,335

4

Mean stream length ratio (RL)

4232

5

Bifurcation ratio (Rb)

1st order

4.2

2nd order

4.5

3rd order

12

4th order

–

6

18.4.1.5

Stream length (Lu)

Value 1st order

Mean bifurcation ratio (Rbm)

6.89

Bifurcation Ratio (Rb)

Bifurcation ratio is the ratio of the number of streams of order (u) to the number of streams of higher-order (u + 1) (Strahler 1964). Chow (1964) stated that the bifurcation ratio values lie between 0 and 7 for those watersheds where geological structures do not influence the drainage pattern (Kumar and Chaudhary 2016). The mean Bifurcation ratio of the watershed is 6.89 (Table 18.3). The higher Rb for basins is the result of the significant variation in frequencies between successive orders and indicates the mature topography. Table 18.3 Bifurcation ratio of Panzara river basin

Stream order

Number of streams

Bifurcation ratio (Rb)

1st order

226

4.18

2nd order

54

4.5

3rd order

12

12

4th order

1

–

18 Morphometric Analysis of Panzara River Basin Watershed, Maharashtra …

409

18.4.2 Areal Aspects The areal aspects are two-dimensional properties of a basin. It is possible to delineate the area of the basin which contributes water to each stream segment. The total area of the basin is 2982.29 km2 . The aerial aspects of the drainage basin such as Drainage Density (Dd), Stream Frequency (Fs), Drainage Texture (T), Elongation Ratio (Re), Circularity Ratio (Rc), and Form Factor Ratio (Rf) were calculated (Table 18.4).

18.4.2.1

Drainage Density (Dd )

The drainage density indicates the closeness of spacing of channels, thus providing a quantitative measure of the average length of stream channel for the whole basin. High drainage density is the resultant of impermeable subsurface material, sparse vegetation, and mountainous relief. Low drainage density leads to coarse drainage texture, while high drainage density leads to fine drainage texture (Strahler 1964). The drainage density (Dd) of the study area is 0.416 km−1 indicating higher drainage densities (Table 18.4). It signifies that impermeable surface materials predominate watershed (Fig. 18.3).

18.4.2.2

Stream Frequency (Fs )

Stream frequency is directly related to the lithological characteristics. The number of stream segments per unit area is called Stream Frequency or Channel Frequency or Drainage Frequency (Fs) (Horton 1945). The total Stream frequency of the study area is 0.098 km−2 (Table 18.4; Fig. 18.4). Table 18.4 Areal parameter of Panzara river basin S. no

Parameter value

Value

1

Basin area (A) (sq.km)

2982.29

2

Basin perimeter (P) (km)

401.89

3

Form factor (Ff)

0.0019

4

Drainage density (Dd)

0.42

5

Drainage texture (Dt)

0.73

6

Texture ratio (T)

0.56

7

Stream frequency (Fs) (no./sq.km)

0.098

8

Elongation ratio (Re)

0.049

9

Circulatory ratio (Rc)

0.23

10

Length of overland flow (Lg) (km)

1.20

11

Infiltration number (If)

0.04

12

Constant of channel maintenance (C)

2.40

410

P. Diwate et al.

Fig. 18.3 Drainage density map of Panzara river basin watershed

18.4.2.3

Drainage Texture (T)

Drainage texture may be defined as the total number of stream segments of all orders in a basin per perimeter of the basin. It is important to understand geomorphology, which means the relative spacing of drainage lines. Drainage texture depends on the underlying lithology, infiltration capacity, and relief aspect of the terrain and natural factors such as climate, rainfall, vegetation, rock and soil type, relief, and stage of development. Smith (1950) has classified drainage texture into five different classes i.e., very coarse (8). The Drainage texture of the whole PRB watershed is 0.72.

18 Morphometric Analysis of Panzara River Basin Watershed, Maharashtra …

411

Fig. 18.4 Stream frequency map of Panzara river basin watershed

18.4.2.4

Elongation Ratio (Re )

Elongation ratio (Re) may be defined as the ratio of the diameter of a circle of the same area as the basin to the maximum basin length. The value of Re (0.049) varies from 0 (in highly elongated shape) to unity i.e., 1.0 (in the circular shape).

412

P. Diwate et al.

Elongation ratio

Shape of basin

0.9

Circular

Thus, the higher the value of elongation ratio, the more circular the shape of the basin and vice-versa. Values close to 1.0 are typical of regions of very low relief, whereas that of 0.6–0.8 are usually associated with high relief and steep ground slope (Strahler 1964). The elongation ratio for the whole basin is 0.1195. It shows that the PRB watershed is less elongated in shape.

18.4.2.5

Circularity Ratio (Rc )

The Circularity Ratio is a similar measure as the elongation ratio, originally defined by Miller (1953) as the ratio of the area of the basin to the area of the circle having the same circumference as the basin perimeter. The value of the circularity ratio varies from 0 (inline) to 1 (in a circle). The circulatory ratio is influenced by the length and frequency of streams, geological structures, land use/land cover, climate, relief, and slope of the basin. The Rc of the whole basin is 0.23. It is a significant ratio that indicates the dendritic stage of a basin.

18.4.2.6

Form Factor (Ff )

The form factor is the numerical index (Horton 1932) commonly used to represent different basin shapes. The value of the form factor is between 0.1 and 0.8. The smaller the value of the form factor, the more elongated will be the basin. The basins with high form factors 0.8 have high peak flows of shorter duration, whereas elongated drainage basins with low form factors have lower peak flows of longer duration. The alluvial basins show a low form factor value representing the elongated nature of the basins. The Ff value for the PRB watershed is 0.0019, indicating an elongated basin with lower peak flows of longer duration.

18.4.3 Relief Aspects Linear and areal features have been considered as the two-dimensional aspects lying on a plan. The third dimension introduces the concept of relief.

18 Morphometric Analysis of Panzara River Basin Watershed, Maharashtra …

413

Table 18.5 Relief parameter of Panzara river basin S. no

Parameters

Value

1

Basin relief (R)

1076

2

Relief ratio (Rr)

0.86

3

Ruggedness number (Rn)

0.496

18.4.3.1

Basin Relief

Basin relief is the elevation difference of the highest and lowest point of the valley floor. Basin Relief plays a significant role in landforms development, drainage development, surface and subsurface water flow, permeability, and erosional properties of the terrain. The relief of the basin is 1076 m above MSL and the relief ratio is 0.86 with ruggedness number 0.496 (Table 18.5).

18.4.3.2

Relief Ratio

It is the ratio of basin relief to basin length. While high values are characteristic of hill regions, low values are characteristic of plains and valleys. Relative Relief =

Maximum basin relief(H) Maximum basin length(Lb)

(18.1)

This is a dimensionless height-length ratio and allows comparison of the relative relief of any basin regardless of the difference in scale or topography. The relief ratio is equal to the right-angled triangle and is identical with the tangent of the angle of slope of the hypotenuse with respect to horizontal (Strahler 1964). Thus, is measure the overall steepness of a drainage basin is an indicator of the intensity of erosion processes operating on the slope of the basin. The relief ratio of the PRB watershed is 0.86 (Table 18.5; Fig. 18.5).

18.4.3.3

Slope

Slope analysis is an essential parameter in geomorphic studies. The slope elements, in turn are controlled by the climatomorphogenic processes in the area having the rock of varying resistance. An understanding of slope distribution is essential as a slope map provides data for planning, settlement, mechanization of agriculture, deforestation, planning of engineering structures, morph conservation practices etc. (Sreedevi et al. 2005). In the present study, Aster DEM was used to prepare a Slope map, DEM, and Aspect map. The slope grid is identified as the maximum rate of change in value from each cell to its neighbor’s, using the methodology described

414

P. Diwate et al.

Fig. 18.5 Absolute relief map of Panzara river basin watershed

in Burrough (1986). The slope varies from 0° to 71.84° with a mean slope of 16.86° and Slope Standard Deviation 11.29° of PRB watershed (Fig. 18.6). The high slope is witnessed in the NE and southern portions and the low slope in the NE and SE part of the PRB watershed.

18 Morphometric Analysis of Panzara River Basin Watershed, Maharashtra …

415

Fig. 18.6 Slope map of Panzara river basin watershed

18.4.3.4

Gradient Ratio

Gradient ratio is the total drop in elevation from the source to the mouth of the trunk channels in each drainage basin. In the present study, the gradient ratio of the PRB watershed is 0.053, which is very low. It indicates that a more nearly level stream bed and sluggishly moving water may carry only small amounts of very fine sediments (Fig. 18.7; Table 18.6).

18.5 Conclusions The morphometric analysis of the drainage network of the PRB watershed exhibits the dendritic pattern and signifies the homogeneity in texture and lack of structural control. Based on the drainage orders, the PRB watershed has been classified as a fourth-order basin. The drainage density (Dd) of the study area is 0.416 km−1 which indicates impermeable surface materials. The drainage texture of the watershed falls under the category of very coarse drainage texture (>2). Elongation and circularity ratios for the basin are 0.049 and 0.23, respectively, showing that PRB Watershed is elongated with the dendritic stage. PRB has a steep slope. The characteristics of the

416

P. Diwate et al.

Fig. 18.7 Aspect map of Panzara river basin watershed

water network, including the discharge density and the stream bifurcation ratio, have directly contributed to increasing the valley’s water discharge. These studies are very useful for rainwater harvesting and watershed management plans. 1st and 2nd order streams are not useful for constructing check dams in the study area because these streams are situated on hilly terrains and the velocity of water discharge through river tributaries in the basin is more due to the presence of a large number of streams of lower-order present in PRB watershed. This study successfully demonstrates the usefulness of Geospatial technology for morphometric analysis of the river basin.

18 Morphometric Analysis of Panzara River Basin Watershed, Maharashtra … Table 18.6 Morphometric parameters of Panzara river basin

417

S. no

Parameter value

Value

1

Basin area (sq.km)

2982

2

Basin perimeter (km)

401

3

Total length of streams (km)

1240

4

Total number of streams (Nu)

293

5

Mean bifurcation ratio (Rbm)

2.29

6

Form factor (Ff)

0.0019

7

Drainage density (km/sq.km)

0.46

8

Drainage texture (no./km)

0.73

9

Texture ratio (T)

0.56

10

Stream frequency (no./sq.km)

0.098

11

Elongation ratio (Re)

0.049

12

Circulatory ratio (Rc)

0.23

13

Length of overland flow (Lg)

1.20

14

Infiltration number (If)

0.04

15

Constant of channel maintenance (C)

2.40

16

Maximum height (H)

1199

17

Minimum height (h)

123

18

Basin relief (R)

1076

19

Relief ratio (Rr)

0.86

20

Ruggedness number (Rn)

0.496

Acknowledgements Authors are grateful to the Director, Centre for Climate Change and Water Research, Suresh Gyan Vihar University, Jaipur for providing all the necessary facilities for carrying out this work.

References Babar MD (2001) Hydrogeomorphological studies by remote sensing application in Akoli watershed (Jintur) Parbhani dist., Maharashtra, India. In: Spatial information technology, remote sensing and GIS-ICORG, vol-II pp 137–143 Babar MD (2002) Application of remote sensing in hydrogeomorphological studies of Purna river basin in Parbhani district, Maharashtra, India. In: Proceeding volume of the international symposium of ISPRS commission VII on resource and environmental. Monitoring held during December 3–6, 2002, vol XXXIV Part 7, pp 519–523 Babar MD (2005) Hydrogeomorphology, fundamental applications and techniques. New India Publishing Agency, New Delhi, pp 1–259 Babar MD (2011) Hydrogeomorphological analysis for watershed development in Jintur Tahsil, Parbhani Dist., Maharashtra. Indian Streams Res J I(5):168–173 Babar MD, Kaplay RD (1998) Geomorphometric analysis of Purna river basin Parbhani district (Maharashtra). Indian J Geomorphol 3(1):29–39

418

P. Diwate et al.

Babar MD, Shah II (2011) Remote sensing and GIS application for groundwater potential zones in Tawarja river sub-basin, Latur district, Maharashtra, India. Int J Earth Sci Eng (IJEE) 4(3) Spec. issue:71–79 Balasubramanian A, Duraisamy K, Thirumalaisamy S et al (2017) Prioritization of subwatersheds based on quantitative morphometric analysis in lower Bhavani basin, Tamil Nadu, India using DEM and GIS techniques. Arab J Geosci 10:552. https://doi.org/10.1007/s12517-017-3312-6 Bedi N, Bhan SK (1978) Application of landsat imagery for hydrogeological mapping in Cuddapah area A.P. In: Proceedings of Joint Indo-UK workshop on remote sensing of water resources. NRSA Hyderabad, pp 115–129 Burrough PA (1986) Principles of geographic information systems for land resource assessment. Monographs on Soil and Resources Survey No. 12, Oxford Science Publications, New York CGWB (2009) Ground water profile manual of Dhule district central groundwater board Chaudhary BS, Kumar S (2018) Soil erosion estimation and prioritization of Koshalya-Jhajhara watershed in North India. Indian J Soil Conserv 46(3):305–311 Chow VT (1964) Handbook of applied hydrology. McGraw Hill Inc., New York Giri P, Diwate P, Mawale YK (2020) Morphometric analysis of Tapi drainage basin using remote sensing and GIS techniques. In: Sustainable development practices using geoinformatics, pp 57–72 Golekar RB, Baride MV, Patil SN, Mohite R, Patil S, Ronad HN (2016) Estimation of hydraulic conductivity from grain size distribution: a case study of sediments from Panzara river, Tapi Basin, Northern Maharashtra (India). Bull Pure Appl Sci-Geol 35(1&2):1–11 GSI (1984) Geology of parts of Dhule and Jalgaon Districts, Maharashtra (Progress Report for the field season 1982–83) By P. Kalyansundaram Hadely RF, Schumm SA (1961) Sediment sources and drainage basin characteristics in upper Cheyenne River basin. United State Geological Survey water-supply paper, 1531-B, pp 137–196 Horton RE (1932) Drainage basin characteristics. Trans Am Geophys Union 13:350–361 Horton RE (1945) Erosional development of streams and their drainage basin, hydrophysical approach to quantitative morphology. Geol Soc Am Bull 56:275–370 Jadhav SI, Babar MD (2014). Linear and aerial aspect of basin morphometry of Kundka Subbasin of Sindphana basin (Beed), Maharashtra, India. Int J Geol, Agric Environ Sci 2(3). ISSN: 2348-0254 Kaplay RD, Babar MD, Panaskar DB, Rakhe AM (2004) Geomorphometric characteristics of 30th September 1993 Killari earthquake area, Maharashtra (India). J Geophys XXV(2 & 3):55–61 Kumar S, Chaudhary BS (2016) GIS applications in morphometric analysis of Koshalya-Jhajhara watershed in northwestern India. J Geol Soc India 88(5):585–592 Kumar S, Chaudhary BS (2021) Integrated watershed conservation and management of KoshalyaJhajhara watershed, North India. In: Geostatistics and geospatial technologies for groundwater resources in India. Springer, Cham, pp 531–549 Kumar S (2017) Remote sensing and GIS based watershed studies in koshalya jhajhara watershed North India. http://hdl.handle.net/10603/190465 Langbein WB (1947). Topographic characteristics of drainage basins. https://doi.org/10.3133/WSP 968C Mesa LM (2006) Morphometric analysis of a subtropical Andean basin (Tucumam, Argentina). Environ Geol 50(8):1235–1242 Miller VC (1953) A quantitative geomorphic study of drainage basin characteristics in the clinch mountain area, Virgina and Tennessee, Proj. NR 389-402, Tech Rep 3, Columbia University, Departmental of Geology, ONR, New York Muley RB, Babar MD, Atkore SM, Ghute BB (2010a) Application of geology and remote sensing in the groundwater potential zones in drought prone areas of Parbhani district, Maharashtra, India. In: Proceeding volume of 3rd international conference on hydrology and watershed management held at Hyderabad held during February 3–6, vol I, pp 410–417 Muley RB, Babar MD, Atkore SM, Ghute BB (2010b) Application of remote sensing and hydrogeological characteristics for groundwater prospect zone in Purna Tahsil (Block), Parbhani district,

18 Morphometric Analysis of Panzara River Basin Watershed, Maharashtra …

419

Maharashtra. In: Proceedings of workshop on “Application of Remote sensing and GIS in water resources management” held at Hyderabad, pp 28–36 Munoth P, Goyal R (2020) Hydromorphological analysis of upper Tapi river sub-basin, India, using QSWAT model. Modell Earth Syst Environ 6:2111–2127. https://doi.org/10.1007/s40808-02000821-x Palanivel S, Ganesh A, Kumaran VT (1996) Geohydrological evaluation of upper Agniar and Vellar Basins, Tamilnadu: an integrated approach using remote sensing, geophysical and well inventory data. J Indian Soc Remote Sens 24(3):153–168 Pawar S (2015) Water budget of bhad river watershed, Panzara river basin, Dhule district of Maharashtra state. Int J Eng Innov Technol (IJEIT) 4(10):53–58 Rai PK, Chandel RS, Mishra VN, Singh P (2018) Hydrological inferences through morphometric analysis of lower Kosi river basin of India for water resource management based on remote sensing data. Appl Water Sci 8(1):1–16 Rai PK, Mishra VN, Mohan K (2017) A study of morphometric evaluation of the Son basin, India using geospatial approach. Remote Sens Appl: Soc Environ 7:9–20 Rai PK, Mohan K, Mishra S et al (2017) A GIS-based approach in drainage morphometric analysis of Kanhar River Basin, India. Appl Water Sci 7:217–232 Rai PK, Singh P, Mishra VN, Singh A, Sajan B, Shahi AP (2019) Geospatial approach for quantitative drainage morphometric analysis of Varuna river basin, India. J Landsc Ecol 12(2):1–25 Ramani RS, Lal Patel P, Vasharambhai Timbadiya P (2021) Key morphological changes and their linkages with stream power and land-use changes in the Upper Tapi River basin, India. Int J Sediment Res 36(5):602–615. ISSN 1001-6279. https://doi.org/10.1016/j.ijsrc.2021.03.003 Rao YS, Raddy TVK, Nayudu PT (1997) Hydrogeomorphological studies by remote sensing application in Niva River basin, Chittor District, Andhra Pradesh. Photonirvachak (J Indian Soc Remote Sens) 25(3):187–194 Rastogi RA, Sharma TC (1976) Quantitative analysis of drainage basin characteristics. J Soil Water Conserv India 26(1&4):18–25 Rawat A, Bisht MPS, Sundriyal YP, Banerjee S, Singh V (2021) Assessment of soil erosion, flood risk and groundwater potential of Dhanari watershed using remote sensing and geographic information system, district Uttarkashi, Uttarakhand, India. Appl Water Sci 11(7):1–13 Schumm SA (1956) Evolution of drainage systems and slopes in Badlands at Perth Amboy. New Jersey, Geol Soc Am Bull 6(7):597–646 Schumm SA (1963) Sinuosity of alluvial rivers on the great plains. Bull Geol Soc Am 74:1089–1100 Smith KG (1950) Standards for grading texture of erosional topography. Am J Sci 248:655–668 Sreedevi PD, Owais S, Khan HH, Ahmed S (2009) Morphometric analysis of a watershed of south India using SRTM data and GIS. J Geol Soc India 73:543–552 Sreedevi PD, Subrahmanyam K, Shakeel A (2005) The significance of morphometric analysis for obtaining groundwater potential zones in a structurally controlled terrain. Environ Geol 47(3):412–420 Strahler AN (1957) Quantitative analysis of watershed geomorphology. Trans Am Geophys Union 38:913–920 Strahler AN (1964) Quantitative geomorphology of drainage basin and channel networks. In: Chow VT (ed) Handbook of applied hydrology. McGraw Hill Book Company, New York, Section 18.4– 18.11 Zaidi FK (2011) Drainage basin morphometry for identifying zones for artificial recharge: a case study from the Gagas river basin, India. J Geol Soc India 77(2):160–166

Chapter 19

Groundwater Geochemistry and Identification of Hydrogeochemical Processes of Fluoride Enrichment in the Consolidated Aquifer System in a Rain Shadow Area of South India Anadi Gayen, Suparna Datta, A. V. Arun Kumar, V. S. Joji, and V. K. Vijesh

Abstract The present study on fluoride contamination in surface water and groundwater in and around Attappady tribal area of Palghat district, Kerala State, India reveals that fluoride dispersal is controlled by country rocks and their structures along with hydrogeomorphology and drainage network. The leaching of F− in groundwater is also controlled by semi-arid climate, which is the result of very less rainfall (936 mm) in the area. Total 42 samples including both surface water and groundwater have been collected during both pre-monsoon and post-monsoon periods and have been analyzed for fluoride apart from other chemical parameters. High fluoride (>1.50 mg/L) in groundwater has been observed in major parts of the Attappady area. The surface water contain fluoride within the range of 1.22–2.86, whereas groundwater in phreatic aquifers have the range of 2.18–2.56 mg/l and deeper fractured aquifers have the fluoride range of 3.20–4.20 mg/l during pre-monsoon and post-monsoon periods, respectively. Maximum fluoride concentration (4.20 mg/L) is recorded in the east-central part of Attappady. Low rainfall and high rate of evaporation promote the dissolution of fluorine-bearing minerals and help in increasing the F− content in groundwater. Fluoride contamination is geogenic in nature and major contribution is made by the hornblende-gneiss formation (1.67 mg/l).

A. Gayen (B) · S. Datta Central Ground Water Board, Eastern Region, Department of Water Resources, River Development and Ganga Rejuvenation, Ministry of Jal Shakti, Kolkata, West Bengal 700091, India e-mail: [email protected] A. V. Arun Kumar · V. K. Vijesh Central Ground Water Board, Department of Water Resources, River Development and Ganga Rejuvenation, Ministry of Jal Shakti, Government of India, Thiruvananthapuram, Kerala, India V. S. Joji Lake Side Campus, Department of Marine Geology and Geophysics, School of Marine Sciences, Cochin University of Science and Technology, Fine Arts Avenue, Cochin, Kerala 682 016, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_19

421

422

A. Gayen et al.

Keywords Semi-arid climate · High fluoride · Dissolution · Geogenic · Fluoride dispersal

19.1 Introduction Fluoride contamination in ground water and its adverse health impact is a burning problem in many parts across the globe. Population explosion, indiscriminate withdrawal of ground water, unscientific disposal of fluoride bearing industrial wastes and insufficient knowledge of adverse impacts of fluoride are the main causes in creating the fluoride problem throughout the world, especially in developing countries. Fluoride is a corrosive poison that affects human health system across a very narrow range of 1.00 mg/L in long term basis. Nearly 90% of the rural population of India depends on ground water for drinking and other domestic purposes. A huge rural population is threatened with serious health hazard of fluorosis caused by fluoride contamination of ground water. Ground water in most countries of the world contains less than 1.5 mg/L fluorides, but in some areas the concentration is more than 5 mg/L. Fluorine in the free-state is more reactive than its’ compounds (fluorides). Fluoride contamination in ground water occurs both in alluvial as well as in hard rock formations. Apart from typical hydrogeological set up in an area, many other influencing factors are also responsible for fluoride contamination in ground water. High fluoride (>1.5 mg/l) may cause various types of fluorosis and other several ailments. Before adopting mitigation measures, understanding about the root causes of fluoride contamination, its occurrence and extension are important to be addressed. Initially the fluoride contamination was reported in India from the 18 states in the year 2013 and further this was the spread has amplified to 21 states distressing 62 million people that includes 6 million children (Adimalla and Li 2019; Adimalla and Venkatayogi 2017). Fluoride is known as the 13th most common element present in the crust of earth, which has both beneficial and adverse effect on human health. The cause of fluoride contamination in ground water is both geogenic and anthropogenic. The geogenic fluoride contamination may include the causes like leaching of minerals containing fluoride, interaction between parent rock and water, weathering, calcite precipitation and parent rock (Maurya et al. 2020; Ahada and Suthar, 2019). Fertilizers and industrial effluents are the main factors having control over the anthropogenic incidence of fluoride contamination in ground water. Fluoride in ground water within permissible limit is beneficial for the bones and teeth, but excess concentration harms health issues in human as well as in other organisms (Kisku and Sahu 2020; Ghaderpoori et al. 2009b, a). High fluoride concentrations in ground water may occur due to the dissolution of fluorite and calcite including phenomenon like alkaline environment and cation exchange, which would be applicable for the ground water types namely HCO3 -Na and SO4 ·Cl-Na type (Liu et al. 2021). Detailed hydrogeochemical studies on fluoride contamination water was carried out by various researchers like Aghapour et al. (2018), Ganyaglo et al. (2019), Hanse et al. (2019),

19 Groundwater Geochemistry and Identification of Hydrogeochemical …

423

Kim et al. (2011). The cumulative factors like geology, geochemistry and anthropogenic activities are having key role to play in the genesis of fluoride in groundwater in NE China (Zuo et al. 2019). Study reveals that the contamination of fluoride in ground water in Telangana state maintains a range in between 0.06 and 4.33 mg/ L (Narsimha and Sudarshan 2017). The occurrence of fluoride in coastal ground water in Andhra Pradesh state is controlled by the factors like alkalinity, prolonged residence time in clay deposits, dissolution of CaF2 by Na+ from saline water and anthropogenic influences (Subba Rao 2017). Fluoride concentration in groundwater in Kanpur Nagar and Kanpur Dehat districts within the Ganga basin is varying from 0.2 to 5.2 mg/L (Nizam et al. 2022). Researchers carried out various types of studies on fluoride contamination in ground water in India may include namely Manivannan et al. (2011), Misra (2013), Chidambaram et al. (2014). The present chapter attempts to study on fluoride contamination in surface water and groundwater in and around Attappady tribal area of Palghat district, Kerala State, India which is governed by semi-arid and rain shadow climatic condition. Hydrogeochemistry, geochemical simulation and geological appraisal were employed together (1) to identify the vital geochemical processes controlling groundwater chemistry, (2) to investigate the spatial distribution of fluoride in shallow and deep aquifers, surface water and spring and (3) to interpret the mechanisms involved in the genesis of F-rich groundwater.

19.2 Study Area The present study encompasses Attappady tribal region that stretches over an area of 745.00 km2 in Mannarghat Taluk of Palghat district of Kerala state (Fig. 19.1). The study area mainly covers the eastern part of the Attappady block (in between 10°55' 10'' and 11°14' 19'' North Latitude and 76°27' 10'' and 76°48' 8'' East Longitude). The terrain is undulated but at places it is very steeply slopping. It has large number of hillocks of varying elevations ranges between 450 and 2300 m above mean sea level. Typical hydrogeomorphological set up allow around 67% of ground water escapes as base flow and only 33% remains available for utilization. Attappady is drained by the main river Bhavani and its tributaries. Bhavani flows in the east direction following a shear zone. It is noteworthy to mention that the Attappady area has a typical rainfall pattern. The eastern portion of Attappady is a rain shadow area with an average rainfall of 936 mm/year mainly dispersed in 100 days, whereas the western portion is in receipt of copious rainfall around 4000 mm/year with distribution over 120 days. The temperature of the area varies from 23° to 33 °C. Dry wind from the eastern Deccan plateau is responsible for faster rate of evaporation and evapo-transpiration during summer months. Wet and dry regions of Attappady are shown in Fig. 19.1b. Sources of drinking water in the area are bore well, dug well, river and spring. Bhavani River is supplying water to the villages (parts) namely Agali, Pudur,

424

A. Gayen et al.

Fig. 19.1 a Location map of Attappady area; b Wet and Dry regions of Attappady

Mattathukad and Kottathara. Anaikatti and parts of Sholayur village are receiving water supply from bore wells as well as springs originated from Varadimala hills. Attappady valley is trending roughly in NE–SW having a broad synformal structure and lies between the Nilgiri hill ranges to the north composed of Charnockite rocks and the Vellingiri hill ranges to the south composed of granitic rocks. Attappady valley is mainly controlled by the Bhavani shear zone running nearly parallel to the axial trace of the synformal structure corresponding to the valley. Major rocks are charnockites, migmatitic gneisses and granite gneisses of the Peninsular Gneissic Complex (PGC). Peninsular Gneissic Complex (PGC) comprises of gneisses of different compositions consisting of granite, biotite-granite gneiss, quartzo-felspathic gneiss, hornblende-biotite gneiss. Other rock types are quartz–biotite schist, bands and lenses of layered mafic/ultramafic rocks represented by talc–tremolite schist, peridotite, meta-pyroxenite, metagabbro (amphibolite), and anorthosite and sillimanite/fuchsite quartzite. Regional geological setting of Attappady is shown in Fig. 19.2.

19.3 Materials and Methods The study area (Attappady block) falls in Survey of India toposheets 58 A/8, 58 A/12, 58 A/16, 58 B/9. Detailed hydrogeological study has been carried out in the study area. Key wells were established in such a way to cover maximum area except forests.

19 Groundwater Geochemistry and Identification of Hydrogeochemical …

425

Fig. 19.2 Regional geological setting of Attappady area

19.3.1 Field Investigations and Sampling for Water and Rock Samples Water samples have been collected from dug wells, bore wells, hand pumps and surface water samples in Attappady block. Both, Pre monsoon and post monsoon samples (42 Nos.) were collected using standard protocol by American Public Health Association (APHA et al. 2012). For analysis of major cations and anions the samples were collected in High-density polyethylene bottles (HDPE) bottles of 1000 ml capacity without any preservations. To avoid exposure to air the containers were sealed immediately after collection and all probable safety measures were taken to avoid or minimize contamination. Fresh rock samples were collected from different zones of the study area to identify the source of Fluoride in Attapaddy.

19.3.2 Analytical Methods for Major Cations and Anions and Rock Samples The chemical analysis of the groundwater samples was carried out in the Regional Chemical Laboratory, CGWB, Kerala region, for 12 basic constituents including

426

A. Gayen et al.

pH, Electrical Conductivity (EC), Calcium (Ca2+ ), Magnesium (Mg2+ ), Sodium (Na+ ), Potassium (K+ ), Nitrate (NO3 − ), Carbonate (CO3 2− ), Bi-carbonate (HCO3 − ), Sulphate (SO4 2− ), Chloride (Cl− ), and Fluoride (F− ) using standard protocols (APHA et al. 2012) for both Pre-monsoon and Post-monsoon. To analyse the rock samples, the samples were grinded and sieved to 300 mesh. Solution were prepared from rock powder (300 mesh) of different rock types and were analysed for fluoride by Fluoride-Ion-Selective Electrode.

19.4 Results and Discussions 19.4.1 Distribution of Water Quality Variables in the Study Area The pH of groundwater solution was in the natural range which depicting the reaction of groundwater with the aquifer materials with neutral in nature. As per BIS guideline, the standard limit of pH in the water is 6.5–8.5 and all the samples in both premonsoon and post-monsoon season was within the permissible limit. The Statistical summary of different measured ions in Attapaddy area has been presented in Table 19.2. The major ions were found in the order of Ca > Na > Mg > K for cations and HCO3 − > Cl− > (SO4 )2 − > NO3 − > F− for anions. The range of Electrical Conductivity (EC) in the study area ranged from 51 to 2800 µS/cm and 83 to 2680 µS/cm, respectively during pre-monsoon and post-monsoon season indicating presence of soluble salts in. The Total Hardness (TH) was in the range of 26–1190 mg/L for pre-monsoon and 22–700 mg/L for post-monsoon season Table 19.1 Method of analysis of the Physico-chemical parameters in the study Parameters

Method adopted

pH

Electrometric Method (pH Meter)

Conductivity

Laboratory Method (EC Meter)

Alkalinity

Titrimetric Method

Chloride (Cl)

Argentometric Method

Sodium (Na)

Flame Emission Photometric Method

Potassium (K)

Flame Emission Photometric Method

Total Harness (TH)

EDTA Titrimetric Method

Calcium (Ca)

EDTA Titrimetric Method

Fluoride

(F− )

SPANDNS method (UV–visible spectrophotometer)

Sulphate (SO4 2− )

Turbidimetric Method

Nitrate (NO3 − )

Colorimetric Method

19 Groundwater Geochemistry and Identification of Hydrogeochemical …

427

Table 19.2 Distribution of water quality constituents in Attappady area in pre-monsoon and postmonsoon with BIS drinking water limit in the study area IS 10500:2012

Premonsoon

Acceptable limit

Permissible limit

Min.

Max.

pH

6.5–8.5

No Relaxation

6.16

7.74

Conductivity (µS/cm at 25 °C)

200

600

51

Total Harness (TH) as CaCO3

200

600

26

Constituents (mg/L)

Postmonsoon Avg.

Min.

Max.

Avg.

6.9

7.1

9.32

8.1

2800

755.8

83

2680

629.5

1190

268.5

22

700

202.3

Calcium (Ca)

75

200

2.4

432

71.6

4

160

37.4

Magnesium (Mg)

30

100

BDL

65.7

21.5

1.5

83

25.7

Sodium (Na)

–

–

3.57

321.3

50.9

Potassium (K) –

–

0.56

40.9

6.4

Bicarbonate (HCO3 − )

–

–

14

1049.2

Sulphate (SO4 2− )

200

400

2.62

750

35.5

350.6

0.3

456

52.4

BDL

28

4.3

3

732

261.4

0.9

350

34.3 41.9

Chloride (Cl− ) 250

1000

13

234

56.8

5.7

312

Fluoride (F− )

1

1.5

0.23

2.42

0.7

BDL

4.2

0.8

Nitrate (NO3 − )

45

No Relaxation

BDL

56.2

9.0

BDL

100

17.0

indicating hardness beyond permissible limit of 600 mg/L as per BIS, 2012 in few locations. Sporadic occurrences of Sulphate and Nitrate was also encountered in the water samples of Attapaddi. Nitrate beyond the permissible limit of 45 mg/L was found in Dug well samples both in pre-monsoon and post-monsoon season. Presence of Alkalinity, Hardness, Nitrate and Fluoride have been reported in previous studies in Attapaddy area (Shaji et al. 2018; Sheeja et al. 2021).

19.4.2 Geochemistry and Hydrochemical Facies Based on Piper Trilinear Diagram The rock water interaction and the water chemistry can be predicted with the study of Hydrochemical facies, hence, Piper Diagram (Piper 1944) is useful to understand the hydrochemistry, reaction kinetics and controlling mechanism. The maximum water samples in the study area come in the Ca–Mg–HCO3 zone as per Piper and

428

A. Gayen et al.

Chadha diagram (Figs. 19.3 and 19.4). Higher HCO3 − concentration accelerates the dissolution of fluorinated minerals in the weak solubility range of fluorite and enhances soluble fluoride concentration (Marghade et al. 2020; Feng et al. 2020). Pearson’s correlation coefficient are also very effective tools to understand the relationship between different constituents in water and was used to study to evaluate geochemistry of fluoride enrichment in the water samples in the study area. The F− shows a positive relation with Na+ (r = 0.7917), K+ (r = 0.76850) and HCO3 – (r = 0.7086) during pre-monsoon and Na+ (r = 0.6698) and HCO3 – (r = 0.7085) during post-monsoon season (Tables 19.3 and 19.4). A positive correlation of fluoride with pH (Table 19.3) suggests more affinity of F− with alkalinity (pH), as it is a controlling parameter of F− content in water (Karunanidhi et al. 2020).

Fig. 19.3 Piper Trilinear diagram for ground water in and around Attappady area

Fig. 19.4 Modified Piper diagram (Chadha 1999) for the water samples in and around Attappady area

0.737262064 0.374337317

0.657438597 0.351051522

0.48439

Na

0.152937937 0.22334247

0.27935

0.473424245

NO3

0.77262082

0.46659

0.534818 0.789885704 0.579957875

Cl

0.178221 0.578866764 0.758183078

SO4

F

0.34759

0.765156 0.657202059 0.459128824

K

HCO3

0.680568927

0.511409 0.826484041 0.961182749

0.792417 0.69197534

Ca (as Ca)

Mg (as Mg)

TH as CaCO3 0.662927 0.892657207 1

1

0.673604 1

Mg

Na

1

K

HCO3

SO4

0.675522 0.67091

0.161053 0.295827 0.036509 0.102026 0.285701

F

0.628824 1

1

C

NO3

−0.08607 0.126911 0.134188 1

0.284311

0.080486

0.090226 −0.16672 1

0.507999 0.522072 0.791731 0.768508 0.708601

0.357385 0.581215 0.87627

0.885836 0.095372 −0.0219

0.250134 0.825295 0.803108 0.654391 1

0.279094 0.395867 0.82635

0.244677 0.560891 1

0.452508 1

1

EC in pS/cm TH as CaCO3 Ca

pH

pH

EC in pS/cm

Pre-monsoon

Table 19.3 Correlation matrix for pre-monsoon samples of the study area

19 Groundwater Geochemistry and Identification of Hydrogeochemical … 429

0.613672 0.804436 0.696861 0.556677 1 0.316074 0.583923 0.85926

0.413519 0.634649 0.883862 0.541359 0.662039 0.914363 1 0.213674 0.528176 0.669889 0.34971

0.569415 0.376815 0.294052 0.245384 0.329097 0.363386 0.501272 0.346222 1

−0.04949 0.869916996 0.847406619

−0.0436

−0.02702 0.913697873 0.599801905

−0.00037 0.627403593 0.435540713

−0.0267

HCO3

SO4

Cl

F

NO3

0.526741256 0.519471683

0.858309803 0.524576803

0.417164924

0.162459 0.547353 1

0.708589 0.446231 0.537175 1

0.489988 0.648652 1

0.417997 0.506657 0.545122 1

−0.06148 0.580740989 0.523927527

K

0.82261585

0.497498 1

0.044621

0.852213551

1

Na

0.790973

0.822428281 1

0.14206

NO3

−0.13532 0.625285662 0.854366272

F

Mg (as Mg)

Cl

Ca (as Ca)

SO4

0.000825

HCO3

−0.00303 1

K

TH as CaCO3

Na

1

Mg

EC in pS/cm

EC in pS/cm TH as CaCO3 Ca

pH

Post-monsoon pH

Table 19.4 Correlation matrix for post-monsoon samples of the study area

430 A. Gayen et al.

19 Groundwater Geochemistry and Identification of Hydrogeochemical …

431

Fig. 19.5 Variations of fluoride contents in groundwater with respect to contour heights and Location map with high Fluoride concentration (>1.5 mg/L) in Attappady area

19.4.3 Meteorological Influence on Fluoride Concentration The Eastern portion of Attappady is a rain shadow area with an average rainfall of 936 mm/year mainly dispersed in 100 days, whereas the western portion is in receipt of copious rainfall around 4000 mm/year with distribution over 120 days (Fig. 19.1b). Hydrogeological survey in Attappady area reveals that Valley fills are located along the valley of the river of Bhavani. In Valley fills area ground water occurs under water table condition in recent sediments of 5–6 m thick deposited by the river and has good ground water development potential. Laterite terrain has a very limited extension. Ground water occurs under water table condition in laterites. In crystalline rock covered area ground water occurs under water table condition in the upper weathered zone of maximum 15 m thick and under semi-confined to confined condition in the fractured zone below the zone of weathering. As eastern part of the area is considered as rain shadow area, low rainfall restricts the dilution as well as groundwater recharge causing indirect influence on Fluoride enrichment in the study area. Depth to ground water level ranges from 1.14 to 10.46 m bgl during premonsoon period and 1.04–10.26 m below ground level during post-monsoon period (Fig. 19.5).

19.4.4 Geogenic Sources of Fluoride in the Study Area As per the previous studies, the main source of fluoride in ground water in the study area is geogenic (Shaji et al. 2018; Sheeja et al. 2021). To establish the fact, solution prepared from rock powder (300 mesh) of different rock types of the study area have been tested for fluoride by Fluoride-Ion-Selective Electrode. The test result indicates that fluoride content in rock powder solution ranges from 1.07 to 1.67 mg/L. Fluoride concentration has also been established to be maximum in the hornblende gneisses. Chemical analysis result for fluoride of different rock types present in Attappady area is shown in (Table 19.5).

432 Table 19.5 Chemical analysis of rock samples (300 mesh powder) for fluoride in the study area

A. Gayen et al.

Rock types

Fluoride (mg/l)

Hornblende—gneiss (Vannathura village)

1.67

Hornblende-Biotite Schist (Sholayur village)

1.60

Magnesite

1.57

Quartz-Biotite Schist (Dasannur village)

1.44

Metagabbro (Vattalakki village)

1.33

Meta Dolerite (Vannathura village)

1.42

Meta—Ultramafic (Narasmukku village)

1.07

Biotite-Gneiss (Sholayur village)

1.52

Biotite-Hornblende-Gneiss (Nallasing village) 1.38 Garnetiferous Granite Gneiss

1.39

To understand the particular factor controlling the overall hydrogeochemistry of the study area, Gibb’s Diagram (Gibbs 1970) has been used. In Fig. 19.6, the Gibbs’s diagrams for post- and pre-monsoon sessions have been presented. From the Gibb’s diagrams, it can be clearly elucidated that dominant factor affecting the hydrogeochemistry of the study area during pre-monsoon and post-monsoon is the rock– water interaction. Hence, dissolution and leaching of fluoride-bearing minerals into groundwater may be designated as a significant source of fluoride ion in groundwater.

Fig. 19.6 Gibbs Diagram for Pre-monsoon and post-monsoon season the study area

19 Groundwater Geochemistry and Identification of Hydrogeochemical …

433

19.4.5 Fluoride Distribution in Different Abstraction Structures Fluoride concentration in water samples collected from different sources during pre-monsoon and post-monsoon is given in Table 19.6. Maximum fluoride concentration (4.20 mg/l) in ground water has been noted in the Naikkarappady bore well (fitted with hand pump) during pre-monsoon period. At present, Attappady has witnessed the maximum fluoride concentration in ground water to the tune of 4.60 mg/l. Ground water of Attappady is mainly CalciumBicarbonate type, which changes to Calcium–Chloride type locally. Microlevel fluoride pollution survey of Attappady area indicates that higher concentration of fluoride has been observed in many villages and its occurrence very sporadic in nature. In 1985–88, maximum fluoride concentration in ground water was 2.62 mg/l. Chemical analysis of water samples from different sources during pre-monsoon and post-monsoon period have been analysed (Fig. 19.7), which indicates that Vattalakki, Kulukkur, Naikkarrappady, Anaikatti, kottathara and Bhutavazhi are the highly fluoride affected villages. Analysis of water samples collected from various sources like dug well, bore well and stream indicates that there are 20 sources in Attappady area where concentration of fluoride exceeds >1.00 mg/l. Comparison of maximum fluoride concentration level in ground water of Attappady region during pre-monsoon and post-monsoon period indicates that for dug well fluoride content ranges in between 0.24–3.5 mg/L and 0.12–3.2 mg/L respectively. For bore well the maximum concentration was witnessed during pre-monsoon season ranging between 0.35 and 4.6 mg/L. The spring sources were identified as the safest for drinking water with maximum Fluoride concentration of 0.6 mg/L. Table 19.6 Fluoride concentration range in ground water for different abstraction structures during pre-monsoon and post-monsoon period Source

Seasons

Min.

Max.

Average

BW/HP

Premonsoon

0.35

4.6

1.33

Postmonsoon

0.14

4.2

1.24

Premonsoon

0.24

3.5

1.03

Postmonsoon

0.12

3.2

1.01

Premonsoon

0.35

0.57

0.49

Postmonsoon

0.24

0.6

0.40

Premonsoon

0.23

2.55

0.82

Postmonsoon

0.06

2.84

0.70

DW Spring Surface water

Fluoride concentration (mg/L)

434

A. Gayen et al.

Fig. 19.7 Distribution of Fluoride concentration for different sources during Pre-monsoon and Post-monsoon seasons in the study area

19.5 Conclusions Eastern part of Attappady area is highly affected by fluoride in ground water. Being a rain shadow area, it receives very less precipitation (636 mm). Scope of recharge to the ground water bearing aquifer is very meager. Therefore, chance of dilution of ground water as well as surface water even after post-monsoon is very less. Especially, ground water is containing high fluoride during pre-monsoon period. High evaporation rate from surface water sources and excessive withdrawal of ground water leading to high concentration of fluoride in surface water as well as ground water. Bore wells drilled in the hard rock tapping fractures are yielding fluoride rich water. Dug wells are constructed within the weathered alluvium lies over the hard rock basement. In addition to this, South-Western, South-Eastern and Southern high land also contributes Fluoride in this area. The overall geochemical appraisal of the groundwater of Attappady area confirms large scale fluoride contamination in various groundwater sources. During premonsoon and post-monsoon period indicates that for dug well fluoride content ranges in between 0.24–3.5 mg/L and 0.12–3.2 mg/L, respectively. For bore well the maximum concentration was witnessed during pre-monsoon season ranging between 0.35 and 4.6 mg/L. Keeping in view, of the alarming situation, following mitigation measures can be adopted to control/minimize high concentration of fluoride in Attappady:

19 Groundwater Geochemistry and Identification of Hydrogeochemical …

435

• Conjunctive use of water resources to minimize the dependency on groundwater and initiation of Surface water-based water supply scheme in fluoride endemic villages. Fluoride affected dug-wells, bore-wells and surface water sources should be avoided and alternative fluoride free water source should be identified. • De-fluoridation methods should be adopted in absence of alternate safe drinking water source. Low-cost domestic de-fluoridation filters for fluoride free drinking water can be an economical alternative for the poor tribal people. • As Attappady is a tribal area and people residing here are poor economic background, balanced diet with adequate nutrition is a constrain for majority. Hence, even low fluoride concentration in drinking water can affect the villagers. Therefore, public in fluoride affected villages is advised to switchover to the nutritive diet (rich in calcium and Vitamin-C), which can encounter the health impacts caused by fluoride intake. • Being rain shadow area, Attappady receives very less rainfall. Still in peak rainy days, water conservation techniques like sub-surface dam (SSD) and roof top rain water harvesting may have special significance in augmenting ground water storage, which may help in improving the quality of ground water in the area. Acknowledgements The authors are would like to place on record their heartfelt thanks to the Chairman, Central Ground Water Board for according kind permission to carry out this research work and to publish the paper in the journal. The authors are really grateful to the Regional Directors of RGNGWTRI, Naya Raipur and CGWB, Kerala Region for kind advice and help in preparation of this paper.

References Adimalla N, Li P (2019) Occurrence, health risks, and geochemical mechanisms of fluoride and nitrate in groundwater of the rock-dominant semi-arid region, Telangana State, India. Hum Ecol Risk Assess Int J 25(1–2):81–103 Adimalla N, Venkatayogi SJEES (2017) Mechanism of fluoride enrichment in groundwater of hard rock aquifers in Medak, Telangana State South India. Environ Earth Sci 76(1):1–10 Aghapour S, Bina B, Tarrahi MJ, Amiri F, Ebrahimi A (2018) Distribution and health risk assessment of natural fluoride of drinking groundwater resources of Isfahan, Iran, using GIS. Environ Monit Assess 190(3):1–13 Ahada CPS, Suthar S (2019) Assessment of human health risk associated with high groundwater fluoride intake in southern districts of Punjab, India Exp. Health 11:267–275. https://doi.org/ 10.1007/s12403-017-0268-4 APHA AW, Greenberg WIA, Clesceri L, Eaton A (2012) Standard methods for the examination of water and wastewater. American Public Health Association, Washington, DC Chadha DK, (1999) A proposed new diagram for geochemical classification of natural waters and interpretation of chemical data. Hydrogeol J 7:431–439 Chidambaram S, Anandhan P, Prasanna MV, Thivya C, Thilagavathi R, Sarathidasan J (2014) Geochemical evaluation of fluoride contamination of groundwater in the Thoothukudi District of Tamilnadu India. Appl Water Sci 4(3):241–250

436

A. Gayen et al.

Feng F, Jia Y, Yang Y, Huan H, Lian X, Xu X, Xia F, Han X, Jiang Y (2020) Hydrogeochemical and statistical analysis of high fluoride groundwater in northern China. Environ Sci Pollut Res 27(28):34840–34861 Ganyaglo SY, Gibrilla A, Teye EM, Owusu-Ansah EDGJ, Tettey S, Diabene PY, Asimah S (2019) Groundwater fluoride contamination and probabilistic health risk assessment in fluoride endemic areas of the Upper East Region, Ghana. Chemosphere 233:862–872 Ghaderpoori M, Khaniki GJ, Dehghani M, Shams M, Zarei A (2009a) Determination of fluoride in bottled water sold in Tehran market. Iran. Am Eurasian J Agri Environ Sci 6(3):324–327 Ghaderpoori M, Khaniki GRJ, Dehghani MH, Shams M, Zarei A (2009b) Determination of fluoride in bottled water sold in Tehran market, Iran Am. Eurasian J Agric Environ Sci 6(2009):324–327 Gibbs RJ (1970) Mechanisms controlling world water chemistry. Science 170(3962):1088–1090 Hanse A, Chabukdhara M, Gohain Baruah S, Boruah H, Gupta SK (2019) Fluoride contamination in groundwater and associated health risks in Karbi Anglong District, Assam Northeast India. Environ Monit Assess 191(12):1–17 Karunanidhi D, Aravinthasamy P, Deepali M, Subramani T, Roy PD (2020) The effects of geochemical processes on groundwater chemistry and the health risks associated with fluoride intake in a semi-arid region of South India. RSC Adv 10(8):4840–4859 Kim Y, Kim JY, Kim K (2011) Geochemical characteristics of fluoride in groundwater of Gimcheon, Korea: Lithogenic and agricultural origins. Environ Earth Sci 63(5):1139–1148 Kisku GC, Sahu P (2020) Fluoride contamination and health effects: An Indian scenario. In: Environmental concerns and sustainable development, pp. 213–233. Springer, Singapore Liu J, Peng Y, Li C, Gao Z, Chen S (2021) A characterization of groundwater fluoride, influencing factors and risk to human health in the southwest plain of Shandong Province North China. Ecotoxicol Environ Saf 207:111512 Manivannan R, Chidambaram S, Anandhan P, Karmegam U, Sinagaraja C, Johnsonbabu G, Prasanna MV (2011) Study on the significance of temporal ion chemistry in groundwater of Dindigul District, Tamilnadu India. J Chem 8(2):938–944 Marghade D, Malpe DB, Subba Rao N, Sunitha B (2020) Geochemical assessment of fluoride enriched groundwater and health implications from a part of Yavtmal District, India. Hum Ecol Risk Assess Int J 26(3):673–694 Maurya J, Pradhan SN, Seema and Ghosh, A.K. (2020) Evaluation of ground water quality and health risk assessment due to nitrate and fluoride in the Middle Indo-Gangetic plains of India. Hum Ecol Risk Assess Int J 27(5):1349–1365 Misra AK (2013) Influence of stone quarries on groundwater quality and health in Fatehpur Sikri, India. Int J Sustain Built Environ 2(1):73–88 Narsimha A, Sudarshan V (2017) Assessment of fluoride contamination in groundwater from Basara, Adilabad district, Telangana state India. Appl Water Sci 7(6):2717–2725 Nizam S, Acharya T, Dutta S, Sen IS (2022) Occurrence, sources, and spatial distribution of fluoride in the Ganga alluvial aquifer, India. Environ Geochem Health: 1–15 Piper AM (1944) A graphic procedure in the geochemical interpretation of water-analyses. EOS Trans Am Geophys Union 25(6):914–928 Shaji E, Gómez-Alday JJ, Hussein S, Deepu TR, Anilkumar Y (2018) Salinization and deterioration of groundwater quality by nitrate and fluoride in the Chittur block, Palakkad, Kerala. J Geol Soc India 92(3):337–345 Sheeja KM, Harilal CC (2021) Ionic flux and geological release of fluoride in groundwater sources of a semi-arid tropical region in south India. Int J Environ Anal Chem: 1–19 Subba Rao N (2017) Controlling factors of fluoride in groundwater in a part of South India. Arab J Geosci 10(23):1–15 Zuo R, Liu X, Yang J, Zhang H, Li J, Teng Y, Yue W, Wang J (2019) Distribution, origin and key influencing factors of fluoride groundwater in the coastal area, NE China. Hum Ecol Risk Assess Int J 25(1–2):104–119

Part III

Forest Resources

Chapter 20

Temporal Areal and Greenness Variation of Marichjhapi Island, Sundarban, India Sipra Biswas

and Kallol Sarkar

Abstract The Sundarban ecosystem bears a unique biodiversity of its own and hosts livelihoods of millions of people. Because of its typical virtues the Sundarban as a whole and many of its parts at the same time, have attracted various researchers and academicians gaudily since a long past. Though individual island in the south eastern parts of the Indian Sundarban in general and Marichjhapi Island in particular are comparatively less studied. Thus, long term variation of land and vegetationarea of Marichjhapi Island was studied with remotely sensed data. In the study it is found that the island lost an estimated land-area of ~4.42 km2 over the period 1972– 2022 with an erosion-rate of 0.18 km2 /year over last 25 years, while the mangrove cover decreased by 1.39 km2 in last 50 years against an increase by 1.24 km2 during 1972–1997 and decrease by 2.63 km2 over the period 1997–2022. Over the period 1972–2022, the mangroves in the island encountered massive structural changes in terms of greenness-density. The area of denser mangroves decreased by 36 km2 between 1972 and 1977, by 31 km2 between 1979 and 1997, while increased by 39 km2 between 1997 and 2022, and the overall greenness-impact was calculated to be 1.50:1.10:1.00:1.12 in the year 1972, 1979, 1997 and 2022. However, such transformations may be attributed mainly to various natural forces like frequent cyclonic storms, tidal and sea current-surges etc. Keywords Island · Erosion · Mangrove · Greenness · Density

S. Biswas (B) Department of Geography, Kultali Dr. B. R. Ambedkar College under University of Calcutta, Kolkata, India e-mail: [email protected] K. Sarkar School of Water Resources Engineering, Jadavpur University, Kolkata, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_20

439

440

S. Biswas and K. Sarkar

20.1 Introduction Intertidal wetland-originated mangroves are mainly confined to the tropical and subtropical regions (Tomlinson, 1986). Being highly productive and magnificent ecological communities (Hogarth, 2015), these typical plant communities play pivotal roles in ecosystem functioning (Nagelkerken et al. 2008). And the mangrove forests in Indian Sundarban Delta (ISD), in this way, contribute a lot as socioecological drivers in addition to protecting the landward bio-geomorphic and anthropo-natural resources. The ISD (also designated as Sundarban Biosphere Reserve (SBR) by the Government of India) is located at the apex of the Bay of Bengal covering southern fringe of West Bengal (WB, an Indian State). And at the same time, it is the most south–west part of Ganga–Brahmaputra Delta (GBD) spreading over an area of about 9,630 sq km (core zone: 1,700 sq km, manipulation zone: 2,400 sq km, restoration zone: 230 sq km and development zone: 5,300 sq km) in 102 islands including 4,263 sq km (Sundarban Tiger Reserve: 2,585 sq km, National Park: 1,330 sq km, Subsidiary Area: 241 sq km, Wildlife Sanctuary: 6 sq km and others: 101 sq km) reserved forests in 48 islands and rest 5,367 sq km forest cleared habitation-area in 54 islands (Gopal & Chauhan, 2006; Biswas et al. 2007; Mistri & Das, 2015; Hazra et al. 2015 and Directorate of Forest, GoWB, 2020). The ISD-area is bordered by the River Hooghly (a distributary of the River Ganga-Bhagirathi in India) to the West, the River Kalindi–Raymangal– Harinbhanga to the East, Dampier & Haudges Line to the North & West and the Bay of Bengal to the South. The entire ISD-area (southern parts in particular) is crisscrossed by dense network of innumerous estuarine rivers, channels, creeks, marshes, swamps, shoals, sand pits, mud flatsetc., resulting in formation of so many islands. The Indian Sundarban Delta (ISD) is a part of the Sundarban Delta (SD) which presently covers about 26,000 km2 (which was, once, about 40,000 km2 in the late 18th century) along the Bay of Bengal from the Meghna River estuary in the east (Bangladesh) to the Hooghly River estuary in the west (India), and reaches about 80 km landward at its broadest point (www.britannica.com/place/Sundarbans). The Sundarban Mangrove Forest (SMF), covering an area of 10,000 km2 (Giri et al., 2007) over both Bangladesh (~60%) and India (~40%) (UNEP, 2005), is designated to be the largest single chunk of mangrove home in the world (Rahman, 2000). The four protected areas (PA) namely Sundarban National Park, Sundarban West, Sundarban South and Sundarban East Wildlife Sanctuaries located in this Sundarban area were enlisted in the UNESCO World Heritage Sites in 1987 due to their unique ecosystems (Giri et al., 2007), and also was designated as Global Biosphere Reserve (GBR) by UNESCO in the year 1989 (Bandyopadhyay, 2012). About 55% of the total area in Indian part of this Sundarban is covered with vegetation-land and the rest 45% consists of waters or waterways like rivers, canals, creeks, swamps etc. (DHDR, 2009). While, those two figures in Bangladesh-part of Sundarban are ~70% and ~30% respectively (Rahman, 2000). However, the Indian Sundarban (IS) hosts about 100 species of vascular plants, 250 species of fishes, 300 species of birds and a variety of reptiles, amphibians, mammals

20 Temporal Areal and Greenness Variation of Marichjhapi Island …

441

etc. and numerous species of benthic invertebrates (like arthropods, molluscs etc.), symbiotics, parasites, phytoplankton, zooplankton, bacteria, fungi etc. (Gopal & Chauhan, 2006). And of course, the iconic Royal Bengal Tiger (Panthera tigris) and various types of mangroves (about 3% of the total world’s mangrove-area) including sunderi tree (Heitiera fomes) (Duke, 1993; Blasco, 1996; Goodbred & Kuehl, 2000; Banerjee, 2002; Gopal & Chauhan, 2006; Biswas et al. 2007; WBFD, 2007; Syvitski et al., 2009; Hanebuth, 2013 and Barik & Chowdhury, 2014). The mangrove plants floristically belong to the Indo-Andaman mangrove province within the speciesrich Indo-west Pacific Group (Duke, 1993). Mandal & Nandi (1989) and Barik & Chowdhury (2014) respectively reported 22 and 24 true mangroves species to be in the Indian Sundarban area, while Chaudhuri and Choudhury (1994) reported 36 species. Of course, several species of them like Aegialitisrotandifoloa, Heritterafoms, Sonneratia apetala etc. are found endemic, and thus, the Indian Sundarban was considered endangered in a 2020 assessment under the IUCN Red List of Ecosystem Framework (Sievers et al., 2020). Above all, the Indian Sundarban is presently supporting an estimated 5.16 million people residing in 19 Community Development (CD) blocks (13 in South 24 Parganas and 6 in North 24 Parganas districts). Though, more than 50% of them, being mainly dependent on fishing, goods, services etc. endowed by the forest environs for their livelihood, are impoverished and forced to bear sub-standard life. The soils in the ISD area are characterised by clay, silt and sandy loams (in order of dominance), and average pH of about 8.0 (Christensen, 1984). The region is dominated by tropical climate and average annual rainfall is around 1700 mm, of which almost 75% is concentrated over June–September during the south-west monsoon. Average temperature varies from 12° to 18 °C in winter to as high as 38 °C in summer, and humidity remains greater than 70% almost throughout the year. The strong summer storms in the tidally active areas sometimes give rise up to 7.5 m instant wave-bulge (Seidensticker and Hai, 1983). This again, in turn, causes erosion & damages of lands, embankments, sudden saline-water intrusion etc., resulting in losses of fisheries, agricultural crops, domestic animals etc. and sometimes even human life. But this typical mangrove ecosystems are subject to thrashing in many parts all over the globe, particularly due to climate changes and resultant sea-level rise (Macintosh and Ashton, 2002, 2004). For example, according to a Food and Agriculture Organization (FAO) estimate, the world-wide mangrove coastline has witnessed a high-pitched decline from 1,98,000 km in 1980 to 1,46,530 km in 2000. And of course, the Sundarban mangrove forest is not an exception. As a result of undertaking so many measures and strategies to protect and conserve the Sundarban Biosphere by both Bangladesh and India, the total area of mangrove forests has been remaining almost same over several decades. But the overall internal structure and density of the Sundarban mangroves has been decreasing substantially, and this can be attributed to occurrence of frequent cyclonic storms, climate change and sea level rise, altered hydrodynamics etc., and of course the human interventions in various forms (Giri et al., 2007).

442

S. Biswas and K. Sarkar

However, change in area coverage and density of mangrove vegetation in the Indian Sundarban may, of course, vary location to location and island to island. And with this background, the present study attempts to investigate and determine the dynamics of vegetation cover in Marichjhapi Island (a mangrove reserved forest within the ISD) with degree of changes, using multi-temporal satellite data. The study can help the academicians and local planners in adopting island-specific further suitable measures for forest management purposes to protect and conserve the Sundarban ecosystem, a magnificent biosphere, for the sake of maintaining proper socio-ecological system (SES) in sustainable manners.

20.2 Literature Reviewed The Sundarban Delta (including ISD) characteristically being a typical one in its type, has vividly attracted both the academicians and the professionals as well over the globe, and has been extensively studied. Thus, exploration of the Sundarban mangroves dates back to the 16th century (Rollet, 19981), as a whole or a part. Of course, such studies and investigations have been done on various themes, subjects and topics, in diverse angles, through different techniques and up to varying dimensions. A large bulk of published literatures exist on various topics and aspects related to both Indian and Bangladesh Sundarban, among them, a few, related and linked to the present study, are felt imperative to mention in this section. Diversity, handiness and changes of mangroves in both Indian and Bangladesh Sundarban has been well studied by Prain (1903), Naskar & GuhaBakshi (1987), Chaudhuri et al. (1994), Hussain & Acharya (1994), Guha Bakshi et al. (1999), Rahman (2000), Blasco et al. (2001), Blasco & Aizpuru (2002), Giri et al. (2007), Bose (2009), Manna et al. (2010), Nandy & Kushwaha (2011), Cornforth & Pettorelli (2013), Ghosh & Ghosh (2013), Rahman et al. (2013), Islam (2014), Shimu et al. (2019), Sahana & Sajjad (2019), Awty-Carroll et al. (2019), Behera et al. (2021), Thakur et al. (2021), Khan et al. (2021) and many others. The mangroves in the Indian Sundarban have been vibrantly documented by Chaudhuri & Choudhury (1994). The history, utilization and conservation strategy of the Indian Sundarban have categorically been demonstrated by Ghosh et al. (2015). Sievers et al. (2020) typically pointed out the endangeredness and way out of mangroves in the ISD. The forest cover and land use/land cover of the Indian Sundarban have also been soundly documented by Datta & Deb (2012), Ranjan et al. (2017), Ranjan & Kanga (2018), Dasgupta et al. (2019), Datta (2018), Mondal et al. (2019a, b) and so on. While, the mangroves in Bangladesh Sundarban have been intensely presented by Chafffey et al. (1985), Anon (1995), Iftekhar & Islam (2004), Emch & Peterson (2006), Uddin et al. (2013), Islam (2017), Rai et al. (2017), Islam & Bhuyan (2018), Abdullah et al. (2019), Islam (2019), Uzzaman et al. (2020), Faruque et al. (2022) etc.

20 Temporal Areal and Greenness Variation of Marichjhapi Island …

443

Beside these, studies on effects of cyclonic storms on mangroves, in various occasions and times, are also plenty. As for example, effects of Amphan on mangrove ecology in Indian Sundarban have been vividly studied by Mishra et al. (2021) and that of Aila on Sundarban ecosystem in Bangladesh have been well studied by Rahman et al. (2017). But contrary to these, study on location and island-wise vegetation and land use & land cover changes are yet studied very little, though some of them are worth mentioning, like- Ali (2006) in western parts of Bangladesh Sundarban, Samanta & Hazra (2012) in Jharkhali Island, Manna et al. (2013) in Jharkhali Island, Ramteke et al. (2017) in Bali Island, Hajra et al. (2017) for Sagar, Ghoramara and Mousani Islands, Debnath (2018) for Gosaba Island, Mandal et al. (2019) for Sagar Island, Kumar et al. (2021) in eastern Bangladesh Sundarban, Paul et al. (2021) for Namkhana etc. At the same time, the islands in the south eastern parts of the Indian Sundarban have been least studied, and no study appeared to have been conducted in respect of Marichjhapi Island vegetation cover as a single entity. With this background, and expecting changes might have been occurred in mangrove cover in the island, the present study aims to investigate the same in respect of Marichjhapi Island.

20.3 Study Area Marichjhapi (21°58' 54'' to 22°11' 23'' N and 88°53' 38'' to 88°59' 09'' E) is a reserved forest island (out of total 48) located in the east-middle of Indian Sundarban (Fig. 20.1), falling within administrative jurisdiction of South 24 Parganas district of West Bengal (WB, an Indian state). The island is bounded by rivers and islands all round- Kumirmari and Kalidaspur Islands to the north (and separated by Garal River), a reserved forest island to the east (and separated by Hariyabhanga River), Satjelia–Luxbagan–Lahiripur Island to the west (and separated by Garal River), and several estuarine islands (reserved forest) of Bidyadhari River and the Bay of Bengal to the south. Climatic, soil, tidal, pH conditions etc. are likely to be that in other parts of the ISD. Total area of this reserve forest island is estimated to be ~91 sq km at present.

20.4 Materials and Methods 20.4.1 Data Sources The study examined areal changes of plan-expanse and different density mangrove covers of Marichjhapi Island over a long period of time. The investigation is mainly based on the secondary data and spatial information extracted from the United States

444

S. Biswas and K. Sarkar

Fig. 20.1 Location of Marichjhapi Island

Geological Survey (USGS) satellite images retrieved from the website: https://earthe xplorer.usgs.govon on several cloud free days, that bears the following particulars: Year

Satellite

Sensor

Date

Spatial resolution

1972

LANDSAT_1

MSS

05.11.1972

60 m

1979

LANDSAT_2

MSS

30.01.1979

60 m

1997

LANDSAT_5

TM

01.01.1997

30 m

2022

LANDSAT_9

OLI_TIRS

15.02.2022

30 m

20.5 Methodological Framework Different techniques, measures and methodological approaches were employed to work with the data and information in this investigation. At first the maps were georeferenced and necessary shapefile was generated. Then the boundaries of the Indian Sundarban vis-à-vis of Marichjhapi Island were digitized in different Geographic Information System (GIS) layers, followed by clipping of red band and NIR band based on the shapefile of Marichjhapi Island from selected satellite images of respective years towards preparation of NDVI maps and area calculations. Lastly the data

20 Temporal Areal and Greenness Variation of Marichjhapi Island …

445

Fig. 20.2 Flowchart showing the major methodologies used

and information were presented in maps and graphs correspondingly and accordingly to identify, determine and characterise different themes and parameters for resulting, analysing & discussing, inferring and concluding. However, the entire process is concisely depicted in the following flowchart (Fig. 20.2).

20.6 Normalised Vegetation Index (NDVI) There are so many approaches to determine vegetation changes in terms of cover and health condition using remotely sensed data. One popular method of which, is to apply vegetation indices relating to the quantity of greenness (Chuvieco, 1998) and the Normalised Vegetation Index (NDVI)-approach is the most commonly used remote sensing index (Bhandari et al., 2012) in monitoring of vegetation dynamics (Vrieling et al., 2013).

446

S. Biswas and K. Sarkar

NDVI is a strong indicator of differential energy between solar energy received and emitted by any object on the Earth. While applied to plant communities, such index determines a value of plants’ greenness. That is, the quantity of green vegetation per unit area along with its healthiness or state of growth is sturdily represented by the NDVI indices. However, the NDVI indices are calculated by relative reflectance of red and near infrared fluxes, i.e., NDVI = (NIR − red)/(NIR + red) The NDVI is a dimensionless index and in theory its values range from −1 to +1. But in practice, its actual values range from about −0.8 to +0.9. The negative values indicate barren lands without vegetation (like sand, barren rock etc.), water, any mechanical structure etc., while the positive values designate greeneries; and the more the value is, higher is the photosynthetic activities (i.e., plant-greenness). In practical senses, the values less than +0.1 represent water bodies, mechanical structures, bare soils including infertile soils, sandy beaches, barren rocks, etc., the values ≥+0.1 and up to 0.2 correspond to very low density and unhealthy vegetation, values greater than 0.2–0.3 represent vegetation cover with moderate density and healthiness, while all the values higher than 0.3 represent vegetation with relatively high density and healthiness (Karaburun, 2010; Chouhan & Rao, 2011 and Pravalie et al. 2014). Classifications based on such ranges were generally employed to distinguish vegetation and no-vegetation, as well as in detecting and determining degree of vegetation-density. Again, no other vegetation cover was considered to exist in the island except mangroves, since the lower ISD’s distinctive soil characteristics, salinity gradients, hydrological dynamics, pH regime, etc. are suitable for those species only. Again, since higher NDVI-values represent more denser photosynthetic greens, infinite number of classifications can be made with infinite number of ranges of NDVI-values against a particular value-range delineated by their highest and lowest values. But for easy, convenient and finite calculations, all the NDVI-values greater than 0.3 (less than 0.3 classified as stated earlier) have been divided with a 0.05 interval to determine further groupings of different mangrove-densities falling within the same highly dense class. Then the individual mean of every range of NDVIvalues (dimensionless) has been multiplied by each corresponding vegetation-area (km2 ) to calculate the greenness weightage of that very range. And all such greenness weightages (corresponding to respective NDVI-ranges) of a particular year, have been summed up to find out the overall greenness-weightage (unit: km2 ) of mangrovevegetation against each of the four years.

20 Temporal Areal and Greenness Variation of Marichjhapi Island …

447

20.7 Results 20.7.1 Areal Change of the Island Like any estuarine region, the deltaic tracts of the Indian Sundarban also are dynamic in nature and hence, are under constant geomorphic changes through accretional and erosional processes resulted out of various natural forces as well as anthropogenic activities (Sahana et al., 2019 and Ganguly et al., 2006). Thus, Marichjhapi Island experienced almost no change in overall area at all over the period 1972–1979 (7 years), and a little change detected during the next 18 years (1979–1997) though, explicit change noticed over the next 25 years (1997–2022). For instance, total land areas of this island were estimated to be 91.04, 90.99, 89.20 and 86.59 km2 respectively in 1972, 1979, 1997 and 2022 (Fig. 20.3), exhibiting decreases over the periods respectively by 0.05%, 1.97% and 2.93%, and ~4.89% decrease of entire island area over the 50-year study period (Figs. 20.3 & 20.4). Such areal changes took place, of course, through erosion and accretion along the shorelines during the periods. Figure 20.4 shows the alignment of shorelines of the island in the year 1972 and 2022, vis-à-vis erosion and accretion over the period (50 years), and also claims that the island faced erosion almost all round, and accretion only in two segments, north and south-west. Figure 20.5 shows that absolute areas of erosion are higher than that of accretion over each and every span of time, and they are ~6.54, ~9.93 and ~30.24 times respectively between 1972 & 1979, 1979 & 1997 and 1997 & 2022. Fig. 20.3 Change of total land (no-vegetation + vegetation) area. Source Primary data

448

S. Biswas and K. Sarkar

Fig. 20.4 Areal change of land-area along the shorelines between 1972 and 2022. Source Primary data

20.8 Mangrove and No-Mangrove Cover Changes Like any other reserved forest island in the Indian Sundarban, Marichjhapi also comprises of both vegetation (mainly different species of mangroves) and novegetation like waters, sandy beaches, barren or unfertile lands etc. Figure 20.3 shows that gradual reduction of total island area over the entire study period has occurred through a combination of both increase and decrease of both no-vegetation area and vegetation (i.e., mangrove) cover (determined with NDVI-approach) over different stretches of time-period.

20 Temporal Areal and Greenness Variation of Marichjhapi Island …

449

Fig. 20.5 Erosion and accretion over different periods. Source Primary data

In the year 1972, 1979, 1997 and 2022 total extents of mangrove cover were estimated to be ~93.80%, ~90.41%, ~97.13% and ~97.06%, and that of no-vegetation areas were determined to be ~06.20%, ~09.59%, ~02.87% and ~2.98% of the total island-area respectively. Thus, during the period from 1972 to 1979 (7 years) the mangrove cover witnessed a decrease of ~3.68% against an increase of no-vegetation area by ~54.79% (which is an abrupt hike by absolute area also) and a decrease of total island area by 0.05%, total island area remaining almost the same; while from 1979 to 1997 (18 years) mangrove cover increased by ~05.32% against the decreases of novegetation and total island areas to the extents of ~70.68% and ~1.97%, respectively. The mangrove cover again experienced a decrease of ~03.04% during the period from 1997 to 2022 (25 years) and no-vegetation area increased by ~0.78% against a decrease of ~2.93% of total land area. And over all, mangrove and no-vegetation areas respectively decreased by ~1.39 km2 (~1.63%) and ~3.06 km2 (~54.26%) against a decrease of total land area of ~4.45 km2 (~4.89%) over the entire study period (50 years). Interestingly, a large chunk of no-vegetation area is found concentrated at a single point of patch i.e., at the north-east tip of the island in 1972 (Fig. 20.6) and expanded further southward in area (pervaded with very low-density vegetation cover within its periphery) in 1979 (Fig. 20.7). And more interesting is that, such no-vegetation area was found completely replaced with moderately dense mangrove in 1997 (Fig. 20.8), and with both moderately and highly dense mangroves covers in 2022 (Fig. 20.9).

450

S. Biswas and K. Sarkar

Fig. 20.6 Different classes of vegetation-cover, 1972. Source Primary data

20.9 Temporal Changes of Different Density Mangrove Area Different values (higher than 0.1) of NDVI, worked out against Marichjhapi Island, qualify different degrees mangrove plants at different patches in terms of their densities, state of healthiness, vigour of growth etc. Mangrove covers of different densities are, all together, found almost evenly distributed over the entire island in the year 1972 and 2022, though with different areas (Figs. 20.6 and 20.9). But in 1979, moderately dense mangroves were found chunked over the middle part, dispersing the highly dense mangrove covers over two distinct skirts- northern and southern (Fig. 20.7).

20 Temporal Areal and Greenness Variation of Marichjhapi Island …

451

Fig. 20.7 Different classes of vegetation-cover, 1979. Source Primary data

Again, in 1997 (Fig. 20.8), highly dense mangroves exist as a very small block in the north-east skirt of the island; whereas, moderately dense mangroves are almost evenly distributed all over the island. In 1972, highly and moderately dense mangroves, respectively accounted for ~81.30% and ~09.30% of the total area of the island; whereas, the area covered with very low-density mangroves was found almost negligible(~3.19%) (Figs. 20.5 & 20.9). But over the next 7 years (1972–1979) the internal structure of mangrove cover underwent (like no-mangrove area) massive changes- the highly dense mangrove cover experienced a decrease in the tune of ~48.73%, while, the moderately dense mangrove area encountered as high as ~294% increase, resulting both of them almost

452

S. Biswas and K. Sarkar

Fig. 20.8 Different classes of vegetation-cover, 1997. Source Primary data

equal (highly dense: ~42%, moderately dense: 37%) in absolute area (Figs. 20.6, 20.7 & 20.10). By this time, very low-density mangrove covers also encountered a huge increase of ~275% (from ~3.19% in 1972 to ~11.98% in 1979). Again, interestingly, over the next 18-year period (1979–1997), the highly dense mangroves witnessed a further abrupt decrease of ~81.74% by area, and moderately dense mangrove area faced a further abrupt increase of ~122.50%, which led to almost equivalent interchange between highly and moderately dense mangroves over the period 1972–1997 (25 years) in terms of area (highly dense: ~81% in 1972, ~8% in 1997 and moderately dense: ~9% in 1972, 83% in 1997) (Figs. 20.6–20.8 & 20.10). But very low-density mangrove area became almost half in 1997 (~6%) as compared to that in 1979

20 Temporal Areal and Greenness Variation of Marichjhapi Island …

453

Fig. 20.9 Different classes of vegetation-cover, 2022. Source Primary data

((~12%) (Figs. 20.6–20.8 & 20.10). Of course, some reverse transitions took place during the next 25 years (1997–2022) when highly dense mangrove cover increased by as high as ~567% (~7.77% in 1997 and ~53.41% in 2022), and moderately dense mangrove cover decreased by ~ 54% (~83.35% in 1997 and ~39.95% in 2022), though very low-density mangrove cover encountered a ~39%decrease (~6.02% in 1997 and ~3.81% in 2022) in area (Figs. 20.6–20.10). However, incidentally, over the entire study period of 50 years (1972–2022) highly dense mangrove forest area reached the apogee(81.31%) and moderately dense mangrove forest reached the nadir (9.30%) in the same year i.e., in 1972; and just reverse was the case in 1997. While, both of very low-density mangrove

454

S. Biswas and K. Sarkar

8.7264

Fig. 20.10 Variations of different classes’ vegetation-cover. Source Primary data

forest and no-vegetation areas reached their peaks in the same year i.e., in 1979, though they respectively reached their nadirs in 1972 and 1997.

20.10 Temporal Variations of Greenness-Impacts The highest density of mangrove cover (that are demonstrated by the highest NDVI values) is found decreasing over the period 1972–1979–1997at considerable rates, after which it remained the same in 2022 (Fig. 20.11), though such highest densities in different years fall under the same (highly dense) class as defined. The overall weightages of greenness of the island in different years are shown in Fig. 20.12. It got maximum in 1972 and thereafter changed in the ratio 1.50:1.10:1.00:1.12. Thus, the years 1972, 2022, 1979 and 1997 stand in descending order in terms of the island’s greenness-impact.

20 Temporal Areal and Greenness Variation of Marichjhapi Island …

Fig. 20.11 Trend of highest values of NDVI. Source Primary data

Fig. 20.12 Greenness weightages in different years. Source Primary data

455

456

S. Biswas and K. Sarkar

20.11 Discussion 20.11.1 Areal Changes The Indian Sundarban, the meeting place of the Himalayan Ganga and many other seasonal rivers with the Bay of Bengal, is dominated by perennial as well as monsoonal non-saline freshets, saline sea-water, tidal and sea currents, transportation and deposition of sediments etc., and are also often beaten by cyclonic winds, floods, storms and submergence etc. As a result, both the lands and mangrove-vegetation in the Indian Sundarban delta (ISD) undergo constant changes in area or volume and internal structure or shape, spatially and temporally. Thus, the Marichjhapi Island, being located in the south-east skirt of the ISD, its overall area has been changed through horizontal as well as vertical erosion and accretion all along the shorelines over the years, as the obvious effects of natural and anthropogenic origin. Again, such areal changes call for changes in areas of both nonvegetation and mangrove-vegetation cover particularly along the island-boundaries. But in case of mangrove-cover, in-land areal changes also take place due to such natural calamities and human-activities. Thus, areal changes of non-mangrove vis-àvis mangrove-cover and the overall island, have taken place over the different spans of time-period with varying rates and degrees. The island faced huge shoreline erosion after 1979 and massive erosion after 1997. Such erosions may be attributed to severe and super cyclonic storms landed on the Indian Sundarban in 1981, 1988, 1990, 1995, 1997 etc., and also in 1998, 2007 (Sidr), 2009 (Aila) etc., in addition to the erosions due to usual reasons. The largest single chunk of no-vegetation area found located at the north-east tip of the island in 1972, demonstrates the mechanical infrastructures constructed by the East Bengal-Dandakar any refugees for settlement, agricultural and fishing purposes, which was then expanded in 1979, and found disappeared in 1997 after the occurrence of ‘1979-Marichjhapi massacre’, as reported by Mallick (1999) and Chowdhury (2011).

20.12 Temporal Variations of Mangrove-Densities With the changes of mangrove-density beyond the threshold limits, area covered by any defined class shall be changed obviously; but shall call for no change in area, if such variations of density remain within the specified threshold limits. Hence, change in area of any itemized class will obviously be reflected in changes of area of any other class, if the total island area remains the same. Results of this study reveal that, changes of total area of the island are not so significant over the 50-year study period, particularly up to 1997. Hence, temporal variations of area and density of mangrovecover, over the entire study period, may be ascribed to changes of in-island mangrovecover and density, resulted out of structural changes of mangrove-vegetation. And

20 Temporal Areal and Greenness Variation of Marichjhapi Island …

457

the areal decrease of denser and increase of moderately dense mangrove up to 1979, may be caused due to both cyclonic storms on multiple occasions and the manmade activities carried out by the refugees. Whereas, further decrease of denser and increase of moderately dense mangrove areas beyond 1979 may be the outcome of the repeating storms only, since the refugees and their settlements were completely exiled in the year 1979. Again, increase of denser mangrove area detected in 2022, may be explained by the sufficient time-period elapsed after the cyclones Sidr and Aila, to grow up the existing and fresh mangroves to great extents.

20.13 Variations of Greenness-Impacts Of course, to justify the significance of temporal variation of greenness-impacts of the mangrove forest in Marichjhapi Island over the years, it is imperative to depend on some formulated guiding factors or indices. Greenness-weightage of mangrovecover, formulation of which is stated in the methodology-section, may be one of such indicating factors or indices, associated by the highest density of mangrove-cover. Temporal variations of such greenness-weightage and highest density of mangrovecover also may be explained with the like-wise contributants as already cited against temporal density-variations of mangrove-cover. However, absolute value of variation in mangrove-cover matters a lot, though percentage variation of the same is less significant as compared to that of novegetation cover, which carries just the opposite weightages in percentage and absolute value.

20.14 Conclusion Despite having one of the highest population densities in the world in the immediate vicinity of the Indian Sundarban Forest comprising of Marichjhapi Island, mangrove-vegetation area in the Sundarbans has not been changed significantly over the decades. However, some of the worth-mentioning harvests of the study, carried out on the basis of multi-temporal analysis of Landsat data, are: • The island experienced considerable land-area decrease (~2.61 km2 ) during the period from 1997 to 2022 due to erosion along the shorelines all round, though not so significant change over the periods 1972–1979–1997. • Total mangrove area decreased a lot, in absolute figure, over the periods 1972– 1979 and 1997–2022, and increased considerably during 1979–1997. Whereas, no-vegetation area increased over 1972–1979, while remained almost unchanged over 1997–2022. • Mechanical infrastructures erected by the refugees at the north-east tip of the island, existed in 1972 and 1979, and thereafter completely disappeared.

458

S. Biswas and K. Sarkar

• Denser mangroves dominated the island in 1972, while moderately dense mangroves dominated in 1997. In 1979 and 2022 they were more or less comparable in area covered. Reasons against such variations need more investigations and reasonable enlightenments. • Denser mangroves are almost evenly distributed all over the island in 1972, whereas dispersed in two (north and south) skirts invading by the moderately dense vegetation in the middle parts in 1979. These also argue more logical explanations. • The highest degrees of mangrove-densities follow a decreasing trend. In terms of the island’s overall greenness impacts, the years 1972, 2022, 1979 and 1997 stand in descending order, the reasons of which also could not be explained with sufficiently strong elucidations.

References Abdullah AYM, Masrur A, Adnan MSG, Baky M, Al A, Hassan QK, Dewan A (2019) Spatiotemporal patterns of land use/land cover change in the heterogeneous coastal region of Bangladesh between 1990 and 2017. Remote Sens 11(7):790 Ali AMS (2006) Rice to shrimp: Land use/land cover changes and soil degradation in Southwestern Bangladesh. Land Use Policy 23(4):421–435 Anon D (1995) Integrated resource management plan of the Sundarbans reserve forest Vol. 1 draft final report of FAO. UNDP project BGD/84/056 integrated resource development of the Sundarbans reserve forest, Khulna, Bangladesh, 585 Awty-Carroll K, Bunting P, Hardy A, Bell G (2019) Using continuous change detection and classification of landsat data to investigate long-term mangrove dynamics in the Sundarbans region. Remote Sens 11(23):2833 Bandyopadhyay S (1997) Coastal erosion and its management in Sagar island, South 24 Parganas West Bengal. Indian J Earth Sci 24(3–4):51–59 Bandyopadhyay S (2000) Coastal changes in the perspective of long-term evolution of an estuary: Hugli, West Bengal, India. In: Rajamanickam V, Tooley MJ (eds) Quaternary Seal Level Variation. Shoreline Displacement and Coastal Environments, New Academic Publishers, New Delhi, pp 103–115 Bandyopadhyay S, Mukherjee D, Bag S, Pal DK, Das AK, Rudra K (2004) 20th century evolution of banks and islands of the Hugli estuary, West Bengal, India: Evidences from maps, images and GPS survey. In: Singh S, Sharma HS, De SK (eds.) Geomorphology and Environment, pp. 235–263 Bandyopadhyay B (2012) A survey of the Sundarban mangrove wetlands of India: an environmental treat. In: Proceedings of the international conference: Meeting on mangrove ecology, functioning and Management (MMM3), 2–6 July 2012, Galle, Sri Lanka Banerjee LK (2002) Sundarbans. In: Shing NP, Shing KP (eds.) Floristic diversity and conservation strategies in India, vol. V, pp. 2801–2829. Botanical Survey of India, Ministry of Environment and Forests: New Delhi, India Barik J, Chowdhury S (2014) True mangrove species of Sundarbans delta, West Bengal, Eastern India Bhandari AK, Kumar A, Singh GK (2012) Feature extraction using Normalized Difference Vegetation Index (NDVI): A case study of Jabalpur city. Procedia Technol 6:612–621 Biswas H, Mukhopadhyay K, Sen S, Jana TK (2007) Spatial and temporal patterns of methane dynamics in the tropical mangrove dominated estuary, NE coast of Bay of Bengal India. J Mar Syst 68(1–2):55–64

20 Temporal Areal and Greenness Variation of Marichjhapi Island …

459

Blasco F, Saenger P, Janodet E (1996) Mangroves as indicators of coastal change. CATENA 27(3– 4):167–178 Blasco F, Aizpuru M, Gers C (2001) Depletion of the mangroves of Continental Asia. Wetlands Ecol Manage 9(3):255–266 Blasco F, Aizpuru M (2002) Mangroves along the coastal stretch of the Bay of Bengal: Present status Bose S (2009) Role of Indian Sundarban mangroves in mitigating climate impacts: An appraisal. IOP Conf Series Earth Environ Sci 6(25), IOP Publishing Chaffey DR, Miller FR, Sandom JH (1985) A forest inventory of the Sundarbans, Bangladesh (Report). Surbiton, England: Land Resources Development Centre Chander G, Markham BL, Barsi JA (2007) Revised Landsat-5 thematic mapper radiometric calibration. IEEE Geosci Remote Sens Lett 4:490–494 Chaudhuri AB, Choudhury A, Hussain Z, Acharya G (1994) Mangroves of the Sundarbans. Check List 10(2):329–334 Chouhan R, Rao N (2011) Vegetation detection in multispectral remote sensing images: Protective role-analysis of vegetation in 2004 Indian ocean tsunami. PDPM Indian Inst Inf Technol Chowdhury D (2011) Space, identity, territory: Marichjhapi massacre, 1979. v 15(5):664–682 Christensen B (1984) Ecological aspects of the Sundarbans, FAO: TCP/BGD/2309 (Mf). FAO, Rome, p 42 Chuvieco E (1998) El factor temporal enteledetección: evoluciónfenomenológica y análisis de cambios. Revista De Teledetección 10:1–9 Cornforth WA, Fatoyinbo TE, Freemantle TP, Pettorelli N (2013) Advanced land observing satellite phased array type L-Band SAR (ALOS PALSAR) to inform the conservation of mangroves: Sundarbans as a case study. Remote Sens 5(1):224–237 Das Gupta R, Hashimoto S, Okuro T, Basu M (2019) Scenario-based land change modelling in the Indian Sundarban delta: An exploratory analysis of plausible alternative regional futures. Sustain Sci 14(1):221–240 Datta D (2018) Assessment of mangrove management alternatives in village-fringe forests of Indian Sundarbans: Resilient initiatives or short-term nature exploitations? Wetlands Ecol Manage 26(3):399–413 Datta D, Deb S (2012) Analysis of coastal land use/land cover changes in the Indian Sundarbans using remotely sensed data. Geo-Spat Inf Sci 15(4):241–250 Debnath A (2018) Land use and land cover change detection of Gosaba Island of the Indian Sundarban Region by using multitemporal satellite image. Int J Hum Soc Sci 7(1):209–217 DHDR (2009) South 24 Parganas. Development and Planning Department, Government of West Bengal, India Directorate of Forest (2020) Government of West Bengal (GoWB.), Wildlife Wing Duke NC (1993) Mangrove floristics and biogeography. Coast Estuar Stud: 63–63 Emch M, Peterson M (2006) Mangrove forest cover change in the Bangladesh Sundarbans from 1989–2000: A remote sensing approach. Geocarto Int 21(1):5–12 Behera RS, Shaoo CK, Sahu RK (2021) “Mangroves—Nature’s shield against natural disasters and climate change”. Social Dhara Faruque MJ, Hasan MY, Islam KZ, Young B, Ahmed MT, Monir MU, Kundu P (2022) Monitoring of land use and land cover changes by using remote sensing and GIS techniques at human-induced mangrove forests areas in Bangladesh. Society and Environment, Remote Sensing Applications, p 100699 Ganguly D, Mukhopadhyay A, Pandey RK, Mitra D (2006) Geomorphological study of Sundarban deltaic estuary. J Indian Soc Remote Sens 34(4):431–435 Ghosh A, Schmidt S, Fickert T, Nüsser M (2015) The Indian Sundarban mangrove forests: History, utilization, conservation strategies and local perception. Diversity 7(2):149–169 Ghosh D, Ghosh S (2013) Sundarban Mangroves—challenges to survival Ghosh T, Bhandari G, Hazra S (2002) Coastal erosion and flooding in Ghoramara and adjoining islands of Sundarban deltaic system. In: Analysis and practice of water resource engineering for

460

S. Biswas and K. Sarkar

disaster mitigation, proceedings of international conference on water related disaster (ICWRD 2002), 1, New Age International Pvt. Ltd., New Delhi, pp. 38–41 Giri C, Pengra B, Zhu Z, Singh A, Tieszen L, L. (2007) Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuar Coast Shelf Sci 73(1–2):91–100 Goodbred SL Jr, Kuehl SA (2000) The significance of large sediment supply, active tectonism, and eustasy on margin sequence development: Late Quaternary stratigraphy and evolution of the Ganges-Brahmaputra delta. Sed Geol 133(3–4):227–248 Gopal B, Chauhan M (2006) Biodiversity and its conservation in the Sundarban mangrove ecosystem. Aquat Sci 68(3):338–354 Guha Bakshi DN, Sanyal P, Naskar K, Roxburgh W (1999) Sundarbans mangal. In: William Roxburgh Memorial Seminar on Sundarbans Mangal (1996: Calcutta, India). Naya Prokash Hajra R, Ghosh A, Ghosh T (2017) Comparative assessment of morphological and landuse/ landcover change pattern of Sagar, Ghoramara, and Mousani island of Indian Sundarban delta through remote sensing. In: Environment and Earth Observation, pp. 153–172. Springer, Cham Hanebuth TJ, Kudrass HR, Linstädter J, Islam B, Zander AM (2013) Rapid coastal subsidence in the central Ganges-Brahmaputra Delta (Bangladesh) since the 17th century deduced from submerged salt-producing kilns. Geology 41(9):987–990 Hazra S, Bhadra T, Roy SP (2015) Sustainable water resource management in the Indian Sundarban Delta. In: Proceedings of the international seminar on challenges to ground water management: vision 2050, CGWS, Kolkata, pp. 324–332 Hogarth PJ (2015) The biology of mangroves and seagrasses. Oxford University Press, Oxford, UK, p 273 Hussain Z, Acharya G (eds) (1994) Mangroves of the Sundarbans., vol 2. International Union for Conservation of Nature and Natural Resources, Bangkok Iftekhar MS, Islam MR (2004) Managing mangroves in Bangladesh: A strategy analysis. J Coast Conserv 10(1):139–146 Islam MT (2014) Vegetation changes of Sundarbans based on Landsat Imagery analysis between 1975 and 2006. Landsc & Environ 8(1):1–9 Islam SM, Bhuiyan MAH (2018) Sundarbans mangrove forest of Bangladesh: Causes of degradation and sustainable management options. Environ Sustain 1(2):113–131 Islam MM, Borgqvist H, Kumar L (2019) Monitoring mangrove forest landcover changes in the coastline of Bangladesh from 1976 to 2015. Geocarto Int 34(13):1458–1476 Islam MM (2017) Tracing mangrove forest dynamics of Bangladesh using historical Landsat data. Student thesis series INES Karaburun A (2010) Estimation of C factor for soil erosion modeling using NDVI in Buyukcekmece watershed. Ozean J Appl Sci 3(1):77–85 Khan AR, Khan A, Masud S, Rahman RM (2021) Analyzing the land cover change and degradation in Sundarbans mangrove forest using machine learning and remote sensing technique. In: International work-conference on artificial neural networks, pp. 429–438. Springer, Cham Kumar KV, Palit AS, Chakraborty AK, Bhan SK, Chowdhury B, Sanyal T (1994) Change detection study of islands in Hooghly estuary using multidate satellite images. J Indian Soc Remote Sens 22(1):1–7 Kumar M, Mondal I, Pham QB (2021) Monitoring forest landcover changes in the Eastern Sundarban of Bangladesh from 1989 to 2019. Acta Geophys 69(2):561–577 Macintosh DJ, Ashton EC (2002) A review of mangrove biodiversity conservation and management. Report to World Bank. Centre for tropical ecosystems research, University of Aarhus, Denmark, viii+71 Macintosh DJ, Ashton EC (2004) Principles for a code of conduct for the management and sustainable use of mangrove ecosystems. Washington (DC): The World Bank, International Society for Marine Ecosystems (ISME), and Centre for Tropical ecosystem Research, University of Aarhus, Denmark, 104.

20 Temporal Areal and Greenness Variation of Marichjhapi Island …

461

Mallick R (1999) Refugee resettlement in forest reserves: West Bengal policy reversal and the Marichjhapi massacre. J Asian Stud 58(1):104–125 Mandal AK, Nandi NC (1989) Fauna of Sundarban mangrove ecosystem, West Bengal, India. In: Fauna of conservation areas: 3, Zoological Survey of India. 116 Manna S, Chaudhuri K, Bhattacharyya S, Bhattacharyya M (2010) Dynamics of Sundarban estuarine ecosystem: Eutrophication induced threat to mangroves. Saline Syst 6(1):1–16 Manna S, Mondal PP, Mukhopadhyay A, Akhand A, Hazra S, Mitra D (2013) Vegetation cover change analysis from multi-temporal satellite data in Jharkhali Island. Sundarbans, India Mishra M, Acharyya T, Santos CAG, da Silva RM, Kar D, Kamal AHM, Raulo S (2021) Geoecological impact assessment of severe cyclonic storm Amphan on Sundarban mangrove forest using geospatial technology. Estuar Coast Shelf Sci 260:107486 Mistri A, Das B (2015) Environmental legislations and livelihood conflicts of Fishermen in Sundarban. India. Asian Profile 44(3):389–400 Mondal B, Saha AK, Roy A (2019a) Mapping mangroves using LISS-IV and Hyperion data in part of the Indian Sundarban. Int J Remote Sens 40(24):9380–9400 Mondal I, Thakur S, Ghosh P, De TK, Bandyopadhyay J (2019) Land use/land cover modeling of Sagar Island, India using remote sensing and GIS techniques. In: Emerging technologies in data mining and information security, pp. 771–785. Springer, Singapore Nagelkerken ISJM, Blaber SJM, Bouillon S, Green P, Haywood M, Kirton LG, Meynecke JO, Pawlik J, Penrose HM, Sasekumar A, Somerfield PJ (2008) The habitat function of mangroves for terrestrial and marine fauna: A review. Aquat Bot 89(2):155–185 Nandy S, Kushwaha SPS (2011) Study on the utility of IRS 1D LISS-III data and the classification techniques for mapping of Sundarban mangroves. J Coast Conserv 15(1):123–137 Naskar KR, Guhabakshi DN (1987) Mangrove Swamps of the Sundarbans—An ecological perspective. Naya Prokash, Calcutta Paul A, Ghosh M, Sardar J, Bandyopadhyay J (2021) Rapid land use and land cover changes in response to environmental variables: A study on Namkhana Abad of Indian Sundarban. Int J Adv Res Dev 6(2):08–18 Prain D (1903) Flora of the Sundribuns. Periodical Expert Book Agency, Delhi Pravalie R, Sîrodoev I, Peptenatu D (2014) Detecting climate change effects on forest ecosystems in Southwestern Romania using Landsat TM NDVI data. J Geog Sci 24(5):815–832 Prithviraj M, Sengupta S, Ghosh SK, Ramanas I, Chakraborty D (1995) Documentation of the long-term coastal geomorphic changes within the Hugli river estuary India. Asian Pac Remote Sens J 7(2):1–9 Rahman M, Ullah R, Lan M, Sri Sumantyo JT, Kuze H, Tateishi R (2013) Comparison of Landsat image classification methods for detecting mangrove forests in Sundarbans. Int J Remote Sens 34(4):1041–1056 Rahman S, Rahman H, Shahid S, Khan RU, Jahan N, Ahmed ZU, Mohsenipour M (2017) The impact of cyclone Aila on the Sundarban forest ecosystem. Int J Ecol Dev 32(1):87–97 Rahman LM (2000) The Sundarbans: A unique wilderness of the world. In: Wilderness Science in a Time of Change Conference: Wilderness within the context of larger systems, Vol. 2, p. 143. US Department of Agriculture, Forest Service, Rocky Mountain Research Station Rai R, Zhang Y, Paudel B, Li S, Khanal NR (2017) A synthesis of studies on land use and land cover dynamics during 1930–2015 in Bangladesh. Sustainability 9(10):1866 Ramteke IK, Obi Reddy GP, Sen TK, Singh SK, Chatterji S, Rajankar PB, Das SN (2017) Land use/land cover change dynamics in coastal ecosystem of Sundarban delta, West Bengal-a case study of Bali Island Ranjan AK, Sivathanu V, Verma SK, Murmu L, Kumar PBS (2017) Spatio-temporal variation in Indian part of Sundarban Delta over the years 1990–2016 using Geospatial Technology. Int J Geomat Geosci 7(3):275–292 Ranjan AK, Kanga S (2018) Dynamic changes in mangrove forest and Lu/Lc variation analysis over Indian Sundarban Delta in West Bengal (India) using multi-temporal satellite data. I-Manager’s J Futur Eng Technol 13(3): 9

462

S. Biswas and K. Sarkar

Sahana M, Hong H, Ahmed R, Patel PP, Bhakat P, Sajjad H (2019) Assessing coastal island vulnerability in the Sundarban Biosphere Reserve, India, using geospatial technology. Environ Earth Sci 78(10):1–22 Sahana M, Sajjad H (2019) Assessing influence of erosion and accretion on landscape diversity in Sundarban Biosphere Reserve, Lower Ganga Basin: A geospatial approach. In: Quaternary Geomorphology in India, pp. 191–203. Springer, Cham Samanta K, Hazra S (2012) Landuse/landcover change study of Jharkhali Island Sundarbans, West Bengal using remote sensing and GIS. Int J Geomat Geosci 3(2):299 Seidensticker J, Hai MdA (1983) The Sundarbans wildlife management plan: Conservation in the Bangladesh Coastal Zone. International Union for Conservation of Nature and Natural Resources (IUCN), Gland, Switzerland Shimu SA, Aktar M, Afjal MI, Nitu AM, Uddin MP, Al Mamun M (2019) NDVI based change detection in Sundarban mangrove forest using remote sensing data. In: 2019 4th International conference on electrical information and communication technology (EICT), pp. 1–5. IEEE Sievers M, Chowdhury MR, Adame MF, Bhadury P, Bhargava R, Buelow C, Friess DA, Ghosh A, Hayes MA, McClure EC, Pearson RM (2020) Indian Sundarbans mangrove forest considered endangered under Red List of Ecosystems, but there is cause for optimism. Biol Cons 251:108751 Syvitski JP, Kettner AJ, Overeem I, Hutton EW, Hannon MT, Brakenridge GR, Day J, Vörösmarty C, Saito Y, Giosan L, Nicholls RJ (2009) Sinking deltas due to human activities. Nat Geosci 2(10):681–686 Thakur S, Maity D, Mondal I, Basumatary G, Ghosh PB, Das P, De TK (2021) Assessment of changes in land use, land cover, and land surface temperature in the mangrove forest of Sundarbans, northeast coast of India. Environ Dev Sustain 23(2):1917–1943 Tomlinson PB (1986) The Botany of Mangroves. Cambridge University Press, Cambridge, p 414 Uddin MS, Shah MAR, Khanom S, Nesha MK (2013) Climate change impacts on the Sundarbans mangrove ecosystem services and dependent livelihoods in Bangladesh. Asian J Conserv Biol 2(2):152–156 UNEP (United Nations Environment Programme) (2005) World Conservation Monitoring Centre (UNEP-WCMC), protected area database Uzzaman KMM, Miah MG, Abdullah HM, Islam MR, Afrad MSI, Hossain MJ (2020) Thirty-year spatiotemporal change record of Sundarban mangrove forest in Bangladesh. Ann Bangladesh Agric 24(2):15–32 Vrieling A, De Leeuw J, Said MY (2013) Length of growing period over Africa: Variability and trends from 30 years of NDVI time series. Remote Sens 5(2):982–1000 WBFD (2007) Annual Report of Sundarbans Tiger Reserve 2006–2007. West Bengal Forest Department, Govt. of WB, Kolkata www.britannica.com/place/Sundarbans, viewed on 04/09/2021

Chapter 21

Estimation of Crop Coefficients Using Landsat-8 Remote Sensing Image at Field Scale for Maize Crop Nirav Pampaniya, Mukesh K. Tiwari, Vijay J. Patel, M. B. Patel, P. K. Parmar, Sateesh Karwariya, Shruti Kanga, and Suraj Kumar Singh

Abstract A widely used method is to estimate crop water requirements using reference evapotranspiration and crop coefficient. The crop coefficients can be estimated using a relationship between satellite-derived vegetation index and crop coefficient values for efficient and timely agricultural water management strategies. In the present decade several remotesensing based vegetation indices are applied to simulate crop coefficients but almost all are based on linear relationship. The N. Pampaniya Department of Natural Resource Management, College of Forestry, Navsari Agricultural University, Navsari, India e-mail: [email protected] M. K. Tiwari Department of Soil and Water Conservation Engineering, College of Agricultural Engineering andTechnology, Anand Agricultural University, Godhra, India V. J. Patel · M. B. Patel · P. K. Parmar Main Maize Research Station, Anand Agricultural University, Godhra, Gujarat 389001, India S. Karwariya Government of Gujarat, Commissionerate of Rural Development, Gandhinagar, Gujarat 3820 010, India Gujarat Water Resource Development Corporation Ltd, Gandhinagar, Gujarat, India S. Karwariya e-mail: [email protected] S. Kanga Centre for Climate Change and Water Research, Suresh Gyan Vihar University, Jaipur, Rajasthan 302025, India e-mail: [email protected] Department of Geography, School of Environment and Earth Sciences, Central University of Punjab, Bathinda, Punjab 151401, India S. K. Singh (B) Centre for Sustainable Development, Suresh Gyan Vihar University, Jaipur, Rajasthan 302025, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_21

463

464

N. Pampaniya et al.

relationship between the NDVI and K c was established by using both a traditional regression method and ANN model. Given the complex meteorological and biophysical phenomena related with crop coefficients and satellite-derived vegetation index, a linear relationship between these two variables is insufficient to extract the non-linearity and non-stationarity between them. Therefore in this study widely applied feed forward back propagation Artificial neural networks (FFBP-ANN), a soft computing techniques for mapping complex input and output relationship, was applied. Performance of FFBP-ANN for mapping crop coefficients with NDVI was also compared with traditional regression method. It was found that FFBP-ANN can be applied to accurately estimate crop coefficient values using the remote sensing derived NDVI values. This advancement in calculating crop coefficient using free satellite images is a significant step forward in the development of agricultural irrigation demand models. As a result, this research paves the way for near-real-time irrigation decision-making systems. Keywords Crop coefficients · Evapotranspiration · Remote sensing · Landsat · ANN

21.1 Introduction One of the most significant elements of the hydrologic cycle, evapotranspiration (ET), can help with efficient water management. ET estimation must be precise, accurate, and timely for different areas of water resources and water management, such as water budgeting, canal operation, drought monitoring, and irrigation scheduling (Gowda et al. 2007). Crop water requirement is generally estimated using the Kc-ETo approach adopted by FAO56 (Allen et al. 1998) where crop evapotranspiration, ETc is estimated by multiplying a reference crop evapotranspiration (ETo) by a crop coefficient (Kc). The ETo is dependent on the weather parameters while the Kc depends upon the crop under consideration ground cover by vegetation, canopy characteristics and aerodynamic resistance. Other approaches for estimating ETc directly from ground crop data include the Penman–Monteith equation and a combination of models. (Monteith 1965; Shuttleworth and Wallace 2009). Thermal remote sensing based approaches are also applied for estimation of ETc such as those based on surface energy balance models (e.g., (Bastiaanssen et al. 2005; Allen 2007; Kustas et al. 2004). These models subtracts the soil heat flux (G) and sensible heat flux (H) from the net radiation (Rn) at the surface for ETc estimation. These models take into consideration the effects of soil water deficit or water vapor pressure for estimation of ETc (Mateos et al. 2013). Crop coefficients can be further derived considering the ETo (Paco et al. 2014; Pakparvar et al. 2014; Pocas et al. 2013). These models are more sophisticated than the Kc-ETo technique and require a considerable amount of data (Mateos et al. 2013). The Kc-ETo approach is widely applied and accepted for operational and research objectives (Pereira et al. 2015).

21 Estimation of Crop Coefficients Using Landsat-8 Remote Sensing …

465

There are several studies where crop coefficients are derived using the remote sensing image derived products by generating a linear relationship between them. Kamble et al. (2013) developed a linear regression model to establish relationship between NDVI obtained using MODIS remote sensing data and crop coefficients obtained from the flux data using AmeriFlux towers. Pimpale et al. (2014) developed a linear regression model for developing relationship between Kc value and remote sensing based NDVI values for chickpea crops. They found very good relationship between Kc and NDVI in terms of higher R2 value and lower RMSE value. Toureiro et al. (2016) showed the potential of remote sensing multispectral images for estimation of crop water and irrigation requirement with high degree of accuracy and spatial representation requirement. Park et al. (2017) developed a linear regression equation to develop Kc values using remote sensing data derived LAI, NDVI, and soil moisture. It was found in the study that NDVI and LAI, showed remarkable influence on the mixed forest sites, whereas soil moisture showed remarkable influence in the paddy fields. Rozenstein et al. (2018) used time series of Sentinel-2 imagery was processed to produce 22 vegetation indices (VIs) based on the sensor’s unique spectral bands for Kc estimation. Javed et al. (2020) used reflectance-based crop coefficients (Kc) were calculated using linear regression between the normalized difference vegetation index (NDVI) of the MOD13Q1 and MYD13Q1 products of the Moderate Resolution Imaging Spectroradiometer (MODIS) and FAO-defined crop coefficients. Dingre et al. (2021) developed regression equations to estimate the seasonal distribution of Kc with NDVI as the dependent variables and ratio of days (t/T) after planting (t) to the total crop period (T) as the independent variable. The relationship between crop Kc and NDVI was characterized with 2nd order polynomial regression but correlation was moderately strong. Because the relation between Kc and NDVI values is complex, with intrinsic non-linearity and non-stationarity, linear models have their own set of limitations and are unlikely to generate universally accepted modelling findings. To the best of our knowledge, this is the first study to look into the possibilities of artificial neural network models for mapping non-linearity between these two variables.

21.2 Material and Methods 21.2.1 Study Area This study was conducted using data from different plots of Main maize research station, Anand Agricultural University, Godhra, located in Panchmahal district, semi arid middle region of Gujarat, India (Fig. 21.1). The location of study site is at 22°46' 56.2'' N latitude and 73°39' 14.1'' E longitude. The mean maximum temperature of this season was 32 °C and mean minimum temperature was 15 °C, mean temperature of the Rabi season was 23.5 °C. The texture of the soil in all the plots

466

N. Pampaniya et al.

Fig. 21.1 Location map of the study area

is sandy loam. The maize fields were irrigated using Furrow irrigation method with total 5–6 irrigation applied during crop period.

21.2.2 Remote Sensing Data and Software Used In this study, Landsat-8 OLI/TIRS data were employed. The data from the Landsat8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) have nine spectral bands, each with a spatial resolution of 30 m. Band 8 (panchromatic) has a resolution of 15 m. It also has two thermal infrared bands with a spatial resolution of 100 m (later resampled into 30 m). The Landsat-8 OLI/TIRS images were downloaded from USGS Earth Explorer www.earthexplorer.usgs.gov/for different dates of pass (Day of year: DOY) during the growing season of maize crop as on 26/ 10/16, 11/11/16, 27/11/2016, 13/12/2016, 29/12/2016, 14/01/2017, 30/01/2017, 15/ 02/2017, 03/03/17, 19/03/2017, 04/04/17 and 20/04/2017. These images were used for generation of NDVI valuesfor different plots during the crop growing period. ArcGIS 10.3 software was used for different remote sensing and GIS operations.

21 Estimation of Crop Coefficients Using Landsat-8 Remote Sensing …

467

Table 21.1 Field data of different plots during Rabi season from main maize research station, Godhra Plot no

Crop

Date of sowing

Fertilizer-1

Fertilizer-2

Fertilizer-3

Date of harvesting

12A

Maize

21/12/ 2016

21/01/2017

7/3/2017

–

20/04/2017

12B

Maize

9/12/ 2016

08/02/2017

06/03/2017

–

–

13

Maize

9/12/ 2016

06/03/2017

–

–

20/04/2017

15

Maize

25/11/ 2016

31/12/2016

07/02/2017

–

18/04/2017

18

Maize

19/10/ 2016

30/11/2016

17/12/2016

21/01/2017

21/03/2017

21

Maize

04/11/ 2016

28/11/2016

03/01/2016

07/02/2017

17/04/2017

25

Maize

07/12/ 2016

–

–

–

01/05/2017

30

Maize (Babycorn)

24/11/ 2016

21/12/2016

07/01/2017

–

31/01/2017

21.2.3 Collection of Field Data Field data from all the eight plots i.e. crop, sowing date, fertilizer application date, irrigation date, harvesting date was collected from MainMaize Research Station (MMRS), Godhra, Gujarat, Indiawere collected and a summary is presented in Table 21.1. A total of 8 plots, numbered as 12A, 12B, 13, 15, 18, 21, 25 and 30 were considered in this study having area of 22, 29, 28, 36, 20, 40, 20 and 29 m2 , respectively.

21.2.4 Calculation of NDVI NDVI is a numerical indicator that is sensitive to vegetation cover, biomass, crop condition, and density and uses reflectance in the red and near-infrared bands of the electromagnetic spectrum (Baruth et al., 2008). The most often used vegetation index for condition assessment and monitoring is the NDVI, which ranges from −1 to +1. It’s also extensively used to classify crops based on NDVI changes as a result of crop growth. In this study 8 different plots of maize were selected and NDVI values were estimated for Rabi season during the period 2016–17 using the following formula NDVI = (NIR − RED) / (NIR + RED)

(21.1)

468

N. Pampaniya et al.

where, NIR = Reflectance in the near-infrared band (Band 5 for Landsat-8 OLI/TIRS image). RED = Reflectance in the red visible band (Band 4 for Landsat-8 OLI/TIRS Image).

21.2.5 Crop Coefficients of Maize Crop Crop coefficients (Kc) for maize crop in all the 8 fields for initial, mid-season and late season of crop growth stages available in FAO-56 Table 12 (Allen et al. 1998) are considered in this study. Crop characteristics, crop planting or sowing date, rate of crop development, length of growing season, and climate conditions all influence crop coefficients. The length of the maize crop’s growth stage was established using local data, and the crop’s growing time was calculated using data from main maize research station, Mehta and Pandey (2016) modified crop coefficients values for maize crop for middle Gujarat region at three crop growth stages (initial, mid-season and late season of crop growth stages) and these values were used in this study. All these values for the three stages were linearly scaled to obtain daily crop coefficient values.

21.2.6 Artificial Neural Networks (ANNs) ANNs are information processing systems having the capabilities to mimic the mathematics of human brain. ANNs are composed of simple processing elements (neurons) connected by weighted synaptic connections (Rumelhart and McClelland 1986; Muller and Reinhardt 1991). ANNs are found to be superior for reconstructing and mapping the complex nonlinear input/output relations. ANNs methodology is found to be fast, efficient and adaptable and flexible in new and noisy environment. ANNs have been extensively applied to tackle real-world issues such as time series prediction, rule-based control, and rainfall-runoff modelling, watershed management, climatic variable forecasting, Due to these properties (Keskin and Terzi 2006; Abbot and Marohasy 2012; Adamowski et al. 2012; Mekanik et al. 2013; Makwana and Tiwari 2014; Deo and Sahin 2015; Kumar et al. 2015; Tiwari et al. 2016; Deo et al. 2017). The input layer of the multilayer feed forward ANN (FF-ANN) is made up of sensory neural units, one or more hidden layers of calculation nodes, and an output layer of computation nodes. The input signal travels through the network in a forward manner, layer by layer. A representative three-layer feed forward ANN is shown in Fig. 21.2. The mathematical form of a three-layer feed forward NN is given as:

21 Estimation of Crop Coefficients Using Landsat-8 Remote Sensing …

(lo)

469

(Vo) (wjo) (wko)

Input Signal

Output Signal

(Ok) (wkj) (li)

(wji)

Input Layer

(Vj)

Hidden Layer

Output

Fig. 21.2 A Basic overview of NN topology (Makwana and Tiwari 2017)

⎡

∑

Ok = g2 ⎣

j

wkj g1

( ∑

)

⎤

wji Ii + wjo + wko ⎦

(21.2)

i

where, I i = Input value to node i of the input layer, V j = Hidden value to node j of the hidden layer, and. Ok = Output at node k of the output layer. I 0 = 1 (An input layer bias term). wj0 = bias weight to hidden layer. V 0 = 1 (an output layer bias term). wk0 = bias weight to output layer. i, j and k are the input, hidden and output layers, respectively. wji = the strength of the connection between the input node i and the hidden node j, and. wkj controls the strength of the connection between the hidden node j and the output node k. g1 and g2 are activation functions for the hidden layer and the output layer, respectively.

470

N. Pampaniya et al.

21.2.7 Development of ANN Model For development of ANN model the 56 dataset of NDVI and Kc values were applied. 40 dataset were taken for ANN model developments, whereas remaining 14 dataset were applied for verification of the developed ANN models. ANN model with one input, one hidden and one output layer was considered for mapping relationship between Daily Kc values and NDVI values for the same day. Depending upon the problem under consideration one input neuron and one output neuron was considered in this study. The values of hidden neurons were changed from 1 to 15 as there is not having any fixed rule for selection of number of hidden neurons. The input and output data were normalized between 0 and 1. Sigmoid function was considered in the hidden neuron whereas linear transfer function was considered in the output neuron. For optimization of the weights and biases of ANN models one of the fast and efficient and widely implemented Levenberg–Marquardt algorithm was applied in the study to minimize the mean squared error between the observed and simulated Kc values.

21.2.8 Performance Indicator The performance of developed linear, non-linear and ANN model is evaluated using five performance indicators viz., Coefficient of determination (R2 ) Nash–Sutcliffe efficiency (E NS ), root mean square error (RMSE), peak percentage deviation (Pdv ) and mean absolute error (MAE). These performance indices are defined below (Makwana and Tiwari 2017; Deo et al. 2017). (i) Coefficient of determination (R2 ) expressed as: ⎞2 _______ )( _______ ) ∑N ( Kcp,i − Kc p,i ⎟ ⎜ i=1 Kco,i − Kc o,i ⎟ (21.3) R2 = ⎜ / ⎝ /∑ ( ) ( ) 2 ∑ 2⎠ _______ _______ N N i=1 Kco,i − Kc o,i i=1 Kcp,i − Kc p,i ⎛

where Kco,i and Kcp,i are the observed and predicted Kc, respectively, Kco,i and Kcp,i are the means of the observed and predicted Kc, respectively, and n is the number of data points. (ii) The Nash–Sutcliffe coefficient (E NS ) is expressed as: ENS

)2 ∑n ( i=1 Kco,i − Kcp,i = 1 − ∑n 2 i=1 (Kco,i − Kco,i )

(21.4)

21 Estimation of Crop Coefficients Using Landsat-8 Remote Sensing …

471

The value of E NS varies between -∞ and 100. The closer the value to 100, the better is the model performance. (iii) Root mean square error (RMSE): ┌ | n |1 ∑ RMSE = | (Kco,i − Kcp,i )2 n i=1

(21.5)

(iv) Mean absolute error (MAE): | 1 ∑ || Kco,i − Kcp,i | n i=1 n

MAE =

(21.6)

(V) Percentage deviation in peak (Pdv ): Pd v =

OKc,peak − PKc,peak 100 OKc,peak

(21.7)

where OKc,peak and PKc,peak are the peak value of observed and predicted Kc values, respectively.

21.3 Results and Discussion 21.3.1 Kc and NDVI Simulation Using Linear Models For simulation of Kc and remote s E NS ing image derived NDVI values, initially linear models are applied and the applied model is tested for simulation of NDVI values and the results are presented in Fig. 21.3. The same linear models is applied to generate Kc values and the results are are presented in Fig. 21.4. It can be observed from the figures that the linear model perform satisfactorily with R2 values 0.409 during the modeling (Fig. 21.3) and R2 as 0.393 during the simulation (Fig. 21.4) of Kc values.

21.4 Kc and NDVI Simulation using Power Function Similar to the linear models non-linear power function model is also applied to model and simulate the NDVI and Kc values. It can be observed that the power function model perform satisfactorily with R2 values 0.493 during the modeling (Fig. 21.5) and R2 as 0.328 during the simulation (Fig. 21.6) of Kc values.

472

Fig. 21.3 Linear model between Kc and NDVI values

Fig. 21.4 Observed and predicted values of Kc using linear model

Fig. 21.5 Power function between Kc and NDVI values

N. Pampaniya et al.

21 Estimation of Crop Coefficients Using Landsat-8 Remote Sensing …

473

Fig. 21.6 Observed and predicted values of Kc using power function

Table 21.2 Performance of linear model and power function HN

R2

E (%)

RMSE

Linear Model

0.394

39.359

0.286

8.650

0.228

Power Function

0.328

18.948

0.330

−35.260

0.250

Pdv (%)

MAE

Performance of linear model and non-linear model during simulation in terms of different performance indicator is presented in Table 21.2. It can be observed from the comparative performance of both the linear and non-linear models that though the power function performs better for modeling the NDVI and Kc values but performance during simulation is not up to the mark, whereas linear models being lesser accurate during modeling is best during simulation period. It shows that linear model remains the best fit model in comparison with non-linear power function model.

21.4.1 Kc and NDVI Simulation Using Artificial Neural Networks (ANNs) ANN models were developed for the simulation of Kc and NDVI values considering the observations that linear and simple non-linear model do not produce results that can be accepted for water resource planning and management with much accuracy. Feed Forward Back Propagation Artificial neural network (FFBP-ANN) with one hidden layer was applied with sigmoidal transformation function in hidden layer and linear transfer function in the output layer. Out of 56 dataset 40 were applied for training whereas remaining 16 dataset were applied for testing of the developed ANN model. The NDVI values were applied as input information whereas Kc values

474

N. Pampaniya et al.

Table 21.3 Performance of ANN models during training under different hidden neurons HN

R2

E NS (%)

RMSE

Pdv (%)

MAE

1

0.426

42.643

0.299

17.808

0.245

2

0.426

42.643

0.299

17.808

0.245

3

0.480

48.038

0.284

19.807

0.237

4

0.489

48.947

0.282

18.758

0.233

5

0.458

45.796

0.290

6.752

0.231

6

0.530

52.978

0.271

8.674

0.206

7

0.717

71.739

0.210

0.225

0.162

8

0.741

74.050

0.201

0.449

0.149

9

0.701

70.044

0.216

6.454

0.167

10

0.607

60.698

0.247

6.800

0.181

11

0.722

72.161

0.208

5.067

0.156

12

0.823

81.989

0.167

2.117

0.110

13

0.879

87.915

0.137

0.000

0.077

14

0.717

71.625

0.210

5.393

0.136

15

0.748

74.763

0.198

2.711

0.134

were presented as target variables. Input and output variables were selected as per the modeling requirement, whereas a trial and error procedure was adopted to select optimum number of hidden neurons varying from 1–15, as there is no direct method to select them. Performance of ANN models for 1–15 hidden neurons is presented in Table 21.3. It can be observed from the Table that ANN performs best with 13hidden neurons in terms of higher R2 and E NS values and minimum RMSE and MAE error values. Performance of ANN model during the training and testing period is presented in Fig. 21.7. It can be observed clearly that linear model with R2 = 0.393 and power function with R2 = 0.328, performance of ANN model is much better with R2 = 0.879 (Fig. 21.7). Further comparing performance of ANN model with remaining two models in terms of different performance indicators shows that ANN are far better than both the remaining models. It shows capabilities of ANN model in capturing complex non-linearity between Kc and NDVI values.

21.5 Conclusions There are several parameters and variables affecting the conversion of remote sensing imagery derived NDVI and ground observed crop coefficient values. Much better performance of ANN techniques over linear regression models shows that linear

21 Estimation of Crop Coefficients Using Landsat-8 Remote Sensing …

475

Fig. 21.7 Estimate and predicted values using ANN model during a training and b testing period

models are not adequate to capture the high non-linearity and non-stationarity associated between NDVI and Kc values. Much higher accuracy can be obtained by ANN modeling for estimation ok precise and accurate Kc values using the remote sensing imagery derived NDVI. Similar studies using ANN model can be performed in future for accurate and precise transformation and mapping between NDVI and other similar indices and the Kc values for formulation and implementation of efficient and timely water resource planning strategies especially estimation of precise crop water requirement, irrigation scheduling, canal operation, etc.

476

N. Pampaniya et al.

References Abbot J, Marohasy J (2012) Application of artificial neural networks to rainfall forecasting in Queensland, Australia. Adv Atmos Sci 29:717–730 Adamowski J, Fung Chan H, Prasher SO, Ozga-Zielinski B, Sliusarieva A (2012) Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada. Water Resour Res 48:W01528. https://doi.org/10.1029/2010WR 009945 Allen RG, Pereira LS, Raes D, Smith M (1998) Crop Evapotranspiration: Guidelines for computing crop water requirements. In: FAO Irrigation and Drainage Paper 56; FAO-Food and Agriculture Organization of the United Nations: Rome, Italy. p. 300 Allen RG, TasumiM TR (2007) Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Model. J Irrig Drain Eng 133:380–394 BaruthB, Royer A, Klisch A, Genovese G (2008) The use of remote sensing within the mars crop yield monitoring system of the European commission. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B8. pp: 936–93 Bastiaanssen WGM, Noordman EJM, PelgrumH DG, Thoreson BP, Allen RG (2005) SEBAL model with remotely sensed data to improve water-resources management under actual field conditions. J Irrig Drain Eng 131:85–93 Deo RC, Sahin M (2015) Application of the Artificial Neural Network model for prediction of monthly standardized precipitation and evapotranspiration index using hydrometeorological parameters and climate indices in eastern Australia. Atmos Res 161–162:65–81 Deo RC, Tiwari MK, Adamowski JF, Quilty JM (2017) Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model. Stoch Environ Res Risk Assess 31(5):1211– 1240. https://doi.org/10.1007/s00477-016-1265-z Dingre SK, Gorantiwar SD, Kadam SA (2021) Correlating the field water balance derived crop coefficient (Kc) and canopy reflectance-based NDVI for irrigated sugarcane. Precision Agric 22(4):1134–1153 Gowda PH, Chavez JL, Colaizzi PD, Evett SR, Howell TA, Tolk JA (2007) ET mapping for agricultural water management: Present status and challenges. Irrig Sci 26:223–237 Javed MA, Rashid AS, Awan WK, Munir BA (2020) Estimation of crop water deficit in lower Bari Doab, Pakistan using reflection-based crop coefficient. ISPRS Int J Geo Inf 9(3):173 Kamble B, Irmak A, Hubbard K (2013) Estimating crop coefficients using remote sensing-based vegetation index. Remote Sens 5:1588–1602. https://doi.org/10.3390/rs5041588 Keskin ME, Terzi O (2006) Artificial neural network models of daily pan evaporation. J HydrolEng 11:65–70 Kumar S, Tiwari MK, Chatterjee C, Mishra A (2015) Reservoir inflow forecasting using ensemble models based on neural networks, wavelet analysis and bootstrap method. Water ResourManag. https://doi.org/10.1007/s11269-015-1095-7 Kustas WP, Norman JM, Schmugge TJ, Anderson MC (2004) Mapping surface energy fluxes with radiometric temperature. Chapter 7. In: Thermal Remote Sensing in Land Surface Processes; Quattrochi, D., Luvall, J., Eds.; CRC Press: Boca Raton, FL, USA, pp. 205–253 Makwana JJ, Tiwari MK (2014) Intermittent streamflow forecasting and extreme event modelling using wavelet based artificial neural networks. Water ResourManag 28:4857–4873 Makwana J, Tiwari MK (2017) Hydrological stream flow modelling using soil and water assessment tool (SWAT) and neural networks (NNs) for the Limkheda watershed, Gujarat, india. Model Earth Syst Environ 3(2):635–645 Mateos L, González-Dugo MP, Testi L, Villalobos FJ (2013) Monitoring evapotranspiration of irrigated crops using crop coefficients derived from time series of satellite images. I. Method validation. Agric Water Manag. 125:81–91

21 Estimation of Crop Coefficients Using Landsat-8 Remote Sensing …

477

Mehta R, Pandey V (2016) Crop water requirement (ETc) of different crops of middle Gujarat. J Agrometeorol 18(1):83–87 Mekanik F, Imteaz M, Gato-Trinidad S, Elmahdi A (2013) Multiple regression and artificial neural network for long-term rainfall forecasting using large scale climate modes. J Hydrol 503:11–21 Monteith JL (1965) Evaporation and environment. 19th Symposia of the Society for Experimental Biology; University Press: Cambridge. CA, USA, pp 205–234 Muller B, Reinhardt J (1991) Neural Networks - An Introduction. Springer-Verlag, Berlin Paço TA, Pôças I, Cunha M, Silvestre JC, Santos FL, Paredes P, Pereira LS (2014) Evapotranspiration and crop coefficients for a super intensive olive orchard. An application of SIMDualKc and METRIC models using ground and satellite observations. J Hydrol 519B:2067–2080 Pakparvar M, Cornelis W, Pereira LS, Gabriels D, Hosseinimarandi H, Edraki M, Kowsar SA (2014) Remote sensing estimation of actual evapotranspiration and crop coefficients for a multiple land use arid landscape of southern Iran with limited available data. J. of Hydroinf 16:1441–1460 Park J, Baik J, Choic M (2017) Satellite-based crop coefficient and evapotranspiration using surface soil moisture and vegetation indices in Northeast Asia. CATENA 156:305–314 Pereira LS, Allen RG, Smith M, Raes D (2015) Crop evapotranspiration estimation with FAO56: Past and future. Agric Water Manag 147:4–20 Pimpale AR, Rajankar PB, Wadatkar SB, RamtekeIK (2014) Determination of spatial crop coefficient of chickpea using remote sensing and GIS. In: American International Journal of Research in Formal, Appl Nat Sci, pp 59–64 Pôças I, Cunha M, Pereira LS, Allen RG (2013) Using remote sensing energy balance and evapotranspiration to characterize montane landscape vegetation with focus on grass and pasture lands. Int J Appl Earth Obs Geoinf 21:159–172 Rozenstein O, Haymann N, Kaplan G, Tanny J (2018) Estimating cotton water consumption using a time series of Sentinel-2 imagery. Agric Water Manag 207:44–52 Rumelhart DE, McClelland JL (1986) Parallel Distributed Processing: explorations in the microstructure of cognition. MIT Press, Cambridge, MA Shuttleworth WJ, Wallace JS (2009) Calculating the water requirements of irrigated crops in Australia using the Matt-Shuttleworth approach. Trans. of ASABE 52:1895–1906 Tiwari MK, Adamowski J, Adamowski K (2016) Water demand forecasting using extreme learning machines. J Water Land Develop 28(1):37–52 Toureiro C, Serralheiro R, Shahidian S, Sousa A (2016) Irrigation management with remote sensing: Evaluating irrigation requirement for maize under Mediterranean climate condition. Agric Water Manag. https://doi.org/10.1016/j.agwat.2016.02.010.

Chapter 22

Spatio-Temporal Assessment of Forest Health Dynamics of Sikkim Using MODIS Satellite Data by AHP Method and Geospatial Techniques Rima Das and Biraj Kanti Mondal

Abstract Forest is the most imperative piece of the environment to maintain ecological balance in any region and it also support the economy of the neighbouring populace. India is a country where forest resource is very much limited; still, some of its states holding the baton to improve the situation and the state Sikkim is one of it. Sikkim is the north-eastern state of India situated in the Himalayan region, which has accounted the forest cover of about 82.31% of its geographical vicinity. In the current effort, the spatio-temporal dynamics of forest health of Sikkim were analyzed using the Moderate Resolution Imaging Spectroradiometer (MODIS) [16-day products (MOD13Q1) of 250 m spatial resolution] satellite data. The Normalized difference vegetation index (NDVI), Enhanced vegetation index (EVI), Leaf area index (LAI), Optimized soil adjusted vegetation index (OSAVI), Infrared percentage vegetation index (IPVI) and Renormalized difference vegetation index (RDVI) have been employed to generate diverse vegetation indices to estimate the temporal variations of forest health from 2000 to 2020. Apart from the appliance of geospatial techniques, the Analytical Hierarchy Process (AHP) method is employed on a GIS platform to recognize the dynamics of forest health pattern to cover the research gap of the earlier studies. It reveals form the outcome of the study that, the forest health of the South and West districts of Sikkim is most affected during the study period. It also found that the very high category of vegetation cover gradually decreased; while the low vegetation cover are comparatively increased. The present investigation provides forest health database, which will definitely help in forest planning and management and the outcomes obtained from this study will aid forest recovery and mitigate the negative effects of forest health deterioration on biodiversity and ecosystem services in the region.

R. Das Faculty, Department of Geography, Bhangar Mahavidyalaya, South 24 Parganas, Kolkata, West Bengal, India B. K. Mondal (B) Assistant Professor of Geography, Netaji Subhas Open University, Kolkata, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_22

479

480

R. Das and B. K. Mondal

Keywords Forest health · MODIS · AHP · Vegetation indices · Spatio-temporal · Sikkim

22.1 Introduction A large tract on earth surface covered with tress is called forest. The FAO (Food and Agriculture Organization of the United Nations 2020) of United Nation’s defines forest as, “Land spanning more than 0.5 ha with trees higher than 5 m and a canopy cover of more than 10%, or trees able to reach these thresholds in situ. It does not include land that is predominantly under agricultural or urban use.” According to this definition, 31% area of earth surface covered by forest cover (FAO 2020). The forest cover maintaining ecological balance, sustain livelihood, hydrological and carbon cycle, reduce contemporary environmental problems like drought, climate change (De Keersmaecker et al. 2014; Measho et al. 2019; Reddy et al. 2020). Hence, spatio-temporal study of forest helps to assess the health of vegetation and suggest better management techniques for protecting vegetation. In the field of forestry and natural resources, the terminology “Forest Health” has been increasingly used (Kolb et al. 1995; Orr 1963). Forest health depicts the production of forest setting that directly meet human needs, as well as the resilience, recurrence, perseverance, and biophysical processes that lead to long-term ecological stability. Forest fire, increasing soil salinity, extreme weather events, plant diseases, overexploitation of forest resources, uncontrolled grazing activity, insects and organism are some factors which are affecting the forest health. Forests ecosystem, like other ecosystems, are prone to a number of stressors that can cause tree mortality or impair their ability to supply a complete range of goods and services, but healthy forests are vital for sustainable forest management. Remote sensing is an important and significant source for analysing vegetation health in large area. Therefore, for this study we use MODIS (Moderate Resolution Imaging Spectroradiometer) data to examine the forest health for last two decades. Advantage of using MODIS data is that, it gives us wide area coverage with decent temporal resolution for monitoring vegetation health (Raghavendra and Mohammed Aslam 2017). An attempt has been made to examine the forest health by using different spectral vegetation indices. The six indices employed in this study were Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), Infrared Percentage Vegetation Index (IPVI), Renormalised Difference Vegetation Index (RDVI), Optimized Soil Adjusted Vegetation Index (OSAVI). Analytical Hierarchy Process (AHP) used for integrating these six indices and helps to prepare forest health map for this study area for the year 2000 and 2020. Saaty (1987) introduced AHP method and now this method extensively applied as an efficient apparatus of multi criteria decision making analysis. The AHP method vigorously used for study the vegetation dynamics and health in recent decades (Dutta et al. 2021).

22 Spatio-Temporal Assessment of Forest Health Dynamics of Sikkim …

481

In the forested area ranking of the world, India stands at number ten, but in respect to percentage of geographical area under forest the rank is 120th. The Forest Survey of India (FSI) estimated a total of 807,276 km2 of forest and tree cover in 2019, which makes up 24.56% of the land area. Forest is the major and significant natural resource of the state Sikkim. State Forest Department of Sikkim holds administrative control of 82.31% of total geographical area in their favour (ENVIS 2019). Several studies shows that, in recent decades the vegetation health of this state defoliating (Banerjee et al. 2019; Basu et al. 2021; Kanade and John 2018; Mandal and Sengupta 2015; Mishra et al. 2020; Sharma et al. 2016). Consequently, in this ground we make an attempt to examine the forest health of Sikkim between 2000 and 2020. The aim of this work is to analyse vegetation dynamics over the last 20 years (2000 to 2020) for the entire state. The specific objectives of this study are: (a) to examine spatio-temporal trends of vegetation health in the last 20 years based on MODIS data; (b) to assess the theme based spatio-temporal dynamics of forest health status using six diverse vegetation indices; (c) to assess the forest health by applying AHP method.

22.2 Study Area The present study carried out at Sikkim which is one of the smallest states in India according to geographical area (Fig. 22.1). It extends between 27° 04' 46'' N to 28° 07' 48'' N and 88° 00' 58'' E and 88° 55' 25'' E, covering an area of 7096 km2 . The study area surrounded by Tibet to the north and north-east, Bhutan to the south-east, West Bengal to the south and Nepal to the West. Politically the states consists of four districts namely, East, West, South and North districts. East district is further sub-divided into four subdivisions, they are Pakyong, Rongli, Rangpo and

Fig. 22.1 Location and satellite image of the study area

482

R. Das and B. K. Mondal

Gangtok; while Soreng, Yuksom, Gyalshing and Dentam are the subdivisions of the West district; Chungthang, Dzongu, Kabi and Mangan are the subdivisions of the North district and Ravongla, Jorethang, Namchi and Yangyang are the subdivisions of the South district. Agriculture is the main occupation of the residents of this study area and the main crops are maize, millet, wheat, barely, orange, tea and cardamom. The elevation of this Himalayan mountainous state, ranging from 280 m in the south to 8586 m in the north. Due to this wide altitudinal variation, the state enjoys tropical, temperate and alpine climatic condition. Sub-tropical climatic condition found in the southern part of the study area and northern parts enjoy alpine condition. Temperate climatic condition observed in all the inhabited regions. Along with monsoon season, the state having four seasons of summer, autumn, winter and spring. The maximum and minimum temperature in summer season ranges from 28 °C to 13 °C and in winter maximum temperature is 13 °C and the minimum temperature is −5 °C and annual rainfall is 325 cm. River Teesta and its tributary Rangeet are the two major river of the study area. The hilly region mainly composed of gneiss and schist which produced poor and shallow brown clay soils by the weathering process. The high concentration of iron oxide and lack of organic and mineral nutrients, make this soil suitable for growing evergreen and deciduous forests. Forestry is the dominant land use of the study area and it occupied 47.61% of the total geographical area of the state (ENVIS, 2019) (Fig. 22.2). On the basis of Champion and Seth (1986) classification of Indian forest, the forest cover of the study area broadly classified into six categories (Table 22.1), i.e. 1. Tropical Semi-evergreen Forests; 2. Sub-tropical Broad-leaved Hill Forests; 3. Himalayan Wet Temperate Forests; 4. Sub-alpine Forests 5. Moist Alpine Forests and 6. Dry Alpine Forests.

22.3 Materials and Methods 22.3.1 Data Sources and Pre-processing MODIS 16-day composites (MOD13Q1, collection v005) (Table 22.2) at 250 m spatial resolution for 2000 and 2020 were used to detect the forest health dynamics for the study area. These products were downloaded from USGS (https://earthexpl orer.usgs.gov/) in HDF format in a sinusoidal projection and were reprojected to the UTM projection (WGS84, zone 45N) and converted to tiff format by using Arc GIS 10.3 and HEG (HDF-EOS To GeoTIFF Conversion) tool. MODIS data are gridded level-3 product and consist of 12 datasets which are NDVI, EVI, VI quality (vegetation index quality indicator), red reflectance, NIR (near-infrared) reflectance, blue reflectance, MIR (mid-infrared) reflectance, view zenith angle, sun zenith angle, relative azimuth angle, composite day of the year and pixel reliability. The product inherently consists two primary vegetation index layers, namely NDVI and EVI. The other indices prepared using different red, NIR, MIR and blue bands.

22 Spatio-Temporal Assessment of Forest Health Dynamics of Sikkim …

483

Fig. 22.2 Methodological flow chart

22.3.2 Analysis of Forest Health Dynamics Terra MODIS data were used to study the forest health dynamics of the study area. After preparation of thematic layers integrated RS-GIS and AHP techniques have been used to analyse forest health status. Analytical Hierarchy Process (AHP) is one of the widely used multi-parametric evaluation (Malczewski and Rinner 2015; Wind and Saaty 1980). It has been used to assign Variable factor weights and individual factor weights for each thematic layer through the pairwise comparison matrix. Consistency Index and Consistency Ratio have been calculated according to the procedure recommended by Saaty (1980). After that, the Weighted Overlay Analysis model has been performed to examine the forest health status of the study area. Forest health status calculated applying the following equation: Vegetation Health =[(NDV Iw ∗ NDV Iwi ) + (EV Iw ∗ EV Iwi ) + (LAIw ∗ LAIwi ) + (IPV Iw ∗ IPV Iwi ) + (RDV Iw ∗ RDV Iwi ) + (OSAV Iw ∗ OSAV Iwi )]

(22.1)

484

R. Das and B. K. Mondal

Table 22.1 Forest types Forest types

Altitude (mts)

Name of tree species

1. Tropical semi-evergreen forests

300–900

Dillenia pentagyna, Mostly found in the foothills, Dysoxylum floribundum, persuaded by physiographic, edaphic and biotic features Gymnema arborea, Lagerostroemia patviflora, Shorea robusta, Toona ciliata

2. Sub-tropical broad-leaved hill forests

900–1800

Macaranga, Schima, Eugenia, Sapium, Castanopsis, Baliospermum, Clerodendrum, Emblica

Tropical to sub-tropical forest mixed with shrubby trees

3. Himalayan wet temperate forests

1800–2700

Suaga (Hemlock), Acer, Rosa, Michelia, Juglans, Rubus, Rhododendron, Berberis and Viburnum, Quercus (Oak), Acer, Populus, Larix, Abies densa

According to altitudinal change the nature of tress gradually changing from sub-tropical to sub-temperate and temperate forest and coniferous species with needle shaped leaves

4. Sub-alpine forests 2700–3700

Gaultheria, Euonymus, Rubus, Vibrunum, Juniperous, Rhododendron

Due elevation rises the temperate types gradually transform into the sub-alpine types

5. Moist alpine forests

Rhododendron, Alpine meadows, the growth of Juniperous, Salix, tress completely stopped Berberis, Rosa, Lonicera

3700–4000

6. Dry alpine forests Above 4000 Berberis, Juniperous, Salix

Characteristics

Scattered thorny scrub

where NDVI = Normalized Difference Vegetation Index, EVI = Enhanced Vegetation Index, LAI = Leaf Area Index, IPVI = Infrared Percentage Vegetation Index, RDVI = Renormalised Difference Vegetation Index, OSAVI = Optimized Soil Adjusted Vegetation Index. Moreover, the subscripts w and wi respectively refer to the normalized weight of a theme and normalized weight of individual features of a theme.

22.3.3 Normalized Difference Vegetation Index (NDVI) NDVI is a dimensionless vegetation indices which is widely used for monitoring vegetation dynamics at different regional level (Anderson et al. 1993; Choubin et al. 2019; Vrieling et al. 2013; Zhu et al. 2013). NDVI represent the band ratio among the Red and near-infrared wavelength of the electromagnetic spectrum and this index

22 Spatio-Temporal Assessment of Forest Health Dynamics of Sikkim …

485

Table 22.2 Characteristics of MOD13Q1 datasets Parameters

Characteristics

Collection

Terra MODIS

Temporal coverage

February 18, 2020—until date

Tile area

~10 × 10 lat/long

Projection

Sinusoidal

Data format

HDF-EOS

Dimension (rows/columns)

4800 × 4800

Resolution

250 m

Scale factor

NDVI, EVI, B, R, NIR, MIR: 0.0001 View Zenith and Sun Zenith: 0.01 Azimuth angle: 0.1

Temporal interval

16 days

Spectral calibration

12 bits

Source

earthexplorer.usgs.gov

introduced by Tucker (Choubin et al. 2019; Dutta et al. 2021; Tucker 1979). The range of this index lies between −1 to + 1, where value less than zero indicates no vegetation such as bare surface, snow cover, water body, glacier, moist surface and value greater than zero indicate completely flourished vegetation cover. The index is calculated using the following algorithm: NDV I = (NIR − Red )/(NIR + Red )

(22.2)

22.3.4 Enhanced Vegetation Index (EVI) EVI was invented by Liu and Huete to enhanced the NDVI by minimizing canopy background variations and atmospheric correction (Huang et al. 2010; Huete et al. 1995; Miura et al. 2001). Similarly, to NDVI, the negative value indicates bare vegetation less surface and positive value indicate greenish vegetation cover. EV I = G ∗ ((NIR − RED) / (NIR + C1 ∗ RED − C2 ∗ BLUE + L)) (22.3) where, NIR is near-infrared band (Band 2 for MODIS data), RED is red band (Band 1 for MODIS data), BLUE is blue band (Band 3 for MODIS data), L is the canopy background amendment factor, G is gain factor; while C1 and C2 indicate the coefficients of the aerosol resistance term. In the MODIS product, L,G,C1 and C2 coefficient values are respectively 1, 2.5, 6 and 7.5. (Ghosh et al. 2016; Raghavendra and Mohammed Aslam 2017).

486

R. Das and B. K. Mondal

22.3.5 Leaf Area Index (LAI) Amount of one-sided green leaf area per unit of ground surface is significant structural biophysical variable of forest ecology commonly known as leaf area index (Myneni et al. 2002; Spanner et al. 1990; Xiao et al. 2016). It is the imperative properties of forest structure because it not only governs foliar gas exchange properties of an ecology, but also powerfully controlling the reflectance nature of vegetation (Chen et al. 1997, 2005; Spanner et al. 1990; Tian et al. 2000). MODISMCD15A3H LAI datasets are widely used leaf area index datasets with high accuracy (Chen et al. 2005; Islam and Bala 2008; Myneni et al. 2002; Ovakoglou et al. 2020).

22.3.6 Infrared Percentage Vegetation Index (IPVI) According to Crippen (1990) the red brightness subtraction in the numerator of NDVI was irrelevant. So, he computes the IPVI index (Crippen 1990). This index functionally equivalent to NDVI and rapidly process large amount of data. In compare to NDVI, this index performs firstly (Mokarram et al. 2015). Like NDVI, the value ranges from 0 to 1. Value near to 0 indicates poor health of vegetation and vice versa. IPV I = NIR / (NIR + RED)

(22.4)

22.3.7 Renormalised Difference Vegetation Index (RDVI) This index is developed to procure more accurate result regarding the forest health by minimize the background reflectance effects (Roujean and Breon 1995). It is insensitive to the effects of soil and sun screening geometry. It helps to linearize the relationship between different biophysical parameters (Roujean and Breon 1995; Vescovo et al. 2012). √ RDV I = (NIR − RED)/ (NIR + RED) (22.5)

22.3.8 Optimized Soil Adjusted Vegetation Index (OSAVI) This index is modified version of Soil Adjusted Vegetation Index (SAVI) (Rondeaux et al. 1996). With the help of reflectance difference between NIR and Red band, Rondeaux et al. (1996) developed this index (Rondeaux et al. 1996; Steven 1998).

22 Spatio-Temporal Assessment of Forest Health Dynamics of Sikkim …

487

In the area of high soil variation, this index can accommodate high variability (Fern et al. 2018). It has the following formulation – OSAV I = (NIR − RED) / (NIR + RED + 0.16)

(22.6)

Here, the value 0.16 is considered as standard value for the canopy background adjustment factor (Mokarram et al. 2015; Steven 1998). This index is best suited the region in which the soil can been seen through the canopy cover. The methodology of the present chapter is represented in a flow chart (Fig. 22.2).

22.4 Result and Discussions 22.4.1 NDVI Analysis For the year 2000 and 2020 the NDVI value ranges from −0.2 to 0.99 but it varies spatially and categorically (Figs. 22.3 and 22.4). NDVI values for each year has been reclassified into five class very low (>0.1), low (0.1–0.3), moderate (0.3–0.4), high (0.4–0.5) and very high (>0.5). Very low zones found mainly in the upper part of the study area, i.e., in the North Sikkim district and the very high zones found in the rest of the three districts of the study area. But, in some part of the district such as East, South and West Sikkim the very high NDVI zones transfer into the low and very low zones. During this 20-year time span, the covering area of this five NDVI classes summarized in the following Table 22.3).

22.4.2 EVI Analysis For the year 2000 the EVI value ranges from −0.2 to 0.9662 and for the year 2020 the range is −0.199 to 0.9951 (Figs. 22.5 and 22.6). EVI values for each year have been reclassified into five class very low, low, moderate, high and very high. The categorisation values for the year 2000 is −0.2 to −0.0079 of very low, −0.0079 to 0.12 of low, 0.12 to 0.23 of moderate, 0.23 to 0.35 of high and 0.35 to 0.97 of very high categories. For the year 2020 the values are slightly differing from 2000 and they are −0.2 to 0.013 of very low, 0.013 to 0.14 of low, 0.14 to 0.27 of moderate, 0.27 to 0.41 of high and 0.42 to 1.00 of very high categories. Very low value found mostly in the snow cover region of the study area, where the very high value found in the very dense forest region of the East, West and South Sikkim districts. But, the result shows that, the overall health of the dense forest degraded in some part. In this regard, the covering area of this five EVI class for the year 2000 and 2020 are summarized in the following table (Table 22.4).

488

R. Das and B. K. Mondal

Fig. 22.3 NDVI map, 2000

22.4.3 LAI Analysis For the year 2000 the LAI value ranges from −0.842 to 3.378 and for the year 2020 the range is −0.838 to 3.482 (Figs. 22.7 and 22.8). LAI values for each year have been reclassified into five class very low, low, moderate, high and very high. The categorisation values for the year 2000 is −0.84 to −0.14 of very low, −0.14 to 0.28 of low, 0.28 to 0.69 of moderate, 0.69 to 1.20 of high and 1.20 to 3.40 of very high categories. For the year 2020 the values are slightly differed from 2000 and they are −0.84 to −0.075 of very low, −0.075 to 0.36 of low, 0.36 to 0.83 of moderate, 0.83 to 1.40 of high and 1.40 to 3.50 of very high categories. Similarly, the previous two indices, very low LAI value found mostly in the snow cover region and water bodies of the northern part study area, where the very high value found in the very dense and moderately dense forest cover of the East, West

22 Spatio-Temporal Assessment of Forest Health Dynamics of Sikkim …

489

Fig. 22.4 NDVI map, 2020 Table 22.3 Area under different NDVI classes

Name of the categories

Area (in %) 2000

2020

Very low

36.22

36.66

Low

14.79

17.66

Moderate

3.70

4.49

High

3.08

4.04

42.21

37.14

Very high

490

R. Das and B. K. Mondal

Fig. 22.5 EVI map, 2000

and South Sikkim districts. But, the result shows that, the overall health of the forest region deteriorates. In this regard, the covering area of these five LAI classes for the year 2000 and 2020 are summarized in the following table (Table 22.5).

22.4.4 IPVI Analysis In the study area IPVI value for both years ranged from 0 to 1 but shows varying spatial difference (Figs. 22.9 and 22.10). IPVI values for each year have been reclassified into five class namely very low, low, moderate, high and very high. The categorisation values for the year 2000 is 0.081 to 0.5010 of very low, 0.5010 to 0.6036 of low, 0.6036 to 0.7297 of moderate,

22 Spatio-Temporal Assessment of Forest Health Dynamics of Sikkim …

491

Fig. 22.6 EVI map, 2020 Table 22.4 Area under different EVI classes

Name of the categories

Area (in %) 2000

2020

very low

18.85

22.62

Low

31.13

32.66

Moderate

20.53

20.30

High

17.92

15.51

Very High

11.47

8.92

492

R. Das and B. K. Mondal

Fig. 22.7 LAI map, 2000

0.7297 to 0.8378 of high and 0.8378 to 0.9964 of very high categories. For the year 2020 the values are slightly differed from 2000 and they are 0.0426 to 0.5156 of very low, 0.5156 to 0.6020 of low, 0.6020 to 0.7184 of moderate, 0.7184 to 0.8310 of high and 0.8310 to 1.00 of very high categories. IPVI result shows similar observation like the previous indices. Very low IPVI value found mostly in the snow cover region and water bodies of the North Sikkim district, where the very high value found in the very dense and moderately dense forest cover of the East, West and South Sikkim districts. But it has been identified from the images, the overall health of the forest region deteriorates. In this regard, the covering area of these five IPVI classes for the year 2000 and 2020 are summarized in the following table (Table 22.6).

22 Spatio-Temporal Assessment of Forest Health Dynamics of Sikkim …

493

Fig. 22.8 LAI map, 2020 Table 22.5 Area under different LAI classes

Name of the categories

Area (in %) 2000

2020

Very low

18.77

22.62

Low

29.75

32.66

Moderate

20.70

20.30

High

18.69

15.51

Very high

12.09

8.92

494

R. Das and B. K. Mondal

Fig. 22.9 IPVI map, 2000

22.4.5 RDVI Analysis In this region range of RDVI value varies for the study period. In the year 2000, the RDVI value ranges from −0.294 to 0.546 and for the year 2020 the range is −0.256 to 0.821 (Figs. 22.11 and 22.12). RDVI values for each year have been reclassified into five class very low, low, moderate, high and very high. The categorisation values for the year 2000 is −0.294 to 0.017 of very low, 0.017 to 0.091 of low, 0.091 to 0.207 of moderate, 0.207 to 0.322 of high and 0.322 to 0.546 of very high categories. For the year 2020 the values are slightly differed from 2000 and they are −0.256 to 0.014 of very low, 0.014to 0.124 of low, 0.124 to 0.247 of moderate, 0.247 to 0.369 of high and 0.369 to 0.821 of very high categories. Very low RDVI value found mostly in the snow cover region and water bodies of the northern part study area, where the very high value found

22 Spatio-Temporal Assessment of Forest Health Dynamics of Sikkim …

495

Fig. 22.10 IPVI map, 2020 Table 22.6 Area under different IPVI classes

Name of the categories

Area (in %) 2000

2020

Very low

24.35

25.33

Low

22.28

23.64

Moderate

10.09

11.55

High

12.07

11.79

Very high

31.21

27.70

496

R. Das and B. K. Mondal

Fig. 22.11 RDVI map, 2000

in the very dense and moderately dense forest cover of the East, West and South Sikkim districts. But, the result shows that, the overall health of the forest region deteriorates. In this regard, the covering area of these five RDVI classes for the year 2000 and 2020 are summarized in the following table (Table 22.7).

22.4.6 OSAVI Analysis OSAVI index value for this study ranged from 0 to 1 for both years, but shows different spatial distribution (Figs. 22.13 and 22.14). OSAVI values for each year have been reclassified into five class namely very low, low, moderate, high and very high. The categorisation values for the year 2000 is −

22 Spatio-Temporal Assessment of Forest Health Dynamics of Sikkim …

497

Fig. 22.12 RDVI map, 2020 Table 22.7 Area under different RDVI classes

Name of the categories

Area (in %) 2000

2020

Very low

16.56

23.23

Low

27.66

27.98

Moderate

16.75

17.51

High

21.47

19.13

Very high

17.56

12.15

498

R. Das and B. K. Mondal

Fig. 22.13 OSAVI map, 2000

0.83 to 0.02 of very low, 0.02 to 0.21 of low, 0.21 to 0.46 of moderate, 0.46 to 0.68 of high and 0.68 to 0.99 of very high categories. For the year 2020 the values are slightly differed from 2000 and they are −0.91 to 0.03 of very low, 0.03 to 0.21 of low, 0.21 to 0.44 of moderate, 0.44 to 0.66 of high and 0.66 to 1.00 of very high categories. OSAVI result shows similar observation like the previous indices. Very low OSAVI value found mostly in the snow cover region and water bodies of the North Sikkim district, where the very high value found in the very dense and moderately dense forest cover of the East, West and South Sikkim districts. But it has been identified from the images, the overall health of the forest region deteriorates. In this regard, the covering area of these five OSAVI classes for the year 2000 and 2020 are summarized in the following table (Table 22.8).

22 Spatio-Temporal Assessment of Forest Health Dynamics of Sikkim …

499

Fig. 22.14 OSAVI map, 2020 Table 22.8 Area under different OSAVI classes

Name of the categories

Area (in %) 2000

2020

Very low

24.53

25.42

Low

22.14

23.50

Moderate

10.04

11.48

High

12.93

11.82

Very high

30.36

27.77

500

R. Das and B. K. Mondal

22.4.7 Analytic Hierarchy Process (AHP) Analysis Multicriteria decision making technique here deployed to examine the forest health of Sikkim between the time span of 2000 and 2020. Various literature showing that, the single index is not enough to express the forest health status accurately, therefore for this study six indices have been selected. NDVI, EVI, LAI, IPVI, RDVI and OSAVI indices were selected to perform the overlay analysis using weighted derived in AHP method. Since, NDVI, EVI, LAI, IPVI, RDVI and OSAVI are positive parameter to access the forest health, therefore these parameters were provided rank positively with descending order (very high-5, high-4, moderate-3, low-2 and very low-1). Thereafter, different layers assigned with different weighted by analytical hierarchy process (AHP). Weighted normalization and comparing the indices done by using pair wise comparison matrix (Saaty, 1987) (Table 22.9). This analysis of all six indices resulted into forest health map for the year 2000 and 2020 and the value ranges from 1 to 5 (Figs. 22.15 and 22.16). Higher value of weighted sum index indicates healthy forest cover region like the higher value of NDVI, EVI, LAI, IPVI and OSAVI indices and lower value indicate low photosynthetic activity. Spatio-temporal variation of forest health observed from the resultant map. In the year 2000, 1325.41 km2 area occupied the very low vegetation health, but in 2020 it raised by 1.30% and this category occupied 1668.92 km2 geographical area. This category found mainly across North Sikkim district around the snow cover region, water bodies and moist surfaces. Area of low vegetative region slightly decreased by 0.008% during the study time. This zone found near water bodies, foothills and rocky land surface. Moderate category vegetation found over agricultural and grass land area and in 2000 this category holds 716.15 km2 area. In 2020 the area became 769.23 km2 by increasing 0.37% in respect to total area. High category holds 1789.99 km2 area in the year 2000 and 1756.39 km2 in 2020. In the 2000, 1182.77 km2 belongs to very high category and 850.69 km2 in 2020 (Table 22.10). Therefore, the high and very high vegetation category reduced by 0.09% and 1.40% respectively during this 20 years period. From this observation we can say, slowly Table 22.9 Pair wise comparison matrix Indices

NDVI

EVI

LAI

IPVI

RDVI

OSAVI

NDVI

1

2

1

3

3

6

EVI

0.5

1

1

2

2

4

LAI

1

1

1

1

1

5

IPVI

0.33

0.5

1

1

1

5

RDVI

0.33

0.5

1

1

1

5

OSAVI

0.17

0.25

0.2

0.2

0.2

1

(Where, Number of comparisons = 15; Consistency Ratio CR = 3.40%; Principal Eigen value = 6.212; Eigenvector solution: 5; Iterations, delta = 3.4E − 8)

22 Spatio-Temporal Assessment of Forest Health Dynamics of Sikkim …

501

Fig. 22.15 Vegetation health status map, 2000

but gradually the forest health of Sikkim changing and this change brought by mainly East and South Sikkim. Wide variation of forest cover has been seen in this region from tropical Dry Deciduous Forests with Sal to the Alpine scrub and grassland in high altitude reported by Forest Survey Report from FSI (2019). Unfortunately, health of this vast natural resource degraded over the past 20 years. The present study reveals that, high and very high categories of forest health defoliate gradually particularly in the East and South Sikkim. Rainfall is the prime factors for growth of forest and vegetation cover. Unlike western Himalaya, the eastern part of Himalaya also vulnerable to natural disaster and that changes the climatic condition of this region (Bawa and Ingty, 2012). In the work Basu et al., (2021), they found that such climate change brought erratic rainfall and raising temperature events which directly effects the vegetation health and agricultural status. In their study they stated that, especially in South Sikkim

502

R. Das and B. K. Mondal

Fig. 22.16 Vegetation health status map, 2020 Table 22.10 Area under different forest health classes Forest health categories

Area in 2000 Hectares

% km2

Area in 2020 Hectares

% km2

Very low

132,541.00

1325.41

19.01

166,891.75

1668.92

23.89

Low

195,779.44

1957.79

28.08

194,180.24

1941.80

27.79

71,615.50

716.15

10.27

76,922.94

769.23

11.01

High

178,998.50

1789.99

25.67

175,639.09

1756.39

25.14

Very high

118,276.93

1182.77

16.96

85,069.23

850.69

12.18

Moderate

22 Spatio-Temporal Assessment of Forest Health Dynamics of Sikkim …

503

region, the farmers who rely on rainfall, agricultural land being left fallow. Due to this hotter and drier climatic condition, forest fires and drought will be more common form of natural disaster by which the vegetation health will be ruined (Basu et al., 2021). In another study done by Mishra et al., (2020) noted that, along the periphery of capital city of the state i.e. Gangtok, the landuse pattern changes drastically in the last two decades. Being an important tourist attraction spot, the city Gangtok expand as urban sprawl revolves in the periphery region (Mishra et al., 2020). The outcomes of the present study was observed in several spots by the authors in varied times and verified with the known personel of the study area, experts’ from the study area and known fields; therefore it canbe stated that the results of the AHP analysis of forest health analysis can be incorporate in the planning and management of the unique resource. Moreover, as the study area is one of the famous tourist destination spots in the country, huge number of tourists arrived every year in different part of Sikkim. Therefore, tourism is one of the prime sources of economy in this state and to sustain the pressure of this economic activity, number of hotels gradually increased. Due to expansion of tourism activity, deforestation and biodiversity loss happening, which negatively correlated to the health of vegetation (Mandal and Sengupta, 2015). So, we can state that, due to changing climatic condition and flourishment of built-up area, the spatio-temporal changing behaviour of vegetation health observed in Sikkim during the study period.

22.5 Conclusion Forest health analysis is a very useful tool to study and describe the changes observed in each vegetation health category. Forest has irreversible value to the society and to support the economy of rural and tribal people. The deterioration of forest health has become an alarming issue which to be addressed at earliest. In this regard, we examine the forest health of Sikkim using six indices, namely NDVI, EVI, LAI, IPVI, RDVI and OSAVI. By using different vegetation, we obtained an effective result and each index is positively correlated with each other. The present analysis reveals that the very high category of vegetation cover gradually decreased; whereas, the very low and low vegetation cover are increased progressively. Moreover, the man induced phenomena, like building and construction work; changes of climatic condition, like erratic rainfall and temperature rise; and continuous increasing immense pressure of tourism activity had altered the vegetation health of the study area. Hence, this condition should be arrested as soon as possible. This thought possibly happen to be effective when the populace became mindful of the natural significance of forest land, which were impractically manhandled to fulfil their requests. The advancement of remote sensing and GIS technology is successfully completed in this current effort. Furthermore, it is imperative to note that with the help of this advanced technology we can monitor the vegetation health spatially over the time; and as well as we can take action immediately when needed.

504

R. Das and B. K. Mondal

Acknowledgements The authors would like to thank to Netaji Subhas Open University for providing the supportive research funding (No. AC/140/2021-22).The authors would like to thank the USGS for the supported RS data used in the study. This effort is a tribute to the researchers, and academicians working on Sikkim from varied disciplines and dimensions to save the forest resources of this greenest state of India.

References [FAO] Food and Agriculture Organization of the United Nations (2020) Food and agriculture organization of the united nations: global forest resources assessment 2020: Terms and Definition FRA. In: Global Forest Resources Assessment -Terms and Definitions, 32. http://www.fao.org/ forestry/58864/en/ Anderson GL, Hanson JD, Haas RH (1993) Evaluating landsat thematic mapper derived vegetation indices for estimating above-ground biomass on semiarid rangelands. Remote Sens Environ 45(2):165–175. https://doi.org/10.1016/0034-4257(93)90040-5 Banerjee A, Chakraborty P, Bandopadhyay R (2019) Urgent conservation needs in the Sikkim Himalaya biodiversity hotspot. Biodiversity 20(2–3):88–97. https://doi.org/10.1080/14888386. 2019.1656547 Basu R, Misra G, Sarkar D (2021) A remote sensing based analysis of climate change in Sikkim supported by evidence from the field. J Mt Sci 18(5):1256–1267. https://doi.org/10.1007/s11 629-020-6534-0 Bawa KS, Ingty T (2012) Climate change in Sikkim: a synthesis. In: Climate change in Sikkim patterns, impacts and initiatives, pp 413–424 Chen JM, Rich PM, Gower ST, Norman JM, Plummer S (1997) Leaf area index of boreal forests: Theory, techniques, and measurements. J Geophys Res Atmos 102(24):29429–29443. https:// doi.org/10.1029/97jd01107 Chen X, Vierling L, Deering D, Conley A (2005) Monitoring boreal forest leaf area index across a Siberian burn chronosequence: a MODIS validation study. Int J Remote Sens 26(24):5433–5451. https://doi.org/10.1080/01431160500285142 Choubin B, Soleimani F, Pirnia A, Sajedi-Hosseini F, Alilou H, Rahmati O, Melesse AM, Singh VP, Shahabi H (2019) Effects of drought on vegetative cover changes: Investigating spatiotemporal patterns. In: Extreme Hydrology and Climate Variability: Monitoring, Modelling, Adaptation and Mitigation (Vol. 2). Elsevier Inc. https://doi.org/10.1016/B978-0-12-815998-9.00017-8 Crippen RE (1990) Calculating the vegetation index faster. Remote Sens Environ 34(1):71–73. https://doi.org/10.1016/0034-4257(90)90085-Z De Keersmaecker W, Lhermitte S, Honnay O, Farifteh J, Somers B, Coppin P (2014) How to measure ecosystem stability? An evaluation of the reliability of stability metrics based on remote sensing time series across the major global ecosystems. Glob Change Biol 20(7):2149–2161. https:// doi.org/10.1111/gcb.12495 Dutta S, Rehman S, Sahana M, Sajjad H (2021) Assessing forest health using geographical information system based analytical hierarchy process: Evidences from southern west bengal, india. Environ Sci Eng (issue August). https://doi.org/10.1007/978-3-030-56542-8_3 FAO (2020) State of the worlds forests–Executive summary Fern RR, Foxley EA, Bruno A, Morrison ML (2018) Suitability of NDVI and OSAVI as estimators of green biomass and coverage in a semi-arid rangeland. Ecol Ind 94(June):16–21. https://doi. org/10.1016/j.ecolind.2018.06.029 Ghosh S, Mishra DR, Gitelson AA (2016) Long-term monitoring of biophysical characteristics of tidal wetlands in the northern Gulf of Mexico—A methodological approach using MODIS. Remote Sens Environ 173:39–58. https://doi.org/10.1016/j.rse.2015.11.015

22 Spatio-Temporal Assessment of Forest Health Dynamics of Sikkim …

505

Huang C, Wylie B, Yang L, Homer C (2010) 10.1080@01431160110106113. September 2012, 37–41 Huete AR, Liu HQ, Batchily K, Leeuwen W Van (1995) 1-s2.0-S0034425796001125-main.pdf. 4257(Table 1) Islam AS, Bala SK (2008) Assessment of potato phenological characteristics using MODIS-Derived NDVI and LAI information. Giosci Remote Sensing 45(4):454–470. https://doi.org/10.2747/ 1548-1603.45.4.454 Kanade R, John R (2018) Topographical influence on recent deforestation and degradation in the Sikkim Himalaya in India; Implications for conservation of East Himalayan broadleaf forest. Appl Geogr 92(June 2017):85–93. https://doi.org/10.1016/j.apgeog.2018.02.004 Kolb TE, Wagner MR, Covington WW (1995) Forest health from different perspectives. General Technical Report—US Department of Agriculture, Forest Service, RM-GTR-267, pp 5–13 Malczewski J, Rinner C (2015) Multicriteria decision analysis in geographic information science. In: Analysis methods (Issue Massam 1993). http://www.amazon.com/Multicriteria-DecisionAnalysis-Geographic-Information/dp/3540747567/ref=sr_1_1?ie=UTF8&qid=1430864854& sr=8-1&keywords=Multicriteria+decision+analysis+in+geographic+information+science Mandal J, Sengupta P (2015) The impact of tourism on livelihood and environment in West Sikkim— a cas study of Pelling. Ilee 38(1):1–9 Measho S, Chen B, Trisurat Y, Pellikka P, Guo L, Arunyawat S, Tuankrua V, Ogbazghi W, Yemane T (2019) Spatio-temporal analysis of vegetation dynamics as a response to climate variability and drought patterns in the Semiarid Region, Eritrea. Remote Sensing, 11(6). https://doi.org/10. 3390/RS11060724 Mishra PK, Rai A, Rai SC (2020) Land use and land cover change detection using geospatial techniques in the Sikkim Himalaya, India. Egypt J Remote Sensing Space Sci 23(2):133–143. https://doi.org/10.1016/j.ejrs.2019.02.001 Miura T, Huete AR, Yoshioka H, Holben BN (2001) An error and sensitivity analysis of atmospheric resistant vegetation indices derived from dark target-based atmospheric correction. Remote Sens Environ 78(3):284–298. https://doi.org/10.1016/S0034-4257(01)00223-1 Mokarram M, Hojjati M, Roshan G, Negahban S (2015) Modeling the behavior of Vegetation Indices in the salt dome of Korsia in North-East of Darab, Fars, Iran. Model Earth Syst Environ 1(3):1–9. https://doi.org/10.1007/s40808-015-0029-y Myneni RB, Hoffman S, Knyazikhin Y, Privette JL, Glassy J, Tian Y, Wang Y, Song X, Zhang Y, Smith GR, Lotsch A, Friedl M, Morisette JT, Votava P, Nemani RR, Running SW (2002) Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens Environ 83(1–2):214–231. https://doi.org/10.1016/S0034-4257(02)00074-3 Orr L (1963) Concepts of forest entomology. For Sci 9(3):337–338. https://doi.org/10.1093/forest science/9.3.337 Ovakoglou G, Alexandridis TK, Clevers JGPW, Gitas IZ (2020) Downscaling of MODIS leaf area index using landsat vegetation index. Geocarto International, March. https://doi.org/10.1080/ 10106049.2020.1750062 Raghavendra BR, Mohammed Aslam MA (2017) Sensitivity of vegetation indices of MODIS data for the monitoring of rice crops in Raichur district, Karnataka, India. Egypt J Remote Sens Space Sci 20(2):187–195. https://doi.org/10.1016/j.ejrs.2016.06.005 Reddy GPO, Kumar N, Sahu N, Srivastava R, Singh SK, Naidu LGK, Chary GR, Biradar CM, Gumma MK, Reddy BS, Kumar JN (2020) Assessment of spatio-temporal vegetation dynamics in tropical arid ecosystem of India using MODIS time-series vegetation indices. Arab J Geosci 13(15). https://doi.org/10.1007/s12517-020-05611-4 Rondeaux G, Steven M, Baret F (1996) Optimization of soil-adjusted vegetation indices. Remote Sens Environ 55(2):95–107. https://doi.org/10.1016/0034-4257(95)00186-7 Roujean JL, Breon FM (1995) Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens Environ 51(3):375–384. https://doi.org/10.1016/00344257(94)00114-3

506

R. Das and B. K. Mondal

Saaty RW (1987) The analytic hierarchy process-what it is and how it is used. Math Model 9(3– 5):161–176. https://doi.org/10.1016/0270-0255(87)90473-8 Sharma M, Areendran G, Raj K, Sharma A, Joshi PK (2016) Multitemporal analysis of forest fragmentation in Hindu Kush Himalaya—a case study from Khangchendzonga Biosphere Reserve, Sikkim, India. Environ Monitor Assess 188(10). https://doi.org/10.1007/s10661-016-5577-8 Spanner MA, Piercej LL, Peterson DL, Running SW (1990) Remote sensing of temperate coniferous forest leaf area index the influence of canopy closure, understory vegetation and background reflectance. Int J Remote Sens 11(1):95–111. https://doi.org/10.1080/01431169008955002 Steven MD (1998) The sensitivity of the OSAVI vegetation index to observational parameters. Remote Sens Environ 63(1):49–60. https://doi.org/10.1016/S0034-4257(97)00114-4 Tian Y, Zhang Y, Knyazikhin Y, Myneni RB, Glassy JM, Dedieu G, Running SW (2000) Prototyping of MODIS LAI and FPAR algorithm with LASUR and LANDSAT data. IEEE Trans Geosci Remote Sens 38(5 II):2387–2401. https://doi.org/10.1109/36.868894 Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8(2):127–150. https://doi.org/10.1016/0034-4257(79)90013-0 Vescovo L, Wohlfahrt G, Balzarolo M, Pilloni S, Sottocornola M, Rodeghiero M, Gianelle D (2012) New spectral vegetation indices based on the near-infrared shoulder wavelengths for remote detection of grassland phytomass. Int J Remote Sens 33(7):2178–2195. https://doi.org/10.1080/ 01431161.2011.607195 Vrieling A, De Leeuw J, Said MY (2013) Length of growing period over Africa: Variability and trends from 30 years of NDVI time series. Remote Sensing 5(2):982–1000. https://doi.org/10. 3390/rs5020982 Wind Y, Saaty TL (1980) Marketing applications of the analytic hierarchy process. Manag Sci 26(7):641–658. https://doi.org/10.1287/mnsc.26.7.641 Xiao Z, Liang S, Wang J, Xiang Y, Zhao X, Song J (2016) Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Trans Geosci Remote Sens 54(9):5301–5318. https://doi.org/10.1109/TGRS.2016.2560522 Zhu Z, Bi J, Pan Y, Ganguly S, Anav A, Xu L, Samanta A, Piao S, Nemani RR, Myneni RB (2013) Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3G) for the period 1981 to 2. Remote Sensing 5(2):927–948. https://doi.org/10.3390/rs5020927

Chapter 23

Forest Degradation Susceptibility and Sustainability: Case Study of Arganeraie Biosphere Reserve, Atlantic High Atlas, Morocco Sarrah Ezaidi, Mohamed Ait Haddou , Belkacem Kabbachi , Abdelkrim Ezaidi, Asmae Aichi, Pulakesh Das , and Mohamed Abioui

Abstract The regional environmental conditions primarily regulate the ecosystem structure, wherein their relationship with sub-surface geological settings is less studied. The Moroccan argan forest (Argania Spinosa) was designated in 1998 by the Man and Biosphere (MAB) program of UNESCO as a terrestrial ecosystem of global importance and called Arganeraie Biosphere Reserve (RBA). Since 2014, UNESCO has also inscribed the RBA as an intangible cultural heritage of humanity. This chapter aims to show the influence of the geological substrate on biodiversity by focusing on the argan grove of the Ida OutananeMountains and the geological formations of S. Ezaidi · M. Ait Haddou · B. Kabbachi · A. Ezaidi · A. Aichi · M. Abioui (B) Geosciences, Environment and Geomatics Laboratory (GEG), Department of Earth Sciences, Faculty of Sciences, Ibnou Zohr University, 80000 Agadir, Morocco e-mail: [email protected] S. Ezaidi e-mail: [email protected] M. Ait Haddou e-mail: [email protected] B. Kabbachi e-mail: [email protected] A. Ezaidi e-mail: [email protected] A. Aichi e-mail: [email protected] P. Das World Resources Institute India, New Delhi 110019, India e-mail: [email protected] M. Abioui MARE-Marine and Environmental Sciences Centre – Sedimentary Geology Group, Department of Earth Sciences, Faculty of Sciences and Technology, University of Coimbra, 3030-790 Coimbra, Portugal © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_23

507

508

S. Ezaidi et al.

the Atlantic High Atlas (Morocco). The ground data species diversity with local soil and geological characteristics have been recorded at various altitudinal ranges. The study showed that the lithological diversity of the main geological formations in the study area influences the distribution of the argan tree and its floral procession. The study capitalizes and promotes the multidisciplinary knowledge acquired in botany and geology within the ecosystem of the RBA. Keywords Geodiversity · Biodiversity · Argan tree · Forest degradation susceptibility and sustainability · Morocco

23.1 Introduction The type of forest cover and ecosystem productivity are influenced by the seasonal availability of water and energy. Local hydrological conditions play a significant role in shaping vegetation life forms and species diversity (Chitale et al. 2012; Das and Behera 2019). Research has highlighted the crucial importance of precipitation. Significant shifts in precipitation patterns can lead to a transformation in life forms, depending on alterations in both wet and dry conditions (e.g. Gougueni et al. 2023; Quesada-Román et al. 2023; Bouchriti et al. 2022; Ait Haddou et al. 2022b; Behera et al. 2018). Climate change and human activities have been identified as the primary factors influencing hydrological conditions at the scale of a river basin (Kostyuchenko et al. 2022). These factors can lead to changes in vegetation phenology and impact the exchange of water with the atmosphere (Das et al. 2018). In their study, (Moukrim et al. 2019) utilized the Maxent model to assess the habitat suitability of argan trees under current and future climate scenarios. Their findings revealed substantial changes in habitat area, with an increase of over 32% compared to the current distribution. The recent observations of the argan (Argania Spinosa L.) forest in the High Atlas of Ida-Ou-Tanane shed new light on the environmental settings. Thegeological substrate and well-distinguished pedological diversity have a key influence on the plant and landscape diversity, plant cover density and flora diversification and their geographical extension from the ocean to the mountains. The Western High Atlas (WHA) hostargan trees and tetraclines are among the important ecosystems conserved in Morocco. The locality of Jbel Taznakht in the WHA is locally famous for supportingthe regeneration of the most beautiful populations of Thuja (Tetraclinis articulata) (covers about 800,000 ha in Morocco) and the Argan tree (Msanda et al. 2021). Due to its richness and uniqueness in the southwest of Morocco, this locality is of paramount importance to clarify the crucial influence of soil type on this plant biodiversity. The study area is part of the Arganeraie Biosphere Reserve (RBA = Réserve de Biosphère de l’Arganeraie in French). The argan tree in this region is exploited by indigenous populations for multiple vocations (Mechqoq et al. 2021). In 1998, UNESCO declared the argan grove to be a terrestrial ecosystem of global importance,

23 Forest Degradation Susceptibility and Sustainability: Case Study …

509

covering an area of 2.5 million hectares (Guillaume et al. 2019). In addition, this zone represents a wooded limit close to the plains where the argan tree needs to be preserved from human disturbances causing clearing and bare soils expansion (Msanda et al. 2005). The bioclimatic conditions of the RBA regulate the species distribution and ecosystem structure. The semi-arid climate condition with prolonged dry phase with higher temperature hosts extreme climate tolerant species dominated by scrubs. Based on the dual thermal and water criteria, the Thuja formations alone or with the argan tree are placed in the arid to the semi-arid lower thermo-Mediterranean stage with a strong oceanic influence (Ait Haddou et al. 2022c; El Aboudi et al. 1992). According to Msanda et al. (2005), it is a rather “oceanic type of the lower thermo-Mediterranean stage”. The territory of the Ida-Ou-Tanane is characterized by vegetation essentially composed of tropical, Macaronesian, Atlantic Mediterranean, and endemic elements (Benabid 1976). This region is characterized by thermophilic endemic flora adapted to the arid climate conditions (Msanda et al. 2021). This study aimed to assess plant biodiversity in argan and tetraclinia induced by soil diversity based on tree density, species richness, and biological spectrum indices. The ecological and pedological characterization of the various stations allows determining the role of soil and disturbances of anthropogenic origin.

23.2 Study Area 23.2.1 Geographic Setting The Ida-Ou-Tanane region is located in southwest Morocco. It belongs to the Western High Atlas in the hinterland of the Agadir city. It covers the entire confederation of Ida-Ou-Tanane, a mid-mountain country culminating at 1423 m to the west at the top of Jbel Taznakht (Adrar n’Taznakht) (Fig. 23.1). The tree canopy cover (TCC) percentage data generated by Hansen et al. (2013) accessed from the Google Earth Engine (GEE) platform shows the vegetation cover in the region in 2000 and changes during 2000–2020. The change maps show significant loss of trees, while no significant gain in TCC is observed during 2000–2020 (Fig. 23.2).

23.2.2 Geomorphologic Context The area of interest in this study shows very diverse geomorphology: . Ridges [Taznakht, Lgouz to the west and Taourirt Moulay Ali (1789 m) to the east], . Valleys (Tinquert, Tamrakht, Tamri, Ouankrim, Tanit, etc.), . Cuvettes (Anklout, Tidilli, Izwaren, etc.),

510

(c)

S. Ezaidi et al.

Tree Canopy Cover (TCC) in % observed in 2000 No tree 0.01 - 5 5.01 - 10 10.01 - 40 40.01 - 76 TCC loss during 2000 – 2020 Loss No Change

Fig. 23.1 Location of the study area a location of the argan forest, b High-resolution Google Earth® image, c Tree canopy cover (TCC) % map of 2000 and TCC loss during 2000–202 [Hansen et al. (2013) and Ezaidi et al. (2022); accessed from Google Earth Engine (GEE)]

. Passes (Tizi Haroun, Tizi Imseker, Tizi Amergas, etc.), . Karstic landscapes (sinkholes of Iggui Lebhar, dolomitic towers of Iguer Aissa, caves of Wintimdouine and Imi n’Ouggoug, etc.), . Travertine and waterfalls of Imouzzer Ida Outanane.

23 Forest Degradation Susceptibility and Sustainability: Case Study …

511

Fig. 23.2 Panoramic view of the geomorphology of the study site: a Izwaren basin, b Izwaren plateau, and c Jbel Taznakht

23.2.3 Geological and Hydrological Framework The sedimentary series in the study area is essentially made up of Jurassic formations that outcrop in the hearts of the anticlines (Anklout, Imouzzer, and Lgouz) and topped by layers of gray and greenish marl from the Berriasian (Lower Cretaceous). The main groundwater resources are therefore associated with the Jurassic sandstone, limestone and marl of the Mesozoic sandstone, limestone and marl of the Ida-Ou-Tanane (Ait Haddou et al. 2023) (Figs. 23.3 and 23.4). a. Middle Jurassic (Dogger) This is the “Ameskhroud red sandstone” Formation, according to Duffaud et al. (1966). It is made up of coarse or fine sandstone and red clay with a thickness varying from 8 to 50 m. At Ameskroud, the Dogger is composed of coarse conglomerates with poorly rolled Paleozoic elements, coarse or fine red sandstones, sandy marls, and red clays reaching its greatest power (300–350 m). The deposit environment is fluvial with marine influence towards the summit (Bouaouda 2004) (Fig. 23.5a). b. Callovian It is represented by the Ouanamane Formation (Adams 1980), a limestone and marllimestone cornice, about 80 m thick, which overhangs the red detrital formations (Fig. 23.5b). The dominant facies in this Callovian series are: Limestones with oolites (Iggui n’Tarhazout member) and Marl-limestones with brachiopods (Somalirhynchia). This formation is the equivalent of the three sedimentary formations defined by Duffaud et al. (1966): Dolomites of Amsittène, Limestones of Anklout,

512

S. Ezaidi et al.

Fig. 23.3 Geological map of the study area (modified from Duffaud 1960)

Fig. 23.4 Synthetic section illustrating the geological formations of the study area (modified from Duffaud 1960). 1–Dogger: silts, red sandstones, and conglomerates. 2–Callovian: limestone and marl-limestone cornice 40 m thick. 3–Lower and Middle Oxfordian: grey-blue laminated marls and marl-limestone. 4–Upper Oxfordian: bioconstructed limestones with coral polyps, Megalodentidae and Nerinea. 5–Lower Kimmeridgian: red, dark, and soft brown clays “chocolate marls”. 6–Upper Kimmeridgian: marl and marl-limestone with numerous evaporitic horizons. 7–Tithonian: alternation limestone and gypsiferous red marls. 8–Berriasian: green marls

and Marl of Anklout. The deposit medium is a distal carbonate ramp, transitioning to the external basin type (Bouaouda 2004). c. Oxfordian The formation of Lalla Oujja can be divided into 2 parts (Adams 1980): . Lower and Middle Oxfordian: grey-blue laminated marl and soft marl-limestone (Fig. 23.6a).

23 Forest Degradation Susceptibility and Sustainability: Case Study …

513

Fig. 23.5 Dogger and Callovian. a red detrital formation of Amskroud. b Callovian limestone and marl-limestone cornice

Fig. 23.6 Lalla Oujja Formation. a The lower Oxfordian marl. b The Oxfordian bioconstruction of Izwaren with coral polyps and microbialites

. Upper Oxfordian: bedded dark-grey limestones and peri-reef and reef limestones. These bioconstructed carbonate facies with coral polyps, Megalodentidae and Nerinea are visible in the localities of Lalla Oujja, Cap Ghir, and Jbel Taznakht (Fig. 23.6b). These bioconstructions are essentially formed by 35% of coral polyps and 26% of microbialites. d. Kimmeridgian Chocolate marls and limestones correspond respectively to the Lower and Upper Kimmeridgian (Fig. 23.7): . The Lower Kimmeridgian (Iggui El Behar Formation) (Adams 1980). The dominant lithological facies is fine limestone, beige dolomites, yellow marls, and crypt-algal laminites. The formation medium is an internal lagoon-type carbonate platform. . The Upper Kimmeridgian (red marls of Imouzzer Formation) is about 140 m thick. The latter contains numerous evaporitic horizons at the top. This formation is distinguished by the presence of red, dark, and soft brown pelites “chocolate marls”. e. Portlandian

514

S. Ezaidi et al.

Fig. 23.7 Chocolate marls and Kimmeridgian limestones. a Lower Kimmeridgian lithologic facies. b Upper Kimmeridgian pelitic series

It is represented by the Tismroura Formation according to Adams (1980) equivalent of the formation of the dolomitic limestones of Ihchahen according to Duffaud et al. (1966). It varies from 140 to 240 m thick and includes dolomitic limestone with gypsum and gray or green marls alternately. In the landscape, this formation forms cliffs on the right bank of Assif Ouankrim (Fig. 23.8). f. Quaternary It is essentially characterized by continental deposits of the Plio-Villafranchian (Ambroggi 1963) formed by soft limestones, tuffs, and travertine, reworked pale pink detrital marls, and poorly consolidated conglomerates.

Fig 23.8 Portlandian series characterized by limestone and marl alternated at the top by gypsiferous marl

23 Forest Degradation Susceptibility and Sustainability: Case Study …

515

23.2.4 Bioclimatic Framework The analysis of rainfall data recorded in the Imouzzer Ida-Ou-Tanane station (Fig. 23.9) for the period 1931–2010 indicates: . Average interannual precipitation of 541 mm, whereina value of more than 500 mm was also reported by Elmouden et al. (2016) and Ait Haddou et al. (2020). . An increasing trend in precipitation is observed from 1931 to 1971, with a historical maximum of 1347.4 mm in 1962 and a minimum of 150 mm in 1943. However, a decrease in average annual precipitation has been observed in the last few decades (since 1971) compared to the average of 1931–1971. Several drought events have been observed during 1971–2010. . High rainfall variability with a standard deviation of 248.31 mm and a coefficient of variance of 45.92% (Ait Haddou et al. 2020). . Annual precipitation showsa five-month-long dryspell with an extremely high temperature extending from May to September. On the contrary, October to April is relatively wet with lower temperatures (Fig. 23.10). Such anomalous climate conditions regulate the ecosystem structure based on the local hydrological settings (Fig. 23.11).

23.3 Materials and Methods Phytosociological surveys according to the method of Braun-Blanquet (1932) were carried out at 3 sites in summer 2021 during the dryperiod. The different homogeneous sites were chosen according to the nature of the soil and the substratum

Fig. 23.9 Interannual variation in rainfall at the Imouzzer Ida-Ou-Tanane station. Source Souss Massa Hydraulic Basin Agency (ABHSM) and High Commission for Water and Forests and the Fight against Desertification (HCEFLCD) and Ezaidi et al. (2022)

516

S. Ezaidi et al.

Fig. 23.10 Ombrothermic diagram of the Imouzzer Ida-Ou-Tanane station (Ezaidi et al. 2022)

Fig. 23.11 Panoramic view of the plant covers in the Imouzzer-Ida-Ou-Tanane area

(Table 23.1). Each floristic survey includes the soil characteristics of the stations (analyzes carried out in the Geosciences and Environment Laboratory, Faculty of Sciences of Agadir) and supplemented by a study of the soil map of Morocco (Panagos et al. 2011). Vegetation structure, cover, abundance-dominance indices for all species present, and tree density (the number of trees on 1-hectare plots) were estimated for each plant survey. The intensity of anthropogenic disturbances were assessed on a scale of 3 levels: low (+), medium (++), and high (+++ ).

23 Forest Degradation Susceptibility and Sustainability: Case Study …

517

Table 23.1 Characteristics of the three stations: major local lithological units: Quaternary (IV), Kimmeridgian (d6), Upper Oxfordian (d5), and Callovian (d4), d: day Site

Latitude

Longitude

Altitude (m)

Slope (%)

Orientation

Soil class

Station 1 (d6)

30.626847°

–9.575367°

1144

15

SSW

Brown soil

Station 2 (d4)

30.608706°

–9.549391°

1090

30

S

Leached soil

Station 3 (Q&d5)

30.604098°

–9.544269°

957

35

SE

Fersiallitic red soil

23.4 Results and Discussion 23.4.1 Soil Diversity The results show that the brown soil develops on limestone crusts, marl, and marllimestone of the Upper Kimmeridgian at the foot of Jbel Taznakht. However, the fersiallitic red soil is observed on land resulting from the karstic dissolution of bioconstructed limestone, particularly on Oxfordian limestone. On the other hand, the leached soil appears on the micritic limestone, gray-blue foliated marl, and Callovian marl-limestone of the Taznakht plateau (Fig. 23.12). On the lithological level, the argan tree is mainly found on the residues of dissolution and the siliceous substratum of the Quaternary with fersiallitic red soil (Station 3) and to a lesser extent on the limestone rocks with leached soil (Station 2). However, Thuja trees are observed in all soil categories and are more abundant in calcareous soils above 1000 m altitude found in station 1, which is characterized by a warmer climate with well-ventilated and leached soils. The argan tree does not form any high-density pure stand in the study area. It is found associated with the Beaumier’s Euphorbia, which competes with other secondary species resulting from the phenomenon of degradation and therophytization (Station 3). Thus, in station 2, it is often mixed with oleaster and thuya, the latter forms wooded matorrals with carob, lentisk, and/or holm oak (Station 1).

Fig. 23.12 Different soil classes. a brown soil. b leached soil. c fersiallitic red soil

518

S. Ezaidi et al.

23.4.2 Floral Biodiversity The results obtained highlight a generally mediocre specific richness on the three types of soil affected by a significant period of drought in recent decades. The tree cover and density are moderate and low in the brown and fersiallitic red soil, respectively. The anthropogenic disturbances are well expressed by illicit cutting and charcoal production in these two stations. The presence of certain remains of traditional charcoal millstones after the carbonization or pyrolysis phase confirms this finding (Fig. 23.13). On the other hand, in the argan grove of the Jbel Taznakht plateau, the recovery rate and the density of trees are high, which could be explained by the presence of rich soil organic matter in a horizon-less and due to lesser anthropogenic disturbances. The comparison of recovery rate, species richness, tree density, and anthropogenic action between the three soil types is illustrated in Table 23.2. We note that in station 3, the argan tree constitutes a group of Argania spinosa, Euphorbia beaumierana, and Acacia gummifera: represents a stage of degradation of the argan tree (8 species), a very frequent case on the southwest slopes extending the Atlantic shores. This group, although generalized in the southwest of Morocco, presents a great ecological interest. It constitutes a typical and emblematic association of the north of Oued Souss and an extension towards the north of the argan tree and its floristic procession. We also note the presence of caulescent or cactoid Macaronesian elements (Euphorbia beaumierana, and Senecio anteuphorbium). Apart from this pre-steppe formation dominated by the argan tree, it individualizes a pre-forest structure at station 2: Argania spinosa, Tetraclinis articulata, and

Fig. 23.13 Traditional charcoal pile

23 Forest Degradation Susceptibility and Sustainability: Case Study …

519

Fig. 23.14 a Plant formation (towards altitude 1000 m). b Plant formation (less than 980 m altitudes) showing (A) Argania spinosa (with fruit), (C) Ceratonia siliqua, (T) Tetraclinis articulata, (O)Olea europaea, and (P) Pistacia lentiscus

Table 23.2 Soil and ecological data on the argan and tetraclinaie. For the three stations, the intensity of disturbances of anthropic origin was evaluated on a scale of 4 levels: absence (0), weak (+), average (++), and strong (+++) Station

Substrate

Recovery

Species richness

Tree count

Human action

1

Brown soil

60

18

220

+++

2

Leached soil

85

10

341

+

3

Fersiallitic red soil

45

8

140

+++

Pistacia lentiscus. It constitutes, with its floristic procession, among the most beautiful spre-forest formations of the greater Agadir region with only a specific richness of 10 species. Finally, in station 1, Thuja appears with various species (18 species) on the Jbel Taznakht: Tetraclinis articulata, Ceratonia siliqua, and Quercus rotundifolia. The geographical distribution, the ecological and dynamic characteristics of these groups are determined by the environmental conditions and primarily defined by the edaphic, climatic, and anthropic drivers. On the phytosociological level, all the associations described above fit into several series: the thermomediterranean series of Argania spinosa, the thermomediterranean series of Olea europaea-Ceratonia siliqua, and the degradation stage of the thermomediterranean series of Quercus rotundifolia, higher units already defined by Benabid (1982). They belong to the class Quercetea ilicis (Benabid 1982). The occurrence of these plants shows dominance of phanerophytes and chamaephytes, reflecting a less evolved sylvatic environment with a very strong impoverishment of geophytes and therophytes. In addition, under such conditions of drought and scarcity of annual and biennial plants, the similarity of the floristic procession between the three records suggests a complex influence of the soil-lithology and disturbance factors. These resulting changes in lithology and soil are accompanied by rarefaction, even the disappearance of certain characteristic species, often restricted to Ida-Ou Tanane. This is particularly the case of Olea europaeas sp. maroccana. This is also observed for Quercus ilex sub sp. rotundifolia,

520

S. Ezaidi et al.

which has only been observed at station 1, near Jbel Taznakht, where its survival is threatened by the various disturbances. Although Thymus satureoidesare considered common in Ida-Ou-Tanane, they wereonly found in a resistance state. Several species are endemic to Morocco (Argania spinosa, Acacia gummifera, Euphorbia beaumierana, Olea europaea L. subsp. Maroccana). Other species are heliophilous and play a fundamental and crucial role in the first phases of starting the process of natural restoration, for example, of Chamaerops humilis, Cistus villosus, Genista sp, etc. (Benabid and Melhaoui 2011). The decrease in the tree density could be attributed to the proximity of the stations to the agroforestry areas. Moreover, it could be indicating human disturbances that have modified the vegetation structure, further accentuated the drying of the substrate, and modified the soil quality and biotic conditions. The comparison of species abundance with previous studies in the Ida-Ou-Tanane region reveals the rapid degradation of the exceptional semi-arid argan and Thuja matorral of the Western High Atlas. a. Primary species The argan ecosystem of the study area is represented by floristic groups characterized by the following main forest species (Table 23.3): b. Secondary species The secondary species in the study area are mainly represented by the following shrubs: Euphorbia beaumierana, Periploca leavigata, Globularia alypum, Chamaerops humilis, Lavandula dentata, Lavandula maroccana, Tymus satureoides, Launaea arborescens, Genista tricuspidata, Cistus villosus, Rhus tripartita, Rhus pentaphylla, Senecio anteuphorbium, and Warionia saharae. We also noted the presence of mosses and lichens on the rocks, trees and shrubs. This microflora is a bio-indicator of the absence of any kind of atmospheric pollution in the study area.

23.5 Challenges and Solutions The exploitation of the results identifiesa few challenges and proposes suitable solutions. The climate aridity, drought episodes, soil dryness, agriculture activities, overgrazing, and illegal cutting are found as the main factors of degradation of emblematic pre-steppe formation in the southwest of Morocco. The fire occurrence in 2013 spread more than 250 hectares devastated massive forest cover composed of argan, Thuja, and oleaster (Fig. 23.15). Drought and fires events have been described as prime factors of Thuja dieback (e.g. Sinha et al. 2023; Ghailoule and Lumaret 2020). The recurrent fireevents in southwestern Morocco cause continuous degradations of argan groves and tetraclinaies. According to HCEFLCD statistics, almost 90% of fires nationwide are caused by human action and favored by rising temperatures. Thus, the forest degradation in this region could be attributed to anthropogenic pressure and climate change (Benabdellah and Ibnezzyn 2018). The ecosystem deterioration due to the widespread anthropogenic disturbances requires immediate restoration and

23 Forest Degradation Susceptibility and Sustainability: Case Study …

521

Table 23.3 The main forest species recorded in the study area Species

Characteristics

Argania spinosa (L.) Skeels

Moroccan endemic species, the only representative of the Sapotaceae family. It adapts to the most diverse geological substrates, except for aeolian dunes and the halomorphic soils of the low terraces of the wadis. In Ida-Ou-Tanane, it reaches an altitude of 950 m, where it is quickly supplanted by Tetraclinis articulate

Tetraclinis articulata (vahl, masters)

It appears at an altitude of 300 m. In our area, it is only between 500 and 1,200 m that it forms true tetracliniae. It accepts all types of substrates provided they are well-drained

Olea europaea L. subsp.Maroccana (Greuter & Burdet)

The Moroccan olive tree is a very significant endemic element in the RBA. Olea europaea sp. maroccana, morphologically close to the olive tree of the Canary Islands, would be a well-differentiated taxon, resulting from an ancestral strain originating from tropical Africa that it is possible to integrate into the mesomegatherm lineages

Ceratonia siliqua L

Morocco is the second-largest producer of carobs in the world

Pistacia lentiscus L

It grows on all types of soils in our study area: clay, sandy, limestone, or clay-silty soils. The appearance of lentisk in the semi-arid stage heralds edaphic humidity

Acacia gummifera Willd

It is especially present in the Ida-Ou-Tanane massif, where it rises to an altitude of 800 m. It is absent in the tetraclinia. It tolerates excess soil humidity and is more resistant to winter cold

Quercus ilex subsp.rotundifolia

It colonizes all limestone and detrital soils from an altitude of 700 m. The species finds its southern limit here in the Moroccan High Atlas

conservation interventions to reduce the rapid decline of the argan trees (Fig. 23.16). . Conservation of the argan and tetraclinaie in most degraded areas. . Alternative livelihood arrangements for the natives and prohibition of the massive tree harvesting for socio-economic interest, such as thyme (Tymus satureoides), lavender (Lavandula dentata), and doum (Chamaerops humilis). . Reserve argan trees for harvesting argan fruits for their nutritional, cosmetic, and medicinal properties. . Prohibition of forestcutting, especially tree species with low natural regeneration capacity; for example, the argan tree, the holm oak (Quercus ilex), and the Thuja, etc. . Increasing awareness among locals, sharing tree cover rights, and engaging locals in forest protection (Turner 2014).

522

Fig. 23.15 Some secondary species of the study area

S. Ezaidi et al.

23 Forest Degradation Susceptibility and Sustainability: Case Study …

523

Fig. 23.16 Natural restoration of the tetraclinaie of Jbel Taznakht after 5 years of the exceptional fire in 2013 (Photo taken in September 2021)

23.6 Recommendations The forest cover management and preservation of the argan grove and tetraclinaie in Jbel Taznakht couldn’t be successful in an unfenced land with a strong anthropogenic impact. Several precautions should be considered as follows: . Rational conservation of biodiversity, which is currently threatened by various natural and anthropogenic aggravations . Ensure charcoal for the natives who still use traditional fuel wood for cooking and many uses . Forest health monitoring in the study area and the southwest of Morocco to assess the impact of climate change and environmental conditions.

23.7 Conclusions The field assessment of soil type and floristic surveys carried out in Jbel Taznakht, considered as a northwestern extension of the Arganeraie Biosphere Reserve. The ground data allowed assessing plant groups their relation to the diversified soils. The pedological diversity could be attributed to the lithological geodiversity and the

524

S. Ezaidi et al.

evolution of soil classes linked to the geological substrate and vegetation distribution. The predominant grouping in the basin is an argan tree and thuja grouping, from which emerges a second Thuja and green oak (Quercus ilex) grouping. These groupings and their composition show variations in the lithological units and soil types. The study highlights the lithological diversity of the main geological formations that influences the argan tree distribution and its floral procession. Acknowledgments We sincerely appreciate the constructive and detailed comments provided by the anonymous reviewers and editors, which have greatly contributed to enhancing the quality of this manuscript. Additionally, the last author, Prof. M. Abioui, gratefully acknowledges the funding provided by the Fundação para a Ciência e Tecnologia, I. P (FCT), under the projects UIDB/ 04292/2020, UIDP/04292/2020, granted to MARE, and LA/P/0069/2020, granted to the Associate Laboratory ARNET. Finally, our thanks also go to the Springer proofreading team for their assistance in managing the manuscript, coordinating reviews, and preparing the final proof.

References Adams AE (1980) The stratigraphy and sedimentology of a Jurassic marine transgression, western high atlas. Morocco. Géol Méditerr 7(3):223–231 Ait Haddou M, Kabbachi B, Aydda A, Gougueni H, Bouchriti Y (2020) Spatial and temporal rainfall variability and erosivity: case of the Issen watershed, SW-Morocco. E3S Web Conf 183:02003 Ait Haddou M, Kabbachi B, Aydda A, Bouchriti Y, Gougueni H, En-Naciry M, Aichi A (2022a) Traditional practices: a window for water erosion management in the Argana basin (Western High Atlas Morocco). E3S Web Conf 337:02002 Ait Haddou M, Ben El Caid M, Aydda A, Bouchriti Y, Wanaim A, Gougueni H, Ezaidi S (2022b) Fencing land impacts on plant biodiversity and argan trees dynamic in the Ida-Ou-Tanane (central western of Morocco). IOP Conf Ser Earth Environ Sci 1090:012023 Ait Haddou M, Kabbachi B, Bouchriti Y, Ben El Kaid M, Gougueni H, Amiha R (2022c) Premier signalement de Thuya, (Tétraclinis articulata, (Vahl) Mast.) à létat naturalisé en bioclimat saharien, basin du Ouarzazate, Maroc. Geo-Eco-Trop (In Press) Ait Haddou M, Bouchriti Y, Ikirri M, Wanaim A, Aydda A, Amarir S, Amiha R, El Boudribili Y (2023) Delineation of groundwater potential zones in a semi-arid region using remote sensing and gis: a case study of Argana Corridor (Morocco). In: Mabrouki J, Mourade A, Irshad A, Chaudhry S (eds) Advanced technology for smart environment and energy. Springer, Cham, pp 257–268 Ambroggi R (1963) Etude géologique du versant méridional du Haut Atlas occidental et de la plaine du Souss. Notes Mém Serv Géol Maroc 157:322p Behera MD, Murthy MSR, Das P, Sharma E (2018) Modelling forest resilience in hindu kush himalaya using geoinformation. J Earth Syst Sci 127(7):95 Benabdellah M, Ibnezzyn N (2018) Évaluation de la contribution de l’arganier dans le revenu du ménage rural et comparaison de sa rentabilité dans différents systèmes de production. Rev Mar Sci Agron Vét 6(4):459–463 Benabid A (1976) Etude écologique, phytosociologique et sylvo-pastorale de la Tétraclinaie de l’Amsittène. Ph.D Dissertation, Aix-Marseille University (In French) Benabid A (1982) Bref aperçu sur la zonation altitudinale de la végétation climacique du Maroc. Ecol Mediterr 8(1):301–315 Benabid A, Melhaoui Y (2011) Écosystèmes naturels à arganier (Argania spinosa), patrimoine national et universel: Bio-écologie, phytosociologie, phytodynamique et ethnobotanique;

23 Forest Degradation Susceptibility and Sustainability: Case Study …

525

Restauration et certification. In: Actes du 1er Congrès International de l’Arganier, Agadir, Maroc, 39–47 Bouaouda MS, Peybernès B, Boutakiout M (2004) Foraminifères benthiques complexes du Bathonien supérieur au Kimméridgien inférieur de la marge atlantique marocaine («bassin» d’Agadir): stratigraphie et paléobiogéographie. Rev Micropaleontol 47(1):13–27 Bouchriti Y, Korrida A, Ait Haddou M, Achbani A, Sine H, Amiha R, Kabbachi B (2023) Mortality and morbidity assessment attributed to short- and long-term exposure to fine particles in ambient air of Agadir city, Morocco: the AirQ model approach. Environ Anal Health Toxicol 38(2):e2023009 Braun-Blanquet J (1932) Plant sociology: The Study of Plant Communities. McGraw Hill, New York Chitale VS, Tripathi P, Behera MD, Behera SK, Tuli R (2012) On the relationships among diversity, productivity and climate from an Indian tropical ecosystem: a preliminary investigation. Biodivers Conserv 21(5):1177–1197 Das P, Behera MD (2019) Can the forest cover in India withstand large climate alterations? Biodivers Conserv 28(8):2017–2033 Das P, Behera MD, Patidar N, Sahoo B, Tripathi P, Behera PR, Srivastava SK, Roy PS, Thakur P, Agrawal SP, Krishnamurthy YVN (2018) Impact of LULC change on the runoff, base flow and evapotranspiration dynamics in eastern Indian river basins during 1985–2005 using variable infiltration capacity approach. J Earth Syst Sci 127(2):19 Duffaud F (1960) Contribution à l’étude stratigraphique du bassin secondaire du Haut-Atlas occidental (Sud-Ouest marocain). Bull Soc Géol Fr 7(6):728–734 Duffaud F, Brun L, Plauchut B (1966) Le bassin du Sud-Ouest marocain. In: Reyre D, Bassins sédimentaires du littoral Africain. Symp New Delhi, Publ Assoc Serv Géol Afr Paris, 1, pp 5–26 El Aboudi A, Peltier JP, Doche B (1992) La carte de la végétation des Aït-Baha (Anti-Atlas occidental, Maroc) et son intérêt pour l’édaphologie. Feddes Repert 103(1–2):121–126 Elmouden A, Alahiane N, El Faskaoui M, El Morjani ZEA (2016) Dams Siltation and Soil Erosion in the Souss-Massa River Basin. In: Choukr-Allah R, Ragab R, Bouchaou L, Barceló D (Eds), The Souss-Massa River Basin, Morocco. The Handbook of Environmental Chemistry, vol 53, Springer, Cham, pp 95–120 Ezaidi S, Aydda A, Kabbachi B, Althuwaynee OF, Ezaidi A, Ait Haddou M, Idoumskine I, Thorpe J, Park HJ, Kim SW (2022) Multi-temporal Landsat-derived NDVI for vegetation cover degradation for the period 1984–2018 in part of the arganeraie biosphere reserve (Morocco). Remote Sens Appl Soc Environ 27:100800 Ghailoule D, Lumaret JP (2020) État des connaissances sur le dépérissement des peuplements de thuya de Berbérie, Tetraclinis articulata (Vahl) Mast. Ecol Mediterr 46(1):83–96 Gougueni H, Ait Haddou M, Ez-zaher L, Bouchriti Y, Amiha R, Boutiti R (2023) Water quality of traditional rainwater storage tanks in Southern Morocco - risk for consumer health. Ecol Eng Environ Tech 24(8):9–21 Guillaume D, Pioch D, Charrouf Z (2019) Argan [Argania spinosa (L.) skeels] oil. In: Ramadan MF (ed) Fruit oils: Chemistry and Functionality. Springer, Cham, pp 317–352 Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO, Townshend J (2013) High-resolution global maps of 21st-century forest cover change. Science 342(6160):850–853 Kostyuchenko Y, Artemenko I, Abioui M, Benssaou M (2022) Global and regional climatic modeling. In: Daya Sagar B, Cheng Q, McKinley J, Agterberg F (eds) Encyclopedia of mathematical geosciences. Springer, Cham Mechqoq H, El Yaagoubi M, El Hamdaoui A, Momchilova S, da Silva Almeida JRG, Msanda F, El Aouad N (2021) Ethnobotany, phytochemistry and biological properties of Argan tree (Argania Spinosa (L.) Skeels) (Sapotaceae) - A review. J Ethnopharmacol 281:114528

526

S. Ezaidi et al.

Moukrim S, Lahssini S, Rhazi M, Alaoui HM, Benabou A, Wahby I, El Madihi M, Arahou M, Rhazi L (2019) Climate change impacts on potential distribution of multipurpose agroforestry species: Argania spinosa (L.) Skeels as case study. Agroforest Syst 93(4):1209–1219 Msanda F, Mayad EH, Furze JN (2021) Floristic biodiversity, biogeographical significance, and importance of Morocco’s Arganeraie biosphere reserve. Environ Sci Pollut Res 28:64156–64165 Msanda F, El Aboudi A, Peltier JP (2005) Biodiversité et biogéographie de l’arganeraie marocaine. Cah Agric 14(4):357–364 Quesada-Román A, Umaña-Ortíz J, Zumbado-Solano M, Islam A, Abioui M, Tefogoum GZ, Kariminejad N, Mutaqin BW, Pupim F (2023) Geomorphological landscape units for regional environmental planning in developing countries. Environ Dev 48:100935 Panagos P, Jones A, Bosco C, Kumar PS (2011) European digital archive on soil maps (EuDASM): preserving important soil data for public free access. Int J Digit Earth 4(5):434–443 Sinha A, Nikhil S, Ajin RS, Danumah JH, Saha S, Costache R, Rajaneesh A, Sajinkumar KS, Amrutha K, Johny A, Marzook F, Mammen PC, Abdelrahman K, Fnais MS, Abioui M (2023) Wildfire risk zone mapping in contrasting climatic conditions: an approach employing ahp and f-ahp models. Fire 6(2):44 Turner B (2014) Neoliberal politics of resource extraction: Moroccan argan oil. Forum Dev Stud 41(2):207–232

Chapter 24

Utilization of PISA Model and Deduced Specific Degradation Over Semi-arid Catchment: Case of Abdelmomen Dam in Souss Basin (Morocco) Mohamed Ait Haddou , Youssef Bouchriti , Belkacem Kabbachi , Mustapha Ikirri, Ali Aydda , Hicham Gougueni , and Mohamed Abioui

Abstract Rainfall-runoff soil erosion is the most frequent form of land degradation. Soil erosion and sediment yield assessment are essential to develop suitable land and water resource management practices. Siltation impact assessment plays a major part in the choice of sustainable development projects for watershed management and can be thus a base to plan appropriate strategies for decision-makers to avoid soil erosion risks and consequently lengthen dams’ life. The current study aims to estimate annual sediments deposit in the Abdelmomen dam, Morocco, using the Previsioni Interimento Serbatoi Artificiali (PISA) model integrated with GIS. Various geospatial layers were used, including ALOS Digital Terrain Model (DTM), historical climate data, high-resolution ESRI land use land cover (LULC) map, etc. M. Ait Haddou · Y. Bouchriti · B. Kabbachi · M. Ikirri · A. Aydda · H. Gougueni · M. Abioui (B) Department of Earth Sciences, Faculty of Sciences, Ibnou Zohr University, Agadir, Morocco e-mail: [email protected] M. Ait Haddou e-mail: [email protected] Y. Bouchriti e-mail: [email protected] B. Kabbachi e-mail: [email protected] M. Ikirri e-mail: [email protected] A. Aydda e-mail: [email protected] H. Gougueni e-mail: [email protected] M. Abioui MARE-Marine and Environmental Sciences Centre – Sedimentary Geology Group, Department of Earth Sciences, Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_24

527

528

M. Ait Haddou et al.

The PISA-based model results reveal an average annual degradation of 1044,71m3 / km2 /yr is estimated, which is equivalent to a soil erosion rate of about 14.62 t/ha/ yr, and to a total annual potential soil loss of 1,900,600 (t y−1 ). These results are significantly higher than that measured in situ by a bathymetric survey (Er), where the ratio Y(PISA)/E(Er) is about 1.24. This model overestimates soil erosion in the study area, as well as for other studies, areas in the Greater Maghreb. In general, the application of the PISA-based model has until now not provided very satisfying results for the prediction of soil loss for large-sized catchments and still encounters severe problems. This model allows an indirect assessment of water erosion and the scope of degradation and soil loss on the siltation of the dam lake. Further, a moderate hazard of water erosion was found throughout the study area. Moreover, the highly eroded areas, which are quite frequented over the study area, require delineation and immediate action. These findings can help the water resource managers to develop a new insight from a multi-scale approach and a new model for delineation of vulnerable areas to water erosion, especially, in mountain regions. Keywords Erosion · Siltation · PISA model · Abdelmomen dam · Argana corridor · Morocco

24.1 Introduction Soil erosion by rain and runoff is the most common form of soil degradation in the world (García-Ruiz et al. 2017; Echogdali et al. 2022a; Maetens et al. 2012). Soil loss is a considerable problem for watershed management in semi-arid environments (e.g. Ait Haddou et al. 2023; Abioui et al. 2023; Elbadaoui et al. 2023; Khaddari et al. 2023; Bel Haj et al. 2023; Bouadila et al. 2023; Ed-Dakiri et al. 2022; Benjmel et al. 2022; Echogdali et al. 2022b;Ikirri et al. 2021, 2022; Ostovari et al. 2017). The silting process of dams limits their use, predicting this phenomenon is important for economic major reasons. The problem is more widely investigated on large dams than on smaller ones, even if it has a higher impact (Alahiane et al. 2016). The problem of a quantitative assessment of the rate of soil loss is one of the main key challenges in the context of sustaining soil productivity and food security of the population (Maltsev and Yermolaev 2019). Water resource management is important is highly important for dry arid and semi-arid regions, which receive limited rainfall and experiences recurrent droughts. Moreover, the population expansion, increasing food demand, scarcity of water for household use, and irrigation exaggerate the water scarcity. The construction of suitable rainwater harvesting structures, reservoirs, dams, etc., will improve the soil moisture and facilitate irrigation in dry periods (Quesada-Román et al. 2023; Gougueni et al. 2021, 2023; Behera et al. 2019). Continuous assessment or monitoring of such water harvesting structures is important to prescribe suitable management plans (Gourfi et al. 2018). The climate change and intense anthropogenic disturbances via land use land cover (LULC) change, including deforestation, urbanization, cropland expansion, etc., are causing

24 Utilization of PISA Model and Deduced Specific Degradation Over …

529

a change in hydrological balances, wherein the inappropriate land management practices, tillage, etc. are leading to massive soil erosion (Das et al. 2018). Soil erosion is common in more than 40% of the total land surface area in Morocco (e.g., Ait Haddou et al. 2022c; Hudson 1990). Among the major disadvantages of water, erosion is soil degradation and siltation in reservoirs, dams and lakes located at the outlet. Such soil deposition becomes a threat when the rate of deposition exceeds the tolerable threshold (10,4 t/ha/year) (Ostovari et al. 2020). Model-based erosion assessment is distinguished by the input data capture and the ability of the model to predict the rate of soil loss through surface runoff (Chadli 2016; Aswathi et al. 2022). Moreover, models are advantageous as they perform consistently, allow repetition with fewer inputs and facilitate monitoring, wherein integration of geospatial data layers allows creating the spatially explicit results to improve the management and conservation plans (Dautrbande and Catherine 2006). However, baseline development, parameterization, and calibration/ validation of such models require several data, so their application at a large scale becomes difficult. Shit et al. (2015) employed the Revised Universal Soil Loss Equation (RUSLE) and various layers on rainfall, soil, topography, crop system, management practices, and ancillary geo-spatial data layers to assess the soil erosion risk in Jhargram, West Bengal, India. Dutta and Sen (2018) applied the Soil and Water Assessment Tool (SWAT) model to estimate soil erosion and sediment yield to assess the soil deposition in the Hirakud dam (Asia’s largest earth dam). Their study highlighted the critical importance of LULC, soil type and cropland management practices in the Mahanadi River basin. Benkadja et al. (2013) applied the prediction model of sediment yield Previsioni Interimento Serbatoi Artificiali (PISA) model to estimate soil loss and siltation K’sob hydrological system, located in the semi-arid region of East Algeria. They concluded that the PISA model well estimated the soil loss as predicted by bathymetric measurements. The objective of this work is to characterize the water erosion of the Issen basin and to develop the extent of degradation and soil losses using an indirect method via the prediction model of sediment yield (PISA) model. A description of this PISA modelbased of estimation and comparison with other siltation of the Abdelmomen dam reservoir by bathymetric measurements in terms of means measurement is provided. Thereafter, we compared the model estimates and actual measurements with other dams in the Greater Maghreb, to develop effective strategies to combat soil erosion and land degradation to revive the detrital lands of the Argana corridor. Moreover, improved land management plans will revamp dam water storage capacity, water quality, and the potential to mobilize water for various purposes.

24.2 Study Area The Issen wadi is the most important tributary descending from the Western High Atlas (WHA). Its watershed extends over a single structural area which is that of the Western High Atlas (Fig. 24.1). It is part of the Argana corridor and its surrounding

530

M. Ait Haddou et al.

Fig. 24.1 Geographical location of Issen watershed, Morocco

valleys covering an area of about 1300 km2 (Ait Haddou et al. 2022a). It is bounded to the north by the reverse fault of Ichemraren-Imin’Tanout and to the south by the fault of El Mnizla, which marks the morphological contact between the Western High Atlas and the plain of Souss (Medina et al. 2001). The main lithological terms are identified from the geological map of Argana drawn by Tixeront (1974). The outcropping facies are those attributed to the great geological formations of the Permo-Triassic and the Paleozoic (Fig. 24.2). Its topography is irregular, with a very uneven relief and a fairly long water concentration time of about 7.5 h (Ait Haddou et al. 2022a, 2022b). The climate is strongly influenced by the altitude of the Atlas Mountains, which attenuates the effect of the ambient aridity by multiplying the contrasts on a local scale. It is characterized by a prolonged dry season and a wet season with sudden and very short rainfall (Ait Haddou et al. 2020). The vegetation cover in the study area is part of the Arganeraie Biosphere Reserve (ABR) of Morocco, Whose assessment and monitoring of vegetation cover changes are essential (Ezaidi et al. 2022). This situation then accentuates runoff and strongly exposes the soils of the watershed to water erosion, hence the importance of sediments retained by the Abdelmomen dam (Fig. 24.3) (Elmouden et al. 2016). Vegetation is sparse and unevenly distributed. Thus, in the long term, these soil-bioclimatic characteristics can slow down the process of pedogenesis, contributing to huge soil losses on the slopes.

24 Utilization of PISA Model and Deduced Specific Degradation Over …

531

Fig. 24.2 Geologic map of the catchment area of the Issen wadi (Qu: Quaternary, UJ: Upper Jurassic, MJ: Middle Jurassic, LJ: Lower Jurassic, B: Basalt, UT: Upper Triassic, LT: Lower Triassic, P: Permian, Or-Si-Dev: Ordovician–Silurian-Devonian, M. Cm & Cm-Ord: Middle Cambrian & Cambro-Ordovician, L.Cm: Lower Cambrian) (modified after Tixeront 1974 and Ait Haddou et al. 2023)

Fig. 24.3 Abdelmomumen dam and its reservoir (September 2021)

24.3 Methodology 24.3.1 Data Availability To estimate the siltation rate in the Abdelmomen dam, the following data were used:

532

M. Ait Haddou et al.

Fig. 24.4 Spatial distribution of sub-catchment of Issen watershed

1. The ALOS Digital Terrain Model (DTM) (12.5 m) was used to recover the hydrographic network and the slopes. The data sets are available free of charge at: https://search.asf.alaska.edu/#/ 2. Historical rainfall data (1981–2017) were collected from the Hydrographic Basin Agency of Souss Massa (ABHSM) and the High Commission for Waters and Forests and the Fight against Desertification (HCEFLCD). This data comprised a time series of average annual rainfall from 13 climatological stations in and near the Issen basin. 3. The land use/land cover data from 2017 to 2021 at 10 m resolution was extracted from the database produced by ESRI, Microsoft, and Impact Observatory. Source: https://www.arcgis.com/apps/instant/media/index.html?appid=fc9 2d38533d440078f17678ebc20e8e2. The application of the PISA model for the Wadi Issen watershed gives a negative value, which makes this model inapplicable on a large scale. For this reason, we proceeded to the extraction of the sub-basins with the help of the hydrology tool ArcGIS (Fig. 24.4).

24.3.2 Model Description Several studies have highlighted the relationship between siltation of reservoirs and soil loss within their basins, which in turn are linked to the physical parameters of these catchments (Peter et al. 2014; Elmouden et al. 2016). The PISA model is

24 Utilization of PISA Model and Deduced Specific Degradation Over …

533

considered the most robust one to complete the assessment of the soil loss model (Drid et al. 2022). It is a mathematical model with statistical parameters that offers the possibility of indirect estimation of water erosion. It is one of the models for the prediction of sedimentation rate in dams (Bazzoffi 2009). The PISA model’s conceptual basis comes from studies conducted since the 1950s in a wide range of environments (Bazzoffi and Rompaey 2003). It is based on the product of a set of morpho-topographic and hydro-climatic indices of the watershed. Its particularity lies in its applicability to watersheds of different sizes. It allows evaluation of the annual average rate expressed in m3 /Km2 of siltation-causing sediments at the dam lake by applying the following Eq. (24.1) (e.g., Bazzoffi 1987; Baldassarre and Palumbo 2009; Benkhadja et al. 2013): Y = 425.9334 − 1.3898 A + 102.9576 (SER)0, 5 − 9.84435 S − 0, 31 Ar + 116.718 D

(24.1)

Y: the siltation index is expressed as the annual volume of wet sediment poured into the dam per unit area of the watershed in m3 /Km2 . A: the surface of the watershed in Km2 . SER: the erodible area corresponding to the tillable area increased by 1/16 of the untellable agroforestry area in Km2 . S: the average slope of the watershed in Grade. Ar: average annual rainfall in mm. D: Drainage density Km/Km2 . Geographic information systems (GIS) are widely used recently to calculate erosion loss of soil at different levels with acceptable accuracy (Maltsev and Yermolaev 2019). In applying the PISA model, the use of mapped field information requires the application of a GIS, which allowed us to extract, overlay, and analyze various factors, such as vegetation cover, slope, and hydrographic network. The determination of the PISA model parameters was performed using Arc-Gis software. The cartographic representation of the slope and the calculation of drainage density are made from the Digital Elevation Model (DEM), ALOS (Advanced Land Observing Satellite), with a resolution of 12.5 m. The processing, visualization, and analysis of DEM and satellite imagery allow the determination of numerous descriptive parameters of relief, hydrographic network, drainage density, and land cover. The approaches adopted are quantitative and analytical.

24.4 Results and Discussion 24.4.1 Land Cover and Erodible Surface Areas The analysis of satellite imagery and the land use map (Karra et al.2021) allowed the individualization of several classes (Fig. 24.5 and Table 24.1). Vegetation formations are mostly sparse and cover an area of about 87,200 ha or 66% of the basin surface, including 2190 ha of cultivated land. The non-tillable agroforestry surface forms a

534

M. Ait Haddou et al.

band that surrounds the entire Argana corridor at the level of the southern plateaus and the slopes of the High Atlas exposed to the west and northwest, better watered. The bare soil appears with variable proportions according to the abundance and coverage of plant species that compose it. One-third (33%) of the land is generally occupied by soils devoid of perennial vegetation due mainly to unfavourable rainfall conditions. This makes them prone to both water and wind erosion, especially in the central and upper basins, where water stress and edaphic deficit are accentuated. The prevalence of clastic floodplain deposits that consist of red sandstone-silty in the Argana Permo-Triassic corridor led to the development of very large gullies. The most obvious consequences of this phenomenon are the loss of agricultural land and the silting up of the Abdelmomen dam.

Fig. 24.5 Land use and land cover map

24 Utilization of PISA Model and Deduced Specific Degradation Over … Table 24.1 Classification of slopes according to their effect on erosion

Class (%) 0–3 3–15

Area (ha) 7863.75 31,707.04

Area (%)

535

Erosion risk

6.0

Very low

24.3

Low

15–30

30,727.74

23.6

Moderate

30–45

24,571.58

18.9

Hight

> 45

35,402.61

27.2

Very hight

Total

130,272.75

100

–

24.4.2 Slope The distribution of the slope classes obtained (Fig. 24.6 and Table 24.1) shows five slope classes with areas varying between 7864 and 35,403 ha. It is clear that all slope classes are represented and that moderate and steep slopes (3–30%) dominate. The areas of low slope (< 3%) are the least preponderant. The entire watershed is inscribed on relatively steep terrain, except for the Argana corridor areas and the extreme northwest of the basin. The average slope is about 33%. The main Quaternary levels forming the colluvial zones with high susceptibility to ablation and transport are located on the Jurassic plateaus of the watershed (1100–1270 m altitude).

24.4.3 Precipitation The arithmetic mean rainfall at the 13 rainfall stations considered is 302 mm/year, associated with a coefficient of variation Cv = 67% over 37 rainfall years, reflecting a high rainfall variability (Ait Haddou et al. 2020b). This rainfall is very near to that observed in the Moroccan mountains (average annual rainfall of 331 mm). The whole basin receives an average of 141 mm in winter and half of this in autumn and also in spring (Fig. 24.7). This high spatial and temporal variability of rainfall on an annual and seasonal scale has an impact on vegetation and is responsible for forest disturbance and soil degradation. Additionally, the impact of rainfall intensity on watershed erosion can be well observed by the sediment depth by the deposition depth (Li et al. 2021).

24.4.4 Drainage Density The drainage density is low, averaging 0.85 km/km2 for the whole basin. It exceeds this value for certain sub-basins where the flow is very ramified in a particular topographic and litho-climatic context, undoubtedly favoring its development. However, the density of the hydrographic network and its ramification are especially important

536

M. Ait Haddou et al.

Fig. 24.6 Slope map and slope classes repartition

from 1200 m altitude on the marl-limestone and friable schistose-gravel mountain slopes framing the Argana corridor. In general, the areas with high drainage and hydrographic density are often concentrated in the ancient massif and the Jurassic plateaus with mountainous relief and less permeable parent rock, and less dense vegetation cover. The hydrographic density is medium and ranges from 0 to 224 km−1 (Fig. 24.8).

24 Utilization of PISA Model and Deduced Specific Degradation Over …

537

Fig. 24.7 Spatial distribution of average annual precipitation

24.4.5 Application of PISA Model 24.4.5.1

Evaluation of the Siltation Rate

The PISA model provides an estimate of the annual volume of wet sediment discharged into the Abdelmomen dam via the calculation of all these statistical parameters using a GIS environment. The average annual degradation expressed by the parameter Y is 1044.71 m3 /km2 /year (Table 24.2). For the whole basin and taking into account the density of the sediments estimated at 1.4, the annual silting would be 1462.60 t/km2 /year. This is equivalent to a soil loss of 14.62 t/ha/year.

538

M. Ait Haddou et al.

Fig. 24.8 Hydrographic density map Table 24.2 PISA model statistical parameters and siltation index values Sub-catchments

Ait Khtab

Ait Moussa

Ait Tourner

Ait Driss

Igounan and Agouni

(Km2 )

126.18

293.48

142.72

311.15

429.22

SER(Km2 )

46.84

108.95

115.51

52.98

159.34

2.42

15.9

20.05

20.47

15.73

171.34

237.31

234.14

3.12

1.59

1.35

1.87

1.97

Y (m3 /Km2 /yr)

1242.45

1048.22

864.61

977.52

1090.76

Y (t/Km2 /yr)

A

Slope (Grade) P (mm) Dd (Km−1)

449.3

365.75

1739.43

1467.51

1210.46

1368.52

1527.07

Y (t/ha/yr)

17.39

14.67

12.10

13.68

15.27

Mean

14.62 t/ha/yr

24 Utilization of PISA Model and Deduced Specific Degradation Over …

539

Table 24.3 Sedimentation, specific degradation, and average soil observed in the dams of the study area (S.Vol: Storage volume, Per. L: Percentage loss (%), Sp. D: Specific degradation (m3 /km2 / year) Dam

Date

S. Vol (Mm3 ) Per. L (%) Sp.D (m3 /km2 /yr) Source

Abdelmomen 1981 214

10.6

842

Elmouden et al. (2016)

1986 0.7

72.1

286

Elmouden et al. (2016)

Dkhila

The first analysis of correlation shows that siltation rate is linked to different factors such as drainage density, catchment area, erodible surface areas, average annual precipitation, and slope index. The Ait Khtab sub-basin has the highest quantity of sediment production with a value of 1739.43 m3 /Km2 /yr while the minimum level of 1210.46 m3 /Km2 /yr was recorded in the Ait Tournet sub-basin. The other sub-basins have a moderate specific degradation.

24.4.5.2

Direct Assessment of Erosion (Er)

In addition to this large dam, the basin has a second small dam at Dkhila, which controls a basin of 100 km2 downstream of Issen. It is intended for drinking water supply, irrigation, and animal watering while contributing to flooding control. The specific degradation and average soil loss were obtained by the bathymetric method via the silting rate of the Abdelmomen and Dkhila dams respectively 842 and 286 m3 /km2 /yr. The importance of the Wadi Issen alluvium on the flow of material delivered to the ocean before the construction of the two dams (Abdelmomen and Dkhila) was well marked on the sediments deposited in the Wadi Souss estuary (Elmouden et al. 2005).

24.4.5.3

PISA Model Versus In-Situ Measures

Validation and evaluation of the results are based on the measured directly by a bathymetric survey and derived from observation data of the erosion forms in the watershed. The difference (∆) between the PISA model (y) and the direct assessment of erosion (Er) is calculated by the following formula (24.2). The results of the calculation are shown in Table 24.4.

∆=

y − Er ∗ 100 Er

(24.2)

We note that the average soil loss evaluated by the PISA model is significantly lower than that measured directly by a bathymetric survey (Er) (Table 24.4).

540

M. Ait Haddou et al.

Table 24.4 The difference in water erosion between the PISA model and measured sediment (Er) Calculation method

Average erosion (m3 /km2 /yr)

Soil loss (t/ha/yr)

Total soil loss (t/yr)

Ecart (%)

Measured sediment

842

11.78

1,900,600

0

PISA model

1044.71

14.62

1,531,400

∆ = 24

Indeed, the ratio Y(PISA)/E(Er) is the order of 1.24. The results indicate that the estimated total annual potential soil loss of 1,900,600 t/yr is slightly superior to the measured sediment of 1,531,400 t/yr during the period 1981–2000. The difference is 24% (Table 24.4). This overestimated value can be explained on the one hand by a loss of a volume of silt during de-silting operations and on the other hand, by a quantity of sediment trapped and deposited during the transport and routing of solid inputs that do not reach the dam lake. The results of monitoring the silting of the Abdelmomen dam reservoir by bathymetric measurements, the average annual silting for the period 1983–2000 is 0.84 Mm3 . The total volume lost is evaluated at 16.25 Mm3 , i.e. nearly 8% of its initial capacity (217.5 Mm3 ). Similarly, this dam has reached 78% of its estimated economic life of 50 years. According to Table 24.5, the siltation rate calculation results are very close to the siltation predictions of the Abdelmomen dam estimated by Grabener (2009). This author also suggests values of up to 12% in 2030. This finding of the current study is found corroborates studies from neighbouring watersheds. Applying the PISA model in a few studies in the Greater Maghreb has yielded results illustrated in Table 24.6. Flaming land erosion in the Issen watershed is an alarming sign of soil deterioration, So, Gully erosion is a pervasive phenomenon that commonly develops in semi-arid regions (Drid et al. 2022). Figure 24.9 shows some forms of water erosion Table 24.5 Siltation predictions for Abdelmomen dam (Grabener 2009) Estimated siltation values of Abdelmomen dam 2000

2010

2020

2030

2070

dm3

%

dm3

%

dm3

%

dm3

%

dm3

%

9046

5

13,807

7

18,568

9

23,329

12

42,373

21

Table 24.6 The siltation rate by PISA model of other dams in the Greater Maghreb Country

Source

Dam

PISA/Er

Morocco

Current study

Abdelmomen

1.24

Algeria Tunisia

Benkhadja et al. (2013)

K’sob

3

Achite et al. (2014)

Sidi M’hamed Benaouda

1.68

Ouechtati and Beldessare (2011)

Siliana

2

Lakhmess

4

24 Utilization of PISA Model and Deduced Specific Degradation Over …

541

in the Issen watershed. Due to the considerable costs of detailed ground surveys of this phenomenon, GIS and remote sensing are appropriate alternatives for estimates of soil erosion, analyzing and assessing the risks of the extension of soil degradation.

Fig. 24.9 Some forms of erosion in the watershed of the wadi Issen. a: Colluvium at the level of the fall of the Ida-Ou-Bouzia plateau on the Argana corridor. b: “Pebbles grow” a symptom of sheet erosion by the lifting of pebbles to the surface by tillage tools near the Timzgadiouine basin. c and d: Quaternary deposit showing claws (c) and gullies (d). e: Generalized gullying, very deep and widely answered on the edge of the Oued Issen makes the banks more prone to slides and/or landslides. f: Slope with high soil degradation affected by gullying processes associated with the slide

542

M. Ait Haddou et al.

The application of the PISA model in the Issen catchment tends to overestimate the value of siltation of the Abdelmomen dam with an order of about 24% for the period (1981–2017). This difference can be explained by: (i) eroded sediments that did not reach the hydrographic network, (ii) the deposition and accumulation of sediments in the watercourses, (iii) the resolution of the satellite image, (iv) the silting up of the cuvettes located in the study area for which we have no data, (v) the reliability of the climatic data provided by the organization concerned. On the other hand, sediment flow observations derived from gauging station measurements in Africa are often based on short measuring periods (less than 5 years old) and are subject to significant uncertainties (Vanmaercke et al. 2014; Kostyuchenko et al. 2022). Predicting specific degradation at the catchment scale is one of the main challenges in geosciences research. The application of the PISAbased model has until now not provided very satisfying results for the prediction of soil loss for large-sized catchments and still encounters severe problems. The explanation for this lies in a combination of several factors and a lack of knowledge to describe erosion process interactions at the catchment scale in a GIS environment. The degree of similarity of the characteristics of the pool studied with those of the experimental pools that were the basis for the construction of the PISA model will influence the results obtained. At present, the effect of soil and water conservation is inconspicuous and the comprehensive management of the watershed in the Issen Wadi has been slightly improved. However, this study only focuses on the same type of water erosion and sedimentation, while there is an opportunity to assess the sediment deposition by wind power and different types of natural or anthropogenic processes. As the research object, the measurement and analysis of the characteristics of the deposit’s sediment provide data support for the research on the sediment deposition, transport process, and a lot of erosion information of the Issen catchment. Despite these specific weaknesses, the PISA model integrated into SIG is also a supportive tool for evaluating soil loss sediment deposits for small-sized catchments. It provides the necessary knowledge of average annual degradation to conduct soil conservation actions.

24.5 Conclusion and Recommendations The PISA model estimates the annual volume of wet sediment discharged into the Abdelmomen Dam via applying statistical approaches in the GIS environment. It allowed estimating a water erosion rate of 14.62 t/ha/year, which causes impressive real losses of soil annually and reduces the storage capacity of the dam by accelerating the rate of siltation. In particular, we note the high susceptibility of the land to water erosion due to the great spatial diversity with the following essential characteristics: (i) importance of impermeable, soft Permo-Triassic clay-detritus outcrops, (ii) the contrasting topography with steep slopes over 46% of the surface, (iii) spatial and temporal variability

24 Utilization of PISA Model and Deduced Specific Degradation Over …

543

of precipitation results in low to moderate climatic aggressiveness, (iv) the diversity of the vegetation cover and its state of degradation. As long as part of the sediment removed from the basin is trapped and deposited during transport, the rate of water erosion is certainly higher than the specific degradation related to the amount of sediment accumulated at the Abdelmomen dam. This study highlights that: (i) the silting degree of the studied dam is 12% for 8 years; (ii) the used model overestimates soil erosion in the study area as for other studies in the Greater Maghreb, and (iii) the difference in water erosion between the PISA model and actual erosion (Er) is ∆ = 1.24. This study can help the water resource managers to develop a new simple approach to generate thematic maps that will be used to delineate vulnerable areas to water erosion in the Issen basin using an appropriate model. Also, this study provides a scaled vision of soil degradation in the Issen watershed, but for future research, it would be useful to take into account prevailing climate change trends, including their effect on future precipitation, land cover, control measures to combat soil erosion and soil’s susceptibility to degradation. So, the following recommendations should be considered: • Contour plowing and other soil erosion protection measures and ways of soil conservation in watering, application of soil saving technologies in agriculture, application of rotational grazing, multiple-cropping, forestation, shrub planting, strip cropping, mulching, and terracing. • Prominent change in the seasonality of monthly average rainfall (mm) and erosivity is extremely important in the course of the development of agricultural activities and the adoption of appropriate soil protection measures. • Application of GIS, Remote sensing (RS) technology, and other models’ local scales would help assess and monitor vegetation cover changes and focus on particular plots which are more prone to soil erosion by using higher resolution satellite data. Acknowledgements An earlier version of this MS was greatly improved by comments made by the editors, and anonymous reviewers. The authors thank also the Souss Massa Hydraulic Basin Agency (ABHSM) and High Commission for Waters and Forests and the Fight against Desertification (HCEFLCD) for collaboration. Additionally, the last author, Prof. M. Abioui, gratefully acknowledges the funding provided by the Fundação para a Ciência e Tecnologia, I. P (FCT), under the projects UIDB/04292/2020, UIDP/04292/2020, granted to MARE, and LA/P/0069/2020, granted to the Associate Laboratory ARNET. We thank ESRI for providing the data on land use/land cover time series (2017-2021). Finally, the authors are much obliged to the Springer proofreading team for handling the work, sending reviews, and preparing the proof.

References Abioui M, Ikirri M, Boutaleb S, Faik F, Wanaim A, Id-Belqas M, Echogdali FZ (2023) GIS for watershed characterization and modeling: example of the Taguenit river (Lakhssas, Morocco).

544

M. Ait Haddou et al.

In: Chatterjee U, Pradhan B, Kumar S, Saha S, Zakwan M (eds) Water, land, and forest susceptibility and sustainability: geospatial approaches and modeling. Elsevier, Amsterdam, pp 61–85. https://doi.org/10.1016/B978-0-323-91880-0.00023-4 Achite M, Toubal AK, Bettahar N, Ballah A (2014) Evaluation de l’envasement du barrage Sidi M’hamed Benaouda dans le bassin versant de la Mina par l’application du modèle P.I.S.A. Int J Environ Glob Climate Change 2(3):86–94 Ait Haddou M, Kabbachi B, Aydda A, Bouchriti Y, Gougueni H, En-Naciry M, Aichi A (2022a) Traditional practices: a window for water erosion management in the Argana basin (Western High Atlas Morocco). E3S Web Conf 337:02002. https://doi.org/10.1051/e3sconf/202233702002 Ait Haddou M, Bouchriti Y, Ikirri M, Wanaim A, Aydda A, Amarir S, Amiha R, El Boudribili Y (2023) Delineation of groundwater potential zones in a semi-arid region using remote sensing and GIS: a case study of Argana Corridor (Morocco). In: Mabrouki J, Mourade A, Irshad A, Chaudhry S (eds) Advanced technology for smart environment and energy. Springer, Cham, pp 257–268. https://doi.org/10.1007/978-3-031-25662-2_21 Ait Haddou M, Kabbachi B, Aydda A, Gougueni H, Bouchriti Y (2020b) Spatial and temporal rainfall variability and erosivity: case of the Issen watershed, SW-Morocco. E3S Web Conf 183:02003. https://doi.org/10.1051/e3sconf/202018302003 Ait Haddou M, Wanaim A, Ikirri M, Aydda A, Bouchriti Y, Abioui M, Kabbachi B (2022) Digital elevation model-derived morphometric indices for physical characterization of the Issen Basin (Western High Atlas of Morocco). Ecol Eng Environ Technol 23(5):285–298. https://doi.org/ 10.12912/27197050/152161 Ait Haddou M, Ben El Caid M, Aydda A, Bouchriti Y, Wanaim A, Gougueni H, Ezaidi S (2022c) Fencing land impacts on plant biodiversity and argan trees dynamic in the Ida-Ou-Tanane (central western of Morocco). IOP Conf Ser Earth Environ Sci 1090:012023. https://doi.org/10.1088/ 1755-1315/1090/1/012023 Alahiane N, Elmouden A, Aitlhaj A, Boutaleb S (2016) Small dam reservoir siltation in the atlas mountains of Central Morocco: analysis of factors impacting sediment yield. Environ Earth Sci 75(12):1035. https://doi.org/10.1007/s12665-016-5795-y Aswathi J, Sajinkumar KS, Rajaneesh A, Oommen T, Bouali EH, Binojkumar RB, Rani VR, Thomas J, Thrivikramji KP, Ajin RS, Abioui M (2022) Furthering the precision of RUSLE soil erosion with PSInSAR data: an innovative model. Geocarto Int 37(27):16108–16131. https://doi.org/ 10.1080/10106049.2022.2105407 Baldassarre G, Palumbo N (2009) Identification and assessment of soil erosion processes in a widely cropped watershed in southern Italy. In: Land degradation and rehabilitation: dryland ecosystems. Series: advances in Geoecology, vol 40. Catena Verlag GMBH, Reiskirchen, pp 207–217 Bazzoffi P (2009) Soil erosion tolerance and water runoff control: minimum environmental standards. Reg Environ Change 9(3):169–179. https://doi.org/10.1007/s10113-008-0046-8 Bazzoffi P (1987) Previsione dell’interrimento nei serbatoi artificiali italiani, modello PISA. Idrotecnica 1:15–18 Bazzoffi P, Van Rompaey A (2003) PISA model to assess off-farm sediment flow indicator at watershed scale in Italy. In: Proceeding OECD: agricultural impacts on soil erosion and soil biodiversity: developing indicators for policy analysis. Rome, Italy, pp 263–272 Behera MD, Biradar C, Das P, Chowdary VM (2019) Developing quantifiable approaches for delineating suitable options for irrigating fallow areas during dry season—a case study from Eastern India. Environ Monit Assess 191(3):805. https://doi.org/10.1007/s10661-019-7697-4 Bel Haj H, El Ouarghi H, El Yousfi Y, Elkhattabi S, Ben Zhir K, Benyoussef S, Aboulhassan MA, Krid A, Abioui M (2023) Reuse of wastewater for irrigation of coriander in Morocco: evaluation of effects on crop growth parameters. World Water Policy. https://doi.org/10.1002/ WWP2.12150

24 Utilization of PISA Model and Deduced Specific Degradation Over …

545

Benjmel K, Amraoui F, Aydda A, Tahiri A, Yousif M, Pradhan B, Abdelrahman K, Fnais MS, Abioui M (2022) A multidisciplinary approach for groundwater potential mapping in a fractured semiarid terrain (Kerdous Inlier, Western Anti-Atlas, Morocco). Water 14(10):1553. https://doi.org/ 10.3390/w14101553 Benkadja R, Boussag F, Benkadja A (2015) Identification et évaluation du risque d’erosion sur le bassin versant du K’sob (Est Algérien). Bull Eng Geol Environ 74(1):91–102. https://doi.org/ 10.1007/s10064-014-0611-y Benkhadja R, Hattab A, Mahdaoui N, Zehar C (2013) Assessment of soil losses and siltation of the K’sob hydrological system (semi-arid area-East Algeria). Arab J Geosci 6(10):3959–3968. https://doi.org/10.1007/s12517-012-0653-z Bouadila A, Bouizrou I, Aqnouy M, En-nagre K, El Yousfi Y, Khafouri A, Hilal I, Abdelrahman K, Benaabidate L, Abu-Alam T, Stitou El Messari JE, Abioui M (2023) Streamflow simulation in semiarid data-scarce regions: a comparative study of distributed and lumped models at aguenza watershed (Morocco). Water 15(8):1602. https://doi.org/10.3390/w15081602 Chadli K (2016) Estimation of soil loss using RUSLE model for Sebou watershed (Morocco). Model Earth Syst Environ 2(2):51. https://doi.org/10.1007/s40808-016-0105-y Das P, Behera MD, Patidar N, Sahoo B, Tripathi P, Behera PR, Srivastava SK, Roy PS, Thakur P, Agrawal SP, Krishnamurthy YVN (2018) Impact of LULC change on the runoff, base flow and evapotranspiration dynamics in eastern Indian river basins during 1985–2005 using variable infiltration capacity approach. J Earth Syst Sci 127(2):19. https://doi.org/10.1007/s12040-0180921-8 Dautrebande S, Catherine S (2006) L’érosion hydrique et les pertes en sol agricoles en Région Wallonne. FUSAGx-UHAGx, Gembloux, p 121 Drid I, Achour Y, Zighmi K, Nouioua T, Khababa O, Rahmani S, Ouadfel O, Atoui B (2022) Designing gully erosion susceptibility maps (GESM) in the Algerian Eastern Tell: a case study of the K’sob River watershed. Arab J Geosci 15(14):1264. https://doi.org/10.1007/s12517-02210566-9 Dutta S, Sen D (2018) Application of SWAT model for predicting soil erosion and sediment yield. Sustain Water Resour Manag 4(3):447–468. https://doi.org/10.1007/s40899-017-0127-2 Ed-Dakiri S, Etebaai I, El Moussaoui S, Ikirri M, Ait Haddou M, Amarir S, Tawfik A, El Talibi H, Cherkaoui Dekkaki H, Abioui M, Damnati B, Mourabit T (2022) Water erosion risks mapping using RUSLE model in the Mohamed Ben Abdelkrim El Khattabi Dam watershed (Central Coastal Rif, Morocco). In: Singh VP, Dubey SK, Jha PK, Gupta PK, Nanda A, Gupta V (eds) Soil-water, agriculture, and climate change—exploring linkages. Springer, Cham, pp 53–75. https://doi.org/10.1007/978-3-031-12059-6_4 Echogdali FZ, Boutaleb S, Taia S, Ouchchen M, Id-Belqas M, Kpan RB, Abioui M, Aswathi J, Sajinkumar KS (2022a) Assessment of soil erosion risk in a semi-arid climate watershed using SWAT model: case of Tata basin South-East of Morocco. Appl Water Sci 12(6):137. https://doi. org/10.1007/s13201-022-01664-w Echogdali FZ, Kpan RB, Ouchchen M, Id-Belqas M, Dadi B, Ikirri M, Abioui M, Boutaleb S (2022b) Spatial prediction of flood frequency analysis in a semi-arid zone: a case study from the seyad basin (Guelmim Region, Morocco). In: Rai PK, Mishra VN, Singh P (eds) Geospatial technology for landscape and environmental management: sustainable assessment and planning. Springer, Singapore, pp 49–71. https://doi.org/10.1007/978-981-16-7373-3_3 Elbadaoui K, Mansour S, Ikirri M, Abdelrahman K, Abu-Alam T, Abioui M (2023) Evaluating the accuracy of EPM and PAP/RAC in predicting soil erosion in mountainous watersheds: an analysis of the Toudgha River Basin in Southeast Morocco. Land 12(4):837. https://doi.org/10. 3390/land12040837 Elmouden A, Alahiane N, El Faskaoui M, El Morjani ZEA (2016) Dams siltation and soil erosion in the souss-massa river basin. In: Choukr Allah R, Ragab R, Bouchaou L, Barceló D (Eds) The souss-massa river basin, Morocco. Springer, Cham, pp 95–120. https://doi.org/10.1007/698_ 2016_70

546

M. Ait Haddou et al.

Elmouden A, Bouchaou L, Snoussi M (2005) Constraints on alluvial clay mineral assemblages in semiarid regions. The souss wadi basin (Morocco, Northwestern Africa). Geol Acta 3(1):3–13 Ezaidi S, Aydda A, Kabbachi B, Althuwaynee OF, Ezaidi A, Ait Haddou M, Idoumskine I, Thorpe J, Park HJ, Kim SW (2022) Multi-temporal landsat-derived NDVI for vegetation cover degradation for the period 1984–2018 in part of the arganeraie biosphere reserve (Morocco). Remote Sens Appl Soc Environ 27:100800. https://doi.org/10.1016/j.rsase.2022.100800 Gougueni H, Ez-Zaher L, Bouchriti Y, Ait Haddou M, Ezaidi A (2021) Health effects of the consumed water from the traditional reservoirs on the population of Tiznit in Morocco. E3S Web Conf 234:00073. https://doi.org/10.1051/e3sconf/202123400073 Gougueni H, Ait Haddou M, Ez-zaher L, Bouchriti Y, Amiha R, Boutiti R (2023) Water quality of traditional rainwater storage tanks in southern Morocco risk for consumer health. Ecol Eng Environ Tech 24(8):9–21. https://doi.org/10.12912/27197050/170168 García-Ruiz JM, Beguería S, Lana-Renault N, Nadal-Romero E, Cerdà A (2017) Ongoing and emerging questions in water erosion studies. Land Degrad Dev 28(1): 5-21. https://doi.org/10. 1002/ldr.2641 Gourfi A, Daoudi L, Shi Z (2018) The assessment of soil erosion risk, sediment yield and their controlling factors on a large scale: example of Morocco. J Afr Earth Sci 147:281–299. https:// doi.org/10.1016/j.jafrearsci.2018.06.028 Grabener J (2009) Changement climatique Vulnérabilité de la population rurale et des écosystèmes dans quatre zones de la Région Souss-Massa-Draa Possibles stratégies: Région Souss-MassaDraa d’adaptation. Fich. part. GTZ—Grabener—ACC RSMD (Report) Hudson NW (1990) Conservation des sols et des eaux dans les zones semi-arides. Bull Pédol FAO 57:182p Ikirri M, Faik F, Echogdali FE, Antunes IMHR, Abioui M, Abdelrahman K, Fnais MS, Wanaim A, Id-Belqas M, Boutaleb S, Sajinkumar KS, Quesada-Román A (2022) Flood hazard index application in arid catchments: case of the Taguenit Wadi watershed, Lakhssas, Morocco. Land 11(8):1178. https://doi.org/10.3390/land11081178 Ikirri M, Faik F, Boutaleb S, Echogdali FZ, Abioui M, Al-Ansari N (2021) Application of HECRAS/WMS and FHI models for the extreme hydrological events under climate change in the Ifni river arid watershed from Morocco. In: Nistor MM (Ed) Climate and land use impacts on natural and artificial systems: mitigation and adaptation. Elsevier, Amsterdam, pp 251–270. https://doi. org/10.1016/B978-0-12-822184-6.00008-9 Khaddari A, Jari A, Chakiri S, El Hadi H, Labriki A, Hajaj S, El Harti A, Goumghar L, Abioui M (2023) A comparative analysis of analytical hierarchy process and fuzzy logic modeling in flood susceptibility mapping in the Assaka watershed, Morocco. J Ecol Eng 24(8):62–83. https://doi. org/10.12911/22998993/165958 Karra K, Kontgis C, Statman-Weil Z, Mazzariello J, Mathis M, Brumby S (2021) Global land use/land cover with Sentinel-2 and deep learning. IGARSS 2021–2021 IEEE International Geoscience and Remote Sensing Symposium. Belgium, Brussels, pp 4704–4707 Kostyuchenko Y, Artemenko I, Abioui M, Benssaou M (2022) Global and regional climatic modeling. In: Daya Sagar B, Cheng Q, McKinley J, Agterberg F (Eds) Encyclopedia of mathematical geosciences. Encyclopedia of earth sciences series. Cham: Springer. https://doi.org/10. 1007/978-3-030-26050-7_144-1 Li M, YongXin N, XiZhi L, QiuFen Z, Nan LL (2021) Study on sedimentary characteristics of warping dam under different silting states in Chabagou watershed. IOP Conf Ser Earth Environ Sci 687:012034. https://doi.org/10.1088/1755-1315/687/1/012034 Maetens W, Vanmaercke M, Poesen J, Jankauskas B, Jankauskiene G, Ionita I (2012) Effects of land use on annual runoff and soil loss in Europe and the Mediterranean: a meta-analysis of plot data. Prog Phys Geogr 36(5):599–653. https://doi.org/10.1177%2F0309133312451303 Maltsev KA, Yermolaev OP (2019) Potential soil loss from erosion on arable lands in the European part of Russia. Eurasian Soil Sci 52(12):1588–1597. https://doi.org/10.1134/S10642293 1912010X

24 Utilization of PISA Model and Deduced Specific Degradation Over …

547

Medina F, Vachard D, Colin JP, Ouarhache D, Ahmamou M (2001) Charophytes et ostracodes du niveau carbonaté de Taourirt Imzilen (Membre d’Aglegal, Trias d’Argana); implications stratigraphiques. Bull Inst Sci 23:21–26 Ostovari Y, Ghorbani-Dashtaki S, Bahrami HA, Naderi M, Dematte JAM (2017) Soil loss estimation using RUSLE model, GIS and remote sensing techniques: a case study from the Dembecha Watershed, Northwestern Ethiopia. Geoderma Reg 11:28–36. https://doi.org/10.1016/j.geodrs. 2017.06.003 Ostovari Y, Moosavi AA, Pourghasemi HR (2020) Soil loss tolerance in calcareous soils of a semiarid region: evaluation, prediction, and influential parameters. Land Degrad Dev 31(15):2156– 2167. https://doi.org/10.1002/ldr.3597 Ouechtati S, Baldassarre G (2011) Evaluation du transport solide et de l’envasement dans le bassin versant de Siliana (Tunisie): cas des barrages Siliana et Lakhmess. Bull Eng Geol Environ 70(4):709–722. https://doi.org/10.1007/s10064-011-0376-5 Peter KD, d’Oleire-Oltmanns S, Johannes BR, Marzolff I, Ait Hssaine A (2014) Soil erosion in gully catchments affected by land-levelling measures in the souss basin, Morocco, analysed by rainfall simulation and UAV remote sensing data. CATENA 113:24–40. https://doi.org/10.1016/ j.catena.2013.09.004 Quesada-Román A, Umaña-Ortíz J, Zumbado-Solano M, Islam A, Abioui M, Tefogoum GZ, Kariminejad N, Mutaqin BW, Pupim F (2023) Geomorphological landscape units for regional environmental planning in developing countries. Environ Dev 48:100935. https://doi.org/10.1016/j. envdev.2023.100935 Shit PK, Nandi AS, Bhunia GS (2015) Soil erosion risk mapping using RUSLE model on Jhargram sub-division at West Bengal in India. Model Earth Syst Environ 1(3):28. https://doi.org/10.1007/ s40808-015-0032-3 Tixeront M (1974) Carte géologique et minéralisations du Couloir d’Argana. Notes Mém Serv Géol Maroc, vol 205, Rabat Vanmaercke M, Poesen J, Broeckx J, Nyssen J (2014) Sediment Yield in Africa. Earth-Sci Rev 136:350–368. https://doi.org/10.1016/j.earscirev.2014.06.004

Chapter 25

Geospatial Practices for Airpollution and Meteorological Monitoring, Prediction, and Forecasting Suvarna Tikle, Vrinda Anand, and Sandipan Das

25.1 Introduction 25.1.1 Geospatial Technologies Geospatial technologies are emerging technologies used to integrate research and development in the geographic mapping and analysis of the Earth. It is rapidly evolving and provides a new framework and process for understanding the meaning of data (Singh et al. 2021). It evolved in mid of 1960 and accelerated in the twentieth century. There are now different types of geospatial technology Geographic Information Systems (GIS), Remote Sensing, Global Positioning System (GPS), Geodatasets, and Internet Mapping Technologies like Google earth (Krishna et al. 2019; Singh et al. 2021) (https://www.aaas.org). Geospatial technologies are used to process all kinds of observational and satellite data in raster and vector format.

25.1.2 Geographic Information System A geographic information system (GIS) is a systemto store, analyze, manage, transfer and present all spatial or geospatial data types. Along with the expert judgment of the S. Tikle (B) Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India e-mail: [email protected] V. Anand Indian Institute of Tropical Meteorology, Ministry of Earth Sciences (MoES), Pune 411008, India S. Das Symbiosis Institute of Geoinformatics, Symbiosis International (Deemed University), Pune 411016, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_25

549

550

S. Tikle et al.

GIS user or analyst, it produces solutions to spatial problems (Kumar et al. 2016). The strides that the field GIS and its components, such as various methods of interpolation such as kriging (Gao and Zha 2010; Wang 2014), are making as an application in all fields of the environmentare incredible to satisfy the spatial data view and analysis demands in the area of meteorology. GIS enables to integrate and analyze several environmental data from different sources that model the overall impact of air pollutants on the environment. This ‘layering’ is encouraged by the fact that all such data includes information on its precise location on the Earth’s surface (https:// www.aaas.org). Air pollution modeling uses geostatistical analysis to assess pollution levels using tools such as variograms, Kriging, and inverse distance weighting (IDW). With geospatial tools, we can calculate the spatial variance of variables based on their distance from two points. The geostatistical analysis is used to analyze and predict the values associated with spatiotemporal phenomena (Pandey et al. 2014). As the deteriorating air quality in India poses a rapidly alarming health risk, a GIS-Based Air Quality Monitoring & Forecast System was designed and developed using Free and Open Source Software for Geospatial Applications (FOSS4G) (Oberai et al. 2022). GIS simplifies decision making by organizing geographic information. Aside from managing statistical and spatial data, it provides advanced functionality in interpolation, extracting data from an interpolated surface, handling multi-dimensional data (Tikle and Gyananath 2010), and creating algorithms to automate the process. Furthermore, it produces results that can be visualized in interactive maps, further simplifying the decision-making process (Shareef et al. 2016). Changing the paradigm of air pollution monitoring The use of a Geographic Information System approach permits a geo-referenced environmental assessment and accurate mapping of air pollution problems (Bozyazi et al. 2015). One of the crucial aspects of GIS is that it can be used to analyze and communicate a wide variety of geospatial data, allowing its large range of geospatial data to be assembled into layers that can be analyzed and communicated to a wider audience, thus making complex themes easier to discuss and analyze.

25.1.3 Remote Sensing Remote sensing is a geospatial technique of acquiring information emitted and reflected electromagnetic (EM) radiation without making physical contact with the Earth’s surface to detect and monitor terrestrial, aquatic and atmospheric. Remote sensing has been widely used for air pollution. A key to retrieving atmospheric components from remotely sensed data is Surface reflectance (Lim et al. 2009). There are two types of ways to measure signals by remote sensors. Radiometric correction mainly estimates the reflectivity of the environment, and Geometric correction corresponds to the spatial and geographic location. Optical atmospheric effects may influence the signal measured in both ways. The use of remote sensing in monitoring air pollution extends the coverage of ground-based observation to a wide scale, which

25 Geospatial Practices for Airpollution and Meteorological Monitoring …

551

complements its limited application (Gao and Zha 2010). Monitoring of atmospheric constituents using the ground surface reflectance data is affected by many factors like aerosol, water vapors, and ozone. The techniques that can be used to range different atmosphere components in the most direct and attractive approach are called active remote sensing (Meei et al. 2011).

25.1.4 Air Quality Monitoring and Mapping GIS and satellite remote sensing are widely used for meteorological and air quality monitoring on the regional and global scale (Michaelides et al. 2017). Advance approaches using both technologies and surface observational data to forecast air quality will help decision makers improve air quality, mitigate the occurrence of acute air pollution episodes, and reduce the associated impacts, particularly in urban areas (Lim et al. 2009; Baklanov and Zhang 2020). A dedicated type of Air Quality Information Service for urban areas at the national level can make the country selfsufficient in providing frontier research based on scientifically accredited robust Air Quality prediction and forecasting (Beiget al. 2018; Beig et al. 2020). Air quality forecasting is a highly specialized area. It requires vast computational calculations in seconds to provide the needed tool focusing on the city areas with high resolution to forecast the air quality along with weather parameters (http://safar.tropmet.res. in). As part of such a system, it is necessary to understand tropical meteorology at different spatial and temporal scales. The Doppler weather radar and satellite observations have provided a better understanding of weather phenomena at all scales, resulting in improved forecasts for the average day (https://mausam.imd.gov.in/). Full-fledged satellite Meteorological Division for providing satellite Metrological services to the nation are established in various countries. Satellite technology is playing a significant role in improving weather forecasting. Satellite or aircraft-based sensors capture the information in terms of energy reflected from the Earth, and final information is obtained in digital image form. Satellite data provide better coverage in time and area extent than any alternative. Most polar satellite instruments observe the entire planet once or twice in 24 h. The fine temporal resolution (12 h) of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery is perfectly suited to study solid atmospheric impurities such as aerosols that tend to change constantly (Gao and Zha 2010). Monitoring Air quality from satellites has played a vital role at global and regional scales in the status of air pollution and trends by providing information on pollutant amounts and transport (Levelt et al. 2018). MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) were reported by many researchers in the literature (Fu et al. 2018). Geospatial data are growing in diversity and size. Due to the scarcity of Ground observational data on the national scale because of the sparse network of air quality monitoring stations. Visualization and analysis of meteorological and climatic data are essential for forecasters and researchers to apply this data (Wang 2014). Satellite data have improved in spatial resolution from the 100 km scale (GOME) to resolutions

552

S. Tikle et al.

of just a few km (TROPOMI), and temporal resolution has been enhanced from days to twice daily (measured by IR) (Kim et al. 2020). Satellite imagery and elevation data at 30 M resolution are readily available for most of the Earth via Landsat and other sources. Very high-resolution satellite images and data like LISS IV(5.8 m), Sentinel-2 (10-60 m), SPOT 6/7 (1.5 m), (Pléiades 1A/1B (0.5 m panchromatic and 2 m multispectral), WorldView (0.5 m–30 cm) https://www.nrsc.gov.in/, https:// eos.com/, https://up42.com/, https://www.satimagingcorp.com/), for selected areas can be obtained. There are also sharper panchromatic satellite images with very high resolution to reveal more details for better major cities. However, estimating pollutants using satellite proxies is still in the nascent stage (Prem et al. 2021) due to the emissions of various air pollutants. Dey et al. (2020) developed a satellite-based national PM2.5 database derived from AOD at a high resolution (1 km) for India over two decades (2000–2019) for air quality management. In addition to high-resolution maps, the implementation also features detailed attributes and current meteorological data for tactical operations (0–12 h), seasonal planning, and 12–48 h of the planning horizon. Rapid updates are also introduced on severe changes in environments. The GIS technology improved the accuracy of the information for users. Weather contours, temperature profiles, wind levels, and other environmental details can be accessed on geotagged images and spatial databases. (India Meteorological Department 2016). As the “new paradigm for air quality monitoring”, the expanded use of low-cost air quality sensors with increasing participation of citizens is recommended by the Environmental Protection Agency (EPA) (Anenberg et al. 2018; Hart et al. 2020; Williams et al. 2018). GEMS (Geostationary Environment Monitoring Spectrometer) was launched in 2020 by the Republic of Korea, allowing scientists to monitor air pollution from space by a far greater distance. This marks a significant leap forward in the ability of scientists to monitor air pollution from space for the hourly monitoring of air pollution levels for almost 20 countries in Asia. Satellite sensors with a strong focus on air quality make up the Geostationary Air Quality (Geo-AQ) constellation. GEMS is a part of the future GEO AQ constellation. Along with the Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument covering North America (Zoogman et al. 2017) and the Sentinel-4 instrument covering Europe (Jhoon Kim et al. 2020).

25.1.5 Modeling, Prediction, and Forecasting Various studies have been published on air quality models for mapping of air quality and challenges, applications, and further advancements in modeling for air quality modeling and forecasting (Grell et al. 2005a, b; Kumar et al. 2015, 2016; Krishna et al. 2019; Baklanov and Zhang 2020; Sartini et al. 2020; Beig et al. 2021; Gunthe et al. 2021). Researchers observed pollutant concentrations by the interpolation analysis, but many researchers (Grell et al. 2005a; Kumar et al. 2016; Shareef

25 Geospatial Practices for Airpollution and Meteorological Monitoring …

553

et al. 2016) depicted that dispersion models underestimate particulate matter (PM) concentrations. A retrospective atmospheric model simulates meteorological parameters (wind, temperature, vertical mixing, etc.) from previous air pollution episodes. The air quality model uses the numerical simulations as input to the photochemical model in the agencies. Multiple Linear Regression Models (Parashar 2019; Varghese et al. 2019) are widely used to forecast air pollution. The machine learning algorithm, the Random Forest method (Singh et al. 2019), provides superior classification and regression capacities (Ye et al. 2017). Multilayer Perception Model (Huang et al. 2020) for prediction of meteorological parameters. An integrated approach to modeling with a real-time weather forecasting system can increase the accuracy. The Weather Research and Forecasting (WRF) Model is a next-generation mesoscale numerical weather prediction system designed for atmospheric research and operational forecasting applications. Air quality models simulate the physical and chemical processes that affect air pollutants during their dispersion and reaction in the atmosphere using mathematical and numerical techniques. A primary pollutant is one that is emitted directly into the atmosphere and is characterized by models based on meteorological data and source information, such as emission rates and stack height. (www.epa.gov). The most commonly used air quality models are simple box models and Gaussian Plume Models. Dispersion Modeling, Photochemical Modeling, and Receptor Modeling to advance the WRF model. Among all AERMOD a steady-state Gaussian plume dispersion model is the most widely used model for air pollution dispersion. For calculating concentrations, meteorological data such as temperature, mixing height, wind direction, and wind speed are used. Early warning systems and emergency response agencies will see a rise in the demand for air pollution information and policy and regulation development in emergencies. Air quality early warning System (AQEWS) integrated with the Decision Support System could become a user-friendly tool for air-quality management, which consist of a framework of two models a high-resolution weather prediction model with an atmospheric chemistry transport model, which also includes datasets of particulate matter of different satellites (https://ews.tropmet.res.in). Further technological disruption in geospatial processing is driven by automation, Artificial Intelligence, machine learning, and sensor technology. A shift toward a more machine-centric world is being driven by technological advances, such as Cloud computing, ubiquitous high-speed connectivity, sensor networks, geospatial analytics, and autonomous smart machines (United Nations Committee of Experts on Global Geospatial Information Management). Indeed, satellite modeling of aerosol optical depth (AOD) has demonstrated a stark picture of PM2.5 with an integrated approach (Beig et al. 2013; Tian et al. 2018; Krishna et al. 2019; Xu and Zhang 2020; Prem et al. 2021). Although Dey et al., 2020 developed a satellite-based national PM2.5 database derived from AOD at a high resolution (1 km) for India over two decades (2000–2019) for air quality management. NOx is one of the major air pollutants (Biswal et al. 2021), which has negative environmental impacts that have not been studied yet using multiple satellites for the long term. The previous studies mainly focus on retrieving PM2.5 from AOD from MODIS (Chowdhury et al. 2019;

554

S. Tikle et al.

Krishna et al. 2019; Xu and Zhang 2020). Yonsei Aerosol Retrieval (YAER) algorithm, version 2, was used for the retrieval of AOD by (Choi et al. 2018) GOCI (GEO) and MODIS (LEO)—Aqua and Terra.). A recent high-resolution study of emission inventory for India reveals a massive overestimation of NOx by existing inventories where ground data is unavailable. Therefore, development and modeling should be high resolution with an upgraded spatial distribution (Sahu et al. 2014). Another type of air quality monitoring instrument aboard satellites is known as the ozone monitoring instrument (OMI) or TROPOspheric Monitoring Instrument (TROPOMI) and is used by NASA (Georgoulias et al. 2020). By measuring the intensity of reflected sunlight at various wavelengths and detecting the absorption features of different atmospheric trace gases, they measure ozone, nitrogen dioxide, sulfur dioxide, and formaldehyde. This technology can observe the entire Earth in a single day and track changes in global air quality over time. TROPOMI/S5P satellite observations (Stavrakou et al. 2020; Vîrghileanu et al. 2020) was launched on 13th October 2017 over large cities worldwide. A few studies used developed GIS-based (Biswal et al. 2021) statistical model for air quality study over the Indian region (Dey et al. 2020). Current deep learning research in the field seeks to utilize these predictive models to learn and predict either spatial or temporal correlations in ambient air pollution, but we seldom see models capable of both (Muthukumar et al. 2021). A global air quality data platform was launched recently by UNEP, UN-Habitat, and IQAir, a Swiss air quality technology company, which will provide real-time data on air pollution from over 4,000 contributors, including citizens, communities, governments, and the private sector, in February 2020 to work towards healthier and more sustainable cities.

25.2 Monitoring and Analysis Geospatial measurement techniques involve mapping of air pollutants and meteorological parameters with fast response instruments and precise GPS to resolve the air pollution and meteorological patterns spatio-temporally. With this help, air quality and meteorological monitoring are possible by geographically mapping the Earth. Remote sensing is one of the major techniques associated with the geospatial monitoring of air pollutants and meteorological parameters, which is geo-referenced and can be further used in GIS to detect patterns spatially. Satellite data is available not only for selected measurement stations but also across the globe. Therefore, satellite data is essential for monitoring air pollution in remote locations. Remote sensing and GIS areusedfor long-term pollution trends analysis. The past air pollution data can be used to create maps that can further createawareness across the world. GIS is used to understand the spatial pattern of the various variables in consideration using different techniques like interpolation methodologies, Kriging, etc. Other geostatistical methods are also used to study spatial variability patterns. GIS manages spatial data by location and examines the interrelationship among various spatial entities. The GIS technology can be used to visualize the air quality forecasted

25 Geospatial Practices for Airpollution and Meteorological Monitoring …

555

Fig. 25.1 Diagram depicting various forms in how GIS can be used

data (Righini et al. 2014; Tirmizi and Tirmizi 2018). Figure 25.1 shows the applications of GIS for Air pollution. GIS can be utilized for air pollution modeling, to locate the monitoring stations, and develop spatial maps of pollutant records which can further be utilized in various decision systems. (Hart et al. 2020).

25.2.1 Satellite Data Different remote sensing options are available to monitor air quality and meteorological parameters. The technological advancements in the miniaturization of sensors, high-speed data transfer, and enhanced storage capabilities have led to a new possibility of satellites specially built for monitoring air pollutants and tracking their emission sources. Various satellites deliver the data on the various air pollutants like Sulphur dioxide, Nitrogen dioxide, Carbon Monoxide, aerosols, etc. The different satellites provide air pollution data directly or from which it can be derived. The Medium Resolution Imaging Spectrometer (MERIS), Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), Scanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) and Global Ozone Monitoring by Occultation of Stars (GOMOS) was onboard Envisat-1,which was active from 2002 to 2012. A new family of satellites was launched as part of this program, known as Sentinels, which measure the different air pollutants and greenhouse gases. The three satellites, MetOp-A, established in 2006, MetOp-B in 2012, and MetOp-C in 2018, are a series of Meteorological Operational (MetOp), which is an ESA programme. Two instruments viz. GOME-2 provides the data on greenhouse gases and the data on ozone profiles, CO, CO2 , N2 O, and CH4 is obtained from Infrared Atmospheric Sounder Interferometer (IASI). The Moderate-resolution

556

S. Tikle et al.

Imaging Spectroradiometer (MODIS) is common to Terra and Aqua and it has 36 spectral bands, ranging in wavelength from 0.4 µm to 14.4 µm, and varying spatial resolutions (2 bands at 250 m, five bands at 500 m and 29 bands at 1 km). MODIS provides information on aerosols, ozone, water vapor, atmospheric temperature, and clouds. Measurement of Pollution in the Troposphere (MOPITT) is another terra instrument that measures CO, with a resolution of 22 × 22 km2 and Multi-angle Imaging Spectro Radiometer (MISR) provides the data on aerosols. The NASA satellite Aura, launched in 2004, has four sensors onboard viz., Microwave Limb Sounder (MLS), Ozone Monitoring Instrument (OMI), High-Resolution Dynamics Limb Sounder (HIRDLS) and Tropospheric Emission Spectrometer (TES), which are all instruments designed to measure pollutants in the lower stratosphere and troposphere. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) provide daily data on aerosols’ vertical distribution. A satellite equipped with five instruments is the Suomi National Polar-orbiting Partnership (Suomi NPP), which includes the Advanced Technology Microwave Sounder (ATMS), the Visible/ Infrared Imager and Radiometer Suite (VIIRS), the Cross-Track Infrared Sounder (CrIS), and the Ozone Mapping and Profiler Suite (OMPS) and Clouds and the Earth’s Radiant Energy System (CERES). A satellite operated by the National Oceanic and Atmospheric Administration (NOAA) since 2017 features five instruments similar to those on Suomi NPP. The Japan Aerospace Exploration Agency (JAXA) has a satellite Greenhouse Gases Observing SATellite (GOSAT) which carries two instruments, viz., TANSO Cloud and Aerosol Imager (TANSO-CAI), providing the data on CO2 , CH4 , and O3 and The Thermal and Near infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) providing aerosol data. The satellite data are publicly available on different portals, which can be used for further analysis and forecasting purposes.

25.2.2 Spatial Data Processing GIS is useful in spatially mapping the ground-based datasets using different statistical techniques to interpolate to a larger area. The geospatial datasets, different meteorological parameters, and various data processing techniques are used to forecast air quality. The various interpolation methods work slightly differently, e.g., in a way to the shape of some mathematical function that is applied to the whole area of interest. The different methods used for spatial data processing are mentioned below.

25.2.2.1

Inverse Distance Weighting (IDW)

IDW interpolation determines cell values using a linearly weighted combination of sample points. This method estimates the pollution concentration, which is inversely correlated to the distance from the measured contamination. The weight is considered as a function of inverse distance. This method assumes that the mapped variable

25 Geospatial Practices for Airpollution and Meteorological Monitoring …

557

decreases influence with distance from its sampled location (Tikle et al. 2012; de Mesnard, 2013; Zalakeviciute et al. 2020). This tool has a limit of approximately 45 million input points. If the input feature class contains more than 45 million points, the tool may fail to create a result (https://www.arcgis.com/). IDW considers that each measured point has a local influence that diminishes with distance. It allocates greater weights to points closest to the prediction location, and the weights diminish as a function of distance (Mesnard 2013). The inverse squared distance (signified by the exponent –2) is typically used to acquire linearly weighted combinations of a set of sample data, and the estimate function of interpolation can be set as below where di O is the distance by which the S0 and the sisi are detached as shown in below equation (Lloyd 2010). 

n

Z (S0 ) =

25.2.2.2

−2 i=1 z(Si )di O  n −2 i=1 di O

Kriging

Kriging is an advanced geostatistical procedure that generates an estimated surface from a scattered set of points with z-values. It is based on the statistical model to produce a prediction surface and gives a measure of the certainty or accuracy of the prediction. Kriging is a common method for obtaining unbiased estimators in a particular location for spatial interpolation. Kriging is a processor-intensive process. The speed of execution is dependent on the number of points. There are two models of Kriging: Ordinary and Universal. Ordinary Kriging is the most commonly used kriging method in different subjects. And the Universal kriging types assume structural componentsare present and the local trend varies from location to location. The basic calculation as shown below in the equation Z (x) =

25.2.2.3

n  n  wi Z (xi ) i=1 k

Spline

The Spline tool interpolates raster using two dimensional minimum curvature spline techniques. It produces a smooth surface that passes through the input points exactly using a mathematical function that minimizes surface curvature. The values entered for this parameter must be equal to or greater than zero. Typical values used are 0, 0.001, 0.01, 0.1, and 0.5.

558

25.2.2.4

S. Tikle et al.

Natural Neighbor

Natural Neighbor Interpolates a raster surface from points using a natural neighbor technique. The perimeter cells of the output raster will be assigned NoData values if their center falls outside the convex hull (defined by the input points). It adopts the structure of the input data locally. It works equally well with regularly and irregularly distributed data. This tool has a limit of approximately 15 million input points. There may be a problem with the tool being unable to create a result if there are more than 15 million points in the input feature class. The various interpolation tools may handle this data condition differently. Due to this process, the output raster may have values that differ from what is expected at some locations. By removing these coincident points, the data can be prepared.

25.2.2.5

Modeling

The Weather Research and Forecasting (WRF) Chem model is used in the real-time forecasting of air pollutants like PM2.5 , O3, etc. In this, the initial and boundary conditions obtained from reanalysis data are used. Further, the initial chemistry conditions are also utilized to develop the final forecast. Thus, this system is used in realtime to obtain the concentration of pollutants in a particular space and time. WRF coupled with atmospheric chemistry (WRF-Chem) is used to simulate the meteorological variables as well as the air pollutants, viz.,particulate matter (PM10 and PM2.5 ) concentration (Grell et al. 2005a, b; Oberai et al. 2022). Various satellite datasets are used to forecast the air pollutant concentration, and along with this, the emissions datasets are also used to simulate the pollutant concentrations better (Fig. 25.2). Air quality forecasting effectively protects the public health by providing an early warning against harmful air pollutants (Titus 1990). The Weather Research and Forecasting Model (WRF) (Skamarock et al. 2008) is an atmospheric model. It is designed for numerical weather prediction (NWP) and research, as indicated by its name. As a result of its long-term development through the interests and contributions of a worldwide community, WRF has become a true community model. It is officially supported by NCAR. WRF has developed over the years to provide remarkable capabilities forvarious of Earth system prediction

Fig. 25.2 Forecasting of air pollutants using the WRF Chem model

25 Geospatial Practices for Airpollution and Meteorological Monitoring …

559

applications, such as air chemistry, hydrology, wildland fires, hurricanes, and regional climate. With the WRF software framework, such extensions are facilitated and a wide range of computing platforms can benefit from efficient, massively parallel computation (Powers et al. 2017). The WRF-Chem model is based on the WRF for the in-line calculation of atmospheric chemistry (Grell et al. 2005a, b). It has applications in a wide range of air quality and chemistry research and has a spectrum of options to handle gas-phase and aqueous chemistry and aerosols. An array of tailored and coupled systems for integrated Earth system modeling will continue to be built on WRF, serving as an effective platform for developing improved representations of physical processes. In fact, WRF is a true community model with a diverse user base that is always looking for new ways to meet their scientific, operational, educational, and commercial needs. Community contributions will advance a vital WRF Model for years to come through innovative applications and contributions (Powers et al. 2017). The HYSPLIT (Hybrid single particle lagrangian integrated trajectory) model is used to understand the trajectories of air masses and predict air pollution concentration. The air masses are responsible for exporting and importing pollutants deposited in the country and neighboring areas. The HYSPLIT model is used as a dispersion model to predict the air pollutant concentration. Pollutant concentrations at groundlevel receptors surrounding pollution sources are typically estimated using these models as part of the permitting process (Stein et al. 2015). Decision Trees Algorithm predictive models were built using a category of the machine learning method called Decision Trees, which is also used for prediction analysis. With the advancement in machine learning techniques and the large availability of datasets, air quality prediction can be done faster and at lower computing costs than the WRF chem model. The artificial neural network turns the characteristics of all problems into numbers and all reasoning into numerical calculations and cannot explain its own reasoning process (Liu et al. 2021). It is essential to use the MLR model to determine the effects of meteorological factors on air pollution concentrations. Using the variance inflation factor (VIF), meteorological factors are calculated to define multicollinearity in these models. In order to analyze multicollinearity for independent variables, we use multicollinearity analysis. Data on air quality and meteorology were our independent variables. Thus, it is assumed that there is no multicollinearity among selected predictors (Zuur et al. 2009; Kliengchuay et al. 2021).

25.3 Findings and Discussion Geospatial techniques can be used to develop various models to characterize air pollutants. Method of air pollution forecasting can broadly be divided into three classical categories: statistical, artificial intelligence, and numerical forecasting methods. To forecast the air quality of specific locations, it is essential to have vast monitoring of the air pollutants and the meteorology of that location and the surrounding regions

560

S. Tikle et al.

that influence it. Only with extensive monitoring can we know about the existing air quality and meteorology patterns and thus be able to forecast both. The conventional ground-based monitoring techniques prove beneficial in understanding a locality’s current features and patterns. With the help of geospatial techniques, the weather systems present in different global locations can be analyzed. Data obtained from various observation stations are used to plot and analyze the weather patterns from a regional to global scale (Mhawish et al. 2018). With the help of GIS, forecasters can easily plot and view maps at different scales, thereby reducing the time taken. GIS helps organize the geographic data to become effective in the decision-making process. The GIS provides advanced functions for managing statistical and spatial data as well as interpolating data to create smooth surfaces, extracting data from the smooth surfaces, and automating these processes. GIS based methodology will help predict the air quality by using the kriging interpolation method. Air pollution concentrations have been estimated using different interpolation methods, such as the Inverse Distance Weighted method (IDW), the Spline method (SPL), the Ordinary Kriging method (OK); the Universal Kriging method (UK); and the Natural Neighbor method (NN). (Shareef et al. 2016). These geostatistical techniques can be used with the monitoring data obtained from the different observational techniques. There have been extensive studies on this topic and different methods prove beneficial for various pollutants. The choice of interpolation technique also depends on the eminence of sample points. If there are few sample points, Kriging can be used where more sample points can be added in areas where the topography changes unexpectedly or recurrently. If the sample points are closely located and have extreme differences in values, then IDW would be the best option. IDW proves as a suitable interpolator for phenomena whose distribution is strongly correlated with distance, such as air, water and noise. The study by (Goutham et al. 2018) reveals there is no universal rule in identifying the best interpolation method. It is better if, for every study area, comparing methods and assessing the accuracy is done via statistical error metrics. Depending on the area of interest and the pollutant of interest and based on the available datasets, the different interpolation techniques will vary. Thus, we need to assess the same as per our requirement. Recent era forecasting uses different techniques and models to predict future weather and air quality. These include statistical methods and chemical transport models. The statistical techniques use prior datasets and the various parameters affecting the air pollutant concentration to forecast air quality. However, this method has a few limitations as sudden extreme events cannot be predicted (Baklanov and Zhang 2020). A regression equation can also be developed to forecast air quality, in which pollutant concentration is calculated from several dependent variables, including air quality in recent days, temperature, and wind speed. Although the development of a regression model requires knowledge of the behavior of the pollutants, variables affecting the contaminants, and execution techniques of regression models, the accuracy of a regression model is highly dependent on the accuracy of the input variables. Thus, using multiple independent variables (which primarily

25 Geospatial Practices for Airpollution and Meteorological Monitoring …

561

include the meteorological parameters), the dependent variable (which is the pollutant of interest) can be predicted. In recent times, artificial neural networks based on numerous statistical techniques are being used to predict air quality. For this purpose, a large amount of monitoring data is required to develop and train a model in context to our requirement. Dispersion modeling is another technique that determines the air pollutant concentration at a distant location than that from the source location. The HYSPLIT model is primarily used for this purpose, as the long-range transport pathways of the pollutants are determined using this model. It is one of the most commonly used models to determine the origin of mass and establish a relation between the sources and receptor (Stein et al. 2015; Liu et al. 2018). The chemical transport models, which are the online-coupled meteorology atmospheric chemistry models (CCMM), have significantly evolved in recent decades. These integrated models are also of interest for numerical weather prediction and climate modeling, although the air quality modeling community mainly develops them. It can consider both the effects of meteorology on air quality and the potentially significant effects of atmospheric composition on the weather. The WRF-Chem model is a widely used chemical transport model for forecasting purposes. Though it is substantially good in simulating the air quality, certain unprecedented emissions cannot be well simulated. Data assimilation (chemical and meteorological measurements) is required to enhance and make the predictions more accurate. The techniques and possibilities of multi-platform (in-situ, ground, aircraft, satellite remote sensing) observations of air pollution and atmospheric parameters over the last decades have dramatically improved, as have the near real-time (NRT) and real-time (RT) capabilities and citizen science opportunities in these fields (Munir et al. 2019). The increased monitoring data available from the different sources over the past years has made assimilation an important aspect. However, apart from this, there are methods to link the chemical transport models and the statistical methods of air quality forecasting by using interpolation techniques, objective analysis, regression analysis, bias correction, and the use of artificial neural networks. In the near future, data fusion methods, machine learning, and artificial intelligence will offer excellent prospects for bridging the gap between global, low-resolution model output and local, highresolution information required by scientists and policymakers (Baklanov and Zhang 2020). Thus, with the help of enhanced monitoring in terms of the available groundbased and satellite data, it is beneficial to study the geospatial patterns. By using geospatial techniques, the locations where the air quality cannot be monitored can be found. Using the different statistical techniques makes air quality forecasting possible; however, chemical transport models will be beneficial to get precise and accurate results at all times (Grell et al. 2005a, b). Furthermore, air quality forecasts should provide appropriate warnings, effects and recommendations for various sectors of health, agriculture, land travel, aviation, and energy. The multilayer perceptron (MLP) model has reliable performance at weather monitoring and a good forecast for one-day weather prediction via the trained models (Huang et al. 2020) as

562

S. Tikle et al.

compared to Multiple Linear Regression Model (Parashar 2019) and Linear Regression Model (Varghese et al. 2019). Many researchers worked on the retrieval of data through different geoprocessing techniques. Detailed retrieval schemes for TROPOMI and OMI data products are provided by Biswal et al. (2021). It was observed that OMI and TROPOMI exhibit a good correlation with the surface observations (Kim et al. 2020; Wang et al. 2020; van Geffen et al. 2020; Biswal et al. 2021) reported that TROPOMI is more superior to OMI. Advance integrated modeling with multiple approaches and AIML can be resulted in robust results with more accuracy and can reduce the.

25.4 Conclusion Many new technologies represent the era of big data science, having an everincreasing influence on geomorphology. Coupling new technologies such as groundbased smart sensors, satellite remote sensing sensors, geospatial technologies, and computational technologies such as artificial intelligence and machine learning can improve the monitoring and prediction of air pollution and meteorological conditions. High resolution satellite data can help the regulator in expanding air quality monitoring to derive valuable findings. Satellite sensors can provide unique views of national pollution information from space. Discovering, sharing, designing, and integrating geospatial data makes the geospatial data more useful. The geospatial data is most useful when discovered, shared, designed, and integrated. In the spatial data infrastructure, geospatial data is discovered and disseminated using geospatial technologies. For successful and sustainable monitoring and prediction system and highlights some emerging trends in the geospatial that will likely impact future use of these concepts. Recent years are marked by rapid growth in sources and availability of geospatial data and information providing new opportunities and challenges for scientific knowledge and geospatial technology solutions on time for data processing. Although there have been advances in the availability and quality of geospatial information, several gaps and challenges remain on the effective use of geospatial information. A technology’s suitability, as well as its advantages and disadvantages, will be determined by its specific characteristics. The future goal of better air quality data can be achieved through innovation in air quality monitoring technologies. It is necessary to adopt a comparatively universal advance integrated multi-satellite data structure model for big geospatial data processing. To emphasize the demand for effective data processing and analysis to strengthen the geospatial technologies for monitoring and predicting air quality and weather forecasting. Satellite-based systems are adaptive in air quality management, and Smart sensor networks with geolocation capabilities may assist developing nations in achieving the Sustainable Development Goals on clean air. Sensors/systems based on geospatial technology are an effective future solution for managing air quality. For enhancing the national economy to produce

25 Geospatial Practices for Airpollution and Meteorological Monitoring …

563

reliable, timely, and accurate analyses, guidance, forecasts, and warnings are essential for protecting lives and property. Global partnerships in weather prediction must be cost-effective to address weather forecasting but are challenging to manage when a weather event threatens a smaller region. For weather events, a logical next step in forecasting due to closer proximity and convenient methods of accessing a weather warning is to make regional partnerships and address the problem at the local level. Developing countries’ goal is to implement long-range and cost-effective weather predictions. To significantly reduce losses at the regional level in all countries, communities and individuals need to become more resilient through actions that integrate weather and climate information in decision-making processes.

References Anenberg SC, Henze DK, Tinney V et al (2018) Estimates of the global burden of ambient PM2:5, ozone, and NO2 on asthma incidence and emergency room visits. Environ Health Perspect 126:1–14. https://doi.org/10.1289/EHP3766 Baklanov A, Zhang Y (2020) Advances in air quality modeling and forecasting. Glob Transitions 2:261–270. https://doi.org/10.1016/j.glt.2020.11.001 Beig G, Chate DM, Ghude SD et al (2013) Quantifying the effect of air quality control measures during the 2010 Commonwealth Games at Delhi, India. Atmos Environ 80:455–463. https:// doi.org/10.1016/j.atmosenv.2013.08.012 Beig G, Sahu SK, Rathod A et al (2021) Role of meteorological regime in mitigating biomass induced extreme air pollution events. Urban Clim 35:100756. https://doi.org/10.1016/j.uclim. 2020.100756 Beig G, Sahu SK, et al (2018) SAFAR-High resolution emission inventory of mega City Delhi for 2018, Special Scientific Report, SAFAR-Delhi-2018-A, ISSN: 0252–1075, Publ.–IITM-Pune, Ministry of Earth Sciences (Govt. of India). IITM-Pune, Ministry of Earth Sciences (Govt. of India) Beig G, Sahu SK, Singh V et al (2020) Objective evaluation of stubble emission of North India and quantifying its impact on air quality of Delhi. Sci Total Environ 709:136126. https://doi.org/10. 1016/j.scitotenv.2019.136126 Biswal A, Singh V, Singh S et al (2021) COVID-19 lockdown-induced changes in NO2levels across India observed by multi-satellite and surface observations. Atmos Chem Phys 21:5235–5251. https://doi.org/10.5194/acp-21-5235-2021 Bozyazi E, Incecik S, Mannaerts C, Brussel M (2015) Analysis and mapping of air pollution using a GIS approach: A case study of Istanbul. Adv Air Pollut 8:431–440 Chowdhury S, Dey S, Di Girolamo L et al (2019) Tracking ambient PM2.5 build-up in Delhi national capital region during the dry season over 15 years using a high-resolution (1 km) satellite aerosol dataset. Atmos Environ 204:142–150. https://doi.org/10.1016/j.atmosenv.2019.02.029 de Mesnard L (2013) Pollution models and inverse distance weighting: Some critical remarks. Comput Geosci 52:459–469. https://doi.org/10.1016/j.cageo.2012.11.002 Dey S, Purohit B, Balyan P et al (2020) A satellite-based high-resolution (1-km) ambient PM2.5 database for India over two decades (2000–2019): Applications for air quality management. Remote Sens 12:1–22. https://doi.org/10.3390/rs12233872 Fu D, Xia X, Duan M et al (2018) Mapping nighttime PM2.5 from VIIRS DNB using a linear mixedeffect model. Atmos Environ 178:214–222. https://doi.org/10.1016/j.atmosenv.2018.02.001

564

S. Tikle et al.

Gao J, Zha Y (2010) Meteorological influence on predicting air pollution from MODIS-derived aerosol optical thickness: A case study in Nanjing, China. Remote Sens 2:2136–2147. https:// doi.org/10.3390/rs2092136 Georgoulias AK, Boersma KF, Van Vliet J et al (2020) Detection of NO2 pollution plumes from individual ships with the TROPOMI/S5P satellite sensor. Environ Res Lett 15. https://doi.org/ 10.1088/1748-9326/abc445 Goutham PM, Jayalakshmi S, Samundeeswari R (2018) A study on comparison of interpolation techniques for air pollution IRS, College of Engineering, Chennai Dept of Civil Engg, University college of Engineering, Panruti. Indian J Sci Res 17:58–63 Grell GA, Peckham SE, Schmitz R et al (2005a) Fully coupled “online” chemistry within the WRF model. Atmos Environ 39:6957–6975. https://doi.org/10.1016/j.atmosenv.2005.04.027 Grell GA, Peckham SE, Skamarock WC, Eder B et al (2005b) Fully coupled “online” chemistry within the WRF model. Atmos Environ 39:6957–6975. https://doi.org/10.1016/j.atmosenv.2005. 04.027 Gunthe SS, Liu P, Panda U et al (2021) Enhanced aerosol particle growth sustained by high continental chlorine emission in India. Nat Geosci 14:77–84. https://doi.org/10.1038/s41561-02000677-x Hart R, Liang L, Dong P (2020) Monitoring, mapping, and modeling spatial–temporal patterns of PM2.5 for improved understanding of air pollution dynamics using portable sensing technologies. Int J Environ Res Public Health 17:1–18. https://doi.org/10.3390/ijerph17144914 Huang ZQ, Chen YC, Wen CY (2020) Real-time weather monitoring and prediction using city buses and machine learning. Sensors(Switzerland) 20:1–21. https://doi.org/10.3390/s20185173 India Meteorological Department (2016) GIS optimizes highway weather forecast Kim J, Jeong U, Ahn M-H et al (2020) New era of air quality monitoring from space: Geostationary environment monitoring spectrometer (GEMS). Bull Am Meteorol Soc 101:E1–E22. https:// doi.org/10.1175/BAMS-D-18-0013.1 Kliengchuay W, Srimanus R, Srimanus W et al (2021) Particulate matter (PM10) prediction based on multiple linear regression: a case study in Chiang Rai Province, Thailand. BMC Public Health 21:1–9. https://doi.org/10.1186/s12889-021-12217-2 Krishna RK, Ghude SD, Kumar R et al (2019) Surface PM2.5 estimate using satellite-derived aerosol optical depth over India. Aerosol Air Qual Res 19:25–37. https://doi.org/10.4209/aaqr. 2017.12.0568 Kumar A, Gupta I, Brandt J et al (2016) Air quality mapping using GIS and economic evaluation of health impact for Mumbai City, India. J Air Waste Manag Assoc 66:470–481. https://doi.org/ 10.1080/10962247.2016.1143887 Kumar P, Kumar S, Joshi L (2015) Socioeconomic and environmental implications of agricultural residue burning Levelt PF, Joiner J, Tamminen J et al (2018) The Ozone monitoring instrument: Overview of 14 years in space. Atmos Chem Phys 18:5699–5745. https://doi.org/10.5194/acp-18-5699-2018 Lim HS, MatJafri MZ, Abdullah K, Wong CJ. (2009) Air pollution determination using remote sensing technique. In: Advances in geoscience and remote sensing. InTech Liu T, Marlier ME, Defries RS et al (2018) Seasonal impact of regional outdoor biomass burning on air pollution in three Indian cities : Delhi, Bengaluru, and Pune. Atmos Environ 172:83–92. https://doi.org/10.1016/j.atmosenv.2017.10.024 Liu B, Jin Y, Li C (2021) Analysis and prediction of air quality in Nanjing from autumn 2018 to summer 2019 using PCR–SVR–ARMA combined model. Sci Rep 11:1–14. https://doi.org/10. 1038/s41598-020-79462-0 Meei C, Gross B, Wu Y, Moshary F (2011) Application of remote sensing instrument in air quality monitoring. In: Air quality monitoring, assessment and management. InTech Mhawish A, Kumar M, Mishra AK et al (2018) Remote sensing of aerosols from space: retrieval of properties and applications. Elsevier Inc.

25 Geospatial Practices for Airpollution and Meteorological Monitoring …

565

Michaelides S, Paronis D, Retalis A, Tymvios F (2017) Monitoring and forecasting air pollution levels by exploiting satellite, ground-based, and synoptic data, Elaborated with regression models. Adv Meteorol 2017. https://doi.org/10.1155/2017/2954010 Munir S, Mayfield M, Coca D, et al (2019) Analysing the performance of low-cost air quality sensors, their drivers, relative benefits and calibration in cities—a case study in Sheffield. Environ Monit Assess 191. https://doi.org/10.1007/s10661-019-7231-8 Muthukumar P, Cocom E, Nagrecha K, et al (2021) Predicting PM2.5 atmospheric air pollution using deep learning with meteorological data and ground-based observations and remote-sensing satellite big data. Air Qual Atmos Heal. https://doi.org/10.1007/s11869-021-01126-3 Oberai K, Saran S, Jha AK, et al (2022) Internet GIS-based air quality monitoring and forecast system for the indian region using FOSS4G. J Indian Soc Remote Sens 4. https://doi.org/10. 1007/s12524-021-01478-4 Pandey M, Singh V, Vaishya RC, Shukla AK (2014) Analysis & application of GIS based air quality monitoring-state of art. Int J Eng Res Technol 2:3788–3796 Parashar A (2019) IoT based automated weather report generation and prediction using machine learning. In: 2019 2nd international conference on intelligent communication and computational techniques (ICCT). IEEE, pp 339–344 Powers JG, Klemp JB, Skamarock WC et al (2017) The weather research and forecasting model: Overview, system efforts, and future directions. Bull Am Meteorol Soc 98:1717–1737. https:// doi.org/10.1175/BAMS-D-15-00308.1 Prem M, Ramya SR, Maheshwarkar P, Sunder RR (2021) Population exposure across central India to PM2.5 derived using remotely sensed products in a three-stage statistical model. Sci Rep 11:1–13. https://doi.org/10.1038/s41598-020-79229-7 Righini G, Cappelletti A, Ciucci A et al (2014) GIS based assessment of the spatial representativeness of air quality monitoring stations using pollutant emissions data. Atmos Environ 97:121–129. https://doi.org/10.1016/j.atmosenv.2014.08.015 Sahu SK, Beig G, Parkhi N (2014) Critical emissions from the largest on-road transport network in South Asia. Aerosol Air Qual Res 14:135–144. https://doi.org/10.4209/aaqr.2013.04.0137 Sartini L, Antonelli M, Pisoni E, Thunis P (2020) From emissions to source allocation: Synergies and trade-offs between top-down and bottom-up information. Atmos Environ X 7. https://doi. org/10.1016/j.aeaoa.2020.100088 Shareef MM, Husain T, Alharbi B (2016) Optimization of air quality monitoring network using GIS based interpolation techniques. J Environ Prot (irvine, Calif) 07:895–911. https://doi.org/ 10.4236/jep.2016.76080 Singh N, Chaturvedi S, Akhter S (2019) Weather forecasting using machine learning algorithm. In: 2019 international conference on signal processing and communication (ICSC). IEEE, pp 171–174 Singh RK, Kumar P, Mukherjee S et al (2021) Application of geospatial technology in agricultural water management. In: Agricultural water management. Elsevier, pp 31–45 Stavrakou T, Müller JF, Bauwens M et al (2020) Satellite evidence for changes in the NO2 weekly cycle over large cities. Sci Rep 10:1–9. https://doi.org/10.1038/s41598-020-66891-0 Stein AF, Draxler RR, Rolph GD et al (2015) Noaa’s hysplit atmospheric transport and dispersion modeling system. Bull Am Meteorol Soc 96:2059–2077. https://doi.org/10.1175/BAMS-D-1400110.1 Tian L, Hou W, Chen J et al (2018) Spatiotemporal changes in PM2.5 and their relationships with land-use and people in Hangzhou. Int J Environ Res Public Health 15:1–14. https://doi.org/10. 3390/ijerph15102192 Tikle S, Saboori MJ, Sankpal R (2012) Spatial distribution of ground water quality in some selected parts of Pune city, Maharashtra, India using GIS. Curr World Environ 7:281–286 Tikle S, Gyananath G (2010) Enrich environment an assessment of drinking water quality around Shahanur dam, Amravati District, Maharashtra Enrich Environment Tirmizi ST, Tirmizi SRUH (2018) GIS based risk assessment of oil spill and gas leakage vulnerable zones in Pakistan. Math Model Eng Probl 5:190–196. https://doi.org/10.18280/mmep.050309

566

S. Tikle et al.

Titus JG (1990) Greenhouse effect, sea level rise, and barrier islands: case study of Long Beach Island, New Jersey. Orig Appear Coast Manag 18:65–90. https://doi.org/10.1061/978078448 0304.034 United Nations Committee of experts on global geospatial information management future trends in geospatial information management: the five to ten year vision van Geffen J, Boersma KF, Eskes H et al (2020) S5P TROPOMI NO2 slant column retrieval: method, stability, uncertainties and comparisons with OMI. Atmos Meas Tech 13:1315–1335. https://doi.org/10.5194/amt-13-1315-2020 Varghese L, Deepak G, Santhanavijayan A (2019) An IoT analytics approach for weather forecasting using raspberry Pi 3 Model B+. In: 2019 fifteenth international conference on information processing (ICINPRO). IEEE, pp 1–5 Vîrghileanu M, S˘avulescu I, Mihai BA et al (2020) Nitrogen dioxide (No2) pollution monitoring with sentinel-5p satellite imagery over europe during the coronavirus pandemic outbreak. Remote Sens 12:1–29. https://doi.org/10.3390/rs12213575 Wang YQ (2014) MeteoInfo : GIS software for meteorological data visualization and analysis. Meteorol Appl 21:360–368. https://doi.org/10.1002/met.13 Wang C, Horby PW, Hayden FG, Gao GF (2020) A novel coronavirus outbreak of global health concern. Lancet 395(10223):470–473 Xu X, Zhang C (2020) Estimation of ground-level PM2.5concentration using MODIS AOD and corrected regression model over Beijing, China. Plos One 15:1–15. https://doi.org/10.1371/jou rnal.pone.0240430 Ye X, Yang X, Xiong X et al (2017) A quality control method based on an improved random forest algorithm for surface air temperature observations. Adv Meteorol 2017:1–15. https://doi.org/ 10.1155/2017/8601296 Zalakeviciute R, Bastidas M, Buenaño A, Rybarczyk Y (2020) A Traffic-based method to predict and map urban air quality. Appl Sci 10. https://doi.org/10.3390/app10062035 Zoogman P et al (2017) Tropospheric emissions: monitoring of pollution (TEMPO). Earth Obs Syst XVIII 186:17–39. https://doi.org/10.1117/12.2024479 Zuur AF, N. EL, Walkar NJ et al (2009) Mixed effects modelling for nested data. In: Mixed effects models and extensions in ecology with R. pp 101–142

Chapter 26

Empowerment of Geospatial Technologies in Conjunction with Information and Communication Technologies (ICT) Aarti Kochhar, Shashikant Patel, Harpinder Singh, P. K. Litoria, and Brijendra Pateriya

Abstract Geospatial technology empowered by its own modern tools has contributed a lot for the benefit of humankind and society. In real world, geospatial technologies intersect with digital technologies for enabling many processes in several applications. Geospatial technologies and digital technologies although are self-driving but the cross linkages can help in solving many complex problems. The empowerment of geospatial technologies in conjunction with key Information and Communication Technologies (ICT) has not been widely pondered over. This paper discusses the influence and significance of geospatial technology and digital technologies in conjunction with one another. The paper is also one of the pioneers in discussing conjunction of all key digital technologies with geospatial in single work. Hardware founded technologies like Internet of Things (IoT), Wireless Sensor Networks (WSN), Robotics and Unmanned Aerial Vehicle (UAV) and software based technologies like Artificial Intelligence (AI), Machine Learning (ML), Data Science and Data Analytics are deliberated in this paper. Other than providing introduction to processes of these technologies, coalition of location parameter and related tools is discussed. The discussions of conjunction are further supported by well-established real life implementations in government or business sector. Keywords Geospatial · Artificial Intelligence (AI) · Machine Learning (ML) · Internet of Things (IoT) · Wireless Sensor Networks (WSN) · Robotics and Unmanned Aerial Vehicle (UAV)

A. Kochhar (B) · S. Patel · H. Singh · P. K. Litoria · B. Pateriya Punjab Remote Sensing Centre, Punjab, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. K. Shit et al. (eds.), Geospatial Practices in Natural Resources Management, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-38004-4_26

567

568

A. Kochhar et al.

26.1 Introduction Geospatial technology has enabled acquisition of geo-referenced data for modelling and analysis using Geographic Information System (GIS), Global Positioning System (GPS) and Remote Sensing (RS). Geospatial technology is itself empowered by its own modern tools but its blend with other digital or Information and Communication Technologies (ICT) has led to solution of many complicated problems. Emergence of Information technologies like Internet of Things (IoT) and Wireless Sensor Networks (WSN), Artificial Intelligence (AI) and Machine Learning (ML), Data Science and Analytics, Robotics and Unmanned Aerial Vehicle (UAV) has paved way of geospatial in many applications like smart city, disaster management, healthcare, agriculture etc. Figure 26.1 explains the corroboration of geospatial and digital or ICT ecosystem. First pillar explains the key technologies of geospatial technology and second pillar shows the enablers of geospatial technology for its outreach to end users in government and business sector. Standardization, interconnected systems and availability of open geospatial data are key enablers of the geospatial technology. Standardization has led to efficient interoperability and cooperation of government sector. Open data led to its exploration and linkages beyond conventional fields. Interconnected systems have supported fast and efficient collection and processing of data. The outreach is driven by strong platforms of digital technologies. These technologies have been further widely discussed below in this section. Framework of conjunction of these technologies with geospatial world has been provided in Sect. 26.2 further. Related case studies are discussed in Sect. 26.3. Paper is concluded in Sect. 26.4.

26.1.1 Internet of Things (IoT) and Wireless Sensor Networks (WSN) IoT is technology that describes a phenomenon of connecting physical things over the internet (Stoyanova et al. 2020). These physical things can be sensors, actuators, appliances, software etc. Things are connected to share data sensed or collected. 10 years ago; no one would have imagined watches being connected to the internet. With the growth of digital electronics and reduction in prices, smart watches replaced mechanical watches. Technological improvements have created microscopic scale sensors that are tiny enough to be embedded into distinctive places (Okwori et al. 2020).Some real world examples of IoT include smart home to control lighting, heating or other appliances of home (Malche and Maheshwary 2017; Jabbar et al. 2018) and smart industry to control machinery and avoid failures (Sisinni et al. 2018; Haverkort and Zimmermann 2017). As shown in Fig. 26.2, these smart physical devices have a sensing unit, Analog to Digital Converter (ADC), embedded processor and a transceiver/communication unit to transmit data. All these units are powered by power unit (Kochhar et al. 2018). The power unit may contain batteries as energy

26 Empowerment of Geospatial Technologies in Conjunction …

569

Fig. 26.1 Conjunction of geospatial and ICT Ecosystem

storage devices that may be powered by ambient energy sources like solar energy, wind energy etc. These devices are connected to internet either through a gateway or directly. As shown in Fig. 26.3, there are different layers in an end-to-end IoT communication (Sikder et al. 2018). Sensing layer senses the data. Preliminary processing can happen here. Network layer is responsible for intra-device communication and sending data over the internet, Bluetooth, cellular network or any other technology. Sensed data can be processed for its presentation again if required. Last layer signifies consumption of the data in an application. WSN is another related technology and can be called as subset of IoT. A collection of only sensors (not devices) to form a network sending data to a base station through a router is called as WSN. WSN consists of only sensors that communicate only wirelessly. These devices communicate following a set of rules called as protocols (Kochhar et al. 2018). Suite of these different protocols responsible for handling different parts of communication is called as protocol stack. Machine to Machine

570

A. Kochhar et al.

Fig. 26.2 Architecture of an IoT physical device

(M2M) Communication and Industrial IoT (IIoT) (Sisinni et al. 2018; Haverkort and Zimmermann 2017) are other related terms.

26.1.2 Artificial Intelligence (AI) and Machine Learning (ML) AI is a broad branch of computer science that conceptualizes a machine being able to mimic a human’s behaviour and perform all the intelligent tasks. As human’s understanding on “how a human mind works” has progressed, the concept has got much deeper insights. Imitating the way a human brain works, algorithm of neural networks was formed to simulate the way a human brain analyzes or processes information. Figure 26.4 shows a single neuron of Artificial Neural Network (ANN). The interconnections among different layers of neurons have weights (w1 , w2 …… wn ). These interconnections are similar to dendrites in biological neuron. Weights select the stimulus of each input (x 1 , x 2 …… x n ). Bias (b) is further added to summation of weighted inputs. Output at this step is further controlled by activation function ( f ) to get the final output. The calculations simulate the cell body of a biological neuron. Weights are adjusted as per the algorithm of the learning procedures. Final output is passed to next neuron through interconnection representing axon in biological neuron (Zhang 2018). Where AI is broader concept, ML is application of AI. Where AI focuses on teaching computers everything they need to know to carry out tasks like humans, ML

26 Empowerment of Geospatial Technologies in Conjunction …

571

Fig. 26.3 Layers of end-to-end IoT communication

focuses on learning from themselves by providing examples of scenarios. Quoting former Chair of the Machine Learning Department at Carnegie Mellon University, Tom M. Mitchell: “Machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience.”

Machine learning has been broadly categorized in two types: Supervised ML and Unsupervised ML. In supervised ML, algorithm learns from a set of examples, generates relationships between input and output/label and then predicts the output/

572

A. Kochhar et al.

Fig. 26.4 ANN in context of biological neuron

label for new data based on developed relationships. Typical example is classification of data (Saravanan and Sujatha 2018). In unsupervised ML, algorithm works on unlabelled data without any prior training. Algorithm tries to learn the hidden pattern in the information itself. Example of unsupervised ML is clustering and association of similar patterns (Wickramasinghe et al. 2021).

26.1.3 Data Science and Analytics Data science is used to mine large datasets and find actionable insights from these large sets of unstructured and structured data. Importance of data science lies in the fact that 90% of the total information available worldwide was produced during the past two years. According to a Forbes article, the whole world generates about 2.5 quintillion bytes of data on daily basis. With the rise of IoT technology, this production is likely to further accelerate. It is predicted to generate 79.4 zettabytes of data by 2025 (Marr 2018). Mathematics and computer science are two major pillars of data science. So as shown in Fig. 26.5, data science is the field of study that is blend of domain knowledge, computer and programming skills, and familiarity of mathematics and statistics to mine meaningful information from data. Few examples of data science are prediction, web recommendations, image, text, audio recognition, product classification and sales or weather forecast. Data science helps in improving decision making, improving risk assessment, forecasting culture and trends etc. Another related term Data Analytics is often confused or used interchangeably with Data Science. Data analytics is more focused subset of data science field with scope limited to processing and performing statistical analysis on existing datasets. Data scientists mainly work on designing algorithms and models. Data analysts identify trends and patterns on the basis of developed models (Cao 2016).

26 Empowerment of Geospatial Technologies in Conjunction …

573

Fig. 26.5 Data science-a cross disciplinary field

26.1.4 Robotics and Unmanned Aerial Vehicles (UAV) Robotics is an interdisciplinary area of science and engineering that integrates software over hardware. Robotics has already been extensively employed in various medical processes because of its perfection and fatigueless capabilities (Kucuk 2020). Other than medical procedures, robots are being stationed on assembly lines of large manufacturing units that may require constant diligence like car assembly. Robots are being operated successfully in military operations and mining zones. In general, people normally perceive robots as humanoids and assess them over their human traits. Humanoids are robots that impersonate the way humans react and respond, imitate their subjective behaviour, look and external presence. All robots are necessarily not humanoids. Robots consist of electro-mechanical components like motors for locomotion, controller acting as brain of robot, sensors like sensing organs, power supply circuitry and at least some level of computer programming telling it what to do. The hardware system integrated with software that ties all the functionalities

574

A. Kochhar et al.

together is called as an embedded system. Controllers are normally evaluated for its processing capability (8-bit, 16-bit, 32-bit), ADC resolution, General Purpose Input Output (GPIO) pins and power consumption. Atmel AVR (Alf and Vegard’s RISC) and PIC (Peripheral Interface Controller) are commonly used microcontrollers for small-scale purposes. Commercial robots have integrated circuits like DSP (Digital Signal Processors), FPGA (Field-Programmable Gate Array), PLC (Programmable Logic Controller) for the computational controller related tasks (Sajkowski et al. 2021; Dutta et al. 2021; Magrin et al. 2021). Snapdragon 820E platform could be perfect for developing integrated IoT applications in collaboration with robotics (Snapdragon 820E embedded platform 2022). UAV a class of flying robots consists of aircraft components, sensors and a Ground Control Station (GCS). These devices do not have any on-board pilot and can be controlled by equipment from the ground manually or autonomously as per a preprogrammed flight plan. Position, speed and altitude are controlled using loop principles integrated with software. Sensors for UAV’s position, velocity and altitude control may include gyroscope and accelerometer also. It has an inbuilt communication module that contains transceiver for uplink and downlink communication between main unit and GCS. Compared to conventional piloted aircraft, UAVs are economical to procure, control and produce images with better spatial resolution.

26.2 Materials and Methods The framework of conjunction of above mentioned ICTs with geospatial technologies has been discussed in this section. This section also discusses the possibilities of conjunction along with data creation and utilization in the cross linkages. Other than framework, this section also throws light on available standards and skill packages that have enabled the conjunction.

26.2.1 IoT and WSN in Conjunction with Geospatial Technology The data produced and collected by IoT has much deeper significance if used in conjunction with geospatial technologies. The location parameter can add a bonus insight into any application. IoT can be used in smart vehicles for urban traffic management and road safety. Detecting location of accident can help an ambulance in reaching the site on time. Location based air pollution sensors can help in analysing the pattern and eliminating the root cause. Combined together the two technologies are also called as geography of things (GoT: combination of geospatial and IoT). Mapping quality of water and soil along with geospatial information can help in gaining better perception of changing patterns and taking future decisions.

26 Empowerment of Geospatial Technologies in Conjunction …

575

Fig. 26.6 Number of results on search of keyword

Location aware IoT, GoT and Location of things are buzz words but not as widely implemented as discussed. Figure 26.6 shows the number of results on search of keyword ‘Geospatial’, ‘IoT’ and combined keyword search for ‘Geospatial and IoT’. The results clearly depict limited research in the area of implementation of IoT for geospatial data management. These keyword searches also include paper that has mere citing of the keyword rather than a discussion. Rieke et al. (2018) widely discussed the challenges of IoT integration with Spatial Data Infrastructures (SDI). Authors discussed the inconsistencies between event driven data and classic data, heterogeneity of approaches, standardization and interoperability issues. Kamilaris and Ostermann (2018) presented a wide review on the interaction of two technologies. The paper presents various geospatial analytical techniques performed over IoT related data. Most of the projects implementing conjunction of two core technologies are based on smart city ideas like City Pulse route planner (Puiu et al. 2016), BALLADE for availability or nearest electric vehicle charging station (Tuning the BALLADE Geospatial Infrastructure for Plug-in Electric Vehicles 2022), traffic flow visualization (Tostes et al. 2013), transport management (Richly et al. 2015), infrastructure health monitoring (Tariq et al. 2020) and geospatial dashboard for complete city management (Lwin et al. 2019). Bhanumathi and Kalaivanan (2019) discuss the role of conjunction for achieving precision agriculture. The conjunction of geospatial technology and IoT has a great potential for solving environmental issues. Popular use cases include applying geospatial analytics on the data collected by an IoT or use of IoT for geospatial data management. Data collected by sensors can be standardized according to Sensor Web Enablement (SWE) specifications of Open Geospatial Consortium (OGC) standards.

576

A. Kochhar et al.

26.2.2 AI and ML in Conjunction with Geospatial Technology The combination of AI and GIS to create new-age machine learning is called as geospatial AI or GeoAI, which is based on geographical components. Few researchers feel that geospatial on this level is a huge dataset that can be utilized by machine learning. Geospatial technologies like Satellite imagery, drone mapping, surveying terrestrial cameras produce a high volume of data. It has been estimated that 80% of the data produced is either geographical or can be georeferenced (IBM: Industry Insights: 2.5 quintillion bytes of data created every day. How does CPG Retail manage it 2022). It becomes virtually impossible for the human mind to arrive at an efficient decision with so much data and parameters involved. AI embedded in spatial technologies can help in analysing multiple factors in real-time with an almosthuman-like perspective but with much better processing and precision. AI can help in geospatial data pre-processing and interpretation (Janowicz et al. 2020). GeoAI can play a huge role in healthcare like identifying population at high risk, analytics for root cause detection of syndromic population, healthcare delivery (Boulos et al. 2019; Yu and Helwig 2021); smart city development (Sharma et al. 2021) like geo-surveillance of videos (García et al. 2017), waste collection and transport (Lella et al. 2017) and environmental contamination (VoPham et al. 2018). Major use cases of machine learning for geospatial analysis are image classification, semantic segmentation, instance segmentation and object detection. For image classification, a label is assigned to a complete image. Each pixel of image is separately classified in semantic segmentation and instance segmentation is for precise 3D object detection. In object detection, an algorithm finds an object within the image (Singh 2019).

26.2.3 Data Science and Analytics in Conjunction with Geospatial Technology To help us understand the workflow of data science, Mason and Wiggins proposed OSEMN (Obtain, Scrub, Explore, Model, and Interpret) framework. As shown in Fig. 26.7, the framework discusses every step of the lifecycle of data science project from beginning to end (Lau 2019).Obtain refers to data collection, Scrub means cleaning and reformatting of data for standardisation and handling missing values. Explore is a way to find patterns and trends within data. Model refers to splitting of data in training and validation dataset, hyperparameters tuning and result evaluation. At final step of the cycle, data is interpreted for gaining insights into results. Combination of Geospatial and data science is also called as Spatial Data Science (SDS). While GIS tells us where something is happening, data science helps us fill in other blanks like what is happening, why it’s happening, and how. SDS has been brought into mainstream by several Python packages for geospatial analytics,

26 Empowerment of Geospatial Technologies in Conjunction …

577

Fig. 26.7 OSEMN framework-a data science process

such as GeoPandas. Data science in conjunction with geospatial science has been used for wide range of applications including biodiversity, health and sustainable environment. SDS has also led to solving many environmental problems. It has widely supported forest and ecosystem conservation efforts (Worthington et al. 2020), ecological predictions (Niu et al. 2014), weather forecasting including disaster forecasting (Chandrasekar and Kutty 2013; Yu et al. 2013; Yucel et al. 2015).

26.2.4 Robotics and UAVin Conjunction with Geospatial Technology Geospatial concepts can be combined with mobile robotics to gather environmental data using GPS and sensors installed on a robot, and then utilize the data to generate and evaluate the matic GIS maps. Robots are actively used to gather data in terrains that are difficult to map or have weak GPS signal. In these cases, robots have georeferenced base maps that are referred to create maps where GPS signals are not evident (Maier and Kleiner 2010). This is an example of using robots for GIS. For navigating in hazardous areas such as coal mines, landmines and military zones, robots can be developed with decision making capability (Baudoin and Habib 2010). Integration of cameras provides real-time information about the terrain and obstacles. Integration of AI helps in finding best way around obstacles. This technology is developed for the Mars rover and other similar vehicles (Fink et al. 2005). Laser range finders and array of sensors are deployed on robotic platforms for collecting data during field surveys. Such a robotic survey can produce a high resolution map with millimetre level accuracy. These terrain plans provide an elevated view of the interior spaces in a building, mines or any tough or unapproachable terrain. The gathered information is either processed in real time or saved for later processing. The ultimate output is a high quality map of the terrain data of the targeted area that can be readily integrated into a GIS. Other than robots, UAVs have progressed their way in geospatial applications. In the field of remote sensing, high spatial resolution aerial images by UAVs have replaced conventional aerial photographs of the earth’s surface. Limitation of UAV is its capability to fly at low altitudes and hence having less coverage area per flight. Flight time is also constrained by battery life. Hence UAVs can be useful in monitoring small terrains where cloud cover often obstructs remote sensing systems in acquiring images like hilly areas and small agricultural fields. By using UAVs, images of crop growth stages can be attained and analysed to monitor the dynamics of crops.

578

A. Kochhar et al.

Robotics operates in collaboration with AI also. AI provides robots a computer vision to navigate, sense and compute their response accordingly. Robots are trained by humans through machine learning or real-time examples are presented for training of robots. Another related field Robotic Process Automation (RPA) is used for automation of repeatable and software based business processes. Since RPA robots are not able to learn or improvise, so it can be employed only with standardized repetitive tasks that need precise repetitive output (Madakam et al. 2019).

26.3 Results and Discussion This section discusses the successfully implemented case studies resulted as output of conjunction of geospatial technologies and mentioned ICTs.

26.3.1 Case Studies: IoT and WSN in Conjunction with Geospatial Technology Barcelona authorities launched a drive to transform the city into a smart city connected via IoT. The Barcelona government setup a system based on IoT in the city. For example, local citizens used an app called ApparkB to find vacant parking slots. Sensors in the tarmac signal if a parking spot is open or not. The app further directs drivers to available locations. Drivers are even provided with an option to pay for parking online via app. In addition to reduction of traffic jams and pollution in the city, the program improved the convenience and accessibility for individual drivers. Within a year of its introduction, the authorities issued 4,000 parking permits a day through the program (Adler 2016). Under same IoT mission, municipal trash cans were fixed with sensors for waste surveillance. The smart trash cans monitors waste levels using IoT, optimizes collection routes using geospatial tools and texts residents when their waste output is high. Many other programs were initiated under same program including smart street lighting, digital transport experience and bike shares. Since implementing its IoT mission, the authorities have saved 58 million US dollars on water and 37 million US dollars annually on lighting, increased its parking revenue by 50 million US dollars per year (Adler 2016; https://www.are averda.cat/en 2016). A sensor integrated system was implemented in Italy for monitoring the environment. The complete application and the database (data sensed from the IoT sensors) were hosted on the cloud environment. The sensors resulted in an alarm, if any environment related issue was sensed. The data gathered was standardized according to SWE specifications of OGC standards (Fazio and Puliafito 2015).

26 Empowerment of Geospatial Technologies in Conjunction …

579

26.3.2 Case Studies: AI and ML in Conjunction with Geospatial Technology Epidemiologist Dr. Gammino supported the Global Polio Eradication Initiative (GPEI) by spotting prospective settlements and navigation patterns using satellite imagery. The Democratic Republic of Congo was highly benefited by the immunization activities of the initiative. The same case scenario development using Geo-AI, later on, helped in Ebola eradication in Nigeria (LaGrone 2020). Many researchers used geospatial machine learning to monitor and analyse the NASA (National Aeronautics and Space Administration) developed daily progression of Australian bush fires from satellite imagery (National Aeronautics and Space Administration: NASA Animates World Path of Smoke and Aerosols from Australian Fires 2020).

26.3.3 Case Studies: Data Science and Analytics in Conjunction with Geospatial Technology Gramener, a data science consulting company developed a spatial data science solution to fight dengue for the World Mosquito Program (WMP) to control deadly mosquito-borne diseases like dengue and malaria. In the lab, WMP modified and neutralized the disease-carrying capabilities of mosquitoes. These modified mosquitoes were then released to mate with the local mosquito population in targeted areas. Over time, the entire mosquito population is neutralized through mating. The biomedical process is costly so to increase its effectiveness releasing zones of modified mosquitoes were identified using SDS. Funded by Microsoft, SDS solution predicts population density from satellite imagery. SDS based solution drafts a release plan to accelerate the effectiveness of the program (Brady et al. 2020).

26.3.4 Case Studies: Robotics and UAV in Conjunction with Geospatial Technology Patricia McSherry, chief Geo Integration Officer (GIO) at Langley Air Force Base, tested the conjunction of technologies through its bounds during a preliminary survey at Langley. During the pilot survey, the robot captured data for over one lakh feet square of space per day, including office spaces like cubicles and other non-traditional geographies. ESRI created a platform named Smart Point to integrate heterogeneous objects like robots and sensors with GIS. This execution ensured that robots and sensors can operate and interact with other elements of geospatial context of the operation (GIS in Defense Installation and Environmental Management 2022). Centre for Robot-Assisted Search and Rescue (CRASAR) deployed UAVs to search for Hurricane Katrina survivors in Florida and Mississippi. UAV was also

580

A. Kochhar et al.

part of damage-assessment activities (Greenwood et al. 2020). UAV has been widely used for fire missions in forest areas. Likewise, for the response to the 2014 Fire in California, Predator UAV was used by the authorities to monitor fire movement (Merlin 2009).

26.4 Conclusion Outlook of modern data has gone beyond the tabular, coordinates or shape file format. Present-day applications also include data in the form of messages, voices, videos etc. Other than the structure, the volume of the data has also increased tremendously. For sustainability of technologies with this unstructured and voluminous data, integration is required. Sensing with IoT and WSN provides eyes and ears to the geospatial tools and IoTis benefitted by the incorporation of location parameter. AI and ML aid in prediction and forecast activities of geospatial data. The handling of velocity, volume and variety of geospatial data is streamlined with tools of data science and analytics. Integration of Robotics and UAV has paved access to geospatial implementations in hazardous and disastrous zones. Thus the paper highlights the role of integration of geospatial and digital technologies for the sustainability of both.

References Adler L (2016) How smart city Barcelona brought the internet of things to life. https://datasm art.ash.harvard.edu/news/article/how-smart-city-barcelona-brought-the-internet-of-things-tolife-789. Last accessed 31 Jan 2022 Bhanumathi V, Kalaivanan K (2019) The role of geospatial technology with IoT for precision agriculture. In: Cloud computing for geospatial big data analytics. Springer, Cham, pp 225–25 Boulos MNK, Peng G, VoPham T (2019) An overview of GeoAI applications in health and healthcare. Int J Health Geogr 18(1):1–9 Brady OJ, Kharisma DD, Wilastonegoro NN, O’Reilly KM, Hendrickx E, Bastos LS et al (2020) The cost-effectiveness of controlling dengue in Indonesia using wMel Wolbachia released at scale: a modelling study. BMC Med 18(1):1–12 Cao L (2016) Data science and analytics: a new era. Int J Data Sci Anal 1(1):1–2 Chandrasekar A, Kutty MG (2013) Studies on the impacts of 3D-VAR assimilation of satellite observations on the simulation of monsoon depressions over India. In: Data assimilation for atmospheric, oceanic and hydrologic applications, vol 2. Springer, Berlin, Heidelberg, pp 643– 705 Dutta A, Tannivar M, Hande B, Sengupta D, Maji A, Shree O, Sarkar M (2021) Robotic arm utilization in global covid-19 management. In: 2021 5th international conference on electronics, materials engineering & nano-technology (IEMENTech). pp 1–7 Baudoin Y, Habib MK (Eds) (2010) Using robots in hazardous environments: Landmine detection, de-mining and other applications. Elsevier Fazio M, Puliafito A (2015) Cloud4sens: a cloud-based architecture for sensor controlling and monitoring. IEEE Commun Mag 53(3):41–47 Fink W, Dohm JM, Tarbell MA, Hare TM, Baker VR (2005) Next-generation robotic planetary reconnaissance missions: a paradigm shift. Planet Space Sci 53(14–15):1419–1426

26 Empowerment of Geospatial Technologies in Conjunction …

581

García RO, Valentín L, Serrano SA, Palacios-Alonso MA, Sucar LE (2017) Geovisualization for smart video surveillance. ISPRS Ann Photogramm, Remote Sens & Spat Inf Sciences 4 GIS in Defense Installation and Environmental Management: https://www.esri.com/content/dam/ esrisites/sitecore-archive/Files/Pdfs/industries/defense/pdfs/gis-in-defense-installation.pdf. Last accessed 31 Jan 2022 Greenwood F, Nelson EL, Greenough PG (2020) Flying into the hurricane: A case study of UAV use in damage assessment during the 2017 hurricanes in Texas and Florida. PLoS ONE 15(2):e0227808 Haverkort BR, Zimmermann A (2017) Smart industry: How ICT will change the game! IEEE Internet Comput 21(1):8–10 https://www.areaverda.cat/en. Last accessed 31 Jan 2022 IBM: Industry Insights: 2.5 quintillion bytes of data created every day. How does CPG & Retail manage it? https://www.ibm.com/blogs/insights-on-business/consumer-products/2-5-qui ntillion-bytes-of-data-created-every-day-how-does-cpg-retail-manage-it/. Last accessed 31 Jan 2022 Jabbar WA, Alsibai MH, Amran NSS, Mahayadin SK (2018) Design and implementation of IoTbased automation system for smart home. In: 2018 International symposium on networks, computers and communications (ISNCC). pp 1–6 Janowicz K, Gao S, McKenzie G, Hu Y, Bhaduri B (2020) GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. Int J Geogr Inf Sci 34(4):625–636 Kamilaris A, Ostermann FO (2018) Geospatial analysis and the internet of things. ISPRS Int J Geo Inf 7(7):269 Kochhar A, Kaur P, Singh P, Sharma S (2018) Protocols for wireless sensor networks: A survey. J Telecommun Inf Technol Kucuk S (2020) Introductory chapter: Medical robots in surgery and rehabilitation. Med Robot-New Achievements LaGrone N (2020) How geospatial technology and AI are transforming Medicine (2020). https:// www.azavea.com/blog/2020/01/17/geospatial-technology-and-ai-transforming-medicine/. Last accessed 31 Jan 2022 Lau CH (2019) 5 steps of a data science project lifecycle. https://towardsdatascience.com/5-stepsof-a-data-science-project-lifecycle-26c50372b492. Last accessed 31 Jan 2022 Lella J, Mandla VR, Zhu X (2017) Solid waste collection/transport optimization and vegetation land cover estimation using Geographic Information System (GIS): A case study of a proposed smart-city. Sustain Cities Soc 35:336–349 Lwin KK, Sekimoto Y, Takeuchi W, Zettsu K (2019) City geospatial dashboard: IoT and big data analytics for geospatial solutions provider in disaster management. In: 2019 International conference on information and communication technologies for disaster management (ICTDM). pp 1–4 Madakam S, Holmukhe RM, Jaiswal DK (2019) The future digital work force: robotic process automation (RPA). JISTEM-J Inf Syst Technol Management 16 Magrin CE., Del Conte G, Todt E (2021) Creating a digital twin as an open source learning tool for mobile robotics. In: 2021 Latin American Robotics Symposium (LARS), 2021 Brazilian Symposium on Robotics (SBR), and 2021 Workshop on Robotics in Education (WRE). pp 13–18 Maier D, Kleiner A (2010) Improved GPS sensor model for mobile robots in urban terrain. In: 2010 IEEE international conference on robotics and automation. pp 4385–4390 Malche T, Maheshwary P (2017) Internet of Things (IoT) for building smart home system. In: 2017 International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC). pp 65–70

582

A. Kochhar et al.

Marr B (2018) How much data do we create every day? The mind-blowing stats everyone should read. https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-createevery-day-the-mind-blowing-stats-everyone-should-read/?sh=476778be60ba. Last accessed 31 Jan 2022 Merlin PW (2009) Ikhana: Unmanned aircraft system western states fire missions. National Aeronautics and Space Administration, Washington, D.C. (2009). https://history.nasa.gov/monogr aph44.pdf. Last accessed 31 Jan 2022 National Aeronautics and Space Administration: NASA Animates World Path of Smoke and Aerosols from Australian Fires (2020). https://www.nasa.gov/feature/goddard/2020/nasa-ani mates-world-path-of-smoke-and-aerosols-from-australian-fires. Last accessed 31 Jan 2022 Niu S, Luo Y, Dietze MC, Keenan TF, Shi Z, Li J, III FSC (2014) The role of data assimilation in predictive ecology. Ecosphere 5(5):1–16 Okwori M, Behfarnia A, Eslami A (2020) Towards microscale NFC-Enabled IoT sensors: Physical and MAC layer design analysis. IEEE Access. 8:88076–88084 Puiu D, Barnaghi P, Tönjes R, Kümper D, Ali MI, Mileo A, Parreira JX, Fischer M, Kolozali S, Farajidavar N et al (2016) Citypulse: Large scale data analytics framework for smart cities. IEEE Access. 4:1086–1108 Richly K, Teusner R, Immer A, Windheuser F, Wolf L (2015) Optimizing routes of public transportation systems by analyzing the data of taxi rides. In: Proceedings of the 1st international ACM SIGSPATIAL workshop on smart cities and urban analytics. pp 70–76 Rieke M, Bigagli L, Herle S, Jirka S, Kotsev A, Liebig T, Malewski C et al (2018) Geospatial IoT— The need for event-driven architectures in contemporary spatial data infrastructures. ISPRS Int J GeoInformation 7(10):385. https://doi.org/10.3390/ijgi7100385 Sajkowski M, Frania K, Stenzel T, Góral M (2021) Image processing based control system for wheeled mobile robots. In: 2021 IEEE 19th international power electronics and motion control conference (PEMC). pp 731–736 Saravanan R, Sujatha P (2018) A state of art techniques on machine learning algorithms: a perspective of supervised learning approaches in data classification. In: 2018 second international conference on intelligent computing and control systems (ICICCS). pp 945–949 Sharma P, Singh R, Srivastava A (2021) Analyzing the role of geospatial technology in smart city development. In: Geospatial technology and smart cities. Springer, Cham, pp 1–20 Sikder AK, Petracca G, Aksu H, Jaeger T, Uluagac AS (2018) A survey on sensor-based threats to internet-of-things (IoT) devices and applications. arXiv preprint arXiv:1802.02041 Singh R (2019) Where deep learning meets GIS. https://www.esri.com/about/newsroom/arcwatch/ where-deep-learning-meets-gis/. Last accessed 31 Jan 2022 Sisinni E, Saifullah A, Han S, Jennehag U, Gidlund M (2018) Industrial internet of things: Challenges, opportunities, and directions. IEEE Trans Industr Inf 14(11):4724–4734 Snapdragon 820E embedded platform. https://www.qualcomm.com/media/documents/files/snapdr agon-820e-embedded-platform-product-brief.pdf. Last accessed 31 Jan 2022 Stoyanova M, Nikoloudakis Y, Panagiotakis S, Pallis E, Markakis EK (2020) A survey on the internet of things (IoT) forensics: challenges, approaches, and open issues. IEEE Commun Surv & Tutorials 22(2):1191–1221 Tariq H, Abdaoui A, Touati F, Al-Hitmi MAE, Crescini D, Mnaouer AB (2020) Design and implementation of cadastral geo-spatial IoT network gateway analyzer for urban scale infrastructure health monitoring. In: 2020 10th annual computing and communication workshop and conference (CCWC). pp 0858–0863 Tostes AIJ, de LP Duarte-Figueiredo F, Assunção R, Salles J, Loureiro AA (2013) From data to knowledge: City-wide traffic flows analysis and prediction using bing maps. In: Proceedings of the 2nd ACM SIGKDD international workshop on urban computing. pp 1–8 Tuning the BALLADE geospatial infrastructure for plug-in electric vehicles. http://sensorsan dsystems.com/tuning-the-ballade-geospatial-infrastructure-for-plug-in-electric-vehicles/. Last accessed 31 Jan 2022

26 Empowerment of Geospatial Technologies in Conjunction …

583

VoPham T, Hart JE, Laden F, Chiang YY (2018) Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology. Environ Health 17(1):1–6 Wickramasinghe CS, Amarasinghe K, Marino DL, Rieger C, Manic M (2021) Explainable unsupervised machine learning for cyber-physical systems. IEEE Access. 9:131824–131843 Worthington TA, Andradi-Brown DA, Bhargava R, Buelow C, Bunting P, Duncan C et al (2020) Harnessing big data to support the conservation and rehabilitation of mangrove forests globally. One Earth. 2(5):429–443 Yu C, Helwig EJ (2021) The role of AI technology in prediction, diagnosis and treatment of colorectal cancer. Artif Intell Review:1–21 Yu X, Park SK, Lee YH (2013) Parameter estimation using an evolutionary algorithm for QPF in a tropical cyclone. In: Data assimilation for atmospheric, oceanic and hydrologic applications, vol 2. Springer, Berlin, Heidelberg, pp 707–715 Yucel I, Onen A, Yilmaz KK, Gochis DJ (2015) Calibration and evaluation of a flood forecasting system: Utility of numerical weather prediction model, data assimilation and satellite-based rainfall. J Hydrol 523:49–66 Zhang Z (2018) Artificial neural network. In: Zhang Z (ed) Multivariate time series analysis in climate and environmental research. Springer, Cham, pp 1–35. https://doi.org/10.1007/978-3319-67340-0_1