Sustainable Development Practices Using Geoinformatics 9781119687115

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Sustainable Development Practices Using Geoinformatics

Scrivener Publishing 100 Cummings Center, Suite 541J Beverly, MA 01915-6106 Publishers at Scrivener Martin Scrivener ([email protected]) Phillip Carmical ([email protected])

Sustainable Development Practices Using Geoinformatics

Edited by

Shruti Kanga Varun Narayan Mishra Suraj Kumar Singh

This edition first published 2021 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA © 2021 Scrivener Publishing LLC For more information about Scrivener publications please visit www.scrivenerpublishing.com. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions. Wiley Global Headquarters 111 River Street, Hoboken, NJ 07030, USA For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com. Limit of Liability/Disclaimer of Warranty While the publisher and authors have used their best efforts in preparing this work, they make no rep­ resentations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchant-­ ability or fitness for a particular purpose. No warranty may be created or extended by sales representa­ tives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further informa­ tion does not mean that the publisher and authors endorse the information or services the organiza­ tion, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Library of Congress Cataloging-in-Publication Data ISBN 978-1-119-68711-5 Cover image: cyber background - Sergey Gavrilichev | Dreamstime.com   planet earth - Alexandr Yurtchenko | Dreamstime.com Cover design by Kris Hackerott Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines Printed in the USA 10 9 8 7 6 5 4 3 2 1

Contents Preface xv Acknowledgement xxi 1 The Impact of Rapid Urbanization on Vegetation Cover and Land Surface Temperature in Barasat Municipal Area Aniruddha Debnath, Ritesh Kumar, Taniya Singh and Ravindra Prawasi 1.1 Introduction 1.2 Study Area 1.3 Datasets and Methodology 1.3.1 Datasets 1.3.2 Methodology 1.4 Results and Discussion 1.4.1 Pattern of LULC in Barasat 1.4.2 Urban Sprawl 1.4.3 Impact of Urban Sprawl on Vegetation Cover 1.4.4 Impact of Urban Sprawl on LST 1.4.5 Relationship Between NDVI and LST 1.4.6 Urban Heat Island 1.5 Conclusion Acknowledgement References 2 Geo-Environmental Hazard Vulnerability and Risk Assessment Over South Karanpura Coalfield Region of India Akshay Kumar, Shashank Shekhar, Anamika Shalini Tirkey and Akhouri Pramod Krishna 2.1 Introduction 2.2 Study Area 2.3 Methodology and Data Used 2.4 Result and Discussion

1 2 4 4 4 4 7 8 9 10 11 12 12 16 19 19 23 24 26 28 30 v

vi  Contents 2.4.1 Thematic Layers of GHI 2.4.1.1 AOT, PWV, and Temperature 2.4.1.2 Land Use/Land Cover 2.4.2 Thematic Layers of SVI 2.4.2.1 Population Density 2.4.2.2 Total Worker 2.4.2.3 Children Age Group (0–6 years) (CAG) 2.4.2.4 Literacy Rate 2.4.3 Geo-Environmental Hazard and Socio-Economic Vulnerability Assessment 2.4.3.1 Geo-Environmental Hazard Index 2.4.3.2 Socio-Economic Vulnerability Index 2.4.4 CMRI Assessment 2.5 Conclusion References Appendix: List of Abbreviations 3 Bistatic Scatterometer Measurements for Soil Moisture Estimation Using Grid Partition–Based Neuro-Fuzzy Inference System at L-Band Ajeet Kumar Vishwakarma and Rajendra Prasad 3.1 Introduction 3.2 Methods and Materials 3.2.1 Bistatic Scatterometer System 3.2.2 Measurement of Soil Moisture Content 3.2.3 Methods 3.2.3.1 G-ANFIS 3.3 Result and Discussions 3.4 Conclusions References 4 Morphometric Analysis of Tapi Drainage Basin Using Remote Sensing and GIS Techniques Pavankumar Giri, Pranaya Diwate and Yadao Kumar Mawale 4.1 Introduction 4.2 Study Area 4.3 Methodology 4.4 Results and Discussion

30 30 32 33 34 36 36 36 37 37 39 39 41 42 44

47 48 49 49 49 51 51 51 53 54 57 57 58 59 60

Contents  vii 4.4.1 Morphometric Analysis of Basin 4.4.1.1 Linear Aspect 4.4.1.2 Relief Aspects 4.4.1.3 Aerial Aspects  4.5 Conclusion Acknowledgments References 5 Efficacy of GOSAT Data for Global Distribution of CO2 Emission Laxmi Kant Sharma and Rajani Kant Verma 5.1 Introduction 5.2 Monitoring of Greenhouse Gases From Space 5.3 GOSAT Satellite 5.3.1 Sensors Description of GOSAT 5.4 Methodology 5.5 Results and Discussion 5.6 Conclusion References

60 60 64 65 69 70 70 73 73 74 74 75 75 80 83 83

6 Development of a Smart Village Through Micro-Level Planning Using Geospatial Techniques—A Case Study of Jangal Aurahi Village of Gorakhpur District 85 Swati Pandey and Gaurav Tripathi 6.1 Introduction 86 6.2 Study Area 87 6.3 Data Used and Methodology 87 6.3.1 Satellite Data 89 6.3.2 Cadastral Data 89 6.3.3 Ground Truth Data 89 6.3.4 Survey of India Toposheet 89 6.3.5 Methodology 89 6.4 Result and Discussion 98 6.4.1 Action Plan Map 98 6.4.1.1 Soil Resources Action Plan 99 6.4.1.2 Water Resources Action Plan 100 6.4.1.3 Action Plan for Waste Water Management 102 6.4.1.4 Action Plan Solid Waste Management 102 6.4.1.5 Action Plan for Land Use Management 103 6.5 Conclusion 105 References 107

viii  Contents 7 Land Appraisal for the Growth of Potato Cultivation: A Study of Sagar Island, India Sabir Hossain Molla, Rukhsana and Asraful Alam 7.1 Introduction 7.2 Study Area 7.3 Materials and Method 7.3.1 Data Source 7.3.2 Generation of Different Thematic Layers for Land Suitability Evaluation of Potato Cultivation 7.3.3 Assigning Weight of Parameters and MCE 7.3.4 Generation of Land Suitability Map (LSM) and Overlaid With LULC Map 7.4 Results and Discussion 7.4.1 Determination of Suitable Zones for Potato Cultivation at Different Land Suitability Parameters 7.4.2 Suitability Map 7.5 Conclusions References 8 Landslide Vulnerability Mapping Using Geospatial Technology Saravanan Kothandaraman, Dinagarapandi Pandi and Mohan Kuppusamy 8.1 Introduction 8.2 Study Area 8.3 Materials and Methods 8.4 Summary References 9 Assessment of Impacts of Coal Mining–Induced Subsidence on Native Flora and Native Forest Land: A Brief Review Ashish Kumar Vishwakarma, Rajesh Rai, Ashwani Kumar Sonkar, Tusarkanta Behera and Bal Krishna Shrivastva 9.1 Introduction 9.2 Material and Methods 9.2.1 Impacts of Subsidence on Forest Lands 9.2.2 Impacts on the Health of Native Floras 9.2.3 Impacts on Soil Functions 9.3 Conclusions References

111 112 113 115 115 116 117 120 121 121 122 124 124 127 128 130 132 137 137 141

142 144 144 145 147 149 149

Contents  ix 10 Application of GI Science in Morphometric Analysis: A Case Study of the Gomati River Watershed in District Bageshwar, Uttarakhand Anand Kumar and Upasana Choudhury 10.1 Introduction 10.2 Study Area 10.3 Materials and Methodology 10.3.1 Extraction of the Gomati River Basin 10.4 Results and Discussion 10.4.1 Aspect 10.4.2 Slope 10.4.3 Linear Aspect 10.4.3.1 Stream Order (Sμ) 10.4.3.2 Stream Number 10.4.3.3 Stream Length 10.4.3.4 Mean Stream Length 10.4.3.5 Stream Length Ratio 10.4.3.6 Bifurcation Ratio 10.4.4 Aerial Aspect 10.4.4.1 Basin Area 10.4.4.2 Drainage Density 10.4.4.3 Drainage Frequency 10.4.4.4 Drainage Texture 10.4.4.5 Form Factor Ratio 10.4.4.6 Elongation Ratio 10.4.4.7 Circulatory Ratio 10.4.5 Relief Aspects 10.4.5.1 Basin Relief 10.4.5.2 Relief Ratio 10.5 Conclusion References 11 Water Audit: Sustainable Strategy for Water Resource Assessment and Gap Analysis Kirti Avishek, Mala Kumari, Pranav Dev Singh and Kanchan Lakra 11.1 Introduction 11.2 Material and Methodology 11.2.1 Pre-Audit Phase 11.2.2 Audit Phase

153 153 155 156 156 160 160 161 161 162 162 163 163 163 163 163 164 164 164 164 165 165 166 166 166 166 167 167 169 169 172 172 172

x  Contents 11.2.2.1 Population Estimation of BIT Campus 11.2.2.2 Water Source Identification 11.2.2.3 Water Demand Assessment 11.2.2.4 Gap assessment 11.2.3 Post-Audit Phase 11.3 Result 11.3.1 Water Demand Assessment 11.3.2 Water Audit Report and Analysis 11.3.2.1 Water Audit of Hostel No. 9 11.3.2.2 Water Audit for Hostel 8 11.4 Conclusions References 12 Multi-Temporal Land Use/Land Cover (LULC) Change Analysis Using Remote Sensing and GIS Techniques of Durg Block, Durg District, Chhattisgarh, India Jai Prakash Koshale and Chanchal Singh 12.1 Introduction 12.2 Study Area 12.3 Materials and Methods 12.3.1 Data Acquisition 12.3.2 Software Used 12.3.3 Methodology 12.4 Result and Discussion 12.4.1 LULC Statistics of October 2005 (Post-Monsoon) 12.4.2 LULC Statistics of October 2016 (Post-Monsoon) 12.4.3 LULC Changes Between October 2005 and October 2016 (Post-Monsoon) 12.4.4 LULC Statistics of February 2006 (Pre-Monsoon) 12.4.5 LULC Statistics of February 2017 (Pre-Monsoon) 12.4.6 LULC Changes Between February 2006 and February 2017 (Pre-Monsoon) 12.5 Conclusion Acknowledgment References 13 Climate Vulnerability and Adaption Assessment in Bundelkhand Region, India Prem Prakash and Prabuddh Kumar Mishra 13.1 Introduction 13.1.1 Climate Change and Vulnerability Assessment

172 172 172 175 175 175 175 176 176 181 181 182

185 186 187 189 189 189 189 191 191 192 195 199 199 200 201 202 202 205 206 206

Contents  xi 13.1.2 LVI for Bundelkhand Region 13.2 Conclusion References 14 Suitable Zone for Sustainable Ground Water Assessment in Dhanbad Block, Jharkhand, India Raghib Raza 14.1 Introduction 14.2 Study Area 14.2.1 Slope 14.2.2 Ground Water Label 14.2.3 LU/LC Mapping 14.2.4 Geology Features 14.2.5 Soil 14.3 Methodology 14.3.1 Overlay Analysis to Find Groundwater Potential Zone 14.4 Results 14.5 Conclusions References

208 213 213 215 216 217 217 218 219 220 221 222 223 223 226 227

15 Detecting Land Use/Land Cover Change of East and West Kamrup Division of Assam Using Geospatial Techniques 229 Upasana Choudhury and Anand Kumar 15.1 Introduction 229 15.2 Study Area 232 15.3 Materials and Methodology 232 15.4 Results and Discussion 232 15.4.1 Land Use and Land Cover Dynamics and Change Analysis 232 15.4.2 The Change Matrix Cross Tabulation 235 15.4.3 Classification Accuracy Assessment 236 15.5 Conclusion 236 References 241 16 Climate Resilient Housing—An Alternate Option to Cope with Natural Disasters: A Study in Fani Cyclonic Storm Affected Areas of Odisha Kiran Jalem and Subrat Kumar Mishra 16.1 Introduction 16.2 Study Area and Methodology

243 244 245

xii  Contents 16.3 Discussion 250 16.3.1 Climate Resilient Housing in the Fani Affected Districts 250 16.4 Policy Recommendation 251 References 252 17 Role of Geo-Informatics in Natural Resource Management During Disasters: A Case Study of Gujarat Floods, 2017 253 Ritambhara K. Upadhyay, Sandeep Pandey and Gaurav Tripathi 17.1 Background 253 17.1.1 Understanding Disasters: Natural and Anthropogenic 253 17.1.2 Disaster-Risk Reduction 255 17.1.3 Disaster Preparedness 256 17.1.4 Disaster Management 257 17.1.5 Role of Geo-Informatics in Disaster Management 258 17.1.6 Structural Measures of Flood Risk Management 259 17.1.6.1 Dams 260 17.1.6.2 Levee and Levee Overtopping 260 17.1.6.3 Flood Diversion 261 17.1.6.4 Transverse Dikes 261 17.1.6.5 Water Traps 261 17.1.6.6 Watershed and Afforestation 261 17.1.7 Non-Structural Measures of Flood Risk Management 262 17.1.7.1 Non-Structural Measures 262 17.1.7.2 Flood Plain Zoning 263 17.1.7.3 Flood Forecasting 263 17.1.7.4 Flood Plain Development 264 17.1.7.5 Flood Insurance 264 17.1.7.6 Flood Proofing 265 17.1.7.7 Catchment Management 265 17.2 Flood Preparedness Measures 266 17.3 Flood Response Measures 268 17.3.1 Components of Flood Response 268 17.3.1.1 Estimation of Severity of Flood 269 17.3.1.2 Emergency Search and Rescue 269 17.3.1.3 Emergency Relief 269 17.3.1.4 Incident Response System 270 17.3.1.5 Control Room Set-Up 271

Contents  xiii 17.3.1.6 Model Action Plan 271 17.3.1.7 Community-Based Disaster Preparedness and Response 272 17.3.1.8 Emergency Logistics and Equipment 272 17.3.1.9 Emergency Medical Response 272 17.3.1.10 State Disaster Response Force 273 17.4 Gujarat Flood Case Study 2017 274 17.5 Preparedness Measures by State Government 278 17.6 Media Handling 278 17.7 Rescue Operation 279 17.8 Relief Work 279 17.9 Speedy Restoration of Essential Services 280 17.10 Use of Drones—New Initiative Adopted 281 References 281 18 Environmental Impacts by the Clustering of Rice Mills, Ernakulam District, Kerala State 283 L. Vineetha and T.S. Lancelet 18.1 Introduction 284 18.2 Environmental Pollution and Rice Processing Industries 284 18.3 Study Area 285 18.4 Methodology and Review of Literature 286 18.5 Spatial Distribution of Rice Mill Clustering 287 18.6 Parboiling Process and Characteristics of Rice Mill Effluents 292 18.7 Description of Rice Mills Taken for Assessing the Impact on Environment 292 18.8 First Model Cluster 292 18.9 Overutilization of Groundwater Resources 292 18.10 Physio-Chemical Analysis of Rice Mill Effluent From Second Model Cluster 293 18.10.1 pH Value 294 18.10.2 Color (Hazen) 296 18.10.3 Total Dissolved Solids/TSSs 296 18.10.4 Chloride and Sulphate 297 18.10.5 Potassium 297 18.10.6 Bio-Chemical Oxygen Demand 297 18.10.7 Chemical Oxygen Demand 297 18.11 Conclusion 298 References 298

xiv  Contents 19 GIS-Based Investigation of Topography, Watershed, and Hydrological Parameters of Wainganga River Basin, Central India Nanabhau Santujee Kudnar 19.1 Introduction 19.2 Study Area 19.3 Methodology 19.4 Results and Discussions 19.4.1 Physiographical Regions Area 19.4.2 Absolute Relief 19.4.3 Digital Elevation Model 19.4.4 The WRB Catchment Area 19.4.5 Land Use Pattern 19.4.6 Hydrology 19.4.6.1 Inflows 19.4.6.2 Rainfall-Runoff Modeling 19.5 Conclusion Abbreviations References

301 302 303 303 305 305 305 306 308 310 311 313 313 315 315 316

Index 319

Preface The sustainable development refers to the qualitative and quantitative stability in the use of natural resources. It involves equilibrium between anthropogenic activities as influenced by social activities, acquired knowledge, applied technology, and food production. Sustainability attempts to address the issues such as resource degradation, deforestation, ecosystem loss, and environmental deterioration from global to local scale. The sustainable use and management of essential natural resources cannot be done without considering the direct and indirect impacts of human. It is required to apply an interdisciplinary approach in order to ensure longterm conservation of natural resources and its sustainable use at ecological and socioeconomic perspectives. Geoinformatics, including Remote Sensing (RS), Geographical Information System (GIS), and Global Positioning System (GPS), has tremendous potential to effectively monitor the natural resources and addressing the concerns related to sustainable development and planning of society. RS is a quick and cost-effective technique to measure the location and spectral properties of earth surface features in comparison to traditional ground-based surveying. It provides reliable geospatial information for comprehensive sustainable development plans, policy making, and decision. GIS is a computer-based system used to digitize remotely sensed data matched with various ground-truth data, which are geo-coded using a GPS. It is able to manipulate, analyze, and display spatial database. Applications of Geoinformatics include land use change and planning, agriculture and soil, water resource management, forest resource mapping and management, glacier mapping and monitoring, climate change, disaster management, and many more. Sustainable applications of Geoinformatics have become more essential in understanding various characteristics of Earth surfaces with the launch of Landsat mission in the 1970s. Many studies of direct relevance to the sustainable development and management have been reported. However, few studies have been reported using the harmonized approach of core science xv

xvi  Preface and research basics, as there are larger concerns of capacity building to use Geoinformatics in sustainable development practices and management. This could be overcome by taking the advantages of Geoinformatics into consideration to the scientific and research communities. The book entitled “Sustainable Development Practices Using Geoinformatics” contains chapters written by well-known researchers, academicians, and experts. The potential readers of this book are scientists, environmentalists, ecologists, policy makers, administrators, university students, urban planners, land managers, and professionals working in the field of sustainable development and management of natural resources. In Chapter 1, multi-temporal Landsat images are used to investigate the change in variability of surface temperature in the Barasat municipal area, West Bengal, India. A correlation analysis is performed between Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST) to show the urban growth and its pattern and trend in relation to surface temperature variation. This study is very useful for investigating the changes in environmental condition due to human activity in an urban area. In Chapter 2, attempts are made to estimate the geo-environmental hazards and risks in South Karanpura Coalfield region using information on land use/land cover (LU/LC), aerosol optical thickness (AOT), precipitable water vapor (PWV), and temperature conditions integrated with socio-economic vulnerability using Geoinformatics approach. Most of the risk-prone zones are found to present in the vicinity of industry and mining areas with higher population density. This study provides a basis to allocate resources for risk mitigation, improve community preparedness, and prepare cost-effective emergency planning. In Chapter 3, a co-polarized radar system is investigated for the estimation of soil moisture along specular direction. The data are collected by indigenously designed ground-based scatterometer system for 20°–60° incidence angles at steps of 10° in the specular direction for HH- and VV-polarizations at L-band. In this study, a hybrid machine learning algorithm combined with fuzzy inference system and artificial neural network called neuro-fuzzy inference system were evaluated for the estimation of soil moisture. The performance index Root Mean Squared Error (RMSE) was used to evaluate the estimation efficiency of the algorithm. This study is very useful for accurate and timely soil moisture estimation for agricultural practices. In Chapter 4, a study is conducted for detailed morphometric analysis of Tapi basin using Geographic Information System (GIS) technique. Different morphometric parameters analyzed, viz., stream order, stream

Preface  xvii length, bifurcation ratio, drainage density, relief ratio, drainage density, stream frequency, texture ratio, form factor, circulatory ratio, elongation ratio, etc., are calculated. The stream order of the basin is mainly controlled by lithological and physiographic conditions of the area. The present study will be helpful for sustainable water resource management and agricultural applications. In Chapter 5, the demand for fossil fuels is increasing speedily with the rapid population growth and development. It is a leading factor of greenhouse gases emission, global warming, and climate change. There are some satellites are available to monitor the concentration of these gases in the atmosphere. This chapter described the importance and capacity of GOSAT satellite to observe and monitor the global distribution of carbon dioxide (CO2). The kriging method is applied to analyze the global distribution of CO2 during 2009 to 2020 for the months of December, January, February, and March. In Chapter 6, a study is performed for micro-level planning and development of natural resources available in Jangal Aurahi village, Gorakhpur district, using high resolution satellite images like CARTOSAT-I, LISS IV merged, and DEIMOS. The basic objectives are to map, monitor, and manage existing resources, facilities, and infrastructures of a village. This kind of study will be very useful for the decision makers and planners to prepare the action plans for all the resources available within the rural area In Chapter 7, land suitability evaluation has been performed for potato crop in the Sagar Island using multi-criteria decision-making (MCDM) and Analytical Hierarch Process (AHP) methods. To find out more accurate suitability for potato crops, the derived suitability zones for the have been veteran by compared criteria-based suitability map and present landuse map using weighted sum overlay techniques in spatial analysis method. The techniques employed in this study provide valuable information that could be utilized by farmers to choose the suitable cultivation areas for potatoes at local level. In Chapter 8, a geospatial technology assisted overlay and index approach is applied to derive a landslide susceptibility zonation map for Western Ghats, India. Different thematic layers responsible for landslide are developed in GIS platform. The sub-class weightage indexes are feed in to the respective thematic layer in the GIS platform to generate landslide vulnerability zonation map into very low, low, moderate, high, and very high categories. An accurate spatial mapping of landslide vulnerability is important for disaster mitigation and regional planning. In Chapter 9, the underground mining activities may have devastating effect on the forest land and its soil. This chapter provided the review of

xviii  Preface existing information of the subsidence impacts on forest lands. It showed that there are reasonably impacts on the topography, hydrology, and soil properties of the area. These multiple impacts need to be considered at local level with particular concern to the interaction of subsidence disturbances with the forest ecosystems. This work can be useful to suggest appropriate adaptation strategies during subsidence for the suitable sustenance of healthy forest environments. In Chapter 10, an approach based on GI Science is demonstrated for Morphometric analysis of Gomati watershed from the lesser Himalaya terrain in district Bageshwar, Uttarakhand. Several morphometric parameters are calculated and analyzed. The drainage density for Gomati river basin is found to be 0.81 km/km² which show the high runoff in the channels. The methods utilized in this study will be helpful for the planners and decision makers in the development and management of the basin. In Chapter 11, water is an essential natural resource for human being. The adequate supply of water is of highest importance for survival. In this paper, water audit has been attempted for the campus of Birla institute of Technology, Mesra, Ranchi with case studies of two hostels. The water audit is assessed lobby wise to conclude the gaps. Water harvesting potentials was assessed for the study area, and recommendations were made for water management and planning. In Chapter 12, this study is conducted to analyze LULC changes during the period of 2006 to 2017 in Durg block of Chhattisgarh state, India using multi-temporal Landsat satellite imageries. Thematic layers and maps for the year of 2005 and 2016 (post-monsoon) and 2006 and 2017 (pre-monsoon) are prepared. A map is generated for LULC change analysis with the help of the intersection tool. The LULC categories showed changing patterns during the period. This type of study can be very useful for policy makers and planners for the management of land resources. In Chapter 13, this study attempts to apply livelihood vulnerability index (LVI) for the assessment of the livelihood risks of the vulnerable communities because of climate change. The socio-economic vulnerabilities suggested by IPCC’s three contributing factors such as exposure, sensitivity, and adaptive capacity of the region are taken into consideration. The study revealed that livelihood options in the region are limited and mainly dependent on agriculture and labor sector. The communities in the region are highly vulnerable due to changing climatic conditions. In Chapter 14, this work is carried out for suitable site selection for the sustainable urban groundwater management in the Dhanbad Block in Jharkhand state, India. Different datasets such as Landsat 8 satellite image, DEM, Toposheet, and secondary data are used in this study. It facilitated

Preface  xix to know the complexities of a dynamic phenomenon like suitability site sustainable water management, land use/land cover benefits, and urban development planning pattern. The weights have been assigned to different layers as per the need for the acceptable site selection for the sustainable groundwater management planning. In Chapter 15, this paper presents a study to detect changes in land use and land cover over a period of 30 years from 1988 to 2018 in the Kamrup district of Assam, India. Multi-temporal Landsat satellite images of year 1988, 1998, 2008, and 2018 are used in this study. The images are classified into different categories using visual interpretation and manual digitization methods. The change matrix approach is used for evaluating the net loss and gain of different land use and land cover classes. This study can be useful for sustainable urban management and land use planning in the region. In Chapter 16, on May 03, 2019, a rare summer cyclone named “Fani” hit Puri, a small coastal town of Odisha, India. This cyclone resulted into the loss of 64 human lives and affected about 16.5 million people in 18,388 villages of the entire state. It also severely affected power, telecommunication infrastructure, and road services. The damage to housing has been extensive, particularly in the Puri district of Odisha. This examines how climate resilient houses with “Build Back Better” features can save valuable human lives through use of eco-friendly, durable, cost effective, and non-pollutant building materials. In Chapter 17, disasters resulting in substantial loss of deaths, disruption of normal life, and the developmental process for years to come. This paper systematically describes the application Geoinformatics technique for disaster management. It has robust data handling capabilities that is ideal for disaster risk reduction, mitigation, and management from global to local scales. This technique is capable to create awareness to dissemination of information during disaster mitigation, preparedness, and response as part of disaster management measures. In Chapter 18, the food processing industries play a key role in economic development of any country. This work analyzes the locational factors how favored in rice mill clustering in Ernakulam district, Kerala state, India. The environmental concerns were identified through field and house hold survey in the select areas or panchayats of Kalady, Okkal, and Koovappady. The physio-chemical analysis of waste water effluent carried out revealed the organic and inorganic presence of the pollutants and its extent. In Chapter 19, this study demonstrates the importance of the Digital Elevation Model (DEM) and satellite images for evaluation of drainage and extraction of their relative parameters for the Wainganga River watershed

xx  Preface area of the Godavari River, India. Several hydrological parameters including drainage analysis, topographic parameters, and land use patterns were evaluated and interpreted. The climatic condition based on hydrological investigation, of the basin is characterized by hot summer from March to May followed by a rainy season from June to September using. This edited book entitled “Sustainable Development Practices Using Geoinformatics” contains chapters written by prominent r­esearchers and experts. The key focus of this edited book entitled “Sustainable Development Practices Using Geoinformatics” is to replenish the available resources on the topic by integrating the concepts, theories, and experiences of the experts and professionals in this field.

Acknowledgement The completion of this edited book entitled “Sustainable Development Practices Using Geoinformatics” could not have been possible without the grace of almighty God. We are grateful to Hon’ble Sunil Sharma, Chairperson, Suresh Gyan Vihar University, Jaipur for his encouragement and support. The words cannot express our indebtedness to Hon’ble Dr. Sudhanshu, Chief Mentor, Suresh Gyan Vihar University, Jaipur for his continuous guidance, expert suggestions and motivation during the completion of this edited book. Special thanks are due to all the reviewers for their time to review the chapters. The editors would like to express heartfelt gratitude to all the members of editorial advisory board for their endless support and valuable instructions at all stages of the preparation of this edited book. We would like to mention the names of the members of editorial advisory board as Prof. M. S. Nathawat, IGNOU New Delhi, India; Dr. (Mrs) Tapati Banerjee, NATMO, Kolkata, India; Prof. Milap Punia, JNU, New Delhi, India; Prof. Rajendra Prasad, IIT (BHU), Varanasi, India; Dr. Devendra Pradhan, IMD, Government of India, New Delhi, India; Prof. Manoj K. Pandit, University of Rajasthan, Jaipur, India, Dr. Snehmani, SASE, DRDO, Chandigarh, India, Prof. Shakeel Ahmed, Jamia Millia Islamia, New Delhi, India; Prof. Suresh Prasad Singh, Himalayan University, Itanagar, India; Mr. Peeyush Gupta, NMCG, Ministry of Jal Shakti, Government of India, New Delhi, India To all the colleagues, friends, and relatives who in one way or another shared their constant and moral support. The editors are eternally thankfully to Scrivener Publishing for giving the opportunity to publish with them. Dr. Shruti Kanga Dr. Varun Narayan Mishra Dr. Suraj Kumar Singh Editors June 2020 xxi

1 The Impact of Rapid Urbanization on Vegetation Cover and Land Surface Temperature in Barasat Municipal Area Aniruddha Debnath, Ritesh Kumar*, Taniya Singh and Ravindra Prawasi Haryana Space Applications Centre, Hisar, Haryana, India

Abstract

India is a developing country and its growing phase is facing the trio of urbanization, modernization, and globalization. The study pertains to find out the impacts of rapid urban development on vegetation cover and its inter-relationship with the variability of Land Surface Temperature (LST). The study area, Barasat municipality, is facing rapid urbanization since mid of 1990s; hence, the number of people residing in Barasat is increasing rapidly, resulting in dense, concrete, and highrise buildings. The Barasat city is adjacent to Kolkata metropolitan city and is a part of Greater Kolkata. Therefore, there is escalation in number of multi-storied buildings along with proliferating population leading to urban sprawl in the study area. These facts promote Barasat to be an Urban Heat Island (UHI). The study aims to show the change in variability of surface temperature from 2001 to 2017 with the help of geospatial techniques and using Landsat data of multiple dates in order to uncover the modification/variation in the urbanization and then correlate it with NDVI (Normalized Difference Vegetation Index), and LST. The 17 years’ time scale is very small period for change detection of urban land use change but enough to show the urban growth and its pattern and trend in relation to surface temperature variation. The remote sensing and GIS provides very useful tool for the analysis of changes in environmental condition due to human activity in the study area. Keywords:  Urbanization, UHI, NDVI, LST

*Corresponding author: [email protected] Shruti Kanga, Varun Narayan Mishra, and Suraj Kumar Singh (eds.) Sustainable Development Practices Using Geoinformatics, (1–22) © 2021 Scrivener Publishing LLC

1

2  Sustainable Development Practices Using Geoinformatics

1.1 Introduction Urban land is primary resultant feature on the Earth surface, induced by human activities from centuries. Urban area is defined as the area having facilities of higher administrative departments in which most of the population belong to secondary and tertiary division, these segments comprises a city or a town, etc. (McGranahan, Satterthwaite, and International Institute for Environment and Development 2014). Urbanization can be simply defined as the conversion of any spatial entity from rural to urban with the help of technology and sustainable uses of resources (Datta 2007). Since ancient era, modification, and transformation of the geographical areas are steady, and great example of this is urban landform. World’s earliest industrial revolution took place in Britain in the 18th century, which caused the rural mass movement toward cities. This era was considered to be the footstep of urbanization. However, in India, the wind of urbanization was initiated by the Britishers, while India being once a domicile of British Empire. The modification in the settlements and settlement zone continue to vary till date, which commences urban sprawl (Narayan 2014). In the phases of urban development, continuous changes on land surface are observed, from small houses to tall buildings, agriculture to industry, pervious surface to impervious (paved) surface, kaccha road to highway, etc. (Grimmond 1998; Gál and Unger 2009). The two most important controlling factors responsible for the development and also retreat of urban region are pull factors and push factors. With the rapid urban sprawl, it results in the increase of inhabitants with a balance of demand and supply. Along with the proliferation of the crowd toward a separate area, there is burgeoning demand of supply for the inhabitants, which further entice entrepreneurs. The urban sprawl cannot be controlled; hence, it appears as an interrelated network of a complex system. The socio-economic development of an urban area is an impact of migration that escalates the growth of urban society. The constant process of growth leads to urban spread and agglomeration, which is continually an ongoing process (Yeh and Li 2001). The scope of application of “Remote Sensing and GIS” is widening day by day from cryosphere to biosphere to hydrosphere to atmosphere, etc. Subject to mankind, most of application parts are broadly used like study of land cover dynamics, spatial growth, trend analysis, rainfall monitoring, zoning of hazard risk assessment mapping, global climatic imbalance, atmospheric phenomenon, etc. (Wijeatne and Bijker 2006). Contemplating the urban application part, it is largely used in the fields of urban morphology structure, urban flooding, urban planning, ventilation mapping,

Vegetation Cover and Land Surface Temperature  3 urban climatic zones, urban pollution, urban population, urban growth modelling, etc. (Grimmond and Oke 1999; Gál and Unger 2009; Mirzaei 2015; Wong, Nichol, and Ng 2011). With the advancement in technologies, it is aimed to gather data from the underground and under water also. Various endeavors were done to discover the prototype of urban growth and examine the several spatial patterns of urban area with the help of various algorithms including geographical weighted regression, Sleuth model, multivariate regression, etc. In India, the urban growth scenario is changing rapidly and poses complexity in measuring urban growth parameters, but use of remote sensing and GIS techniques are becoming handy in to perform analysis on urban growth and its impact on natural vegetation and local surface air temperature. Urban sprawl is a continuous process, which leads to decrease in the amount of green space and increase in the density of concrete garden of buildings (Capozza and Helsley 1989). To demarcate the consistency of vegetation canopy layer, NDVI (Normalized Difference Vegetation Index) is a very useful index (Bhandari, Kumar, and Singh 2012; Volcani, Karnieli, and Svoray 2005). The increase density of buildings is the major cause of increasing surface air temperature that is trapped by the building infrastructure (Unger, Sümeghy, and Zoboki 2001). From this point of view, the concept of Land Surface Temperature (LST) is inspired, which is the temperature of the near surface area within specified limit, but it is entirely different from atmospheric temperature. The LST is a new emerging concept in the field of remote sensing and it plays a key role in establishing an inter-relation between NDVI and LST (Deng et al., 2018). The relationship between LST and NDVI ponders on the concept of surface temperature in cram-full areas (Yuan and Bauer 2007). From this point of view, the area can be delineated as a Heat Island as the core area of the city experiences relatively high temperature than the surrounding and rural areas. The domain of UHI can be easily detected using these two crucial indices. The urban area that is comparatively hotter than the surrounding area can be considered as a UHI (Tso 1996). India is home of 1,210,193,422 people (Census of India, 2011) and having a population density about 382 persons/km2, which represent a mass of population pressure on less amount of land. As a developing country, India is bound to see increase in urban area or converting land use into boundless built-ups. India is facing force of population toward urban areas and converting land use into boundless built-ups. Since independence, the growth rate of urban population is gradually rising and recorded 17.46% as per Census of India, 2011. Kolkata is one of the renowned metropolitan cities having the

4  Sustainable Development Practices Using Geoinformatics population density of approximately 24,000 persons/km2 (Census of India, 2011), one of the highest in the world. The suburbs (Barasat, Barracpore, Kalyani, Kashba, Rajarhat, etc.) are having density of almost 9,000 persons/km2, which is increasing rapidly. Because Kolkata is having limited land, the suburbs are developing at faster pace than the core city from last few decenniums. Barasat city is adjacent to Kolkata; therefore, the branches of Kolkata city are expanding toward the outskirt areas at faster rate and that can be clearly estimated from the difference in 2001 and 2011 Census.

1.2 Study Area Barasat city is in the northern outer periphery of Kolkata city, in West Bengal, India, facing problems of unplanned urbanization in a short span of time after getting declared as district head quarter town within the jurisdiction of Kolkata Metropolitan Development Authority (KMDA). It has a total area of 31.41 km2 and extends between 88°27’ E and 88°31’E longitude and between 22°40’58” N and 22°44’44”N latitude (Figure 1.1). There are 32 wards in Barasat municipality. The growth rate of population in this town is very high and approximates around 3.5% per year. As per the provisional reports of census of India, population of Barasat in 2011 is 283,443. Barasat has a population density of 9,023 persons/km2.

1.3 Datasets and Methodology 1.3.1 Datasets The satellite images of Landsat 5 TM for the year 2001, Landsat 5 TM for the year 2011, and Landsat 8 OLI and TIRS for the year 2017 were obtained from USGS official website and processed for analysis (Table 1.1), and to quantify the changes due to urbanization. The datasets used for various analyses and for preparing different maps along with census data are listed in Table 1.1.

1.3.2 Methodology Landsat images of different time are very helpful for the analysis of land use and land cover change pattern and to measure the increase in urban

Vegetation Cover and Land Surface Temperature  5 WEST BENGAL

INDIA

NORTH 24-PARGANAS

88°30'0"E

22°42'0"N

22°42'0"N

22°44'0"N

22°44'0"N

88°28'0"E

N W 0

1

E S 2

4 km

88°28'0"E

88°30'0"E

Figure 1.1  Location map of Barasat municipality.

Table 1.1  List of datasets used for the study. Satellite/Sensor

Date

Source

Landsat 5 (TM)

January, 2001

USGS

Landsat 5 (TM)

January, 2011

USGS

Landsat 8 (OLI and TIRS)

January, 2017

USGS

MOD11A1

January: 2001, 2011, 2017

USGS

Census

2011

Census of India

Barasat municipality boundary

2014

Barasat municipality

6  Sustainable Development Practices Using Geoinformatics built-up area (Song et al., 2001). To meet the objectives of the study, the following procedures were done. Pre-processing is an essential step for removing the atmospheric noise and haze, which is there in the image due to atmospheric scattering of solar radiation due to atmospheric elements (Chander, Markham, and Helder 2009). Satellite images were classified using Maximum Likelihood classification algorithm because it gives better accuracy than other available techniques like box classifier, minimum distance to mean, etc., available in published literature (Lyon et al., 1998; Reis 2008; Patidar and Sankhla 2015). The study area was classified into six classes inclusive of built-up area, agricultural fallow, bare land, water body, green space, and built up with green space. The main aim was to measure the increase in built-up area, which is an indicator of urbanization. Further, accuracy of the classified images was calculated, and confusion matrix generated to highlight the user accuracy, producer accuracy, and Kappa statistics thus obtained (Foody 2002; Berberoglu and Akin 2009). The urban growth is a process of urbanization, which always follows a pattern of development. In urban areas, the pattern differs from core to periphery region. In this study, two different types of pattern had been observed, one was concentric, and another one was linear. The concentric circular pattern was found in the areas of tri-junction of roads or in the “Y” point where the development is very rapid and shows multiplier effect of growth. The linear pattern of development was observed from 2001 to 2017 along the sides of roads and railway lines (Figure 1.2), which is very

A

B

C D

P H

E

Legend Railway Road Road Junction Barasat Municipality area

Figure 1.2  Road and railways network in Barasat municipality.

Vegetation Cover and Land Surface Temperature  7 common in this region. Hence, both the developmental patterns are simultaneously affecting the environment of the area significantly. NDVI is a very popular vegetation index used to measure the biomass content of vegetation with respect to its spatial entity (Volcani, Karnieli, and Svoray 2005). Two different bands, NIR and Red of remote sensing data, are used for calculating the NDVI (Bhandari, Kumar, and Singh 2012; Yin et al., 2012). The formula for NDVI (Eq. 1.1) calculation is as follows:

NDVI =



NIR   −  R NIR   +  R

(1.1)

Remote sensing helps in deriving LST, which is used in various studies related to local climatology, meteorology, and climate change, etc., as the observations collected from the ground cannot provide much detailed information over a larger area (Wu et al., 2015). LST is the temperature radiated by the surface and measured within limited boundary of lower atmosphere from the surface, which is proportionally dependent on land surface emissivity (Wan and Dozier 1996; Wang et al., 2015; Isaya Ndossi and Avdan 2016). The LST is technically different from the Atmospheric temperature and is largely affected by the urban canopy layer. LST calculation was done with the help of Inversion of Planck’s Function (Isaya Ndossi and Avdan 2016; Zhang, Wang, and Li 2006; Srivastava et al., 2014; Artis and Carnahan 1982; Sobrino, Caselles, and Becker 1990). The formula for LST calculation (Eq. 1.2) is as follows:



Ts =

BT − 273.15   λ ∗ BT    1 +  ρ  ∗ ln ε 

(1.2)

where Ts = land surface temperature (°C), BT = brightness temperature (K), λ = wavelength of the emitted radiance, ρ = (h * c/σ) = 1.438 *10-2mK, and ε = land surface emissivity

1.4 Results and Discussion The results of the data analyzed for this study are discussed in four sections.

8  Sustainable Development Practices Using Geoinformatics

1.4.1 Pattern of LULC in Barasat The temporal images of study area were classified in six classes with the help of Maximum Likelihood algorithm. The classification scheme of Land Use/Land Cover (LULC) includes the classes (1) built-up area, (2) built-up with green space, (3) agricultural fallow, (4) bare land, (5) green space, and (6) water bodies (Figure 1.3). A drastic change is observed in the ratio of built-up area and built-up area with green vegetation in the center of the city. Moreover, the aggregation and expansion in built-up area changes rapidly after 2011. The total area of the Barasat municipality is about 34.5 km2 as obtained by the digitized vector layer. The observed change in built-up area is ranging from 6.79% in 2001 to 29.23% in 2017, and simultaneously, there 2001

2011

2017

Agricultural Fallow

Built-up Area

Bare Land

Built-up With Green Space

0

2

4

Green Space

8

Water Body

km

Figure 1.3  Land Use/Land Cover map of Barasat municipality (2001, 2011, and 2017).

Table 1.2  Area statistics of LULC in Barasat municipality. Area in % Class Name

2001

2011

2017

Bare land

16.70

13.39

8.25

Water body

2.40

2.36

1.47

Agricultural fallow

7.92

4.75

3.74

Built-up area

6.79

16.38

29.23

Built-up with green space

38.20

40.79

45.80

Green space

27.99

22.33

11.50

Vegetation Cover and Land Surface Temperature  9 is highest decrease observed in green space ranging from 27.99% in 2001 to 11.50% in 2017 (Table 1.2). Built-up area with green space is increasing, although at a slower rate than the pure built-up area, which implies that the municipality or concerned agency for greenery in urban space is not giving serious thought to the importance of green space in urbanization. There is decrease in surface area of water body and reduction in number of water body within the study area, which may lead to water crisis in near future.

1.4.2 Urban Sprawl It is imperative from above classified (Figure 1.3) remote sensing images of different time period that the LULC in Barasat municipality area is more inclined toward urbanization. Moreover, the rate of urbanization in the study area has changed rapidly within second decade than the first decennia. It is clear from Figure 1.4 that there are two peculiar patterns of urban growth found in the study area. Initially, the urbanization in the 2001 A

2011 A

2017 A

2001 B

2011 B

2017 B

0 Road Junction

2

4

Built-up Area

8 km Road

Circular pattern

Figure 1.4  Urban Sprawl pattern from 2001 to 2017.

Railway Linear pattern

Barasat Municipality Area

10  Sustainable Development Practices Using Geoinformatics

2001 *1

2017 *6

2011 *2

(A) 2001 *1

2011 *2

2017 *4

(B)

Figure 1.5  (A) The Circular pattern of increasing population from center of the city. (B) The Linear pattern of increasing population alongside road.

study area shows the linear pattern followed by concentric development in built-up area. The Figure 1.4 clearly shows that in the year 2001, there was only few clusters of built-up area, which is showing trend of linear growth in the year 2011 followed by again concentric development of built-up area in the year 2017. Moreover, due to such sequential developmental pattern in the study area, there is no space for easy air circulation within the urban domain. The scale of growth in urbanization in concentric pattern starting at scale of 1 in the year 2001, it became 2 times in the year 2011 and has increased to 6 times in the year 2017 (Figure 1.5A). However, the scale of growth in urbanization in linear pattern has changed manifold, starting at a scale of 1 in the year 2001, it became 2 times in the year 2011 and in turn it has increased to 4 times in the year 2017 (Figure 1.5B). The impact of concentric urbanization is greater in relation to the linear urbanization pattern during 2011 to 2017 than during 2001 to 2011 (Figure 1.4).

1.4.3 Impact of Urban Sprawl on Vegetation Cover LULC analysis of temporal imageries clearly shows the impact of urban growth on vegetation cover in the study area. The analyzed remote sensing dataset shows that there is inverse relationship between built-up area and green space. Figure 1.6 shows the amount of changes in green space based on NDVI from 2001 to 2017 through 2011. Moreover, it is also evident

Vegetation Cover and Land Surface Temperature  11 2001

2011

2017

High : 0.76

High : 0.60

High : 0.42

Low : –0.33

Low : –0.10

Low : –0.06

Figure 1.6  NDVI images for the year 2001, 2011, and 2017.

from the NDVI images that there is reduction not only in spatial extent of vegetation cover but also there is reduction in overall biomass and chlorophyll as well during the study period. In the year 2001, the NDVI value was ranging from −0.33 to 0.76, while in the year 2011, the lower value is around −0.10 and higher value is around 0.60 and during year 2017 the NDVI is showing lower value of −0.06 and higher value of 0.42. However, there is increase of population density in the central part and radially outward from the city since year 2011 to 2017. Moreover, the temporal NDVI images also shows that there is decrease in vegetation cover as one moves from central part of the city to edges of the study area.

1.4.4 Impact of Urban Sprawl on LST LST plays an important role in local weather phenomena. The study area was also analyzed for the changes in LST due to rapid urbanization in the study area during 2001 to 2017. LST shows a direct relationship with the built-up area. Moreover, with the increase in built area, there is gradual rise in average surface air temperature in Barasat municipal area. The temporal variation in LST as analyzed from remote sensing data are shown in Figure 1.7. It is evident from the LST obtained from analysis of temporal 2001

2011

2017

High : 21.9

High : 26.3

High : 27.1

Low : 16.1

Low : 18.4

Low : 20.0

Figure 1.7  Land Surface Temperature (LST) map for the year 2001, 2011, and 2017.

12  Sustainable Development Practices Using Geoinformatics remote sensing images that in the year 2001, the minimum and maximum temperature was around 16.1°C and 21.9°C with average of around 19°C in the study area. Moreover, in the year 2011, the LST increased to 26.3°C as compared to maximum of 21.9°C in the year 2001 with an average of 22.3°C. However, the rise in LST of the study area for the year 2017 was observed around 20.0°C as lower value and 27.1°C as the higher with an average of around 23.5°C.

1.4.5 Relationship Between NDVI and LST The vegetation cover of a locality significantly influences LST. LST can reflect the scenario of land surface water and heat exchange process broadly. Therefore, in this study, we retrieved and tried to explore relationship between LST, NDVI, and LULC using temporal Landsat 5 and Landsat 8 remote sensing images. The results obtained shows that the spatial distribution of LST and NDVI is having negative relationship, analyzed after removing water body data (Figure 1.8A, 1.8B & 1.8C). Moreover, it is clear from the figures given below that in all directions from center of the city the NDVI is showing inverse relationship in 2001, 2011, and 2017. However, it is also deduced from the analysis of LST against land use (LULC) that built-up area shows highest LST as against green space and built-up area with green space (Figure 1.9). Moreover, the present analysis also shows that there is average rise in temperature from 2001 to 2017 through 2011 for each land use class. The LST observed for the green space is also comparable to the water body. The coefficient of regression is found to be negative as observed from regression analysis between NDVI and LST for the Barasat Municipality (Figure 1.10).

1.4.6 Urban Heat Island We have analyzed the Barasat municipality for Urban Heat Island (UHI) formation and found that, although it is a small city in comparison to Mega city like Delhi, still, it is showing a very clear UHI formation over

Vegetation Cover and Land Surface Temperature  13 N W

E

2001 LST

N W

E S

S

High : 1 Low : 0

1

2

4 km

0

1

18.50

0.40

18.00

0.30

17.50

0.20

17.00

0.10

16.50

0.00

20.50 20.00 19.50 19.00 18.50 18.00 17.50 17.00 16.50

Central to North (m)

0.30 0.20 0.10 0.00

NDVI

0.60

20.50 20.00 19.50 19.00 18.50 18.00 17.50 17.00 16.50 16.00 15.50

0.50 0.40 0.30 0.20 0.10

0 180 136 539 719 989 1079 1259 1439 1618 1798 1978 2158 2338 2517 2697 2877 3057 3237 3417

LST (°C)

NDVI

LST (°C)

0 119 238 357 476 595 713 832 951 1070 1189 1308 1427 1546 1665 1784 1903 2022

NDVI

0.40

Center to Westward 0.500 0.450 0.400 0.350 0.300 0.250 0.200 0.150 0.100 0.050 0.000

LST

0.50

LST

Center to Southward

Central to South (m)

0.60

Central to East (m)

NDVI

20.00 19.50 19.00 18.50 18.00 17.50 17.00 16.50 16.00

0.70

0 150 299 449 599 749 898 1048 1198 1347 1497 1647 1796 1946 2096 2246 2395 2545 2695 2844 2994

0.50 LST (°C)

19.00

NDVI

0.60

LST

4 km

Center to Eastward

19.50

0 120 240 360 480 600 720 840 959 1079 1199 1319 1439 1559 1679 1799 1919 2039 2159

LST (°C)

Center to Northward

2

NDVI

0

Degree celsius High : 21.94 Low : 16.10

0.00

Central to West (m)

LST

NDVI

Figure 1.8A  The relationship pattern between NDVI and LST from center to outward (north, south, east, and west) of Barasat municipality for the year 2001.

NDVI

2001 NDVI

14  Sustainable Development Practices Using Geoinformatics N W

E

2011 LST

N W

E S

S

High : 1 Low : 0

2

4 km

0

1

0.30

22.00

0.25

21.50

0.20

21.00

0.15

20.50

0.10

20.00

0.05

19.50

0.00

LST (°C)

22.50

NDVI

0.35

23.50 23.00 22.50 22.00 21.50 21.00 20.50 20.00 19.50 19.00 18.50

Central to North (m)

LST

LST

22.50

0.25

25.00

0.20

20.00

0.10

20.50

10.00 5.00

19.50

0.00

0.00

LST

NDVI

0 180 136 539 719 989 1079 1259 1439 1618 1798 1978 2158 2338 2517 2697 2877 3057 3237 3417

0.05

Central to South (m)

0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00

15.00

20.00

0 119 238 357 476 595 713 832 951 1070 1189 1308 1427 1546 1665 1784 1903 2022

LST (°C)

0.15

LST (°C)

30.00

NDVI

0.30

21.00

NDVI

Center to Westward

Center to Southward 23.00

21.50

0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00

Central to East (m)

NDVI

22.00

4 km

Center to Eastward

23.00

0 120 240 360 480 600 720 840 959 1079 1199 1319 1439 1559 1679 1799 1919 2039 2159

LST (°C)

Center to Northward

2

NDVI

1

0 150 299 449 599 749 898 1048 1198 1347 1497 1647 1796 1946 2096 2246 2395 2545 2695 2844 2994

0

Degree celsius High : 26.25 Low : 18.38

Central to West (m)

LST

NDVI

Figure 1.8B  The relationship pattern between NDVI and LST from center to outward (north, south, east, and west) of Barasat municipality for the year 2011.

NDVI

2011 NDVI

Vegetation Cover and Land Surface Temperature  15 N W

E

2017 LST

N W

E S

S

High : 1 Low : 0

1

2

4 km

0

1

Center to Northward 24.50

0.25

0.15

23.00

0.10

22.50

0.05

21.50

0.00 Central to North (m)

0 119 238 357 476 595 713 832 951 1070 1189 1308 1427 1546 1665 1784 1903 2022

21.50

Central to South (m)

LST

NDVI

0.050 0.000

NDVI

0.30

25.00 24.50 24.00 23.50 23.00 22.50 22.00 21.50 21.00 20.50

0.25 0.20 0.15 0.10 0.05 0.00

0 180 136 539 719 899 1079 1259 1439 1618 1798 1978 2158 2338 2517 2697 2877 3057 3237 3417

22.00

LST (°C)

22.50

NDVI

LST (°C)

23.00

0.100

Center to Westward 0.20 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00

23.50

0.150

LST

Center to Southward

24.00

0.200

Central to East (m)

NDVI

24.50

0.250

25.00 24.50 24.00 23.50 23.00 22.50 22.00 21.50 21.00 20.50 0 150 299 449 599 749 898 1048 1198 1347 1497 1647 1796 1946 2096 2246 2395 2545 2695 2844 2994

22.00

NDVI

0.20

23.50

0 120 240 360 480 600 720 840 959 1079 1199 1319 1439 1559 1679 1799 1919 2039 2159

LST (°C)

24.00

LST (°C)

0.30

25.00

4 km

Center to Eastward

25.00

LST

2

NDVI

0

Degree celsius High : 27.08 Low : 20.00

Central to West (m)

LST

NDVI

Figure 1.8C  The relationship pattern between NDVI and LST from center to outward (north, south, east, and west) of Barasat municipality for the year 2017.

NDVI

2017 NDVI

16  Sustainable Development Practices Using Geoinformatics 24.0 23.0 LST (˚C)

22.0 21.0 20.0 19.0 18.0 17.0

Green Space Bare Land Water Body

2001

Built-up

2011

Agricultural Built-up Fallow with Green Space 2017

Figure 1.9  Relationship of LST with LULC classes.

the study area (Figure 1.11). Temporal MODIS night time LST product (MOD11A1) downloaded from USGS were used to assess the UHI formation in Barasat. The plotted graph as shown in Figure 1.11 is clearly showing the formation of UHI in the year 2001 with lesser flattening in the center of the city, which gradually increased in 2011 and 2017. Moreover, for the same time period, Landsat data was also analyzed for daytime UHI formation, and it was found that, although UHI formation is subdued in comparison to the night time UHI, still, it is significant enough to be reported.

1.5 Conclusion In summary, the satellite-based remote sensing images are very impressive dataset for urban sprawl study. Moreover, GIS platform provides toolset for analyzing temporal LST and NDVI retrieval over a period, giving chance for comparison and trend delineation. Barasat municipality, although a small city is showing UHI formation, may be due to poor planning and indiscriminate urbanization. However, Barasat municipality being a suburban part of Kolkata is facing problem of rapid urbanization and degradation of natural vegetation. Due to poor planning and giving no space for surface air ventilation and greenery in the city, the city is under environmental stress leading to rise in LST and UHI formation. Availability of legacy remote sensing data provides good chance for comparison of changes in environmental conditions in the study area in wholesome perspective.

20.00 0.00

16.50 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70

NDVI

20.50

21.00

21.50

22.00

22.50

23.00

23.50

17.00

17.50

18.00

18.50

19.00

19.50

20.00

y = –3.0621x + 19.503 R2 = 0.271

2001

LST(°C)

Figure 1.10  Regression analysis of LST vs. NDVI.

LST(°C)

20.50

0.10 NDVI

0.20

0.30 0.40

y = –6.5176x + 22.734 R2 = 0.3881

2011

21.00 0.00

22.00

23.00

24.00

25.00

0.05

0.10

NDVI

0.15

0.20

0.25

0.30

y = –4.6079x + 23.653 R2 = 0.1503

2017

Vegetation Cover and Land Surface Temperature  17

LST(°C)

18  Sustainable Development Practices Using Geoinformatics MODIS Night Time 13

19

12

18 17

2001

LULC MAP

20 LST IN °C

LST IN °C

Landsat Day Time

10

3014 6058 WEST TO EAST

18

26 25 24 23 22 21 20 19

1462 3289 5115 WEST TO EAST

18 LST IN °C

LST IN °C

19 18 17 16 15 14 13

1462 3289 NORTH TO SOUTH

19

23 22 21 20 19

17 16 15 14

1493 3014 NORTH TO SOUTH

1462 3289 NORTH TO SOUTH

19

25

18

24

LST IN °C

LST IN °C

11

3014 6058 WEST TO EAST

24

23 22 21

LST IN °C

24 23 22

1493

17 16 15 14

3014 6058 WEST TO EAST

25 LST IN °C

12

10

1493 3014 NORTH TO SOUTH

LST IN °C

LST IN °C

17

2017

1462 3289 5115 WEST TO EAST

13

19

LST IN °C

LST IN °C

20

2011

11

3014

18 18 17 17 16 16 15

1462 3289 5115 WEST TO EAST

1462

3289

NORTH TO SOUTH

NORTH TO SOUTH

Builtup

Agricultural Fallow

North to Southward

Waterbody

Green Space

West to Eastward

Builtup with Green Space

Bare Land

Urban sprawl Boundary

Figure 1.11  Urban Heat Island (UHI) formation in Barasat municipality area in the year 2001, 2011, and 2017.

Vegetation Cover and Land Surface Temperature  19

Acknowledgement Authors are thankful to Haryana Space Application Center (HARSAC), Hisar, India for providing software and infrastructures to carry out this work successfully.

References Artis, David A., and Walter H. Carnahan. 1982. “Survey of Emissivity Variability in Thermography of Urban Areas.” Remote Sensing of Environment 12 (4): 313–329. Berberoglu, S., and A. Akin. 2009. “Assessing Different Remote Sensing Techniques to Detect Land Use/Cover Changes in the Eastern Mediterranean.” International Journal of Applied Earth Observation and Geoinformation 11 (1): 46–53. Bhandari, A.K., A. Kumar, and G.K. Singh. 2012. “Feature Extraction Using Normalized Difference Vegetation Index (NDVI): A Case Study of Jabalpur City.” Procedia Technology 6: 612–21. https://doi.org/10.1016/j. protcy.2012.10.074. Capozza, Dennis R., and Robert W. Helsley. 1989. “The Fundamentals of Land Prices and Urban Growth.” Journal of Urban Economics 26 (3): 295–306. Chander, Gyanesh, Brian L. Markham, and Dennis L. Helder. 2009. “Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors.” Remote Sensing of Environment 113 (5): 893–903. Datta, Pranati. 2007. “Urbanisation in India.” Deng, Yuanhong, Shijie Wang, Xiaoyong Bai, Yichao Tian, Luhua Wu, Jianyong Xiao, Fei Chen, and Qinghuan Qian. 2018. “Relationship among Land Surface Temperature and LUCC, NDVI in Typical Karst Area.” Scientific Reports 8 (1). S. Foody, Giles M. 2002. “Status of Land Cover Classification Accuracy Assessment.” Remote Sensing of Environment 80 (1): 185–201. Gál, T., and J. Unger. 2009. “Detection of Ventilation Paths Using High-Resolution Roughness Parameter Mapping in a Large Urban Area.” Building and Environment 44 (1): 198–206. https://doi.org/10.1016/j.buildenv.2008.02.008. Grimmond, C. S. B. 1998. “Aerodynamic Roughness of Urban Areas Derived from Wind Observations.” Boundary-Layer Meteorology 89 (1): 1–24. Grimmond, C. S. B., and Timothy R. Oke. 1999. “Heat Storage in Urban Areas: Local-Scale Observations and Evaluation of a Simple Model.” Journal of Applied Meteorology 38 (7): 922–940. Isaya Ndossi, Milton, and Ugur Avdan. 2016. “Application of Open Source Coding Technologies in the Production of Land Surface Temperature (LST) Maps

20  Sustainable Development Practices Using Geoinformatics from Landsat: A PyQGIS Plugin.” Remote Sensing 8 (5): 413. https://doi. org/10.3390/rs8050413. Lyon, John G., Ding Yuan, Ross S. Lunetta, and Chris D. Elvidge. 1998. “A Change Detection Experiment Using Vegetation Indices.” Photogrammetric Engineering and Remote Sensing 64 (2): 143–150. McGranahan, Gordon, David Satterthwaite, and International Institute for Environment and Deve lopment. 2014. Urbanisation: Concepts and Trends. London: IIED. Mirzaei, Parham A. 2015. “Recent Challenges in Modeling of Urban Heat Island.” Sustainable Cities and Society 19 (December): 200–206. Narayan, Laxmi. 2014. “Urbanization and Development.” International Journal of Research 1 (8): 901–908. Patidar, Savitree, and Vimit Sankhla. 2015. “Change Detection of Land-Use and Land-Cover of Dehradun City: A Spatio-Temporal Analysis.” International Journal of Advanced Remote Sensing and GIS 4 (1): pp–1170. Reis, Selçuk. 2008. “Analyzing Land Use/Land Cover Changes Using Remote Sensing and GIS in Rize, North-East Turkey.” Sensors 8 (10): 6188–6202. https://doi.org/10.3390/s8106188. Sobrino, J. A., V. Caselles, and F. Becker. 1990. “Significance of the Remotely Sensed Thermal Infrared Measurements Obtained over a Citrus Orchard.” ISPRS Journal of Photogrammetry and Remote Sensing 44 (6): 343–354. Song, Conghe, Curtis E. Woodcock, Karen C. Seto, Mary Pax Lenney, and Scott A. Macomber. 2001. “Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects?” Remote Sensing of Environment 75 (2): 230–244. Srivastava, Prashant K., Saumitra Mukherjee, Manika Gupta, and Tanvir Islam, eds. 2014. Remote Sensing Applications in Environmental Research. Society of Earth Scientists Series. Cham: Springer International Publishing. Tso, C. P. 1996. “A Survey of Urban Heat Island Studies in Two Tropical Cities.” Atmospheric Environment, Conference on the Urban Thermal Environment Studies in Tohwa, 30 (3): 507–19. https://doi. org/10.1016/1352-2310(95)00083-6. Unger, János, Zoltán Sümeghy, and Judit Zoboki. 2001. “Temperature CrossSection Features in an Urban Area.” Atmospheric Research 58 (2): 117–27. Volcani, A., A. Karnieli, and T. Svoray. 2005. “The Use of Remote Sensing and GIS for Spatio-Temporal Analysis of the Physiological State of a Semi-Arid Forest with Respect to Drought Years.” Forest Ecology and Management 215 (1–3): 239–50. Wan, Zhengming, and Jeff Dozier. 1996. “A Generalized Split-Window Algorithm for Retrieving Land-Surface Temperature from Space.” IEEE Transactions on Geoscience and Remote Sensing 34 (4): 892–905.Wang, Fei, Zhihao Qin, Caiying Song, Lili Tu, Arnon Karnieli, and Shuhe Zhao. 2015. “An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data.” Remote Sensing 7 (4): 4268–89.

Vegetation Cover and Land Surface Temperature  21 Wijeatne, I. K., and W. Bijker. 2006. “Mapping Dispersion of Urban Air Pollution with Remote Sensing.” In ISRRS Technical Commission II Symposium, Vienna, 12:14. Wong, Man Sing, Janet Nichol, and Edward Ng. 2011. “A Study of the ‘wall Effect’ Caused by Proliferation of High-Rise Buildings Using GIS Techniques.” Landscape and Urban Planning 102 (4): 245–53. https://doi.org/10.1016/j. landurbplan.2011.05.003. Wu, Penghai, Huanfeng Shen, Liangpei Zhang, and Frank-Michael Göttsche. 2015. “Integrated Fusion of Multi-Scale Polar-Orbiting and Geostationary Satellite Observations for the Mapping of High Spatial and Temporal Resolution Land Surface Temperature.” Remote Sensing of Environment 156 (January): 169–81. https://doi.org/10.1016/j.rse.2014.09.013. Yeh, A. G. O., and X. Li. 2001. “Measurement and Monitoring of Urban Sprawl in a Rapidly Growing Region Using Entropy.” Yin, He, Thomas Udelhoven, Rasmus Fensholt, Dirk Pflugmacher, and Patrick Hostert. 2012. “How Normalized Difference Vegetation Index (NDVI) Trendsfrom Advanced Very High Resolution Radiometer (AVHRR) and Système Probatoire d’Observation de La Terre VEGETATION (SPOT VGT) Time Series Differ in Agricultural Areas: An Inner Mongolian Case Study.” Remote Sensing 4 (11): 3364–89. Yuan, Fei, and Marvin E. Bauer. 2007. “Comparison of Impervious Surface Area and Normalized Difference Vegetation Index as Indicators of Surface Urban Heat Island Effects in Landsat Imagery.” Remote Sensing of Environment 106 (3): 375–86. Zhang, Jinqu, Yunpeng Wang, and Yan Li. 2006. “A C++ Program for Retrieving Land Surface Temperature from the Data of Landsat TM/ETM+ band6.” Computers & Geosciences 32 (10): 1796–1805.

2 Geo-Environmental Hazard Vulnerability and Risk Assessment Over South Karanpura Coalfield Region of India Akshay Kumar1*, Shashank Shekhar1, Anamika Shalini Tirkey2 and Akhouri Pramod Krishna1 Department of Remote Sensing, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India 2 Department of Geoinformatics, School of Natural Resource Management, Central University of Jharkhand, Brambe, Jharkhand, India

1

Abstract

Coal is the major energy resource in India, as well as in the world. It is a prime source of energy for industrial growth but coal mining and uses can harm the land, air quality, surface and sub-surface water, and human health. In the present study, attempts were made to estimate the geo-environmental hazards and risks in South Karanpura Coalfield region using information on land use/land cover (LU/ LC), aerosol optical thickness (AOT), precipitable water vapor (PWV), and temperature conditions integration with socio-economic vulnerability in geographic information system (GIS) environment. AOT, PWV, and temperature were measured using a MICROTOPS-II Sunphotometer instrument during the month of January 2011. Census data were used to examine the socio-economic vulnerability of the region through computation of population density, total workers, children below ages 0–6 years and literacy rate. Results indicated that 32.03% (122.16 km2) of the area is in a high to very high risk zone in the central and eastern part of study area. The majority of the risk-prone areas are present in the vicinity of industry and mining areas also have a higher population density. Keywords:  Geo-environmental hazards, socio-economic vulnerability, risk, coal mining, GIS

*Corresponding author: [email protected] Shruti Kanga, Varun Narayan Mishra, and Suraj Kumar Singh (eds.) Sustainable Development Practices Using Geoinformatics, (23–46) © 2021 Scrivener Publishing LLC

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24  Sustainable Development Practices Using Geoinformatics

2.1 Introduction Fossil fuels such as coal, petroleum, and natural gas are the primary sources of energy worldwide. Among all these abovementioned energy sources, coal is a principal source of energy in many countries due to its widespread abundance and low cost. Coal provides 30% of global primary energy needs and generates over 40% of the world’s electricity [1]. India is the second largest coal producer in the world after China [2]. In recent years, coal mining and production have increased tremendously in India to meet the growing demand for energy. Coal mining is one of the core industries that contribute to the economy and development of a country; however, it can causes noticeable deterioration to the environment and drastic changes in the local area [3, 4]. The process of coal mining both surface and underground can harm the land, surface water, groundwater, and the air. Coal mining involves the excavation of the coal by removing overburdens by using large mining equipment. This process is associated with the production of large quantities of mine spoils and the release of dust particles. Methods of mining tend to make a notable impact on the environment, the impact varying in severity depending on whether the mine is working or abandoned, the mining methods used and the geological conditions [5]. Evaluating and monitoring of land use/land cover (LU/LC) change in the coal mining area has become an important priority for scientists, land managers, and policymakers as LULC changes are typically associated with mining [6]. For example, vast forest areas are removed for mining impacting soil biological properties that leads to land degradation. Air pollution due to particulate matter (PM) originated through coal mining activities is also one of the major problems in the coal mining regions. According to the US Clean Air Act of 1971, PMs or aerosols are considered to be one of six criteria pollutants, others are carbon monoxide, lead, ground-level ozone, nitrogen dioxide, and sulfur dioxide [7]. The PM also known as aerosols that are tiny particles found in either solid or liquid state of matter, suspended in the air (excluding cloud particles) with an extensive combination of sizes, ranging from 10−2 μm to 102 μm [8, 9]. Aerosols and their association with gaseous pollutants significantly affect the Earth’s atmosphere by either scattering or absorbing the incoming solar radiation at a global, regional, and local level [8, 10, 11]. It includes modification of cloud properties, global climate change, acid rain, ozone depletion, visibility reduction, and soiling of monuments [12–16]. Air pollution also has a severe impact on human health. When fine and ultra-fine

Geo-Environmental Hazards and Risks Assessment  25 materials are inhaled by people, they get carried deep into the lungs and enter the circulatory system where they can lodges in organs such as the heart and liver. Aerosols influence adverse health effects such as chronic pulmonary diseases like cancer, bronchial asthma, chronic bronchitis, premature delivery, and lower birth weight [17, 18]. Apart from aerosols, precipitable water vapor (PWV) and meteorological parameters also have a severe impact on human health due to their association with aerosols. Water vapor is the most abundant greenhouse gas that plays a substantial role in many atmospheric processes, such as radiative cooling, latent heat, and convective activity [19]. Degradation of air quality is a major concern associated with surface or opencast coal mining. Different mining operations such as quarrying, drilling, blasting, loading, unloading, and transportation contribute a huge amount of PM in the surrounding atmosphere that is very harmful to the environment and human health. Therefore, changes in LU/LC, aerosol concentration, PWV, and ambient temperature constitute the main hazards associated with coal mining-related activities. Coal mining and allied activities constitute a threat to livelihoods of the people in the surrounding region and the ability to maintain their health and food production. The severity of these hazards can turn into a disaster due to the existence of high population density (PD) with low socio-economic status. Therefore, a study of the vulnerability of society to these hazards with respect to socio-economic conditions of the population existing in these regions is advisable. Assessment of the vulnerabilities is necessary to identify the relevant actions that should be taken to mitigate the problems before there is severe and permanent damage [20]. Remote sensing (RS) and geographic information system (GIS) are valuable techniques that provide an excellent platform for vulnerability and risk analysis study. Pandey et al. [21] utilized geospatial technology for flood and waterlogging vulnerability and risk assessment in the northern Bihar plains. They used multi-temporal satellite data (1975–2008) to evaluate area statistics and dynamics of waterlogging, whereas census data was used to examine the socio-economic characteristics of the region. The prepared flood-waterlogging risk map that indicated the highest risk to the central districts with 50.95% of the total area, whereas 20.61% and 28.44% are the proportions medium and low-risk zones in the study area. Yaduvanshi et al. [22] estimate the drought hazard and risk using spatial and temporal datasets of Tropical Rainfall Measuring Mission (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS) in integration with socio-economic vulnerability. Their results indicated that 36.90% of the area is facing high to very high drought risk over the north-eastern

26  Sustainable Development Practices Using Geoinformatics and western parts of South Bihar. Gautam et al. [23] reviewed the contribution of air pollution of opencast coal mining areas through a direct and dependent relationship between the composition of PM and exposure time in coal mining operations. They also discussed the adverse effects of PM on health due to inhalation in opencast coal mining areas. The present study addresses vulnerability and risk assessment of coal mining hazards in the South Karanpura Coalfield region of Jharkhand. Opencast and underground active coal mines, abandoned coal mines, and coal-based industries generate environmental hazards in the study area, not only because of the vast extent of some of these activities but also because population centers have tended to develop around them. This study also provides a basis from which local planners, administrators, and responders can create or update the regional district’s emergency plan, allocate resources for risk mitigation, enhance community preparedness, and prepare budgets for cost-effective on-going emergency planning.

2.2 Study Area The study area comprising 381 km2 in the Survey of India (SOI) topographical sheet no. 73 E/6, on a scale of 1:50,000, is situated in Ramgarh and Hazaribagh district of Jharkhand (Figure 2.1). It lies between longitude 85° 15’ to 85° 27’ and latitude 23° 35’ to 23° 44’ at an altitude of 348 m above mean sea level (MSL). The South Karanpura coalfield forms an elongated strip along the Chingara fault. It occupies different formations of the Lower Gondwana system such as Talchir, Karharbari, Barakar, Barren Measure, and Raniganj Formation that overlie with the Precambrian basement. The Coalfield considerably recognized for its excellent quality non-coking coal used by various industries such as power station, steel, cement, fertilizer, bricks-manufacturing, and many other medium and small-scale industries. The general climate of the area is tropical with maximum temperatures between 40°C and 2°C. The area receives an average rainfall between 1,102 mm and 1472 mm with significant seasonal variation. From November to the middle of February, there is the occasional cold weather, which follows rain. The Damodar River drains the area with the Nalkari River as its chief tributary. The main urban settlements in the area are Patratu, Bhurkunda, Barkakana, Saunda, Gidi, Lapanga, Dari, and Simratanr. Patratu is the leading industrial town, and it is well known for coal mining activity and thermal power. Various other medium and smallscale industries also present here are responsible for the degradation of air quality and land degradation in the area.

Orissa

Indonesia

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Thailand

Myanmar

0

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Dari Sponge Fc. Anindita Steel Ltd.

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Chaingada Sponge Fc.Hehal Sponge Fc.

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P.T.P.S. Patratu J.S.P.L. Patratu

Patratu

Sayal

Urimari

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Figure 2.1  Location map of the study area showing village boundaries along with water bodies and coal mining areas.

Industrial Emission Coal-fire Locations

Legend

Bhutan

China

Bangladesh

Jharkland State

Bihar

Sri Lanka

India

Nepal

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Uttar Pradesh

Afghanistan Iran Pakistan

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23°45'0"N

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Turkmenistan Tajikistan

Geo-Environmental Hazards and Risks Assessment  27

28  Sustainable Development Practices Using Geoinformatics

2.3 Methodology and Data Used In the present study, a satellite image of Landsat TM (pertaining to year 2011) was used for the preparation of LU/LC map. The Landsat Thematic Mapper (TM) data was downloaded from the website of the United States Geological Survey (USGS) (http://glovis.usgs.gov). The topographical map pertaining to sheet number 73 E/6 provides the details of the study area and was obtained from the SOI on 1:50,000 scale for ground reference. Visual interpretation techniques were used for delineating and mapping of LU/LC classes of the study area. Categorization of various LU/LC classes such as built-up land (urban/rural), industrial settlement, cropland/fallow land, forest, wasteland, water body within coal mine, water body/river/reservoir, and coal mining area are envisaged based on classification scheme developed by National Remote Sensing Agency (NRSA) [24]. The minor modification in the categories at Level-II is done because of coal mining activity in the area. Further, the hazard rating (1 to 4) was assigned in each LU/LC class based on their relative influence toward inducing geo-environmental hazards. Aerosol optical depth (AOD), also called aerosol optical thickness (AOT), PWV, and ambient temperature were simultaneously measured using MICROTOPS-II Sunphotometer instrument during January 2011. The Sunphotometer has five accurately aligned optical collimators, viz., 340, 500, 870, 936, and 1,020 nm with a full field view of 2.5° [25]. The finer wavelength, i.e., 340 nm was used to assess the status of atmospheric pollution in and around the coal mining area. The observation revealed considerable variation in AOT at the wavelength of 340 nm. The PWV is estimated from the measurements of solar intensity at 936 and 1020 nm. Ambient temperature was measured during day time to assess the vulnerability condition over industrial, mining, urban and forest area. The inverse distance weightage (IDW) technique was used to interpolate the AOT, PWV, and temperature concentration over the study area. This technique applies the spatial correlation of variables and based on observed values predicts the variables at unobserved locations [26]. For the preparation of the geo-environmental index model, each subclass of indicator maps assigns a numerical rating within a scale of 1–4 according to their relative influence of hazard proneness. Finally, a composite geo-environmental hazard map was prepared by the addition of the entire ratings assigned to different hazard inducing factor maps and divided by total number of parameters in GIS environment. For each of the above four geo-environmental hazard indicators, four classes of hazard such as low, moderate, high, and very high were defined. The composite

Geo-Environmental Hazards and Risks Assessment  29 geo-environmental hazard index (GHI) of the integrated layers is calculated by using the following formula:

GHI = AOT(340 nm)r + PWVr + Tr + LULCr/n

(2.1)

Where AOT(340 nm)r represents ratings assigned to AOT; PWVr represents ratings assigned to PWV concentration in the winter season; Tr, represents the ratings assigned to temperature in the winter season; LULCr represents ratings assigned to LU/LC map; and n represents the number of indicators. Census data of the year 2011 were used for computation of village-wise socio-economic indicators of PD, workers, literacy rate, and children below ages 0–6 years. Numerical ratings within a scale of 1 to 4 were assigned to each indicator class according to their assumed vulnerability to geoenvironmental hazards. A composite vulnerability map was generated by integrating the village level thematic maps of all socio-economic indicators and divided by total numbers. For the socio-economic vulnerability map, four classes of vulnerability such as low, moderate, high, and very high were defined. The composite socio-economic vulnerability index (SVI) of the integrated layers is calculated by using the following formula:

SVI = PDr + TWr + CAGr + LRr/n

(2.2)

where PDr represents the ratings assigned to population density; TWr represents ratings assigned to total worker density; CAGr represents ratings assigned to the population of children in the age group 0–6 years; LRr represents ratings assigned to percentage population of literacy rate; and n represents the number of indicators. Risk is the probability of adverse consequences or expected losses resulting from interactions between hazards and vulnerable conditions [20, 27, 28]. The final coal mining risk index (CMRI) is prepared by multiplication of both GHI and SVI in the GIS domain using the following formula:

CMRI = (GHI) × (SVI)

(2.3)

where CMRI represents coal mining risk index; GHI represents village level geo-environmental hazard index; and SVI represents the village level socio-economic vulnerability index. All thematic layers were converted into a raster format, maintaining the same resolution (30 m) and coordinate system before they are taken into

30  Sustainable Development Practices Using Geoinformatics Satellite Data Landsat TM (2011) Georeferencing Visual Interpretation and Digitization

Geo-environmental Hazard

Census Data (2011)

AOT at 340 (nm)

Population Density

Perceptible Water Vapour (cm) Temperature (°C)

Total Workers Children in Age Group (0–6) Literates

Land use/Land cover Map

Layer Addition in GIS

Layer Addition in GIS

Geo-environmental Hazard Map

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Coal Mining Risk Zone Map

Figure 2.2  Flow chart of methodology adopted for coal mining risk mapping.

the GIS environment. The software, ArcGIS (version 10.5) and ERDAS Imagine (version 2015) were, respectively, used for GIS analysis and image processing in the digital framework. The details of the overall methodology adopted for the present study are depicted in Figure 2.2.

2.4 Result and Discussion 2.4.1 Thematic Layers of GHI 2.4.1.1 AOT, PWV, and Temperature The ground-based concentration of AOT, PWV, and temperature were simultaneously measured at 41 locations using the MICROTOPS-II Sunphotometer instrument during January 2011. Further, measured datasets were spatially analyzed using GIS to identify the geo-environmental condition of the study area (Figures 2.3a–c). An aerosol is one of the primary pollutants that affect air quality in urban as well as the rural environment of the world [29]. Various coal mining activities such as mine fire, overburden dumping, transportation, and other anthropogenic activity are responsible for increasing these types of pollutants in the atmosphere. The spatial distribution map of AOT(340 nm) indicates a higher concentration (>1.5) over industrial areas (Bhurkunda,

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Figure 2.3  Map showing spatial distribution of (a) AOT at wavelength of 340nm, (b) PWV, (c) temperature, (d) field photographs showing emissions of gases from various sources.

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Geo-Environmental Hazards and Risks Assessment  31

32  Sustainable Development Practices Using Geoinformatics Chaingada, Jindal Steel and Patratu thermal power station) followed by active mining regions (Potonga, Railgada, Sayal, Giddi-C, and Bhurkunda colliery). Higher frequency of transportation activities, loading/unloading of coal, smoke emissions from power plants, and sponge iron factories are the primary cause for dispersion of dust particles into the atmosphere in the area (Figure 2.3d). Whereas AOT is lower in the residential and vegetation areas (1.00 dS/m) and organic carbon (45

36–45

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Figure 10.4  Slope of Gomti River Basin.

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162  Sustainable Development Practices Using Geoinformatics 79°30'0"E

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Figure 10.5  Stream order of Gomti River Basin.

10.4.3.1 Stream Order (Sμ) Stream order is defined as a measure of the position of a stream in the hierarchy of tributaries as shown in Figure 10.5. The first-order streams have no tributaries and these channels normally flow during the wet seasons. The second-order streams have only first-order streams as tributaries. Similarly, third-order streams have first- and second-order streams as tributaries, etc. In the present study, maximum frequency is observed in the first-order streams. We found the Gomati River as the 5th order river. Entire detailed about orders are shown in Table 10.2.

10.4.3.2 Stream Number Gradually, the number of streams diminishes as there is increment in the stream order. High value of first-order streams demonstrates that there is

GI Science in Morphometric Analysis  163 a probability of unexpected sudden flash floods after substantial precipitation in the downstream segments. There are 154 first-order streams, 38 second-order streams, 9 third-order streams, 3 fourth-order streams, and 1 fifth-order stream.

10.4.3.3 Stream Length Stream length is the total length of the stream having same order. The stream length is measured from mouth of the river to the drainage divide near the source to its confluence with other stream. The derived total stream length is 303.20 km.

10.4.3.4 Mean Stream Length The mean stream length is determined by dividing the total stream length of given order and number of stream of that order. The mean length of channel sections of a given order is higher than that of the following lower order, however less than the following higher order, showing that the watershed development observes disintegration laws following up on geologic material with homogeneous enduring disintegration attributes.

10.4.3.5 Stream Length Ratio Stream length ratio as per Horton (1945) can be determined by separating the estimations of mean stream length of higher order by the estimation of LSM of past lower order. The estimation of stream length proportion are appeared in Table 10.2.

10.4.3.6 Bifurcation Ratio The strategy for bifurcation ratio was given by the Schumn in (1956) as the ratio of the stream number of some random order and the stream number of next higher order. The bifurcation ratio for Gomati stream bowl ranges from 2 to 4.30 representing homogeneous rock type.

10.4.4 Aerial Aspect The areal aspect of a basin is a noteworthy morphometric parameter influencing the spatial distribution of various morphometric properties and controlling variables which includes drainage density, drainage frequency, and drainage texture, form factor ratio, elongation ratio, circulatory ratio, and so forth.

164  Sustainable Development Practices Using Geoinformatics

10.4.4.1 Basin Area Such locale where precipitation collects and depletes off into a common outlet, for example, a stream or bay or other kind of water body known as river basin. The complete region of Gomati river basin has been determined and is given in Table 10.3.

10.4.4.2 Drainage Density Drainage density is the entire length of all stream order per unit area. It is an essential parameter as it controls the texture of the drainage system. Low value of drainage density is a symptomatic factor of highly porous area and a big amount of precipitation permeate the ground. It declines the potential of channels to carry the surface run off. High drainage density represents the high amount of run-off. The drainage density for Gomati river basin has been calculated which show the high run-off in the channels given in Table 10.3 and shown in Figure 10.6.

10.4.4.3 Drainage Frequency The total number of drain per unit area can be termed as drainage frequency. The drainage frequency is directly associated with run-off. Higher the drainage frequency higher is the run-off and vice-versa. The calculated drainage frequency has been given in Table 10.3.

10.4.4.4 Drainage Texture Entire number of stream section of order per perimeter of that area Horton (1945). According to Smith (1950), there are five different textures, i.e., Table 10.3  Numeric detail of areal morphometric parameters. Basin Area

371.95 km²

Drainage Density

0.81 km/km²

Drainage Frequency

0.426

Drainage Texture

2.04

Form Factor Ratio

0.30

Elongation Ratio

0.17

Circulatory Ratio

0.46

GI Science in Morphometric Analysis  165 79°40'0"E

30°0'0"N

Drainage Density of Gomti River, Uttarakhand

79°50'0"E N W

E S

30°0'0"N

79°30'0"E

Legend Drainage Density Km/Sq Km 0.9 – 2.07 2.08 – 3.37 3.38 – 5.08

0

4

8

79°30'0"E

16 KM 79°40'0"E

5.09 – 8.52

29°50'0"N

29°50'0"N

0 – 0.9

Boundary

79°50'0"E

Figure 10.6  Drainage density of Gomti River Basin

very course (8). We found the fair texture for the Gomati basin assessment given in Table 10.3.

10.4.4.5 Form Factor Ratio The ratio between the basin area and total length of basin is termed as form factor ratio. Smaller value of form factor will represent the elongated basin while the basin with high form factor will have high peak flows of shorter duration. The calculated form factor value for Gomati basin is given in Table 10.3. The value of the form factor should always be less than 0.7854 (for a perfect circular basin).

10.4.4.6 Elongation Ratio The elongation ratio assists in influencing the shape of the basin (circular or elongated). It ranges from 0 to 1. Higher the value of elongation ratio more circular is the basin while lower value will show the elongated basin. In this study, we found the Gomati basin is near circular as its elongation ratio approaching 1.

166  Sustainable Development Practices Using Geoinformatics

10.4.4.7 Circulatory Ratio Circularity ratio is the ratio of the basin area and the area of a circle with the same perimeter as that of the basin. The value of ratio is equal to unity when the basin shape is a perfect circle and is range 0.4–0.5 when the basin shape is strongly elongated and highly permeable homogeneous geologic materials. The circularity ratio is influenced by the slope, relief, and geologic structure of the basin.

10.4.5 Relief Aspects The relief aspects involves three dimensional parameters such as basin relief (H), relief ratio (Rh), and so forth.

10.4.5.1 Basin Relief Basin relief is the most extreme vertical distance from the mouth of the stream to greatest point on the partition. It plays a significant job in representing the land-form qualities and geomorphic process. The basin relief for the Gomati River is 1,694 as given in Table 10.4. The least relief of the basin is 849 (m), saw as the highest and the most lowermost part of the basin, while highest is more than 2543 (m) as witnessed in the central part of the basin.

10.4.5.2 Relief Ratio It can be defined as the ratio between the relief of the basin and length of the basin. It helps in understanding the steepness of the watershed. The value of relief ratio in this study is 48.58 shown in Table 10.4.

Table 10.4  Numeric detail of relief morphometric parameter. Maximum Basin Height (Z) (m)

2,543

Minimum Basin Heigth (z) (m)

849

Basin Relief (H) (m)

1,694

Relief Ratio (Rh)

48.58

GI Science in Morphometric Analysis  167

10.5 Conclusion The present investigation has demonstrated that the geo processing method utilized in GIS is a viable apparatus for calculation and examination of different morphometric parameters of the basin and comprehends different landscape parameters, for example, nature of the bedrock, invasion limit, surface spillover, and so forth. Watershed of the Gomati River Basin was selected for morphometric analysis. A quantitative study of the drainage basin has been undertaken to understand the existing relations among various morphometric parameters. The main purpose of the present study was to analyze the morphometry of the drainage basin and the channel characteristics as a major component of the fluvial system, which will bring forth detailed geomorphic characteristics and provide an idea about the evolution of the whole landscape by hydrological processes. The Gomati river basin is well drained in nature with stream order ranging from 1 to 5. The drainage density for Gomati river basin has been calculated as 0.81 km/km² which show the high run-off in the channels. Moreover, in this study, we found the Gomati basin is 0.17 which is near circular as its elongation ratio approaching 1. The basin relief for the Gomati basin is 1,694, the lowest relief of the basin is 849 (m) witnessed in the uppermost, and the lowest part of the basin while highest is more than 2,543 (m) witnessed in the central part of the basin. The quantitative examination of linear, aerial, and relief parameters utilizing GIS is observed to be of immense utility in river watershed assessment, basin prioritization for soil and water protection, and common asset the executives. The geo processing methods utilized in this examination will help the planners and decision makers in basin development and management studies.

References Chopra, R., Dhiman, R. and Sharrna, P.K (2005) Morphometric analysis of sub-watersheds in Gurdaspur District, Punjab using Remote Sensing and GIS techniques Journal of the lndmn Society of Remote Sensing, 33(4): 531-539. Patel DP, Dholakia M, Naresh N, Srivastava PK (2012) Water harvesting structure positioning by using geo-visualization concept and prioritization of mini-watersheds through morphometric analysis in the lower Tapi basin. J Indian Soc Remote Sens 40(2):299–312. doi:10.1007/s12524-011-0147-6.

168  Sustainable Development Practices Using Geoinformatics RASTOGI, R.A. and SHARMA, T.C. (1976) Quantitative analysis of drainage basin characteristics. Jour. Soil and water Conservation in India, v.26(1&4), pp. 18–25. Thakkar AK, Dhiman S (2007) Morphometric analysis and prioritization of mini watersheds in Mohr watershed, Gujarat using remote sensing and GIS techniques. J Indian Soc Remote Sens 35(4):313–321. Srivastava PK, Mukherjee S, Gupta M, Singh S (2011) Characterizing monsoonal variation on water quality index of River Mahi in India using geographical information system. Water Qual Expo Health 2(3):193–203. doi:10.1007/ s12403-011-0038-7. Srivastava PK, Gupta M, Mukherjee S (2012a) Mapping spatial distribution of pollutants in groundwater of a tropical area of India using remote sensing and GIS. Appl Geomat 4(1):21–32. doi:10.1007/s12518-011-0072-y. Srivastava PK, Han D, Gupta M, Mukherjee S (2012b) Integrated framework for monitoring groundwater pollution using a geographical information system and multivariate analysis. Hydrol Sci J 57(7):1453–1472. doi:10.1080/02626 667.2012.716156. Srivastava PK, Han D, Rico-Ramirez MA, Bray M, Islam T (2012c) Selection of classification techniques for land use/land cover change investigation. Adv Space Res 50(9):1250–1265. doi:10.1016/j.asr.2012.06.032. Magesh NS, Jitheshlal K, Chandrasekar N, Jini K (2012) GIS based morphometric evaluation of Chimmini and Mupily watersheds, parts of Western Ghats, Thrissur District, Kerala, India. Earth Sci Inform 5:111–121. Mukherjee S, Sashtri S, Gupta M, Pant MK, Singh C, Singh SK, Srivastava PK, Sharma KK (2007) Integrated water resource management using remote sensing and geophysical techniques: Aravali quartzite, Delhi, India. J Environ Hydrol 15. Paper no 10. Mukherjee S, Shashtri S, Singh C, Srivastava PK, Gupta M (2009) Effect of canal on land use/land cover using remote sensing and GIS. J Indian Soc Remote Sens 37(3):527–537. SREEDEVI, P.D., SUBRAHMANYAM, K. and SHAKEEL, A. (2005) Morphometric Analysis of a Watershed of South India Using SRTM Data and GIS, Vol.73, April 2009, pp. 543–552. Singh PK, Singh UC (2009) Water resource evaluation and management for Morar River basin, Gwalior District, Madhya Pradesh using GIS. E-journal Earth Sci India 2:174–186. Strahler AN (1964) Quantitative geomorphology of drainage basins and channel networks. In: Chow VT (ed) Handbook of applied hydrogeology. McGrawHill, New York, pp. 4–76. Nautiyal MD (1994) Morphometric analysis of drainage basin, district Dehradun, Uttar Pradesh. J Indian Soc Remote Sens 22:252–262.

11 Water Audit: Sustainable Strategy for Water Resource Assessment and Gap Analysis Kirti Avishek*, Mala Kumari, Pranav Dev Singh and Kanchan Lakra Department of Civil and Environmental Engineering, Birla Institute of Technology, Mesra, Ranchi, India

Abstract

Water is essential natural resource for life and environment. The survival of all living things depends on the availability of water. The supply of this key natural resource in adequate quantity, quality, and time is of utmost importance for survival. However, rapid decline in water quality and quantity is observed due to changes in surface and ground water bodies, rainfall fluctuations, and consumption patterns resulting in a gap between water demand and availability. Water resource audit is a technique for assessing these gaps for any defined boundary. In this study, water audit has been attempted for Birla institute of Technology, Mesra, Ranchi with case studies of two hostels. Lobby-wise water audit was assessed to finally conclude the gaps. Water harvesting potentials was assessed for the study area and recommendations were made for water management and planning. Keywords:  Water audit, demand-supply, gaps, planning, harvesting

11.1 Introduction Water is essential natural resource for life and environment. The survival of all living things depends on the availability of water. The supply of this key natural resource in adequate quantity, quality, and time is of utmost importance for survival (Bandyopadhyay and Mallik, 2003). The total water available on earth surface is 71%, out of which 97.5% of this water is *Corresponding author: [email protected] Shruti Kanga, Varun Narayan Mishra, and Suraj Kumar Singh (eds.) Sustainable Development Practices Using Geoinformatics, (169–184) © 2021 Scrivener Publishing LLC

169

170  Sustainable Development Practices Using Geoinformatics saltwater and unsuitable for drinking. According to United Nations, only 2.5 % of earth’s water is fresh water and almost three quarter of it is frozen in the ice caps, only 0.3% is available from rivers, lakes, and reservoirs. In addition, 93% of the available water is used in agricultural sector, 4% for industrial sector, and 3% for domestic sector. By 2025, it is estimated that about two thirds of the world’s population—about 5.5 billion people—will live in areas facing moderate to high water stress (United Nations, 2002). In view of increasing demand of water for various sectors, special emphasis is usually laid on for the proper utilization of water resources (Hazra et al., 2011). Issues of water security has attracted greater importance in research and policy matter during last few decades (Gleick, 1993; Cosgrove and Rijsberman, 2000) along with growing interests in economics of water resources (Briscoe, 1996; Gibbons, 1986). The implementation of rain water harvesting system has the potential to mitigate the ongoing water supply crises experienced by many urban centers (Colombes et al., 2002). Roof water harvesting differs from other water supply modes in that there is no need to transport water, since it is used within a few meters of where it falls as rain. Roof top rainwater harvesting is a technique through which rain water is captured from the roof catchment and stored in reservoirs. Harvested rain water can be stored in sub-surface ground water reservoirs by adopting artificial recharging techniques to meet the household needs through storage in tanks. As most of the water goes off as runoff, the recharge into ground water is less. Soil in Jharkhand is thus one of the components responsible for water stress in the state. Thus, steps should be taken to capture the running water. This can be done by adopting measures like contour bunding, water harvesting, ponds, and other physical methods (Hazra et al., 2011). Thus, water management measures should be taken for capturing the running water (Ramasastri, 2002) so that water stress is reduced. The objective of the study is to assess the water resource gaps in Birla Institute of Technology (BIT), Mesra Campus (Figure 11.1) using water audit approach. BIT Mesra lies at 23°25’N latitude and 85°26’E longitude. The campus is residential, providing accommodation to undergraduate and postgraduate students and 2,500 members of faculty and staff. A water audit is a systematic review of a site that identifies the quantities and characteristics of use of water. The overall objective of conducting a water audit is to assess the process of water usage and gaps in water distribution system for overall improvement. Ideally, the process is divided into three phases: pre-audit, audit and post-audit.

Figure 11.1  Map study area, BIT Mesra, Ranchi District, Jharkhand State, India.

JHARKHAND MAP

SOURCE: IRS 1C LISS 2004

STUDY AREA

Water Resource Assessment and Gap Analysis  171

172  Sustainable Development Practices Using Geoinformatics

11.2 Material and Methodology 11.2.1 Pre-Audit Phase It is process of having an overview of the process and system of water quality and quantity and study area visit. This phase thus focused on the following: field survey of the study area to assess the water system of BIT; discussion with the water supply department employees, staff, and the motor operators on the various water uses and the source of water supply; interactions with the residents of the campus for the water problems and possible solutions.

11.2.2 Audit Phase 11.2.2.1 Population Estimation of BIT Campus Population of the campus was assessed through data collected from Dean Students Welfare office. Floating population was taken as 600 based on discussions with Water Supply department. Family population was estimated by counting the number of households with an average population of 4.

11.2.2.2 Water Source Identification Based on discussion with the water supply department, there are two sources of water supply for BIT campus: rivers (Jumar and Subarnarekha) and bore wells. Bore well point and discharge data were obtained from Water Supply department. Water source location was plotted using GPS. Table 11.1 shows the bore well data along with geographic coordinates. Table 11.2 shows the water withdrawal from Jumar River.

11.2.2.3 Water Demand Assessment 11.2.2.3.1 Hostel Water Requirement

It was based on per capita water demand. Hostel water demand is achieved by the number of persons multiply with the per capita norm for water supply 135 L/day/person.

11.2.2.3.2 Residential Area Requirement

It was based on quantity of water currently used. Residential water is achieved by the storage tank availability in quarters and estimated water consumption in a day on the basis of survey report.

Location of Bore Well

Hostel no. 7

Hostel no. 8

Hostel no. 9

Hostel no. 10

Hostel no. 12

Water supply

Rocketery dept.

Medicinal plant

S. No

1

2

3

4

5

6

7

8

10

6

16

18

16

16

16

8

Operation Time

3,000

1,500

4,500

7,200

6,600

8,400

4,800

1,500

Flow Rate (L/h)

Table 11.1  Bore well location of BIT Mesra.

30,000

9,000

72,000

129,600

105,600

134,400

76,800

12,000

Total Water Withdrawal (L/day)

RS hostel and Qtrs.

Rocketry dept.

Residential/ Institutional Area

Qtrs. behind H-5 and H-6

H-10

H-8 and H-9

H-8

H-7

Supply Location

23° 25’ 30.73”N

23° 24’ 36.08”N

23° 24’ 40.39”N

23° 24’ 08.06”N

23° 25’ 08.88”N

23° 24’ 58.09”N

23° 24’ 56.58”N

23° 25’ 27.86”N

Latitude

(Continued)

85° 26’ 13.79”E

85° 26’ 39.89”E

85° 26’ 20.52”E

85° 26’03.27”E

85° 26’05.32”E

85° 26’40.27”E

85° 26’28.95”E

85° 25’53.76”E

Longitudes

Water Resource Assessment and Gap Analysis  173

Doctor colony

Durga puja Pandal

Hostel 7

10

11

12

6

10

8

16

Operation Time

1,500

1,800

1,800

10,800

Flow Rate (L/h)

(Source: Water Supply Department BIT Mesra).

Total

Pump house jungle

9

S. No

Location of Bore Well

783,600

9,000

18,000

14,400

172,800

Total Water Withdrawal (L/day)

Table 11.1  Bore well location of BIT Mesra. (Continued)

H-7

H-8

Qtrs.

Pump house jungle

Supply Location

23° 24’ 28.32”N

23°24’ 56.46”N

23°24’ 59.52”N

2° 24’ 39.72”N

Latitude

85° 25’ 55.97”E

85° 26’ 34.13”E

85° 26’ 02.96”E

85° 26’ 20.96”E

Longitudes

174  Sustainable Development Practices Using Geoinformatics

Water Resource Assessment and Gap Analysis  175 Table 11.2  Water withdrawal from Jumar River. Month

Water withdrawal per day (Liters)

January

600,000

February

600,000

March

600,000

April

600,000

May

400,000

June

400,000

July

600,000

August

600,000

September

600,000

October

600,000

November

600,000

December

400,000

(Source: Water Supply Department BIT Mesra).

11.2.2.4 Gap Assessment Identification of demand-supply gaps for water management planning.

11.2.3 Post-Audit Phase Suggesting measures for water resource improvement and water resource planning.

11.3 Result 11.3.1 Water Demand Assessment Hostel Water Requirement: It was estimated based on per capita water demand (Table 11.3). Residential Area W ater Requirement: Table 11.4 shows the water demand from residential and other facilities of the campus.

176  Sustainable Development Practices Using Geoinformatics Table 11.3  Water requirement. S. No

Hostel

Total population

Water Requirement in Liters/Day

1

H-1

135

18,225

2

H-2

135

18,225

3

H-3

150

20,250

4

H-4

150

20,250

5

H-5

305

41,175

6

H-6

300

40,500

7

H-7

325

43,875

8

H-8

620

83,700

9

H-9

750

101,250

10

H-10

780

105,300

11

H-11

500

67,500

12

H-12

220

29,700

13

H-13

220

29,700

14

H-14

100

13,500

4,690

633,150

Total

11.3.2 Water Audit Report and Analysis Hostel 8 and Hostel 9 were selected as sample hostel for the study.

11.3.2.1 Water Audit of Hostel No. 9 Total population including students and working staff is 750. According to the standard water requirement of 135 L/day, total water demand of Hostel 9 is 101,250 L/day. The intake reservoir is situated just behind the hostel premises where water comes mainly from two sources, river water, and from bore wells. According to the field survey, it has been found that the water scarcity problem arising in post-monsoon season (July to December) is less; as compared to summer months witnessing extreme scarcity (March to May). Figure 11.2 shows the intake reservoirs

Water Resource Assessment and Gap Analysis  177 Table 11.4  Water demand in campus facilities. Total No. of Quarters

Average No. of Persons

Water Requirement

ASPL

1

4

1,000

BSPL

6

24

6,000

A1

2

8

2,000

B1

2

8

2,000

C1

26

104

26,000

D1

54

216

54,000

E1

80

320

80,000

F1

18

72

18,000

G1

31

124

31,000

NCC

8

32

8,000

912

228,000

Quarter Type Inner campus

Outer Campus B 11

8

32

8,000

C 11

80

320

80,000

D 11

40

160

40,000

E 11

18

72

18,000

F 11

6

24

6,000

G 11

31

124

31,000

GR 11

9

36

9,000

TH

35

140

35,000

FM

8

32

8,000

940

235,000 463,000

178  Sustainable Development Practices Using Geoinformatics

Figure 11.2  Intake reservoir of Hostel 9.

in Hostel 9. The exact amount of water supply cannot be calculated due to lack of metering system in hostel; thus, a water audit is based on average consumption of water in two sectors. Total capacity of intake reservoirs was calculated as follows: Dimension of Tank: 350 m × 895 m × 160 m Reservoirs capacity = 50.12 m3 = 50,120 liters. To estimate the flow rate of the intake reservoir, the bucket method was used. Ten-liter bucket was used to estimate the time required to fill. This estimation was further used to determine the time required for intake reservoir to fill. As per the water supply department of BIT Mesra supply runs for 16 h/day. Considering this, the total water supply for Hostel 9 was calculated to be 74,304 L/day which is apparently less than the total demand. As per estimated flow rate water supply during post-monsoon season is 74,304 L/day and during pre-monsoon season is approximately 53,712 L/day. Table 11.5 shows the average flow rate based on bucket method. Table 11.6 provides the total overhead storage-tank capacity of Hostel 9 and per head availability of water under the ideal condition of completely filled overhead water tank. Overhead water storage consists

Water Resource Assessment and Gap Analysis  179 Table 11.5  Average water flow rate. Post-monsoon season S.no

Water supply

Bore Well

7 am

0.60 L/s

0.63 L/s

1 am

0.68 L/s

0.66 L/s

4.30 am

0.63 L/s

0.69 L/s

Pre-monsoon season 6 am

0.55L/s

0.60 L/s

8 am

0.61L/s

0.61 L/s

10 am

0.45 L/s

0.57 L/s

12 pm

0.41 L/s

No supply

3 pm

0.66 L/s

0.46 L/s

5 pm

0.5 L/s

No supply

of cement tank, syntax tanks, and hot water cisterns; water storage capacity of each is calculated separately and in each lobby. After calculating the total overhead capacity per lobby, average water availability per person per lobby is calculated. Water availability per person differs in each lobby depending on overhead capacity and the number of students in the lobby; somewhere, it is positive, and somewhere, it is negative as compared with the standard per day water consumption per person data which is 162 L/day/person. In actual, water supply in 16 h, even under ideal condition, such as constant electricity, water availability in bore wells, etc., is much less than the storage water demand capacity. In addition to above, water supply is affected by most practical problems such as power cut, drying up of Jumar River, bore well water level decrease, faulty pipelines and taps, and negligence of students and staffs in hostel, etc. Thus, it is found that the total demand for Hostel 9 is 101,250 L/ day, whereas the supply is only 74,307 L/day during post-monsoon and 53,712  L/day during premonsoon condition. Thus, a gap of 26,946– 47,538 L/day is observed.

15,684

11,472

1

2

3

14,260

Kitchen

Total

16,220

7

6

5

14,260

15,684

Lobby No.

4

Cement Tank Capacity (L)

2,000

1

1

500

3,000

1

1

5,000

2,000

5,000

1

2

1

No. of Syntax

Capacity of Syntax (L)

1

1

1

1

1

No. of Syntax for Hot Water

Table 11.6  Water availability based on OHT of Hostel 9.

500

500

300

200

200

Cap. of Hot Water Syntax

84

76

80

42

120

150

120

No. of Girls in Each Lobby

108,780

14,260

17,220

10,000

14,760

4,000

11,772

15,884

20,884

Total Capacity of Water in Storage tank (L)

161.8

205

131.5

184

95

98

105

174

Water Availability (Liters per Person)

+

+

−

+

−

−

−

+

GAP

180  Sustainable Development Practices Using Geoinformatics

Water Resource Assessment and Gap Analysis  181 Table 11.7  Water supply and demand gaps at Hostel 8. Hostel 8 Total population

640

Water demand

83,700 L/day

Water supply

76608 L/day (post-monsoon season) 52848 L/day (pre-monsoon season)

Overhead storage tank

97,940 L

Overall Water Gap/day

7,092 L/day (post-monsoon) 30,852 L/day (pre-monsoon)

11.3.2.2 Water Audit for Hostel 8 Based on the process followed in Hostel 9, Table 11.7 shows the water demand-supply gap. Similar to this, water audit was performed for all the hostels and residential areas of the campus, based on which, it was observed that the gap was at least by 50% with more crisis during summer season.

11.4 Conclusions Post-audit phase focuses upon conclusions and recommendations from pre- and post-audit phase. Survey shows that water problem persists in the campus and the residents are unsatisfied with the quality and quantity of water. It is also concluded that water problem is more in summer seasons and in outer campus. Water facilities are inadequate. Being a closed campus water demand can be accurately estimated which lagging in the campus. Based on questionnaire survey, it was found that most of people are unsatisfied with quality of water regard to color, taste, and smell.  Rapid sand filtration has very little effect on taste and smell and dissolved impurities of drinking water, unless activated carbon is included in the filter medium. Rainwater harvesting potential is as high as 2,527,868.86m3/year in the area based on built up and non-built up area estimation. Water storage capacities in hostels are less than what is required and thus power failures result in water scarcity during peak hours. Water flow to outer campus is low and thus poses water problems. Water supply systems are weak. Water

182  Sustainable Development Practices Using Geoinformatics supply department have the willingness to work but manpower skill is a major issue. Rain water harvesting can be a good option to recharge the lowering water table of the region. This may be effective only in long term. But, for sustainable future, it will be an effective mechanism. Water treatment needs to be improvised and maintained regularly. Wastewater treatment facilities should be improved as the reuse of treated wastewater will be the future of the country. Increasing water storage tanks in hostels and outer campus can be beneficial. Deep bore well is required in outer campus. Formal trainings to manpower can be beneficial. Gray water from kitchens and wash basins can be used for watering the gardens at individual homes. Thus, it is suggested that gravity-based system is created that can be directly link the gray water usage. Water meter should be installed in all houses/hostels and other premises for estimating exact water demand. Sensor-based system is to be installed for assessing the pipeline water losses. It has been observed that due to irregular distribution of tank capacity, hostels having different lobby faces water shortage problem. Therefore, the tank should be placed depending upon the size of population. Hence, water audit has identified the gaps between demand and supply and has highlighted the key gaps arising in environmental infrastructures, manpower, usage, and wastage.

References Bandyopadhyay, J, Mallik, B (2003). Ecology andeconomics in sustainable water resourcedevelopment in India. In: Chopra, K; Rao, C HH; Sengupta, R (ed) Water Resources,Sustainable Livelihood and Ecosystem Services. Concept Publishers, New Delhi, p.55–96. Briscoe, J (1996). Water as an economic good: the idea and what it means in practice, Paper presented at the world congress of ICID, The World Bank. Washington. Cosgrove,W., Rijsberman, F R (2000). World Water Vision: Making water everybody’s business, Earthscan, London. Colombes, P.J, kuczera, G., kalma, J.D and John, R.A (2002) “An evaluation of the benefits of source control measures at the regional scale”. Urban Water, 4(4), pp307–320. Gleick, P H (1993) Water in Crisis: A Guide to the Worlds Freshwater Resources, Oxford University Press, Oxford. Gibbons, D C (1986). The economic value of water resources for the future, Washington. Hazra, M., Avishek, K., Pathak, G., Nathawat, M.S (2011). Water Stress Assessment in Jharkhand State using Soil Data and GIS. Journal of applied Sciences and

Water Resource Assessment and Gap Analysis  183 Environmental Management. ISSN: 1119-8362, March, 2011. Vol. 15 (1), 63–67. Ramasastri, K.S., 2002. Water resource management-some vital issues. In. proceedings of international Conference on hydrology and watershed management. Vol 2,1–5, BS publications. Hyderabad. India.

12 Multi-Temporal Land Use/Land Cover (LULC) Change Analysis Using Remote Sensing and GIS Techniques of Durg Block, Durg District, Chhattisgarh, India Jai Prakash Koshale1* and Chanchal Singh2 Chhattisgarh Council of Science and Technology, CGSAC, Raipur, Chhattisgarh, India 2 C3WR, Suresh Gyan Vihar University, Jaipur, Rajasthan, India

1

Abstract

Land use and land cover (LULC) is an important component to understand global land status; it shows the present as well as the past status of the earth surface. Land use and land cover are two separate terminologies that are often used interchangeably. This study aims to find out the LULC changes of the Durg block during 11 years in pre-monsoon (February 2006 to February 2017) and post-monsoon (October 2005 to October 2016) season. Here, population growth is very high so urbanization and industrialization activity is also high in this block. In the present study, multi-temporal Landsat satellite imageries are downloaded and image processing has been done with the help of on-screen visual interpretation techniques on GIS platform. Thematic layers and maps for the year October 2005 and October 2016 (post-monsoon) and February 2006 and February 2017 (pre-­ monsoon) are prepared. With the help of the intersection tool, change map with a change database is generated for LULC change analysis. Lots of LULC changes have been observed between both pre-monsoon (February 2006 to February 2017) and post-monsoon (October 2005 to October 2016) maps; it is observed that in pre-monsoon (February 2006 to February 2017), 0.000153 km2 agriculture land converted into built-up land, 85.43 km2 agricultural land converted into the wasteland, while 10.46 km2 wasteland is converted into built-up, where 0.32 km2 area of water bodies converted into wastelands. In post-monsoon (October 2005 to October 2016), the area of agricultural land which is converted into built-up *Corresponding author: [email protected] Shruti Kanga, Varun Narayan Mishra, and Suraj Kumar Singh (eds.) Sustainable Development Practices Using Geoinformatics, (185–204) © 2021 Scrivener Publishing LLC

185

186  Sustainable Development Practices Using Geoinformatics land is 4.86 km2, 21.21 km2 agriculture land converted into a wasteland, 6.47 km2 wasteland converted into built-ups, whereas 0.45 km2 water bodies are converted into a wasteland analysis reveals lots of information about the area, for instance, during post-monsoon season, agricultural land has shown a decreasing trend by 05.95 km2, while built-up land increased by 11.11 km2, due to an increase in population growth. During 11 years, areas covered by water bodies are increased but it doesn’t match with the population growth. Land use planners require up-to-date and spatially accurate time series land resources information and changing patterns for future management. Keywords:  Satellite imageries, GIS, Change analysis, Visual interpretation, LULC, Durg block

12.1 Introduction Land cover is a biophysical state of the Earth’s surface, which can be used to estimate the interaction of biodiversity with the surrounding environment (Dimyati et al., 1996). On the other hand, the land which is used by human beings for their socio-economic activity is termed as land use. Nowadays, land use and land cover (LULC) analysis plays an important role in the field of environmental science and natural resource management (Maya Kumari et al., 2014; Ayele et al. 2018). It is also very helpful in future town and country planning. Remote sensing and GIS (geographical information system) techniques play a vital role in LULC change analysis. Multi-temporal study for LULC change analysis is a very useful technique. It is evident that remote sensing is a crucial trend analysis tool for multi-temporal LULC change (Roy and Inamdar, 2019). LULC changes are affected by human intervention and natural phenomena such as agricultural demand and trade, population growth, and consumption Patterns, Determining the effects of LULC change on the Earth system depends on an understanding of past land-use practices, current LULC patterns, and projections of future land use and cover (Satyawan et al., 2015). However, one fundamental factor behind city change both in terms of size and pattern remains the same for most cities, i.e., “population growth”. Nevertheless, other factors attributing to LULC change are directly or indirectly dependent on population growth (Kafi et al., 2014). High economic pressures have been strongly influencing agricultural practices over the years (EEA, 2000). Considering the importance of agricultural land, it is urgent to have information regarding the dynamics of LULC changes at regional levels over time to promote

LULC Analysis Using Remote Sensing and GIS Techniques  187 parsimonious use of the available resources (FAO, 2010, Baessler et al., 2006, Noszczyk et al., 2019). Now, advanced geospatial technologies have further improved the efficiency of mapping of LULC type at landscape level. Thus, integration of these techniques forms a potential tool for LULC and change detection (Yadav et al., 2012). With the help of remote sensing and GIS techniques, LULC mappings are done in various fields to find out the changes. Khan and Jhariya (2016) have done LULC change mapping and find out its impact on groundwater quality in Raipur City, Chhattisgarh, India. Raza et al. (2019) find out the spatial change of LULC with urban land surface temperature and precipitation level in between the years 1998 and 2018 for the period of 20 years. The different part of earth surface having different geomorphology and its exhibits different geomorphological phenomena such as erosion, glacial lake outburst floods, debris flows, and landslides—all of which can turn into hazards once elements are at risk. The LULC mapping further used for landslide susceptibility mapping of the district, which will support the governmental authorities in order to prevent natural hazard losses (Mukhiddin et al., 2018). Sharda Singh apply simple NDVI (Normalized Difference Vegetation Index), which is a simple numerical indicator that can be used to analyze remote sensing measurements and access whether the target being observed contains live green vegetation is being used. It helps to reveal the land cover change. Andualem et al. (2018) have done LULC change detection for Rib watershed. Satellite images downloaded and classified using ERDAS Imagine software. The results of the study indicated that there was a dramatic LULC change over 11-year period of time in Upper Rib watershed. With the review, importance of LULC change detection mapping can be easily understood.

12.2 Study Area Geographically, the Durg district is the smallest district of Chhattisgarh state, Durg district further classified into three blocks: Durg, Dhamdha, and Patan. The Durg block is located in the center of Chhattisgarh state; it is a highly populated block of the Durg district. The Durg block is highly populated in Durg district based on census report, 2001 and 2011 population of the Durg district increased relatively 997,848 and 1,126,731 (Statistical booklet, 2013–14). As districts’ headquarter and another administrative office are situated here, urbanization growth is very fast. It is bounded by the Dhamdha block on the north, Patan block in the east,

188  Sustainable Development Practices Using Geoinformatics Rajnandgaon district of Chhattisgarh in the west. The extent of the entire study area of the Durg block is about 642.766 km2 (Figure 12.1), The area of Durg block mainly covered by Survey of India Toposheets no. 64 G/8, while some portion covered by the Toposheets no. 64 G/3, 64 G/4, and 64 G/7. It falls between latitude 21° 02’ 00” N to 21°22’ 00” N and longitude

N

LOCATION MAP

INDIA

CHHATTISGARH

81°30'0

21°0'0"N

21°10'0"N

21°10'0"N

21°20'0"N

81°20'0"E

21°20'0"N

81°10'0"E

DURG DISTRICT

0

2

4

8

SCALE

81°10'0"E

Figure 12.1  Location map of the study area.

12

16 KM 81°20'0"E

DURG BLOCK 81°30'0

LULC Analysis Using Remote Sensing and GIS Techniques  189 “81°08’ 00” E to 81° 28’ 00” E. The altitude of Durg block is 317 m from mean sea level (MSL).

12.3 Materials and Methods 12.3.1 Data Acquisition For the LULC change study, two-season satellite imageries are required that are pre-monsoon (February 2006 to February 2017) and post-monsoon (October 2005 to October 2016) seasons, and it is collected from USGS (United States Geological Survey). Satellite imageries downloaded from the (http://glovis.usgs.gov/) website. Characteristics of Landsat satellite imageries are illustrated in (Table 12.1). Table 12.1  Characteristics of Landsat satellite imageries. Satellite

Sensor

Landsat 5

TM

Landsat 5

TM

Landsat 8

OLI/TIRS

Landsat 8

OLI/TIRS

Path/Row

143/45

Acquisition Date

Spatial Resolution (m)

09/10/2005

30

14/02/2006

30

23/10/2016

30

28/02/2017

30

12.3.2 Software Used ArcGIS 10.3: ArcGIS software is used for the preparation of LULC thematic layer, analysis, and map composition. MS Word: Microsoft word software is used for database preparation.

12.3.3 Methodology Multi-temporal satellite imageries play a vital role in quantifying spatial and temporal phenomena which is otherwise not possible to attempt through conventional mapping (Rawat and Kumar, 2015). Change detection can be defined as the process of identifying differences in the state of an object or phenomenon by observing it at different times

190  Sustainable Development Practices Using Geoinformatics (Singh, 1989). To prepare the LULC MAP from satellite imageries, a classification scheme which defines the LULC classes is preferred based on the requirement of the specific project for a particular application (Arora and Mathur, 2001). For the LULC change analysis, image processing and the thematic layer are required and these are discussed below: a. First-stage: Layer stacking of satellite imageries in falsecolor composite (FCC) format and sensor calibration in the spatial domain is done. b. Second stage: Survey of India toposheets (64 G/8, 64 G/3, 64 G/4, and 64 G/7) is geo-referenced and mosaicking of the toposheets is done. With the help of this toposheets, administrative block boundary of Durg was identified and shapefile was derived. c. Third stage: Image classification has been done. Jensen (2005) defines image classification as the process of categorizing an image into a smaller number of individual classes based on the reflectance values. LULC mapping is done by preparing a thematic layer in five categories (classes) such as agriculture land, built-up, forest, wasteland, and water body/river beds; this each class is digitized by using on-screen visual interpretation key, i.e., tone, texture, shape, size, pattern, association, and reorganization. The thematic layer prepared, and if somewhere doubt occurs for identifying the area, then it is rectified with the help of Google earth and field visit. By following the above process, LULC map of pre-monsoon and post-monsoon season of the study area was prepared. d. Fourth stage: The intersection process was applied. Were intersection method is performed in between both premonsoon maps and post-monsoon maps. Pre-monsoon (February 2006) thematic layer was intersected with the pre-monsoon (February 2017) thematic layer and post-monsoon (October 2005) thematic layer was intersected with post-monsoon (October 2016) thematic layer and compared to find out the LULC changes. The LULC change statics are generated to understand the LULC changes. The flow chart of the methodology is shown in Figure 12.2.

LULC Analysis Using Remote Sensing and GIS Techniques  191 Data Sources SOI Toposheet 1:50,000

Landsat Satellite Imageries Pre-Monsoon Post-Monsoon Year 2006, 2017, Year 2005, 2016, February Month October Month Geo-referencing

Visual Interpretation of Satellite Imageries Thematic Layer Preparation

Ground Trouthing

Analysis using Intersect tool between (2005 to 2016) and (2006 to 2017) Vector layer Vector Layer Separation Change analysis

LULC Change matrix Map Composition

Figure 12.2  Flow chart showing methodology.

12.4 Result and Discussion 12.4.1 LULC Statistics of October 2005 (Post-Monsoon) With the help of Figure 12.3, it is observed that the study area is classified into five classes: agriculture, built-up, forest, wasteland, and waterbodies/ riverbeds. With the help of Table 12.2, it is seen that most of the area is

192  Sustainable Development Practices Using Geoinformatics 81°10'0"E

81°20'0"E

N

Legend DURG TALUKA BOUNDARY AGRICULTURE LAND

21°20'0"N

21°20'0"N

BUILT-UP LAND FOREST LAND WASTE LAND

COORDINATE SYSTEM:- GCS WGS 1984 DATUM:- WGS 1984 UNIT:- DEGREE MINUTES SECONDS REFERENCE SCALE:- 1:125,000

81°10'0"E

0 1.5 3

SCALE 6 9 81°20'0"E

12 KM

21°0'0"N

21°0'0"N

21°10'0"N

21°10'0"N

WATER BODY/RIVER BEDS

Figure 12.3  LULC map of October 2005.

covered by agriculture land; it is about 407.63 km2 because in post-monsoon season, most of the area are used for paddy cultivation. The area which is covered by the built-up is 123 km2. Similarly, the area which is covered by wasteland is 71.00 km2 and the area which is covered by the water body/riverbeds is 37.98 km2. Here, the forest-covered area is very less and it is about 3.17 km2.

12.4.2 LULC Statistics of October 2016 (Post-Monsoon) Map of 2016 is generated and it is also classified into five classes. With the help of Figure 12.4 and Table 12.2, it is observed that the agriculture land

37.98

642.766

Forest Land

Waste Land

Water body/ Riverbeds

Total

3

4

5

71.00

3.17

123.00

Built-up Land

2

407.63

October 2005 (area in km2)

Agriculture Land

LULC Feature Class

1

Sl

100

5.91

11.05

0.49

19.14

63.42

Area in (%)

642.766

38.92

64.45

3.61

134.11

401.67

October 2016 (area in km2)

100

6.05

10.03

0.56

20.87

62.49

Area in (%)

+0.94

−6.54

+0.44

+11.11

−5.95

Change Area in (km2)

Table 12.2  Eleven-year LULC change statistics during October 2005 and October 2016 (post-monsoon).

0.15

1.02

0.07

1.73

0.93

Change Area in (%)

LULC Analysis Using Remote Sensing and GIS Techniques  193

194  Sustainable Development Practices Using Geoinformatics 81°10'0"E

81°20'0"E

N

Legend DURG TALUKA BOUNDARY AGRICULTURE LAND

21°20'0"N

21°20'0"N

BUILT-UP LAND FOREST LAND WASTE LAND

COORDINATE SYSTEM:- GCS WGS 1984 DATUM:- WGS 1984 UNIT:- DEGREE MINUTES SECONDS REFERENCE SCALE:- 1:125,000

81°10'0"E

0 1.5 3

SCALE 6 9 81°20'0"E

12 KM

21°0'0"N

21°0'0"N

21°10'0"N

21°10'0"N

WATER BODY/RIVER BEDS

Figure 12.4  LULC map of October 2016.

covered about 401.67 km2 areas; the area which is covered by built-up land is 134.11 km2, the area which is covered by wasteland is 64.45 km2, and the area which is covered by the water body/riverbeds is 38.92 km2, while, here, the forest-covered area is also very less and it is about 3.61 km2, The comparative study of the post-monsoon maps showed the changing pattern of agricultural land; it is reduced by 5.95 km2; the built-up area increased by 11.11 km2. The forest area increased by 0.44  km2, respectively, the area of wasteland decreased by 6.54 km2, whereas the area which is covered by the water body/riverbeds increased by 0.94 km2.

LULC Analysis Using Remote Sensing and GIS Techniques  195

12.4.3 LULC Changes Between October 2005 and October 2016 (Post-Monsoon) With the help of Figure 12.5 and Table 12.3, changes between particular classes can find out here; it is seen that the 4.86 km2 area of agriculture land is converted into the built-up land, the 21.21 km2 area of agriculture land is converted in the wasteland, the 6.47 km2, area of wasteland is converted into built-up land, and the 0.45 km2, area of water body/

81°10'0"E

81°20'0"E

N

Legend DURG TALUKA BOUNDARY AGRICULTURE TO BUILT-UP LAND

21°20'0"N

WASTE LAND TO BUILT-UP LAND

21°0'0"N

COORDINATE SYSTEM:- GCS WGS 1984 DATUM:- WGS 1984 UNIT:- DEGREE MINUTES SECONDS REFERENCE SCALE:- 1:125,000

81°10'0"E

0 1.5 3

SCALE 6 9 81°20'0"E

Figure 12.5  LULC change map of October 2005 to October 2016.

12 KM

21°0'0"N

21°10'0"N

WATER BODY/RIVER BEDS TO WASTE LAND

21°10'0"N

21°20'0"N

AGRICULTURE TO WASTE LAND

196  Sustainable Development Practices Using Geoinformatics Table 12.3  Eleven-year LULC change in class-wise statistics during October 2005 to October 2016 (post-monsoon).

SI

LULC Feature class (October 2005)

LULC Feature class (October 2016)

1

Agriculture Land

2

LULC Changes

Change Value (Area in km2)

Built-up Land

Agriculture to built-up Land

4.86

Agriculture Land

Waste Land

Agriculture to waste Land

21.21

3

Waste Land

Built-up Land

Wastelands to built-up Land

6.47

4

Water body/ Riverbeds

Waste Land

Water body/ Riverbeds to waste Land

0.45

riverbeds is converted into a wasteland. With the help of Table 12.3, change area of different classes during 11 years in post-monsoon season can be seen easily where + sign shows increment and – sign shows decrement.

12.4.4 LULC Statistics of February 2006 (Pre-Monsoon) With the help of Figure 12.6, it is observed that the study area is classified into five classes: agriculture land, built-up land, forest land, and wasteland land, and water body/riverbeds. Table 12.4 shows LULC change statistics during February 2006. It is found that the area which is covered by agriculture land is 104.16 km2. Because during pre-monsoon, here, cultivation activity is very less on this period agriculture land which is

LULC Analysis Using Remote Sensing and GIS Techniques  197 81°10'0"E

81°20'0"E

N

Legend DURG TALUKA BOUNDARY AGRICULTURE LAND

21°20'0"N

FOREST LAND WASTE LAND

21°0'0"N

COORDINATE SYSTEM:- GCS WGS 1984 DATUM:- WGS 1984 UNIT:- DEGREE MINUTES SECONDS REFERENCE SCALE:- 1:125,000

81°10'0"E

0 1.5 3

SCALE 6 9 81°20'0"E

12 KM

21°0'0"N

21°10'0"N

WATER BODY/RIVER BEDS

21°10'0"N

21°20'0"N

BUILT-UP LAND

Figure 12.6  LULC map (February 2006).

not used for cultivation and it is calculated in wasteland so the area of wasteland got increased and it is about 374.97 km2. The area which is covered by built-up land is 123 km2, and the area of water body/riverbeds is covered about 36.84 km2, while, here, the forest-covered area is very less and it is about 3.54 km2.

36.84

642.766

Agriculture Land

Built-up Land

Forest Land

Waste land

Water body/ Riverbeds

Total

1

2

3

4

5

374.97

3.54

123.26

104.16

LULC Class

SI

February 2006 (area in km2)

100.00

5.73

58.34

0.55

19.18

16.20

Area in (%)

642.77

38.58

431.26

3.61

134.65

34.66

February 2017 (area in km2)

100.00

6.00

67.09

0.56

20.95

5.39

Area in (%)

0.00

+1.74

+56.30

+0.07

+11.39

−69.50

Change area (km2)

Table 12.4  Eleven-year LULC change statistics during February 2006 and February 2017 (pre-monsoon).

0.27

8.76

0.01

1.77

10.81

Change Area in (%)

198  Sustainable Development Practices Using Geoinformatics

LULC Analysis Using Remote Sensing and GIS Techniques  199

12.4.5 LULC Statistics of February 2017 (Pre-Monsoon) With the help of Figure 12.7 and Table 12.4, it is found that in February 2017 during pre-monsoon periods, the area of wasteland is about 431.26 km2, the agriculture land is 34.66 km2, built-up land is about 134.65 km2. The area which is covered by the water body/riverbeds is 38.58 km2, and the area of forest land is covered by 3.61 km2,

81°10'0"E

81°20'0"E

N

Legend DURG TALUKA BOUNDARY AGRICULTURE LAND

21°20'0"N

FOREST LAND WASTE LAND

21°0'0"N

COORDINATE SYSTEM:- GCS WGS 1984 DATUM:- WGS 1984 UNIT:- DEGREE MINUTES SECONDS REFERENCE SCALE:- 1:125,000

81°10'0"E

Figure 12.7  LULC map (February 2017).

0 1.5 3

SCALE 6 9 81°20'0"E

12 KM

21°0'0"N

21°10'0"N

WATER BODY/RIVER BEDS

21°10'0"N

21°20'0"N

BUILT-UP LAND

200  Sustainable Development Practices Using Geoinformatics

12.4.6 LULC Changes Between February 2006 and February 2017 (Pre-Monsoon) With the help of Figure 12.8 and Table 12.5, changes between particular classes can find out and it is observed that during 11 years, difference of 85.43  km2 area of agriculture land is converted into wasteland; on the other hand, 10.46 km2. Area of wasteland is used for built-up land. It is also observed that 0.32 km2. Area of water body/riverbeds converted into a wasteland and a very small fraction of the agricultural land converted into built-up land.

81°10'0"E

81°20'0"E

N

Legend DURG TALUKA BOUNDARY AGRICULTURE TO BUILT-UP LAND

21°20'0"N

WASTE LAND TO BUILT-UP LAND

21°0'0"N

COORDINATE SYSTEM:- GCS WGS 1984 DATUM:- WGS 1984 UNIT:- DEGREE MINUTES SECONDS REFERENCE SCALE:- 1:125,000

81°10'0"E

0 1.5 3

SCALE 6 9 81°20'0"E

12 KM

21°0'0"N

21°10'0"N

WATER BODY/RIVER BEDS TO WASTE LAND

21°10'0"N

21°20'0"N

AGRICULTURE TO WASTE LAND

Figure 12.8  LULC change map of pre-monsoon (February 2006 to February 2017).

LULC Analysis Using Remote Sensing and GIS Techniques  201 Table 12.5  Eleven-year LULC change in class-wise statistics during February 2006 to February 2017(pre-monsoon).

SI

LULC Feature Class (February 2006)

LULC Feature Class (February 2017)

1

Agriculture Land

Built-up Land

Agriculture to built-up Land

0.000153

2

Agriculture Land

Waste Land

Agriculture to waste Land

85.43

3

Waste Land

Built-up Land

Wastelands to built-up Land

10.46

4

Water body/ riverbeds

Waste Land

Water body/ Riverbeds to waste Land

0.32

LULC Change

Change Value Area in (km2)

12.5 Conclusion This study helps us to assessed and monitored the LULC changesthe pattern of Durg block, for this study, Landsat 5 TM sensor sand Landsat 8 OLI/TIRS sensors are used from Pre-monsoon (February 2006 and February 2017) and post-monsoon (October 2005 and October 2016), this comparative study finds out the major LULC changes, for instance, increasing trend of built-up land in both seasons, the agriculture land and wasteland is also showing an increasing trend in both pre-­monsoon and post-monsoon season, due to the massive population growth of Durg block urbanization/industrialization is very fast. During these 11 years of comparative studies, it is also found that the built-up land rapidly increased, in post-monsoon (October 2005 to October 2016), built-up land increased about 11.11 km2, similarly, in pre-monsoon (February 2006 to February 2017) built-up land is increased by 11.39 km2. It is also concluded that, in 11 years, agriculture land shows the decreasing trend in the post-monsoon season by 5.95 km2 and by 69.50 km2 in premonsoon season. The area of forest increased by 0.44 km2 in post-monsoon, while in pre-monsoon, it is increased by 0.01%. The area of waste land

202  Sustainable Development Practices Using Geoinformatics decreased by 6.54 km2 in post-monsoon while in pre-­monsoon, it is decreased by 8.76 %. The water body/river beds covered the 0.27% area in pre-monsoon. The agricultural percentage ratio is reducing with the time frame and it is replaced by man-made built-up land activities. Most of the agriculture land, wasteland, and water bodies affected by urbanization and industrialization, and most agriculture lands converted to wasteland, as the population increase water resources are not sufficient and it is very difficult to manage. At the present, condition of the forest cover area is not good. Therefore, sustainable phase-wise management is required for the betterment of the area.

Acknowledgment This study was carried out in the part of Durg district so I am thankful to the district administrative department. I am also thankful to USGS for providing Landsat satellite imageries and to SOI for providing toposheets. I gratefully acknowledged “CCOST” for providing such a wonderful platform and continuous support during research work and want to give sincere thanks to friends who support directly or indirectly of this research work.

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LULC Analysis Using Remote Sensing and GIS Techniques  203 Jensen, J.R., Introductory Digital Image Processing: A Remote Sensing Perspective. 3rd Edition. Prentice Hall Series in Geographic Information Science, Pearson Education, Inc., New Jersey, (2005). Juliev, M., Pulatov, A., Fuchs, S. & Hübl, J. Analysis of Land Use Land Cover Change Detection of Bostanlik District, Uzbekistan. Polish J. Environ. Stud. 28, 3235–3242 (2019). Kafi, K. M., Shafri, H. Z. M. & Shariff, A. B. M. An analysis of LULC change detection using remotely sensed data; A Case study of Bauchi City. IOP Conf. Ser. Earth Environ. Sci. 20, (2014). Khan, R. & Jhariya, D. C. Land Use Land Cover Change Detection Using Remote Sensing and Geographic Information System in Raipur Municipal Corporation Area, Chhattisgarh. Sci. Soc. Adv. Res. Soc. Chang. SSARSC Int. J. Geo Sci. Geo Informatics 3, 2348–6198 (2016). Mishra, P. K., Rai, A. & Rai, S. C. Land use and land cover change detection using geospatial techniques in the Sikkim Himalaya, India. Egypt. J. Remote Sens. Sp. Sci. 1–11 (2019) doi:10.1016/j.ejrs.2019.02.001. Munthali, M. G., Botai, J. O., Davis, N. & Adeola, A. M. Multi-temporal analysis of land use and land cover change detection for dedza district of Malawi using geospatial techniques. Int. J. Appl. Eng. Res. 14, 1151–1162 (2019). Noszczyk, T.; Rutkowska, A.; Hernik, J. Exploring the land use changes in Eastern Poland: Statistics-based modeling. Hum. Ecol. Risk Assess. 1–28. [CrossRef] (2019). Rawat, J. S. Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. Egypt. J. Remote Sens. Sp. Sci. 18, 77–84 (2015). Raza, D. et al. Satellite Based Surveillance of LULC with Deliberation on Urban Land Surface Temperature and Precipitation Pattern Changes of Karachi, Geography & Natural Disasters. 9, 1–8 (2019). Roy, A. & Inamdar, A. B. Multi-temporal Land Use Land Cover (LULC) change analysis of a dry semi-arid river basin in western India following a robust multi-sensor satellite image calibration strategy. Heliyon 5, e01478 (2019). Satyawan, M. S. And Gairola, S. (2015) ‘Land Use/Land Cover Change Detection Using Geospatial Technique: A Case Study of Sahaspur Block in Dehradun District (Uttarakhand)’, Forest, 13(3.35), pp. 9–62. Singh, A. Review Articlel: Digital change detection techniques using remotely-sensed data. Int. J. Remote Sens. 10, 989–1003 (1989). Singh, A., Change detection in the tropical forest of northeastern India using Landsat, Remote Sensing and Tropical Land Manage ment (M.J. Eden and J.T. Parry, editors), Chichester Wiley Press, London, United Kingdom, pp. 237– 254 (1986). Viana, C. M., Girão, I. & Rocha, J. Long-term satellite image time-series for land use/land cover change detection using refined open source data in a rural region. Remote Sens. 11, (2019).

204  Sustainable Development Practices Using Geoinformatics Yadav, P. K., Kapoor, M. & Sarma, K. Land Use Land Cover Mapping, Change Detection and Conflict Analysis of Nagzira-Navegaon Corridor, Central India Using Geospatial Technology. Int. J. Remote Sens. GIS 1, 90–98 (2012). Yuan, J., Bian, Z., Yan, Q., Gu, Z. Y. & Yu, H. C. An approach to the temporal and spatial characteristics of vegetation in the growing season in Western China. Remote Sens. 12, (2020).

13 Climate Vulnerability and Adaption Assessment in Bundelkhand Region, India Prem Prakash1* and Prabuddh Kumar Mishra2 Department of Geography, Delhi School of Economics, University of Delhi, New Delhi, India 2 Department of Geography, Shivaji College, University of Delhi, New Delhi, India 1

Abstract

In Central India, the regional climate model projections indicate that there are possible changes in future weather patterns. These changes will significantly affect climate-sensitive agriculture, water, and health sectors in the entire region. Development deficit and widespread reliance on agriculture as a source of nourishment and income make Bundelkhand region particularly vulnerable to changes in the climate. Due to exposure to variable climatic conditions, Bundelkhand region has high physical vulnerability. The region is largely rainfed with variable trend of precipitation. Drought like conditions is very frequent in this region, which leads to unstable socio-economic conditions. Monsoon remains a critical determinant of the sowing time, which has been varying drastically in the past few years, causing significant loss to the farmers due to lack of correct and timely information. For vulnerability assessment in the region, Livelihood Vulnerability Index (LVI) evaluated and reviewed. The present study attempts to apply LVI for a comprehensive evaluation of the livelihood risks of the vulnerable communities posed by climate change. The local rapid assessment needs of the present study this methodology is most suitable. It takes into account the socio-economic vulnerabilities of the region using IPCC’s three contributing factors to vulnerability: exposure, sensitivity, and adaptive capacity. The results of the study show that livelihood options in the region is limited and primarily based on agriculture and labor sector. Due to high reliance on primary sector for the livelihood, the communities in the region are highly vulnerable due to changing climatic conditions.

*Corresponding author: [email protected] Shruti Kanga, Varun Narayan Mishra, and Suraj Kumar Singh (eds.) Sustainable Development Practices Using Geoinformatics, (205–214) © 2021 Scrivener Publishing LLC

205

206  Sustainable Development Practices Using Geoinformatics Keywords:  Climate, vulnerability, exposure, adaptation, capacity, livelihood

13.1 Introduction The livelihood of rural communities in India is mainly dependent on primary activities. The impacts of climate change on these primary activities are now considerably recognized. Climate change is emerging as a new challenge to the food security and livelihood of vulnerable communities all around the world (Qaisrani, 2018). The scientific research body and IPCC’s Fourth Assessment Report has continued to highlight global climate change for its perilous nature and potential impacts on natural phenomenon (IPCC, 2001). Rural communities in India have high reliance on climate-sensitive sectors such as agriculture, forestry, and fisheries for their livelihoods. Among them, agriculture is to be largely impacted by climate change on which nearly 70% people are dependent. Climate change will alter the distribution and availability water resources and quality of other natural resources. These conditions of resource depletion will further have significant impact on Indian economy and the associated livelihoods. The exposure of Bundelkhand region toward climatic and non-climatic hazards makes the agrarian rural livelihood more vulnerable toward climate change (Gupta, 2013). Such hazards also affect the underlying sensitivity of the natural resource base. Bundelkhand region is consists of seven southern-most districts of Uttar Pradesh and six northern districts of Madhya Pradesh. In this region, agriculture has a major role in the state’s economy, accounting for about 38% of state domestic product and more than 66% of the rural labor force. In this region, the most important crops are wheat, sorghum, rice, pulses, maize, and groundnuts (Madhuri, 2014). About 90% of the population, which is engaged in the farming practice, are either small, marginal, or landless (Thomas, 2014). The main objective of livelihood vulnerability index (LVI) for Bundelkhand region is to identify various places and people who are most vulnerable for both climatic and non-climatic hazard. In order to reduce or minimize the livelihood vulnerability, the present study attempts to explore some sustainable livelihood options.

13.1.1 Climate Change and Vulnerability Assessment According to the Intergovernmental Panel on Climate Change (IPCC) definition, climate change is the change in the state of the climate that can be identified by changes in the mean (and/or the variability) and that persists

Climate Vulnerability and Adaption Assessment  207 for a prolonged period, generally decades or longer. Whereas, on all temporal and spatial scales, the variations in mean and other statistics of climate beyond that of individual weather events are referred as climate variability. For systematic examination and integration of human and their physical surroundings, various vulnerability assessment tools are commonly used. The vulnerability assessment has been taken into account in several contexts including the World Food Programme’s (WFP) Vulnerability Analysis and Mapping tool, particularly to target food aid Famine Early Warning System (FEWS). The vulnerability assessment tools have been used for various geographic analyses as well as to combine data on health status, poverty, biodiversity, and globalization. The most common aspect of climate change and vulnerability assessment is to quantify the multidimensional vulnerability using several indicators. In this type of assessment, indicators are combined into a composite index in which many variables are integrated. For example, the Human Development Index (HDI) incorporates several indicators and sub-indicators such as health, life expectancy, education, and standard of living to get a complete scenario of national well-being. Similarly, a gap method for calculation of Water Poverty Index (WPI) was also used (Sullivan, 2002) to assess the water provision and how its uses deviates from a pre-set standard. Both the WPI and HDI are considered good examples of composite indices, which is calculated by using weighted averages of separate individual indicators. Sometimes, these weighted methods tend to vary. Due to recent advancements and changing environmental conditions, climate vulnerability assessment has emerged as a fundamental tool to quantify to what extent the communities are vulnerable and how they will adapt to the changing climatic conditions. To confront these changing climatic conditions, various researches and new methodologies have tried to bridge the gaps between natural, physical, and social sciences. Most of these studies and researches primarily rely on the working definition of IPCC for climate vulnerability. Vulnerability is defined as a function of exposure, sensitivity, and adaptive capacity (IPCC, 2001). Vulnerability is also the propensity of ecological as well as human systems to get affected and their ability to respond to such stress, which is imposed as a result or outcome of climate change. In this case, exposure is the duration of climate-related exposure such as a change in precipitation or draught and magnitude of the same phenomenon. The measure of the degree to which a system is affected by the exposure is known as sensitivity. At the same time, the system’s ability to withstand or recover from the exposure is defined as adaptive capacity by the IPCC (Figure 13.1). The assessment of climate vulnerability also suggests about the need for sustainable

208  Sustainable Development Practices Using Geoinformatics Sensitivity

Exposure

Potential Impact

Adaptive Capacity

Vulnerability

Figure 13.1  IPCC framework for assessing vulnerability. Source: IPCC framework report, 2007.

future agricultural development policy and practice to include both longterm and short-term planning which incorporates knowledge of climate change and understanding in order to sufficiently respond to the reality of changing climate. Climate change may have a long-term significant impact on our agricultural systems. The adverse impact of climate change is leading to reduced crop production and other associated livelihood options. It is further causing food insecurity, reduction in the availability of fuel wood and fodder, biodiversity loss, and out-migration of people. As the availability of pasture land is shrinking, pressure on the livestock is also increasing at the same time.

13.1.2 LVI for Bundelkhand Region For developing a LVI, several earlier approaches have been reviewed. To design development program at the community level, Sustainable Livelihood Approach has been used, which uses five types of household assets: physical, natural, human, social, and financial (Chambers and Conway, 1992). This approach has remained very useful for the assessment of household’s ability to withstand the magnitude of shocks such as any disaster, epidemic, or civil conflict. For the vulnerability assessment, climate change adds more complexity in this approach of household livelihood security. This approach addresses the complexity of climate change assessment to a limited extent of assessing sensitivity and adaptive capacity to climate change. The new approach for the livelihood vulnerability

Climate Vulnerability and Adaption Assessment  209 assessment also integrates the exposure toward climate change and incorporates household adaptation practices to comprehensively estimate risk toward livelihood, which is resulting from the climate change. One such approach and methodology is Livelihood Vulnerability Assessment method, which was earlier used by Hahn et al. (2009). This is a comprehensive method, which evaluates the risks associated with the livelihood of vulnerable communities posed by climate change. The LVI method requires the use of both climate data and secondary information, which needs to be verified by primary survey. It uses three contributing factors for vulnerability assessment. These three contributing factors are exposure, sensitivity, and adaptive capacity as used by IPCC. The LVI provides a framework for aggregating and grouping at district level. This methodology is very critical for the development and adaptation planning, which makes it suitable for Bundelkhand region. This methodology provides better scope for the sub-components and weighted measures of LVI, which can be adjusted according to the relevance for the need of local communities. Lastly, in this methodology, the indicators for socio-­economic vulnerability assessment are standardized. Therefore, it is designed in such a way that it provides policymakers, development organizations, and public health practitioners a more practical tool to understand various demographic, health, and social factors, which contribute for climatic vulnerability at the community as well as district level. The biggest advantage of this tool is its applicability and variation. It is also a very flexible method in which the framework can be tailored accordingly by the researchers and planners to meet the needs of some unique geographical areas such as the Bundelkhand region. For Bundelkhand region, climatic data form metrological department and secondary information from census and states statistical records can be obtained to calculate the vulnerability profile of all the districts in the region. The same results can be further verified by conducting a primary data survey. The climatic data will provide a better understanding of climatic variability and long-term variability of those selected parameters. To conduct a vulnerability assessment for the region, various indices are required to be computed. To derive the factors contributing the vulnerability such as exposure (E), sensitivity (S), and adaptive capacity (A) can be taken from secondary data for all the districts in Bundelkhand region. The formula for LVI uses a simple approach of applying equal weights to all major components. LVI requires several steps and an equation for the calculation is given as follows:

210  Sustainable Development Practices Using Geoinformatics Step 1 Indicators Values for all the indicators are required to be standardized for all the district of Bundelkhand



Ia − I (min) I (max − I (min)

Indicator  Index (Ix)  =  

Where, Ix = Standardized value for the indicator. Ia = Value for the Indicator I for a particular district, d. I (min) = Minimum Value for the indicator across all the districts. I (max) = Maximum Value for the indicator across all the districts. Step 2 Profile Indicator index values are combined to get the values for various profiles



Profile (P) =  

∑I = 1 Indicator  Index

1

n

Where, n = Number of indicators in the profile Indicator Index. i = Index of the ith indicator. Step 3 Component Components values of the profile a under component are required to be combined in order to get the value of that component n



Component (C) =  

∑ W Pi ∑ WPi i −1

i −1

Where, WPi is the weightage of the Profile i.

Pi

Climate Vulnerability and Adaption Assessment  211 Weightage of the profile will depend on the number of indicators under it such that, within a profile, each indicator has equal weightage. Step 4 Livelihood Vulnerability Index The combination of the value of the three components will give the vulnerability Index.

Vulnerability Index = (exposure − Adaptive Capacity) × Sensitivity. Scaling in done from -1 to +1 indicating low to high vulnerability. This methodology provides a holistic approach to the study. The above-mentioned indicators represent the contributing factors for vulnerability such as exposure, sensitivity, and adaptive capacity. These selected indicators act as representatives of socio-economic and livelihood vulnerabilities for the climate-sensitive districts of Bundelkhand region. The study of exposure as contributing factor for this research includes both climatic and demographic components. Under climatic components, meteorological data of the previous thirty to 40 years can be used according to the availability of data at various stations. Through the detailed analysis of this dataset, temperature variability and variability in average annual rainfall can be calculated. If the analysis indicates that there is higher inter-annual rainfall variability, it means there is a high probability of unanticipated amount of rainfall throughout the year. This could also lead to drought, flooding, or, in simple words, the below or above-average rainfall will have an impact on agriculture and associated livelihood of the people. In the Bundelkhand region, rainfall remains the only source of agriculture and water recharge. In addition to this, variability in temperature exposes this region by affecting the productivity of crops due to uncertainties, decrease in soil moisture, and increase in evapotranspiration loss. For demographic analysis, sub-components such as percentage of rural population to the total population and sex ratio has been taken into account (Table 13.1) In the Bundelkhand region, a large section of the population is primarily engaged in the agricultural sector and their livelihoods and subsistence are highly dependent on agriculture. It correlates with climate change sensitivity because agriculture is the most sensitive sector to a variable climate because a large population of Bundelkhand region is entirely dependent on subsistence form of agriculture in this region. The women who are under cultural and social pressure and low sex ratio increase their sensitivity toward climate change. In the region, generally, women are responsible for the fulfilment of basic household requirement

212  Sustainable Development Practices Using Geoinformatics Table 13.1  Major components and sub-components comprising Livelihood Vulnerability Index developed for Bundelkhand region. Contributing Factors

Components

Sub-Components

Exposure (E)

Climate

Variability of temperature Average annual rainfall

Demographic

% of rural population to the total population Sex ratio

Sensitivity (S)

Ecosystem

% of forest cover Total area under wasteland Net annual availability of groundwater

Agriculture

Intensity of irrigation Intensity of crops Per capita production of food grains Number of cultivators

Adaptive Capacity (A)

Socio-economic

Total illiterate population Number of health care centers per thousand person Total no. of BPL families

Source: Prepared by the author, 2019.

such as fuel and fodder collection and securing drinking water. Due to climatic variability, conditions like drought and erratic rainfall will further deteriorate their existing vulnerable position. This will also result into large number of schools drop out to help in domestic works. To study sensitivity, ecosystem and agricultural sensitivity have been taken into account. The sub-components under ecosystem sensitivity are percentage of forest cover, area of wasteland and net annual groundwater availability. The composition and distribution of forest resource in Bundelkhand region can be affected due to climatic uncertainties because they are highly sensitive toward the impact of climate change.

Climate Vulnerability and Adaption Assessment  213 This will make forest resources prone to degradation due to disturbance in the delicate balance of biogeochemical cycle. This will also result in habitat loss and shift in fauna of this region. Similarly, climate change is very likely to affect the situation of wasteland due to loss of land fertility in this semi-arid topography. For the study of agricultural sensitivity, irrigation intensity, cropping intensity, per capita food grain production and the number of cultivators in the region may be taken into account as sub-components. Similarly, for the analysis of adaptive capacity as a contributing factor for climate vulnerability, the socio-economic vulnerability can be analyzed which includes the total illiterate population in the region, number of public health care center per thousand people, and the total number of vulnerable families. These are some of the factors, which make the communities more vulnerable toward the impact of climate change.

13.2 Conclusion In conclusion of this concept paper, it can be said that the LVI method is very inclusive, which incorporates various components and sub-components of climate vulnerability. This methodology provides a reasonable framework for aggregation and grouping the indicators of vulnerability assessment at district level. These characteristics of the present methodology may be critical for preparation of adaption strategy and development planning. The LVI also provides socio-economic vulnerability index of a region or community in which the related indicators can be standardized accordingly. This methodology also uses a balanced weighted average approach in which various components contributes equally to overall LVI. This method is also widely verified in many studies, related to public health, food security accessibility to safe drinking water, etc. Therefore, for the Bundelkhand region, which is a climate-sensitive semi-arid area, LVI can be a better approach to study and suggest policy plans for the most vulnerable community.

References Anil K Gupta, S. S. (2013). Vulnerability Assessment and Mitigation Analysis for Draught in Bundelkhand Region. New Delhi: ICSSR. Ayesha Qaisrani, M. A. (2018). What Defines Livelihood Vulnerability in Rural Semi-Arid Areas? Evidence from Pakistan. Earth System and Environment, 2(3), 455-475.

214  Sustainable Development Practices Using Geoinformatics Chambers, R. C. (1992). Sustainable Rural Livelihoods: Practical Concepts for 21st Century. IPCC. (2001). Climate Change 2001: Impacts, Adaptation and Vilnerability. Contribiution of working group II to the Third Assessmnet Report. Cambridge, UK: Cambridge University Press. IPCC. (2007). Climate change Synthesis report. Contribution of working groups I. II and III to the fourth assessment report of the intergovernmental panel on climate change. Madhuri, T. H. (2014). Livelihood vulnerability index analysis: An approach to study vulnerability in the context of Bihar. Jàmbá: Journal of Disaster Risk Studies, 6(1), 1-13. Micah B. Hahn, A. M. (2009). The Livelihood Vulnerability Index: A pragmatic approach to assessing risks from climate variability and change- A case study in Mozambique. Global Environment Change(19), 74-88. Sullivan, C. (2002). Calculating a Water Powerty Index. Worl Developmnet, 30, 1195-1210. Thomas, T. J. (2014). Comprehensive evaluation of the changing drought characteristics in Bundelkhand region of Central India. Meteorol. Atmos. Phys, 127(2), 163-162.

14 Suitable Zone for Sustainable Ground Water Assessment in Dhanbad Block, Jharkhand, India Raghib Raza

*

Quantum Asia Pvt. Ltd., Jaipur, Rajasthan, India

Abstract

The work includes the findings and results of suitable site selection for the sustainable urban groundwater management, within the Dhanbad Block in Jharkhand. The precise aim of this present study is to seek out the suitable site for urban ground water management planning in Dhanbad block. Using Landsat 8 satellite image, DEM image, Toposheet, and other data of Dhanbad block monitor the land use/land cover (LU/LC) pattern for sustainable urban ground water issue and identify the management of groundwater for urban development with various utility services of Dhanbad block. It facilitated to know the complexities of a dynamic phenomenon like suitability site sustainable water management, LU/ LC benefits, urban development planning pattern. A serious component of this is survey and analysis. This comprises the profile of the study area that provides an in depth account of location of study area, extent and aerial coverage of the study area at the Dhanbad District, Jharkhand, India. Thus, the weighted value is included to the features as per the need for the acceptable site selection for the sustainable groundwater management planning. The very good site is suitable for future urban development. Keywords:  RS, GIS, AHP, ground water management

Email: [email protected] Shruti Kanga, Varun Narayan Mishra, and Suraj Kumar Singh (eds.) Sustainable Development Practices Using Geoinformatics, (215–228) © 2021 Scrivener Publishing LLC

215

216  Sustainable Development Practices Using Geoinformatics

14.1 Introduction Ground water for urban development is the social, cultural, economic, and physical development of cities, as well as the underlying causes of these processes. Dhanbad (Jharkhand, India) are growing at a very fast rate and acquired a complex urban structure over the years. The central part or the core has gone through unusual changes in terms of social and physical transformations. Planning theory is the body of scientific concepts, definitions, behavioral relationships, and assumptions that define the body of knowledge of urban planning (Almedia B., 2005). Groundwater has created as a fundamental source to meet the water requirements of various portions including the genuine customers of water, like water framework, family unit, and organizations. The reasonable improvement of groundwater resource requires definite quantitative examination reliant on reasonably authentic sensible norms. Very heightened headway of groundwater in certain area of the country has achieved over-abuse provoking abatement in groundwater levels. The assessed proportion of groundwater availability for the country is 399 billion cubic meter (MOWR2009). Mega urban planning attracts considerable attention due to varying population size, economic, socio-cultural, and political boundaries and having various geological challenges. Land use and land cover (LU/LC) are the biophysical state of our earth and subsurface are the main source of extracting groundwater and minerals (Steffen et al., 1992). The main source for hampering the groundwater is the temperature, as the temperature is high in urban areas in comparison to the rural areas, and for the suitable sites for sustainable ground water assessment, we have to consider several degrees difference in planning area (Mather, 1986). Remote sensing and geographic information system (GIS) integration has been applied and different types of modern modeling and tools of GIS used in detecting the results (Ehlers et al., 1990; Treitez et al., 1992, Harris and Ventura, 1995). The goal of this paper is to demonstrate the modern tools and modeling of GIS to detect the suitable sites and show the potential condition of our natural and man-made earth structures. Nowadays, we have a multispectral, multi resolution, and multi temporal data through remote sensing satellites. By this modern technique analysis, we can keep growth for our nation and easy to cover a large area for the well planning and finding the resource for long livelihood.

Suitable Zone for Sustainable Ground Water Assessment  217

14.2 Study Area Dhanbad area is one of the 24 locales of Jharkhand state, India. Dhanbad is the managerial home office of this region. Starting at 2011, it is the second most crowded region of Jharkhand. As indicated by the 2011 evaluation, Dhanbad region has a populace of 2,682,662, generally equivalent to the country of Kuwait or the US territory of Nevada. This gives it a positioning of 148th in India (out of a sum of 640). The locale has a populace thickness of 1,284 occupants for each square kilometer (3,330/sq. mi). Its populace development rate throughout the decade 2001–2011 was 11.91%. Dhanbad has a sex proportion of 908 females for each 1,000 males, and an education pace of 75.71% (Wikipedia). The scope and longitude of the study region is 86018’46.73”E to 86029’23.40”E longitude and 23051’8.31”N to 23042’3.82”N scope, as examine in territory map in Figure 14.1.

14.2.1 Slope Slope surface recognizes greatest pace of progress in an incentive from every cell to its neighbors or a proportion of progress in surface an N

Study Area

INDIA

Bag hm

r

ara

Gov ind pu

Bal

a ari Jh

Figure 14.1  Study area.

ur

Jharkhand

ia p

Dhanbad

86°25'0"E

86°27'30"E

86°30'0"E

23°47'30"N

86°22'30"E

23°47'30"N

23°50'0"N

86°20'0"E

23°50'0"N

218  Sustainable Development Practices Using Geoinformatics

Slope In Degree 0 – 1.63

23°45'0"N

23°45'0"N

Legend

1.63 – 15.31 23°42'30"N

20.11 – 23.38 23.38 – 30.16 30.16 – 41.86

23°42'30"N

15.31 – 20.11

41.86 – 47.24 0

1

2

4

86°20'0"E

6

47.24 – 52.15

8 KM 86°22'30"E

52.15 – 59.63 86°25'0"E

86°27'30"E

86°30'0"E

Figure 14.2  Slope map.

incentive over separation, communicated in degrees or as a rate. The lower the incline esteem that complement the territory, the higher the slant worth or level of slant (Figure 14.2), indicating the slant guide of the investigation zone. The examination region Dhanbad square slant map speaks to the surface incline in the investigation zone, the most elevated slant is 64.31°.

14.2.2 Ground Water Label Water assumes a fundamental job in each organic culture in the globe. The financial improvement of a district transcendently relies upon the accessibility of good quality water (AmitGhosh, 2015). In the examination zone, Dhanbad square, four zones are isolated into ground water table: the 4- to 6-m zone demonstrates that the accessible of ground water is extremely high; 12- to 16-m zone shows that the accessible of ground water is low; and other two zones are moderate, as appeared in Figure 14.3.

Suitable Zone for Sustainable Ground Water Assessment  219 86°20'0"E

86°22'30"E

86°25'0"E

86°27'30"E

86°30'0"E

23°50'0"N 23°47'30"N

23°47'30"N

23°50'0"N

N

12–16 6–8

8–10

23°45'0"N

23°45'0"N

4–6

Legend

0

1

2

4 86°20'0"E

6

4–6 6–8 8–10 12–16

8 KM 86°22'30"E

86°25'0"E

86°27'30"E

23°42'30"N

23°42'30"N

Water Label In Meter

86°30'0"E

Figure 14.3  Ground water depth map.

14.2.3 LU/LC Mapping The Landsat 8 of 2018 image covering the study area was classified to obtain LU/LC for the suitable site selection for urban development planning. Satellite was clipped into ward of a block. An unsupervised classification was performed to obtain the LU/LC information classes into eight classes. The LU/LC map is shown in Figure 14.4 and it shows percentage of LU/LC area distribution in the study area. SETTLEMENT: The majority of the area is under land utilization type for settlement with nearly 25% of the total area being occupied 31.01 km2 area is cover by settlement. VEGETATION: A vast extent of the area is found on vegetation land, nearly about 27% of the area. Under vegetation, 33.22 km2 area is covered. AGRICULTURE LAND: 19% is kept currently for agriculture area. Agricultural land is useful for cropping and it is use full for the farmers to meet there daily life need. Agricultural land is cover 24.25 km2 in the study area. GRAZING LAND: 12% of the total area is under grazing land. This area is seasonally useful. In the different seasons, only it is used, for other days,

220  Sustainable Development Practices Using Geoinformatics 86°22'30"E

86°25'0"E

86°27'30"E

86°30'0"E

23°42'30"N 0

1

2

4 86°20'0"E

6

8 KM 86°22'30"E

86°25'0"E

86°27'30"E

23°45'0"N

Legend Land Use/Land Cover Water Bodies Waste Land vegetation settlement road mining grazing barren agriculture

23°42'30"N

23°45'0"N

23°47'30"N

23°47'30"N

23°50'0"N

N

23°50'0"N

86°20'0"E

86°30'0"E

Figure 14.4  Land use/land cover map.

it only contains scrubs or act as a waste land; 14.52 km2 area is covered under the total area. MINING LAND: In study area, 6% of its area is under open cast mining. Mining area is conserved of 7.78 km2. WASTE LAND: Waste land is about 2% in the study area, 2.99 km2. WATER BODY: The inland water body covers 1.29 kq km about 1% of total study area. RIVER: River and water bodies both contain only 1% of the study area 0.52 km2. BARREN LAND: Mostly barren land area is about 8%. They appear very distinctly on the satellite data. Nearly, a quarter of the area is less than 10.23 km2.

14.2.4 Geology Features In study area, geological features taken from geological survey of India is divided into four parts (Figure 14.5). Northern part which consists of Barakar formation is consist of white to buff color coarse medium sandstone

Suitable Zone for Sustainable Ground Water Assessment  221 86°20'0"E

86°22'30"E

86°25'0"E

86°27'30"E

86°30'0"E

23°50'0"N 23°47'30"N 23°45'0"N

23°45'0"N

23°47'30"N

23°50'0"N

N

Legend ARCHEAN BARAKAR FORMATION BARREN MEASURE

0

1

2

4

6

86°20'0"E

8 KM 86°22'30"E

TALCHIR FORMATION

86°25'0"E

86°27'30"E

23°42'30"N

23°42'30"N

Geology

86°30'0"E

Figure 14.5  Geology map.

and grit shale; Barren measure rang is the main geological formation of the lower part of the study area; Talchir formation is formed due to the mass flow of sediments and minerals; and upper middle portion which is approximate half of the area is covered by gneiss and schist (Archean Formation). Strictly speaking, there are no large stretches of what may be called as plains in this Basin area.

14.2.5 Soil Soils are complex mixtures of minerals, water, air, organic matter, and countless organisms that are the decaying remains of once-living things. It forms at the surface of land—it is the “skin of the earth” (ICAR). The map, as shown Figure 14.6, represents the study area soil feature which is under 80 and 82 soil categories. So, 80 is a fine loamy, mixed, hyperthermia Typic Haplustalfs Loamy, mixed, hypothermic Lithic Ustorthents, Area (hect) is 467, and % of TGL is 22.39, and 82 is a fine loamy, mixed, hypothermic Typic Haplustalfs Fine, mixed, hypothermic Aeric Endoaqualfs, Area (hect) 609, and % of TGL is 29.20.

222  Sustainable Development Practices Using Geoinformatics 86°20'0"E

86°22'30"E

86°25'0"E

86°27'30"E

86°30'0"E

23°45'0"N

23°45'0"N

23°47'30"N

23°47'30"N

23°50'0"N

23°50'0"N

N

Soil Map 80 0

1

2

4 86°20'0"E

6

82

8 KM 86°22'30"E

86°25'0"E

86°27'30"E

23°42'30"N

23°42'30"N

Legend

86°30'0"E

Figure 14.6  Soil map.

14.3 Methodology In this study, the following three steps are used to evaluate groundwater potential zone. Logical Hierarchy Process (AHP) is a multi-criteria investigation, chose as the most feasible choice technique to recognize appropriate locales for rainwater harvesting (RWH) with GIS stage. A wide utilization of AHP has been done in many research examines for the recognizable proof of potential RWH destinations (Krois and Schulte, 2014). In AHP strategy, the mind boggling choices are composed and dissected in an organized manner dependent on information of mathematic and specialists. The principle code of AHP is to symbolize the parts of any issue progressively to show the connections between each dimension. The primary objective (objective) ought to be on the highest dimension for settling an issue and the lower level comprise of increasingly point by point criteria that impact the fundamental target. For the most part, in AHP technique, a network of pairwise correlations is connected

Suitable Zone for Sustainable Ground Water Assessment  223 to decide the loads for every model. The appropriateness appraisals of a given target which include two criteria are controlled by their relative significance with the assistance of pairwise correlations. The 9-point nonstop scale is utilized to look at and rate the two criteria. The odd values 1, 3, 5, 7, and 9 relate individually to similarly, modestly, unequivocally, in all respects firmly, what’s more, critical criteria when contrasted with one another, and the even qualities 2, 4, 6, and 8 are moderate qualities. Figure 14.7 shows the following flow chart of overall water management with Ground water potential.

14.3.1 Overlay Analysis to Find Groundwater Potential Zone The groundwater potential index (GWPI) is computed by the weighted linear combination method (Ma+.lczewski, 1999; Machiwal et al., 2011) is given by n



GWPI =

m

∑∑[α (β x )] i

i =1

ij ij

(14.1)

j =1

Where βij = weight of the jth class of ith theme obtained by AHP and αi = weight of the ith theme obtained by ANP, n = total number of thematic layers, and m = total number of classes in a thematic layer, xij is the pixel value of the jth class of the ith theme. Tables 14.1 to 14.3 show assigned and normalized weights of the features of themes for the delineation of the groundwater potentials in the study area. Consistency ratio is 0.006.

14.4 Results In this study, an AHP/ANP-based methodology that supports the relative importance of various thematic layers and their corresponding classes affecting groundwater has been used to evaluate groundwater potential zone. Table 14.3 represents the weight of each thematic layer using ANP and weights of their corresponding classes using AHP. The groundwater potential evaluated by the weighted linear combination of these weights is shown in Figure 14.8.

224  Sustainable Development Practices Using Geoinformatics Collection of Data

Existing Maps, SOI Toposheet

Sentinel 2A Data

Aster DEM Data

Geometric correction and Georeferencing Geology, Soil

Interpretation and Classification

Land use/Land cover

Slope, Contour and Aspects map

Geo-database

Network model for Analytic hierarchy Process

Construct Pairwise comparision matrix

Obtain the weights of Sub-themes using AHP

Weighted linear combination to obtain GWPZ

Suitable zones for sustainable ground water assessment

Figure 14.7  Shows methodology flow chart.

Suitable Zone for Sustainable Ground Water Assessment  225 Table 14.1  Pairwise comparison matrix. Ground Water Depth

Geology

Geomorphology

Soil

Slope

LU/LC

Ground Water Depth

9/9

9/8

9/6

9/5

9/4

9/3

Geology

8/9

8/8

8/6

8/5

8/4

8/3

Geomorphology

6/9

6/8

6/6

6/5

6/4

6/3

Soil

5/9

5/8

5/6

5/5

5/4

5/3

Slope

4/9

4/8

4/6

4/5

4/4

4/3

LU/LC

3/9

3/8

3/6

3/5

¾

3/3

Parameter

Table 14.2  Saaty’s ratio index for different values of n. n

1

2

3

4

5

6

7

8

9

10

RI

0

0

0.58

0.89

1.12

1.24

1.32

1.41

1.45

1.49

Table 14.3  Weights of the thematic maps of the potential groundwater. Themes

Assigned Weight

Normalized Weight

Ground Water Depth

9

0.196

Geology

8

0.178

Geomorphology

6

0.1303

Soil

5

0.108

Slope

4

0.089

LU/LC

5

0.102

226  Sustainable Development Practices Using Geoinformatics 86°10'0"E

86°24'30"E

86°28'0"E

23°48'30"N Legend

23°45'0"N

23°45'0"N

23°48'30"N

N

Suitable Zone for Sustainable Ground Water assessment Highly suitable Suitable Low Suitable Not suitable 0

1

2

4

6 km

86°10'0"E

86°24'30"E

86°28'0"E

Figure 14.8  Suitable site for sustainable ground water assessment.

14.5 Conclusions The present study demonstrated the efficiency of remote sensing and GIS as a tool in the study of various mapping pattern just as used for the study of LU/LC changes. To fight with the problems faced by the rapid urban sprawl in the nation, various scientific perspectives are necessary for a critical assessment of urban conditions. The serious issues related with the urban focuses in India are that of impromptu extension, changing area use/ land spread zones. For this, remote detecting symbolism, with its tedious and concise review capacities, together with GIS, is significant apparatuses to delineate and screen the adjustments in the urban development. This work concentrated on the multi-scale approach with remote detecting, to help urban administration with zone wide and forward-thinking datasets. The used satellite image of Landsat 8 gives the information for investigation capable the checking of the grouping extra time to comprehend the elements and qualities of the territory of Dhanbad square. This incorporates additionally data about the course of urban arranging. In this manner, remote detecting and GIS gave to be an extremely valuable reason for an increasingly definite examination of the spatial dispersion, a developing

Suitable Zone for Sustainable Ground Water Assessment  227 megacity which is essential for a sensible arranging of specialized foundation. The authorities of different government divisions ought to be given through presentation and preparing of remote detecting and GIS for its application execution in the urban improvement plans. This multilayer spatial data permits breaking down and foreseeing advancements to help future arranging procedures. The study investigated the urban development planning phenomenon occurring in the Block and Nagarnigam area and found that there has been an overall Settlement area is 25% in 2011. Based on the analysis and overall data, we can conclude that, in the study area, overall area is covered with settlement and vegetation, i.e., about 25% area is under settlement and 27% is under vegetation. Dhanbad administrative ward shows the population pressure which is largely present in the area Bhuli, Dhanbad, Hirapur, Godhar, loyabad, chotaputki, PandarKanale, Jagigora, Nichitpur, Sendra, Sijua, and Bherhnpur. Dhanbad is also known as mining area, but in the study area, only 6% are under open cast mining. Hence, in this area, only few chances for the sustainable ground water assessment. All the features were present on the suitable site selection for the urban development planning, i.e., low population pressure, having surface water bodies, suitable geology conditions, and low ground water label.

References Almedia B. (2005), “A GIS Assessment of Urban developmentin Richmond, Virginia”, published M.Sc. dissertation submitted to the faculty of Virginia Polytechnic Institute and State University, Blacksburg. AmitGhosh, (2015) “A GIS based DRASTIC model for assessing groundwater vulnerability of Katri Watershed, Dhanbad, India”. Basudeb Bhatta (2011) “Remote Sensing and GIS”, second edition, OXFORD University press. Ehlers, M., Jadkowski, M. A., Howard, R. R., and Brostuen, D. E., (1990), “Application ofSPOT data for regional growth analysis and local planning”. Photogrammetric Engineering and Remote Sensing, 56, 175–180. Horn, B. K. P. (1982), “Hill shading and the reflectance map”. Geo-Processing 2:65-146 Harris, P. M., and Ventura, S. J., (1995), “the integration of geographic data with remotelysensed imagery to improve classification in an urban area”. Photogrammetric Engineering and Remote Sensing, 61, 993–998. https://en.wikipedia.org/wiki/Dhanbad_district. Mather, A. S., (1986), “Land Use, London: Longman”.

228  Sustainable Development Practices Using Geoinformatics National Bureau of Soil Survey and Land Use Planning (ICAR) Regional Centre, Kolkata In collaboration with: Dept. Of Soil Science & Agricultural Chemistry, BAU, Ranchi, Jharkhand. Steffen, W. L., Walker, B. H., Ingram, J. S., and Koch, G. W., (1992), “Global change and terrestrial ecosystems: the operational plan”. IGBP Report No. 21, International Geosphere–Biosphere Programme, Stockholm. Taylor, Nigel (2007), “Urban Planning Theory since 1945”, London, Sag Treitz, P.M.,Howard, P. J., and Gong, P., (1992), “Application of satellite and GIS technologies for land-cover and land-use mapping at the rural-urban fringe: a case study”. Photogrammetric Engineering and Remote Sensing, 58, 439–448.

15 Detecting Land Use/Land Cover Change of East and West Kamrup Division of Assam Using Geospatial Techniques Upasana Choudhury* and Anand Kumar Centre for Excellence for NRDMS in Uttarakhand, Department of Geography, Kumaun University SSJ Campus, Almora, India

Abstract

The mechanism of recognizing the transformations in the form of an object or phenomenon by perceiving it at different times is termed as change detection. For efficient management and utilization of the earth’s natural resources, it is obligatory to establish a strong empathetic interactive relationship between human beings and natural environment, which requires precise and timely information of the change occurring on the Earth surface. Vegetation cover is one of the most dynamic phenomenon’s occurring on the surface of the earth, and detection of its change across a time period proves to be very critical for various ecosystem services, such as fortification of the land surface, the modification or enrichment of the native weather conditions, the conservation of perilous ecosystem processes, and the maintenance of biodiversity. This paper presents an overview of the change detection over a period of 30 years from 1988 to 2018 change matrix and evaluating the net loss and gain of different land use and land cover. Keywords:  Vegetation cover types, change detection, land use and land cover, change matrix

15.1 Introduction Vegetation forms a fundamental part of the ecosystem, composed of flora genres which are consequences of long continuing development process *Corresponding author: [email protected] Shruti Kanga, Varun Narayan Mishra, and Suraj Kumar Singh (eds.) Sustainable Development Practices Using Geoinformatics, (229–242) © 2021 Scrivener Publishing LLC

229

230  Sustainable Development Practices Using Geoinformatics which is concomitant with the places they squat (Velázquez and Romero, 1999). The various environmental factors influencing the growth of various sets of flora genres which cohabit in defined region and time span is understood as the resultant vegetation of a particular locality (Velázquez and Romero, 1999). Generally, vegetation can be categorized as two: cultural and natural. Cultural vegetation can be explained as the continuous anthropological activities, which regulates its development and perpetuity; for example, planted forest and agricultural areas which are of relatively recent origin (Matteucci and Colma, 1982). In contrast, natural vegetation is the mechanisms of the various geomorphological, ecological, climatic, and edaphic parameters which functions continuously and concurrently over prolonged time span (Matteucci and Colma, 1982). Vegetation cover is one of the most dynamic phenomenon occurring on the surface of the earth, detection of its change across a time period proves to be very critical for various ecosystem services, such as fortification of the land surface, the modification or enrichment of the native weather conditions, the conservation of perilous ecosystem processes, and the maintenance of biodiversity (Hölzel et al., 2012). The study of vegetation classification reveals the different levels of vegetation restoration, its succession and its relationship with the physical properties of the earth surface such as soil salinity, soil erosion, surface runoff etc. (Jiao et al., 2008a, b; Nagase and Dunnett 2012; Qiu et al., 2010; Wang et al., 2011). The assessment of various environmental impacts on the regeneration of the vegetation growth, conservation of the water and soil quality, and maintaining ecological stability depends on the dispersion, measure, and restoration of diverse types of vegetation cover (Jiao et al., 2008a, b; Wang et al., 2011). Quantification of the vegetation cover reveals valuable information on the difference between land cover and land use patterns through delineating and mapping vegetation cover class (Egbert et al., 2002; He et al., 2005). Since the interventions of human beings on the earth, they have been in constant struggle to extract the maximum benefits encouraging rapid destruction and loss of vegetation cover over the decades (Burrows, Colin J., 1990). The deprivation of the vegetation cover leads to desertification, resulting in the echelons, which causes disequilibrium in the terrestrial ecosystem (Anyamba and Tucker, 2005; Olsson et al., 2005). The interactions between terrestrial ecosystem, landscape ecology, and biodiversity with the human induced and natural land cover changes affect the climate globally (Houghton, 1994; Reid et al., 2000). Assessment of such phenomenal degradation of the ecosystem due to the modification in the levels of vegetation activity through various vegetation indices and phonological indicators is critical (Reed et al., 2003). In order to maximize the information content from radiometric

Detecting Land Use/Land Cover Change in Assam  231 measurements, computation of vegetation indices like NDVI (Normalized Differential Vegetation Index) is the most practical method (Asrar et al., 1985). Normalized Differential Vegetation Index is one of the most commonly used indices for vegetation monitoring, as it is closely related to plant biomass and leaf area (Rouse et al., 1973). The significance of choosing the Kamrup district as the study is because it is enriched with diverse variety of natural vegetation. There are about 23 reserve forests in the district. The climate and the physiographic of the region in respect to altitude slope and soil greatly influence the diverse nature of the natural vegetation found in this region. However, due to industrialization, the commercial species are taking up the old useful species which is major concerned a need immediate attention. 79°30'0"E

79°45'0"E

90°0'0"E

90°0'0"E

95°0'0"E

91°30'0"E

92°0'0"E

26°0'0"N

EAST & WEST KAMRUP

26°30'0"N

90°0'0"E

90°0'0"E

26°0'0"N

26°30'0"N

25°0'0"N

30°0'0"N

ASSAM

15°0'0"N 25°0'0"N

30°0'0"N 15°0'0"N

30°0'0"N

95°0'0"E

INDIA

75°0'0"E 91°0'0"E

30°15'0"N

75°0'0"E

30°0'0"N

30°15'0"N

STUDY AREA MAP

Legend Value High : 1129

91°30'0"E

0

3

6

79°30'0"E

Figure 15.1  Showing study area map.

12

92°0'0"E

18

24

KM

79°45'0"E

29°45'0"N

29°45'0"N

Low : –4 91°0'0"E

232  Sustainable Development Practices Using Geoinformatics

15.2 Study Area The capital of Assam, Kamrup district lies on the western part of the state and is situated between 25°43′ and 26°51′ N latitude and between 90°36′ and 92°12′ E longitude (Figure 15.1). The district is bounded by Goalpara and Barpeta district on the west, Darrang and Nagaon district on the east, Meghalaya Plateau on the south, and the foothills of Bhutan and Nalbari district on the north side of the region. The Kamrup district covers a total area of 4,345 km2 which is about 5.5% of the total geographical area of the state, i.e., Assam. The Kamrup district is enriched with diverse variety of natural vegetation. There are about 23 reserve forests in the district. The climate and the physiographic of the region in respect to altitude slope and soil greatly influences the diverse nature of the natural vegetation found in this region. In areas having average temperature of 25° and an average rainfall of 2,000 mm, with flat alluvial plains supports the growth of moist mixed deciduous forest. Moreover, the district embraces the growth of evergreen, semi-evergreen, deciduous, and swamps.

15.3 Materials and Methodology To analyze the dynamic changes in LULC in East and West Kamrup Division from 1988 to 2018 for a period of 30 years of interval, Landsat images were used. Radiometric and atmospheric correction was performed on Erdas Imagine software. In this study, 10 LULC classes were delineated, namely, agriculture, forest, barren land, blank forest, fallow land, settlement, mixed built-up, sandbars, sandbars with vegetation, water body. Through visual interpretation and manual digitization, the classified maps of the years 1988, 1998, 2008, and 2018 were produced. The accuracy assessment is done for the final map to proceed for the calculation of change detection. Change matrix tables are produced to represent the extent of LULC changes occurring in different classes.

15.4 Results and Discussion 15.4.1 Land Use and Land Cover Dynamics and Change Analysis The land use and land cover (LULC) classification maps of East and West Kamrup Division for the year 1988, 1998, 2008, and 2018 are done

Detecting Land Use/Land Cover Change in Assam  233 based on visual interpretation and manual digitization. The region is classified into 10 classes which are forest, agriculture, barren land, fallow land, settlement, mixed built-up, blank forest, sandbars, sandbars with vegetation, and water body. The classified maps (Figures 15.2, 15.3, 15.4 and 15.5). give us a visual explanation of how the landscapes have altered over the years from the 1988 to 2018. The Table 15.1 shows the changes in area of LULC classes of the study area, from 1988 to 2018 within an interval of 30 years. The areas of the classified classes are shown in square kilometer and the change is shown in percentage for better understanding. The positive value in percentage change indicates increase in area, and a negative value indicates decrease in area. The Table 15.1 gives us an insight of the per class changes during 30-year interval. The most concern and affected classes that have gone a drastic change over the years are forest and mixed built-up. In 1988, the forest cover was 357.93 km2 which reduced to 224.72 km2 in 2018 that gives a negative change −37.22%. Unfortunately, in case of mixed built-up, the area under 1988 was only 189.49 which increased to 750.72 sq that gives a positive increase in area by 296.18%.

91°20'0"E

91°30'0"E

91°40'0"E

91°50'0"E

92°0'0"E

Landuse and Landcover Map

92°10'0"E N

0

5

10

91°0'0"E

20

91°10'0"E

30

Agriculture Barren land Blank Forest Fallow Land Forest Mixed Builtup

40 Km

91°20'0"E

91°30'0"E

91°40'0"E

Figure 15.2  Land use and land cover map (1988).

91°50'0"E

Sandbar Sandbar With Vegetation Settlement Water body

92°0'0"E

92°10'0"E

25°50'0"N

LEGEND

25°40'0"N

25°40'0"N

25°50'0"N

26°0'0"N

26°0'0"N

26°10'0"N

26°10'0"N

East and West Kamrup Division 1998

26°20'0"N

91°10'0"E

26°20'0"N

91°0'0"E

234  Sustainable Development Practices Using Geoinformatics 91°20'0"E

91°30'0"E

91°40'0"E

91°50'0"E

92°0'0"E

Landuse and Landcover Map

92°10'0"E N

0

5

10

91°0'0"E

20

30

91°10'0"E

Agriculture Barren land Blank Forest Fallow Land Forest Mixed Builtup

40 Km

91°20'0"E

91°30'0"E

91°40'0"E

Sandbar Sandbar With Vegetation Settlement Water body

91°50'0"E

92°0'0"E

92°10'0"E

91°50'0"E

92°0'0"E

92°10'0"E

25°50'0"N

LEGEND

25°40'0"N

25°40'0"N

25°50'0"N

26°0'0"N

26°0'0"N

26°10'0"N

26°10'0"N

East and West Kamrup Division 1988

26°20'0"N

91°10'0"E

26°20'0"N

91°0'0"E

91°10'0"E

91°20'0"E

26°20'0"N

91°0'0"E

91°30'0"E

91°40'0"E

Landuse and Landcover Map

N

0

5

10

91°0'0"E

20

91°10'0"E

30

Agriculture Barren land Blank Forest Fallow Land Forest Mixed Builtup

40 Km

91°20'0"E

91°30'0"E

Figure 15.4  Land use/land cover map (2008).

91°40'0"E

91°50'0"E

Sandbar Sandbar With Vegetation Settlement Water body

92°0'0"E

92°10'0"E

25°50'0"N

LEGEND

25°40'0"N

25°40'0"N

25°50'0"N

26°0'0"N

26°0'0"N

26°10'0"N

26°10'0"N

East and West Kamrup Division 2008

26°20'0"N

Figure 15.3  Land use/land cover map (1998).

Detecting Land Use/Land Cover Change in Assam  235 91°20'0"E

91°30'0"E

91°40'0"E

91°50'0"E

92°0'0"E

Landuse and Landcover Map

92°10'0"E N

0

5

10

91°0'0"E

20

91°10'0"E

30

Agriculture Barren land Blank Forest Fallow Land Forest Mixed Builtup

40 Km

91°20'0"E

91°30'0"E

91°40'0"E

91°50'0"E

Sandbar Sandbar With Vegetation Settlement Water body

92°0'0"E

92°10'0"E

Figure 15.5  Land use/land cover map (2018).

Table 15.1  Area of LULC classes in km2. Area (km2) Land use/Land Cover Classes

1988

2018

Percentage Change

Agriculture

634.50

544.68

−14.16

Barren land

220.02

34.77

−84.20

Blank forest

0.52

1.14

119.23

Fallow land

222.35

211.27

−4.98

Forest

357.93

224.72

−37.22

Mixed Built-up

189.49

750.72

296.18

Sandbars

119.12

158.95

33.44

Sandbars with vegetation

112.36

95.04

−15.41

Settlement

123.04

10.47

−91.49

Water body

152.08

153.00

0.60

25°50'0"N

LEGEND

25°40'0"N

25°40'0"N

25°50'0"N

26°0'0"N

26°0'0"N

26°10'0"N

26°10'0"N

East and West Kamrup Division 2018

26°20'0"N

91°10'0"E

26°20'0"N

91°0'0"E

236  Sustainable Development Practices Using Geoinformatics

15.4.2 The Change Matrix Cross Tabulation The transition matrix (Table 15.2) reveals that the major forest and agriculture areas of 1988 have changed to mixed built-up and settlements in 1998, which is 40 and 34 km2, respectively. This kind of change is logically obvious because of possible growing of societies in the region which require more land to settle and work. The second transition table (Table 15.3) reveals that from 1998 to 2008, the scenario have taken a drastic change mainly the forest areas are decreasing which is being transformed to mixed built-up and settlement zones which is out of total 1339 km2 forest area, 87 km2 is converted to mixed built-up and settlement, and 63 km2 is transformed to agricultural land because agricultural area converted to mixed built-up zones, which is out of total 720 km2, 65 km2 is converted to mixed built-up zones. So, forests are converted to agricultural zones. The third table (Table 15.4) reveals that the transition state of the region has really undergone drastic transformation over a period of 30 years (1988–2018). Out of the total forest lands which have reduced to 1,225 km2, 65 km2 is converted to agricultural areas. Moreover, a drastic change is also noticed that agricultural lands are also decreasing due to increased mixed built-up over the years. The numbers are, out of the total 768 km2, 192 km2 is converted to mixed built-up zones.

15.4.3 Classification Accuracy Assessment The overall accuracy = number of correct pixels/total number of points, which is found to be 183/200 = 0.915. Table 15.4 shows the confusion matrix of the change that has occurred from 1988 to 2018. The diagonal shows the pixels which are correctly classified. The user’s accuracy is computed by dividing the total number of pixels that were correctly classified in each category by the total number of pixels that were classified in that category, i.e., row total. The producer’s accuracy is computed by dividing the total number of pixels that were classified in each category by column total. Also, in this study, KAPPA analysis is performed. The KAPPA analysis results a KHAT statistic which is a measure of accuracy or agreement. The computation of the KHAT for the error matrix table (Table 15.5) was measured to be K = 0.90.

15.5 Conclusion The aim of this paper is to show a geospatial approach in analyzing and predicting land use and land cover changes of East and West Kamrup

Land Use and Land Cover 1988

554

14

0

81

38

4

5

19

0

4

719

Barren land

Blank Forest

Fallow Land

Forest

Mixed Built-up

Sandbar

Sandbar With Vegetation

Settlement

Water body

Grand Total

Agriculture

Agriculture

(1988–1998)

Barren land 184

1

0

0

0

0

6

0

0

162

15

Blank Forest 3

0

0

0

0

0

3

0

1

0

Fallow land 170

0

0

0

0

0

3

141

0

16

10

Forest 1,339

0

1

0

0

16

1,313

0

0

0

8

244

1

9

0

0

159

33

0

0

8

34

Mixed Built-up

Land Use and Land Cover 1988 (Area in km2)

Table 15.2  LULC transition matrix (1988–1998).

Sandbars 71

17

0

20

26

0

0

0

0

0

8

Sandbars with vegetation 60

14

0

31

15

0

0

0

0

0

Settlement 140

0

111

0

1

2

7

0

0

19

0

Water body 246

115

2

42

73

0

0

0

0

0

14

Grand Total 3,176

152

123

112

120

181

1,403

222

1

219

643

Detecting Land Use/Land Cover Change in Assam  237

Land Use and Land Cover 1998

589 15 0 72 63 21 4 0 0 2 766

Barren land

Blank Forest

Fallow Land

Forest

Mixed Built-up

Sandbar

Sandbar With Vegetation

Settlement

Water body

Grand Total

Agriculture

Agriculture

(1998–2008)

Barren land 82

0

0

0

0

0

0

4

0

74

4

Blank Forest 1

0

0

0

0

0

1

0

2

0

0

Fallow land 188

0

0

0

0

0

0

88

0

90

10

Forest 1,225

0

0

0

0

20

1,188

3

1

0

11

Mixed Built-up 359

1

0

0

0

200

85

3

0

5

65

133

56

0

36

17

0

0

0

0

0

24

Sandbars

Land Use and Land Cover 2008 (Area in km2)

Table 15.3  LULC Transition matrix (1998–2008).

Sandbars with vegetation 105

60

0

7

31

0

0

0

0

0

7

Settlement 147

1

141

0

0

3

2

0

0

0

0

Water body 172

125

0

17

20

0

0

0

0

0

10

Grand Total 3,176

245

141

60

72

244

1,339

170

3

184

720

238  Sustainable Development Practices Using Geoinformatics

Land Use and Land Cover 2008

467 4 0 2 31 33 2 0 0 5 544

Barren land

Blank Forest

Fallow Land

Forest

Mixed Builtup

Sandbar

Sandbar With Vegetation

Settlement

Water body

Grand Total

Agriculture

Agriculture

(2008-2018)

Barren land 35

1

0

0

0

5

0

1

0

21

7

Blank Forest 1

0

0

0

0

0

1

1

0

Fallow land 210

1

0

0

0

10

2

166

0

20

11

Forest 1,220

0

2

0

0

18

1,125

1

0

1

72

Mixed Built-up 510

4

0

0

0

241

55

18

0

0

192

158

55

0

62

36

0

0

0

0

0

5

Sandbars

Land Use and Land Cover 2018 (Area in km2)

Table 15.4  LULC transition matrix (2008–2018).

Sandbars with vegetation 96

12

0

13

71

0

0

0

0

0

0

Settlement 251

2

144

0

4

50

10

0

0

33

8

Water body 154

92

1

30

20

2

1

0

0

2

6

Grand Total 3,176

172

147

105

133

359

1,225

188

1

81

768

Detecting Land Use/Land Cover Change in Assam  239

0 29 90

Settlement

Sandbars with vegetation

Sandbars

Barren land

Agriculture

Forest

Blank forest

Mixed Built-up

Column Total

Producer’s Accuracy

3

4

5

6

7

8

9

10

0

0

0

0

1

0

2

0

Fallow land

2

26

Water body

Water body

1

Classified

Fallow land 100

14

0

0

0

0

0

0

0

0

14

0

Settlement 100

33

0

0

0

0

0

0

0

33

0

0

Sandbars with vegetation 33

9

0

0

0

0

0

6

3

0

0

0

Sandbars 81

16

0

0

0

0

0

13

0

0

0

3

93

15

0

0

0

0

14

0

0

0

1

0

Barren land

Table 15.5  Theoretical error matrix of the change detection (1988–2018). Agriculture 91

33

0

0

0

30

0

1

0

0

2

0

Forest 100

29

0

0

29

0

0

0

0

0

0

0

Blank forest 50

2

0

1

1

0

0

0

0

0

0

0

Mixed Builtup 20

20

0

0

0

0

0

0

0

0

0

Row Total 200

20

1

30

30

14

21

3

35

17

29

User’s Accuracy 183

100

100

8.7

100

100

61.90476

100

94.28571

82.35294

89.65517

240  Sustainable Development Practices Using Geoinformatics

Detecting Land Use/Land Cover Change in Assam  241 Division using Landsat imagery of four different times: 1988, 1998, 2008, and 2018. The approach involved GIS, change detection technique, and remote sensing. The results show that the forests zones have degraded over the years and the mixed built-up and settlement areas are growing tremendously which was only 159 km2 in 1998 and have increased to 241 km2 in 2018. The kappa accuracy assessment was performed to get a better result and the accuracy was found to be 90%. Thus, change detection analysis is quiet challenging and shows the direct change undergoing around us.

References Anyamba, A., Tucker, C.J., 2005. Analysis of Sahelian vegetation dynamics using NOAA AVHRR NDVI data from 1981 to 2003. Journal of Arid Environments 63, 596–614. Asrar, G, E. R. Kanemasu, and M. Yoshida, 1985, Estimates of leaf area index from spectral reflectance of wheat under different cultural practices and solar angle, Remote Sens. Environ. Vol. 17, pp. 1–11. Burrows, Colin J. (1990). Processes of vegetation change. London: Unwin Hyman. p. 1. ISBN 978-0045800131. Egbert SL, Park S, Price KP, et al. (2002) Using conservation reserve program maps derived from satellite imagery to characterize landscape structure. Comput Electron Agric 37:141–56. He C, Zhang Q, Li Y, et al. (2005) Zoning grassland protection area using remote sensing and cellular automatamodeling—a case study in Xilingol steppe grassland in northern China. J Arid Environ 63:814–26. Hölzel, N., Buisson, E., &Dutoit, T. (2012). Species introduction—a major topic in vegetation restoration. AppliedVegetation Science, 15, 161–165. Houghton, R.A., 1994. The worldwide extent of land-use change. Bioscience 44 (5), 305–313. Jiao, J., Tzanopoulos, J., Xofis, P., &Mitchley, J. (2008a). Factors affecting distribution of vegetation types on abandoned cropland in the hilly-gullied Loess Plateau Region of China. Pedosphere, 18, 24–33. iao, J., Zhang, Z., Jia, Y., Wang, N., & Bai, W. (2008b). Species composition and classification of natural vegetation in the abandoned lands of the hilly-gullied region of North Shaanxi Province. ActaEcologicaSinica, 28, 2981–2997. Matteucci SD, Colma A (1982) Metodología para el estudio de la vegetación. SerieBiología OEA.Monografía 22. Washington. Nagase, A., &Dunnett, N. (2012). Amount of water runoff from different vegetation types on extensive green roofs: effects of plant species, diversity and plant structure. Landscape and Urban Planning, 104, 356–363. Olsson, L., Eklundh, L., Ardo, J., 2005. A recent greening of the Sahel-trends, patterns and potential causes. Journal of Arid Environments 63, 556–566.

242  Sustainable Development Practices Using Geoinformatics Qiu, L., Zhang, X., Cheng, J., & Yin, X. (2010). Effects of black locust (Robiniapseudoacacia) on soil properties in the loessial gully region of the Loess Plateau, China. Plant and Soil, 332, 207–217. Reed, B.C., White, M.A., Brown, J.F., 2003. Remote Sensing Phenology. In: Schwartz, M.D. (Ed.), Phenology: An Integrative Science. Kluwer Academic Publishing, Dordrecht, The Netherlands. Reid, R.S., Kruska, R.L., Muthui, N., Taye, A., Wotton, S., Wilson, C.J., Mulatu, W., 2000. Land-use and land-cover dynamics in response to changes in climatic biological and socio-political forces: the case of south-western Ethiopia. Landscape Ecol. 15 (4), 339–355. Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the great plains with ERTS. Third Earth Resources Technology Satelite-1 Symposium (pp. 309-317). Washington, D.C. Telesca L., and Lasaponara R., 2006, Quantifying intra-annual persistent behaviour inSPOT-VEGETATION NDVI data for Mediterranean ecosystems of southern ItalyRemote Sensing of Environment, Volume 101, Pages 95-103. Velázquez A, Romero FJ (eds) (1999) Biodiversidad de la región de montaña del sur de la Cuenca de México: bases para el ordenamientoecológico. UAM-XSEMARNAP. ISBN: 754 24 2867 8. Wang, L., Wei, S., Horton, R., & Shao, M. A. (2011). Effects of vegetation and slope aspect on water budget in the hill and gully region of the Loess Plateau of China. Catena, 87, 90–100.

16 Climate Resilient Housing—An Alternate Option to Cope With Natural Disasters: A Study in Fani Cyclonic Storm Affected Areas of Odisha Kiran Jalem and Subrat Kumar Mishra* National Institute of Rural Development and Panchayati Raj, Hyderabad, India

Abstract

A rare summer cyclone named “Fani” hit Puri, a small coastal town of Odisha on 3rd May 2019. As per the report of Indian Meteorological Department (IMD), the maximum sustained surface wind speed of 175–180 km per hour (kmph) gusting to 205 kmph was observed during landfall. Although human casualties have been relatively low when compared to earlier cyclones experienced by the state, cyclone Fani resulted loss of 64 human lives and affected about 16.5 million people in 18,388 villages of the entire state. Puri, Khurda, Cuttack, Jagatsinghpur, and Kendrapara were the five most affected districts of state. Due to the cyclonic storm Fani, power, telecommunication infrastructure and road services were severely affected. High wind speed also resulted in catastrophic damage to about 3.62 lakh dwelling units which left many homeless. The damage to housing has been extensive, particularly in Puri district of Odisha. The most affected were people in rural areas, urban slums, and those residing in settlements along the coast line living in kutcha/semi-pucca houses with low resilience against cyclonic winds. The current paper critically examines how climate resilient houses with “Build Back Better” features can save valuable human lives through use of eco-friendly, durable, cost effective, and non-pollutant building materials. Keywords:  Catastrophic damage, climate resilience, eco-friendly

*Corresponding author: [email protected] Shruti Kanga, Varun Narayan Mishra, and Suraj Kumar Singh (eds.) Sustainable Development Practices Using Geoinformatics, (243–252) © 2021 Scrivener Publishing LLC

243

244  Sustainable Development Practices Using Geoinformatics

16.1 Introduction Odisha is one of the major state of India with population of 4.2 crore comprises of 3.4% of India’s population. The state is the eighth largest in terms of landmass having 4.7% of the country’s geographical area. There are 30 districts in Odisha out of which 83% of the total population lives in 51,349 villages of 6,801 Gram Panchayats (GPs) and the remaining 17% live in urban areas. Agriculture is mainstay of the state’s economy which provides livelihood to 62% population of Odisha on varying degrees. As per the official data, 33% of population of Odisha were living below the poverty against the national average of around 22% in the year 2011–2012. Due to its geographical location and climatic condition, Odisha is highly prone to disasters. The location of state on the east coast of India makes it one of the six most cyclone prone areas in the world. The coast of Odisha has the distinction of having the highest vulnerability in terms of cyclonic landfall. The Odisha coast experienced 890 cyclonic disturbances out of the 1,019 in the Indian subcontinent during the last century. Out of these, 260 cyclonic disturbances had their landfall along the Odisha coast. The major cyclones which hit Odisha in the last two decades are the 1999 super cyclone, Phailin and Titli in the year 2013 and 2018, respectively. There are 10 major river systems in Odisha which cause flood to state at regular intervals. 1.40 lakh hectares of state’s geographical area are prone to floods out of total area of 15,571 lakh hectares. Vulnerability of the state to other disasters like flash floods, landslides, earthquakes, and tsunami is also high [1]. A rare summer cyclone named “Fani” hit Puri, a small coastal town of Odisha on 3rd May 2019. As per the report of Indian Meteorological Department (IMD), the maximum sustained surface wind speed of 175– 180 km per hour (kmph) was observed during landfall. Although human casualties have been relatively low when compared to earlier cyclones experienced by the state, cyclone Fani resulted loss of 64 human lives and affected about 16.5 million people in 18,388 villages of the entire state. Puri, Khurda, Cuttack, Jagatsinghpur, and Kendrapara were the five most affected districts of state. Due to the cyclonic storm Fani, power, telecommunication infrastructure and road services were severely affected. Trees were uprooted resulting blocking of major roads and damaged culverts with complete power outage in several parts of the state for about a week. High wind speed also impacted in catastrophic damage to about 3.62 lakh dwelling units which left many poor people homeless. There had been considerable damage to agriculture, fisheries, and livestock in all affected districts of the state. As per the estimate of the Forest and Environment

Climate Resilient Housing  245 Department, Government of Odisha, 21.9 lakh trees were uprooted or damaged across the state, including urban and rural areas as well as sanctuaries [2]. The scholars and researchers have opined on various aspects of climate resilient housing in their study. Ross Gillard et al. (2015) in their study on resilient and affordable housing in Kochi and Trivandrum cities of India opine that housing projects of all nations need to be located in such ways that enhance economic opportunity and social participation of all sections of people while minimizing ecological risks [3]. In a case study on community consultation for climate resilient housing in Vietnam, Tran Tuan Anh and Tran Van Gialphong’s (2014) research outcome in Vietnam demonstrates the importance of social relationships in building climate resilient housing. They further conclude that there is wide gap between “at-risk” grassroots communities and technical service providers for safe and resilient housing construction [4]. Singh and Singh (2016) in their paper based on the study in Gorakhpur city of Uttar Pradesh state of India find lack of technical capacities of local masons on climate resilient building techniques affect construction of climate resilient houses [5].

16.2 Study Area and Methodology The study was carried out in the five most affected districts due to Fani cyclonic storms. Puri, Khurda, Cuttack, Jagatsinghpur, and Kendrapara were the districts where the study was undertaken. Analysis on the damages caused to buildings and settlements in the affected areas was the major objective of the study. Exploring the scope for climate resilient housing based on the perception of common people and executing departments was also the other objective of the study. Extensive consultations were made with the common people, civil engineers, and executing contractors to know their perception on climate resilient housing to cope with natural disasters. It can be seen from Figure 16.1 that the cyclone Fani affected 14 districts of Odisha. Puri, Khurda, Cuttack Jagatsinghpur, and Jajpur are the five most affected districts due to the cyclone. It has been estimated that about 3.61 lakh houses have been affected due to Fani. The district-wise damage cause to housing sector due to the cyclone in affected districts of Odisha has been given in the table. Table 16.1 depicts that out of total damages caused to the housing sector, 82% of the damages were in rural areas and the remaining damages happened in urban areas. The kutcha houses of rural areas and urban slums have been mostly affected due to the cyclone Fani. The speed of the wind

246  Sustainable Development Practices Using Geoinformatics 86°0'0"E

87°0'0"E 23°0'0"N

23°0'0"N

85°0'0"E

Map showing vulnerable districts of fani cyclone in Odisha N W

E

22°0'0"N

22°0'0"N

S

Mayurbhanj Baleshwar

Nayagarh

Ganjam

Bhadrak BhadrakKendrapara

Kendrapara Kendrapara

Jagatsinghapur Khordha Puri

Jagatsinghapur Jagatsinghapur

Puri

Ganjam Ganjam

19°0'0"N

20°0'0"N

Cuttack

Bhadrak

21°0'0"N

Jajapur

Dhenkanal

Baleshwar

20°0'0"N

Anugul

Baleshwar

Ganjam

Moderately affected

19°0'0"N

21°0'0"N

Kendujhar

0

25 50

85°0'0"E

100

150

86°0'0"E

Kilometers 200

87°0'0"E

18°0'0"N

18°0'0"N

Severely affected

Figure 16.1  Houses damaged in Puri town near the sea beach.

was so high that most of the kutcha houses have been leveled to grounds which made many families homeless and compelled them to stay in the houses of neighbors or in temporary tarpaulin tents. The predominant type of kutcha buildings in Odisha is constructed either with mud walling of varying thickness (300–450 mm) or with bamboo reinforced mud walls. The roofs consist of bamboo under-structures, with closely-spaced-split bamboo grids, like a mesh supporting bunched up straws on top. The front verandas also follow the same roofing system, supported on bamboo, or timber posts. It has been observed during the cyclone that while high wind suction blew off the straws in many kutcha houses, the under-structure remained intact (Figure 16.2). The kutcha houses which were in poor condition with gable walls have collapsed. The affluent families in rural Odisha reside in three to four pucca rooms with a few kutcha rooms and verandas; the kutcha structures

Climate Resilient Housing  247 Table 16.1  Damage caused to houses due to cyclone Fani in Odisha. Settlement Pattern

Type of Houses

Nature of Damage

No. of Affected Houses

Total No. of Affected Houses

Rural

Pucca

Completely

947

2,95,703

Substantially

14,181

Partially

61,074

Completely

21,015

Substantially

75,556

Partially

122,940

Completely

379

Substantially

5,435

Partially

27,352

Completely

3,125

Substantially

12,571

Partially

17,178

Kutcha

Urban

Pucca

Kutcha

Total

66,040

3,61,743

Source: Odisha Disaster Management Authority, Government of Odisha, 2019.

Figure 16.2  Affected districts due to cyclone Fani.

248  Sustainable Development Practices Using Geoinformatics have been destroyed during the cyclone Fani. A large number of semipucca buildings had brick or laterite stone walls asbestos as roof cladding material lay on mostly timber trusses. Many primary and upper primary schools and relatively affluent households had this kind of building pattern. This pattern was observed during the course of study. The common observation on all of the damaged buildings was missing purlins and roof covers, while the trusses were intact. It was revealed through close observation that the timber purlins with the asbestos roof covering were blown away by the high wind leaving very small portion of the roofing materials. However, many of the timber trusses were intact after the cyclone. It was observed during the field study that the building did not have any maintenance since long causing deterioration of the structure and hence loss of strength. The anchorage of the truss sitting on the wall was found to be grossly inadequate. Some of the well-built kutcha buildings could have survived the cyclone had there been cross bracings, ensuring truss action against the wind load. The use of bracing was not observed at the time of study even in the buildings and sheds with RCC pillars and CGI sheet structures owned by relatively affluent people. These buildings had shallow roof slopes which made them vulnerable to wind suction. About 40 of 60 such sheds collapsed like a pack of cards, resulting massive financial losses to the affected people due to Fani. Most of the semi-pucca buildings in the affected districts had laterite, brick, and fly-ash brick walls. The families in affected areas living without RCC roofs had adopted mono-pitch roofs cladded with asbestos or CGI sheets with timber or steel under-structures. These kinds of roofs did not have adequate anchorage with the wall to withstand the high wind suction. These roof structures should have been suitably tied with the wall through RCC band to avoid uprooting due to high wind suction. Another reason which contributed to the damage of the roof cladding materials such as asbestos and CGI was the lack of wind arresters. The roof sheets were only bolted to the purlins of the under-structure due to which the failure started from the weakest joint that could have been due to a construction error or due to the aging of the structure. This happened due to stress concentrating at the bolts which led to sudden failure. Many buildings could have been saved with proper application of two to three wind arresters (mild, steel-flat members) on the top of the roof sheets and anchored to the under-structures as an additional wind stopper. The lack of wind arresters causing the blowing off of roof sheets was also observed during the field study in the high-end structures of affected districts. The sheet-roofed buildings were the most affected structures with varying degrees of damage to the roof portion. The reason for damage of these buildings was due to the wind suction displacing and breaking

Climate Resilient Housing  249 them and from flying objects and falling trees. It was observed during the field visits that many toilets were found to be roofless. In summary, it may be concluded that the major reasons of cyclone damage was due to noncompliance with the safety norms prescribed by the National Building Code (NBC) and the related Bureau of Indian Standards (BIS) codes. The damage assessment of houses was done by Panchayati Raj and Drinking Water Department, Government of Odisha immediately after the cyclone Fani as per the prevailing schedule of rate with 10 to 15% cost escalation for urban settlements. Since all the damaged kutcha houses need reconstruction, the value of damage was assessed at 25% of the reconstruction cost of Pucca houses @ Rs 71,250/- per dwelling unit. The cost of pucca houses which were completely or substantially damaged had been considered as equivalent to the reconstruction cost, i.e., Rs 2.85 lakh per house. The semi-pucca houses which were partially damaged, the cost of damage were assessed as 27% of the reconstruction cost which was to the tune of Rs 79, 950/- per dwelling unit. The cost of damage assessment arrived by Government of Odisha has been given in Table 16.2. It can be seen from Table 16.2 that according to the assessment of Government of Odisha, substantial damage has also been made to kutcha houses of poor people properties which comes to the tune of 58% of the total estimated cost of damages caused to housing sector. According to the reports of Government of Odisha, 2.736 lakh houses require reconstruction, the major chunk of which would be in rural areas to the extent of 2.346 lakh dwelling units. Similarly, 0.885 lakh houses need repair and retrofitting out of which the target for rural areas comes to the tune of 0.61

Table 16.2  Assessment of damage caused to housing due to Fani. Nature of damage

No. of Damaged Houses

Damage Assessment (Rs in Crore)

Damage of Kutcha houses

252,385

1,798.24

Substantial damage to Pucca houses

20,932

596.56

Semi-Pucca houses partially damaged

88,426

680.44

Total

361,743

3,075.24

Source: Odisha Disaster Management Authority, Government of Odisha, 2019.

250  Sustainable Development Practices Using Geoinformatics lakh and the remaining are in the urban areas. In Puri alone, 1.68 lakh houses require reconstruction including both kutcha and pucca units [6].

16.3 Discussion 16.3.1 Climate Resilient Housing in the Fani Affected Districts Odisha is one of the few states that attempt to mitigate the adverse effects of natural disasters by not only building resilient infrastructures but also by improving access to basic services, imparting new skills and strengthening livelihood security to common masses with well-coordinated actions as part of its “Build Back Better” strategies. The process of “Build Back Better” commences with the commitment to deliver the best with the available resources of the state. Government of Odisha also mobilises funding from the external sources like Government of India and international bodies with focus on completing the projects within specified time limits. The Build Back Better strategies have been adopted by Odisha in order to instil confidence among the communities who are psychologically traumatized and economically devastated due to unprecedented disasters. The focus of “Build Back Better” strategy is on prevention and mitigation of recurrent disaster risks in the state. The principles of Build Back Better Strategies are based on the principle of retrofitting the partially damaged houses to multi-hazard safe houses by following standards prescribed in the NBC of India 2016. The mandate of Government of Odisha is to retrofit each house with multi-hazard resistant safety features as prescribed in the NBC-2016 to withstand the risks of frequent flood and cyclone disasters experienced in the state. Odisha Disaster Management Plan, 2018 also accords utmost priority to multi-hazard resistant dwelling units with cyclone and earthquake proof features. Each retrofitted house would act as a good example of resilient safe dwelling unit. The aim of the “Build Back Better” strategy is to inspire, educate, and motivate others in the community to adopt the same for embedding the climate resilience culture into the housing construction practices. The state of Odisha is also endowed with fly-ash based construction materials. FALG is an eco-friendly material which is acceptable, durable, cost effective, and non-pollutant in nature. To reduce the consumption of clay burnt bricks in the construction of buildings, rat-trap-bonded walls have been used during initial phases of construction of houses. While being more labor intensive, it has the potential to save cement and bricks to the extent of 20% of existing consumption. Housing Facilitation Centres

Climate Resilient Housing  251 (HFC) have been constituted in each block of the affected districts to provide techno-managerial support to the affected people. In the reconstruction works, the house owners selected the materials under the guidance of HFCs. In consultation with the government engineers at block level, the facilitators of HFC have prepared the estimates, working drawings, models, etc, which are be displayed at the offices of municipality, block, and the GP of the respective affected districts. The masons, engineers, Block Development Officers, and support staffs play key role in in the Build Back Better strategy since this is not just a technical issue. Resilient development is only possible when the teams at the rural and urban local bodies understand philosophy of the same. Therefore, training for all involved in housing construction has been accorded with top priority and a part of the immediate response of Government of Odisha. The first group has been trained by including the masons to develop their skills to use green technologies with multi-hazard safety features. Five training programmes were organized by Government of Odisha for the 99 HFCs @ 40 masons per batch by which a total number 19,800 master masons have been created within a span of six months. Each of these trained masons has worked with five semiskilled/helpers. Hence, 99,000 BBB-trained masons and helpers have been created in the five districts to carry forward reconstruction of climate resilient houses. The training of masons has been conducted with the help of appropriate skilled training providers. The HFCs after completing the reconstruction work are to work in the sector, supporting the private and government housing programmes. The facilitators are also to act as human resources for future for facilitating climate resilient and ecofriendly dwelling unit in the state.

16.4 Policy Recommendation Based on the field study on affected areas due to cyclone Fani and consultation with multiple stakeholders, the following recommendations are made for climate resilient housing to cope with disaster. All the houses for the poor community need to be as Multi-Hazard Resistant, with cyclone and earthquake proof features. The plinth area of each house may be of minimum 350 ft2 with a hall, bedroom, kitchen, and toilet to meet the expectations of community. All the houses should be integrated with rainwater-harvesting structures. Fly ash bricks need to be used in construction which is stronger and eco-friendlier than the common red bricks. In bigger settlements, Common Effluent Treatment Plants need to be established. All the houses should be provided with a staircase to serve multiple purposes.

252  Sustainable Development Practices Using Geoinformatics It will also be useful to the beneficiaries when they construct the first floor in future. The habitations should be developed with adequate space to enable easy evacuation at times of emergencies with amenities of permanent nature and with suitable rainwater harvesting. As a precautionary step against future natural calamities, bio-shields in major re-­settlements and near all existing habitations need to be taken up. The design and construction of houses need to be done in consultation with the “at risk” communities. The technical service providers should demonstrate the newly adopted technology to the local community for better acceptance of technology in disaster prone areas. More number of masons should be created at village level with regular capacity building programmes on climate resilient building techniques in housing sector. The kutcha houses of poor communities may be replaced with multi-hazard proof pucca houses out of flagship programmes on rural housing like Pradhan Mantri Awas Yojana (PMAY) and other state sponsored programmes with token contribution from the beneficiaries.

References 1. State Disaster Management Plan, 2018, Odisha State Disaster Management Authority, Govt of Odisha, 2018. 2. Cyclone Fani, Damage, Loss & Need Assessment, Odisha State Disaster Management Authority, Govt of Odisha, 2019. 3. Ross Gillard, Abhijit Datey, Andrew Sudmant, Lucy Oates, and Andy Gouldson (2015), Resilient and affordable housing for all: Lessons on house building from Kochi and Trivandrum, India, International Journal of Public Administration 34 (3): pp-171–179. 4. Tran Tuan Anh and Tran Van Gialphong (2014), Housing and Climate Change-related Disasters: A Study on Architectural Typology and Practices, Procedia Engineering, Volume 165, 2016, pp-869–875. 5. Dillip Singh & Binay Singh (2016), Scaling up climate resilient housing in Gorakhpur, India, working paper published by Institute of Social and Environmental Transition, Nepal. 6. Cyclone Fani, Damage, Loss & Need Assessment, OSDMA, Govt of Odisha, 2019.

17 Role of Geo-Informatics in Natural Resource Management During Disasters: A Case Study of Gujarat Floods, 2017 Ritambhara K. Upadhyay1, Sandeep Pandey2 and Gaurav Tripathi3* Centre of Advanced Studies in Geology, Panjab University, Chandigarh, India 2 Gujarat Institute of Disaster Management, Gandhinagar, Gujarat, India 3 Department of Geoinformatics, Central University of Jharkhand, Brambe, Ranchi, Jharkhand, India 1

Abstract

Disasters, be it natural, anthropogenic, or biological, are generally sudden and intense, resulting in considerable loss in terms of destruction, injuries and deaths, disruption of normal life, as well as the process of development for years to come. Vulnerability of disasters is further enhanced with burgeoning population and socio-economic setup as there is increase in the magnitude, frequency, and economic impact of the disasters. The geo-climatic condition of India with high population density renders it highly vulnerable to all sorts of disasters. Geo-informatics, with robust data handling capabilities, is the ideal for disaster management. It has immense potential from generation of awareness to dissemination of information during disaster mitigation, preparedness, and response as part of disaster management measures. Keywords:  Disasters, disaster management, vulnerability assessment, remote sensing, geo-informatics, flood monitoring

17.1 Background 17.1.1 Understanding Disasters: Natural and Anthropogenic Disaster has been defined by the United Nations as “the occurrence of a sudden or major misfortune which disrupts the basic fabric and normal *Corresponding author: [email protected] Shruti Kanga, Varun Narayan Mishra, and Suraj Kumar Singh (eds.) Sustainable Development Practices Using Geoinformatics, (253–282) © 2021 Scrivener Publishing LLC

253

254  Sustainable Development Practices Using Geoinformatics functioning of a society (or community).” “Disaster” is a generally used term to denote any intense event, be it natural or man-made/anthropogenic, which brings about destruction of life, property, infrastructure, essential services, and means of livelihood to an extent that it becomes very hard to cope with the situation due to it being beyond the normal capacity of the affected communities to deal with the situation unaided from states or nations [1]. It is characterized by immense loss or damage to property, both immovable and movable; or loss of human life or injury or illness to human beings on a large scale (e.g., outbreak of epidemic like COVID-19); or environmental degradation which takes a long time to recover. Disasters can be classified as natural and man-made or anthropogenic. A natural disaster can result from an instantaneous extreme effect or a longterm process leading to the disruption of normal life of social, economic, and traditional system to a substantial extent. The natural disasters can be classified on the basis of their origin, viz., wind and/water-related disasters (floods, droughts, cyclones, and tsunami); climate-related disasters (global warming, seal-level rise, ozone depletion, heat and cold waves); mountainousregion disasters (landslides, snow avalanches) and geological disasters (earthquakes, volcanic eruptions). Man-made or anthropogenic or human-induced disasters result from a man-made event, sudden or progressive, which adversely affects the community and it has to respond by taking prompt and exceptional measures including help from outside the community. In fact, anthropogenic disaster acknowledges that all disasters are caused by humans, because humans are responsible for these disasters as they have chosen, whether advertently or inadvertently, or whatever reason to be where natural phenomena occurs, which result in adverse impacts. They can arise from unintentional or inadvertent activity, as a fall out of poor maintenance, low quality work or human error, indiscriminate industrialization, overpopulation, increased consumerism, use of hazardous substances or processes, or simply accidents of various types system or process malfunctioning as in the case of nuclear radiation, gas leak, explosion, and fire. On the other hand, they can also result from willful, deliberate, and intentional activity, such as mischief, mob fury, revenge, sabotage, riots, or enemy attack. Negligence on the part of professionals as well as the public along with ignorance increases the possibility of man-made disasters. The man-made disasters can be broadly categorized as accidents (road, rail, air, river, sea, transport of hazardous material, building collapse); warfare (conventional, chemical, biological, and nuclear); poisoning (food, hooch, water supply); fires (buildings, coal mines, oil exploration sites, refineries, storage depots, forest fires); civil conflicts (arson, sabotage, terrorist, and other criminal activities; industrial

Role of Geo-Informatics in Natural Resource Management  255 and technological mishaps (leaks, fires, explosion, sabotage technical system failure, plant safety failure); nuclear hazards (radioactive leaks, thefts, transportation, waste disposal, reactor meltdown); ecological (air pollution, water pollution, noise pollution, soil erosion and degradation, waste accumulation including toxic waste, disease and epidemics, loss of biodiversity, loss of habitat, deforestation, global warming, sea level rise, depletion of stratospheric ozone, and increase in tropospheric ozone). The intensity of a disaster is determined on the basis of disruption to the normal pattern of life; impacts like loss of life and property, injury, hardship, and adverse effects on health; community needs, specially shelter, food, clothing, medical assistance, and social care; damage to infrastructure, buildings, communications; and the requirements of rehabilitation. The misery of the affected community is aggravated by poverty, high population density, weak infrastructure, proximity to river, sea or mountains, lack of cooperation within the community, and poor governance.

17.1.2 Disaster-Risk Reduction In the present scenario, the human understanding of disasters has taken a great leap and progressed from an exclusive techno-centric approach to a social and ecological one to resolving the phenomenon associated with the disaster. This quintessential shift in the understanding of disasters over the years has given considerable weightage to the sociological processes that determine or undermine a community’s coping capacity, resilience, and response to disasters. International as well as national communities pioneered by the United Nations have attempted to make thorough analysis of the disasters and device an inventory of causes that lead to a particular disaster, the extent of damage occurred, appropriate mitigation measures required to be taken and where these measures have been successfully implemented. Hazard is the process, phenomenon, or human activity arising from a variety of biological, geological, hydrological, meteorological, or technological sources. Location, intensity or magnitude, frequency, and probability are the main factors determining the hazard. Exposure denotes the impact of the disaster on inhabitants, infrastructure, housing, production capacities, and other tangible human assets located in hazard-prone areas. The suitable measures include the number of people or types of assets in the region, specific vulnerability and quantitative risks associated with it. [2]. Disaster Risk (DR) is the potential losses to the community or society in terms of lives, health status, assets, livelihoods, and services that could

256  Sustainable Development Practices Using Geoinformatics hamper the normal functioning of the society over some specified period of time due to some disaster. Vulnerability is the physical, social, economic, and environmental factors or processes which increase the susceptibility of an individual, a community, assets, or systems to the impacts of hazards [3].  It depends on a range of economic, social, cultural, institutional, political, and psychological factors that are instrumental in shaping people’s lives and the environment that they live in. With the changing lifestyle and poorly managed urban development, vulnerability is on the rise in many countries and regions of the world. As there is exposure to hazards, the vulnerability of the affected community and their capacity to cope with the disaster also increases. Vulnerability assessment is part of risk assessment and takes into consideration the differential vulnerability of communities in differential areas of disaster impact like the degree of hazard proneness. Vulnerability analysis takes into account the socio-cultural, ecological, and developmental aspects for developing a comprehensive strategy for disaster mitigation. Due emphasis is also laid on poverty alleviation and community empowerment, sustainability of livelihoods through effective governance. The prime goal of Disaster Risk Reduction (DRR) is to strengthen resilience and achieve sustainable development so as to prevent new disaster, reduce the disaster risk, and manage the residual risk. In accordance with the Sendai Framework for Disaster Risk Reduction 2015–2030, DRR should be aimed at measures for preventing the disaster risk, reducing existing risk, and strengthening economic, social, health, and environmental resilience.

17.1.3 Disaster Preparedness Irrespective of whether the disasters are natural or man-made/anthropogenic/human-induced, their impact is usually long lasting. Thus, it becomes imperative to design and implement support system that serves long-term goals. Earlier, humans heavily relied on the mercy of nature and sheer luck, but now, they are in a position to possess the knowledge, capacity, and capability to deal with and lessen the worst impact of disasters [4–7]. Disaster Risk Management (DRM) is the systematic implementation of strategies, policies, and state-of-the-art coping capacities through administrative directives and operational skills of the states or nations to minimize the adverse impacts of hazards and possibility of disaster. The systematic process of using administrative directives, organizations, and operational skills and capacities to implement strategies, policies, and improved coping capacities in order to lessen the adverse impacts of hazards and the

Role of Geo-Informatics in Natural Resource Management  257 possibility of disaster is disaster risk management. Capacity is the capability of an organization, community, society, state, or nation to manage and reduce disaster risks and strengthen resilience like vulnerability by making use of all the strengths, attributes, and resources available with them. Economic, physical (infrastructure and physical means), social (societal coping abilities, human knowledge, skill, social relationships, leadership, management), or environmental connotations determine the capacity of the organization, community, society, state, or nation. Prevention, preparedness, and mitigation are inter-connected and form an integral part of disaster management strategy. Disaster prevention is preventive planning through research, building of robust and sound information database, creating state-of-the-art infrastructure, and establishing linkages between all knowledge-based institutions. Thorough research is needed for a comprehensive vulnerability and objective risk assessment. For planning, warning, and assessment of disasters, an exhaustive database of demography, land use/land cover, availability of infrastructure and current information on climate, weather and man-made structures at local, state and national level along with resource inventories of personnel and equipment with governmental and non-governmental organizations for efficient mobilization and optimization of response measures is required. A holistic approach to disaster mitigation is anchored to frontline research and development. Availability of modern, state-of-the art technologies these days has led to the upgradation of the disaster management system. All frontier areas related to disasters like biological, space applications, information technology, nuclear radiation, etc., should have sound research inputs for a continuous flow of high quality basic information for sound disaster management planning (through warning or forecasting). All the above goals for dealing with the natural, anthropogenic, and biological disasters can only be achieved through collaboration of community, disaster managers, and decision makers.

17.1.4 Disaster Management Resilience includes risk management measures adopted by the system, community, or society exposed to the hazards to resist, accommodate, absorb, transform, and recover from the aftermath of hazards in a timely and efficient manner through preservation and restoration of its essential basic structures and health remedies. Response is the first stage of disaster management cycle after the onset of disaster and includes putting contingency plan in action, issuing warning, action for evacuation of people to safer places, rendering medical aid to the victims [8]. It can be achieved

258  Sustainable Development Practices Using Geoinformatics through communication and coordination between the various agencies, assessment of on-going situation, and mobilization of resources during the emergency period. Response measures are divided into three phases, viz., pre-, during, and post-disaster. As soon as the information on the impending disaster is received, the pre-disaster response activities such as setting up control rooms and evacuation of people from the site of disaster to safer places come into play to reduce the impact of disaster on life and property. Response activities during disasters are aimed at ensuring the fulfilment of needs of the victims to alleviate and minimize their sufferings. Postdisaster response activities are designed to achieve quick, long-lasting, and sustainable recovery. Disaster management is a comprehensive approach for reduction of disasters through prevention, preparedness, mitigation, and response. There has been a remarkable shift of emphasis over the last decades from disaster relief and rehabilitation to prevention and mitigation strategies. This has improved the disaster management measures for protection of lives and property.

17.1.5 Role of Geo-Informatics in Disaster Management Technological advancements have revolutionized the way we communicated and transformed the entire world into a global village. State-ofthe-art communication system has immense potential in effective disaster management through generation of awareness and dissemination of information during disaster mitigation, preparedness, and response. Remote Sensing (RS) and Geographical Information System (GIS) technologies hold immense potential in effective support during the disastrous events of cyclones, landslides, floods, droughts, forest fires, earthquakes, outbreak of pandemic, etc. Remote Sensing (RS) has a vital role to play in efficient disaster management measures. It has immense potential in minimizing the potential risks associated with by aiding in early warning strategies, formulation and implementation of developmental plans, mobilization of aid, rehabilitation and post-disaster reconstruction. The RS applications are crucial for formulating appropriate strategies for disaster preparedness and operational framework for monitoring, assessment, and mitigation measures. They are instrumental in identifying the gaps and suggesting measures for disaster mitigation keeping in view space and ground segments. It provides a robust database for past disasters which could be used for future prediction of disasters in the form of hazard maps indicating the regions that are potentially vulnerable to disaster. Pre-disaster management applications

Role of Geo-Informatics in Natural Resource Management  259 include disaster warning, risk analysis and mapping, flood monitoring and assessment, cyclone tracking, estimation of crop and forestry damages, etc., and also for monitoring of land-use changes in the aftermath of a disaster. Meteorological satellites monitor weather patterns, detect and track storm systems, and keep a track of frosts and floods. It also provides large spatial coverage and thus is apt tool for damage assessment, aftermath monitoring and providing a quantitative base for relief operations as disasters usually affect large areas. Real-time monitoring of the disasters is also facilitated by RS to minimize the impact of disaster that leaves behind a trail of massive destruction. The Synthetic Aperture Radar (SAR) sensors overcome cloud cover that poses difficulty in procuring RS data over the affected areas and help in supporting relief operations through an integrated GIS database. The earth observation and communication satellites provide continuous, accurate and timely monitoring and assessment of location of the disaster-stricken area, disaster alerts, current status of damage in postdisaster situation, and coordination with various agencies for efficient delivery of aid. Geographical Information System (GIS) is primarily instrumental in geographical and computer-generated maps as an interface for integrating and accessing massive amounts of location-based information. It serves as an effective tool for disaster management planning in terms of scientific investigation and collaboration, emergency declaration, resource management, disaster preparedness and quick, continued disaster response and continued surveillance/monitoring, analyze past events, and predict future events. A classic example could be the real-time monitoring of natural disasters like floods, tsunamis, etc. It is the best tool for efficient handling of robust geo-spatial data, capacity building, and mapping of priority sectors.

17.1.6 Structural Measures of Flood Risk Management Structural measures are physical in nature and aim to prevent flood waters from reaching potential damage centres, this is the measures, which alter the physical characteristics of the floods (reservoir operation, upstream catchments management, channel modifications, levees, operation of hydraulics works) [9–12]. Structural measure refers to those mitigation tools which have physical entity such as embankment, dam, etc., a measure to control the physical process of flooding. Structural measures with adequate appurtenant structures and proper water management practices create condition for increasing productivity from land and other developmental activities. Structural measures aim at protecting an area up to certain level of flooding. It can be divided into five categories:

260  Sustainable Development Practices Using Geoinformatics a. b. c. d. e.

Storage reservoir or basins to restrict overflow. Retarding basins to lower the flow of flooding. Levees and floodwalls to confine floodwaters. Improvement of channel capacity. Some structural measures such as Flood Embankment, Channel Improvement, River Training, Coastal Embankment, etc., to combat the flood sufferings.

Among these structural measures, construction of embankment is most popular practice. There are some options of structural measures: • Dams and reservoirs for impounding excess runoff. Detention basin, retention pond to lower the level of flooding downstream. • Embankment, dyke, polder, levee, bund, or flood wall to block the movement of water from rivers to floodplain. • Flood bye pass, flood diversion • Watershed management and afforestation.

17.1.6.1 Dams Barriers that impound hydrologic flows, dams retain floodwaters before they reach areas at risk. For example, during high-precipitation periods, dams hold upstream floodwaters that are released gradually to minimize the likelihood of damage to downstream communities. However, during exceptionally large events, the storage capacity of a dam can be exceeded and uncontrolled flood flows are passed downstream. Under these circumstances, downstream levees may not be able to contain floodwaters and will fail.

17.1.6.2 Levee and Levee Overtopping A levee, floodbank or stopbank, is a natural or artificial embankment. Levee is to prevent flooding of the adjoining areas; however, they also confine the flow of the river resulting in higher and faster water flow. During a flood event, the risk of a levee overtopping can be significant and the consequences can be catastrophic. Controlled overtopping of levees or engineered overtopping involves designing a levee to force overtopping in the least hazardous location. This can be done by using different levee heights, known as superiority, or notches or openings in a desired location.

Role of Geo-Informatics in Natural Resource Management  261 The advantages of controlled overtopping in a designated area are: 1. reducing the impact of overtopping failure in the selected area and in other parts of the levee system, 2. reducing the likelihood of overtopping in less desirable areas (i.e., areas with more development), and 3. reducing levee maintenance and repair costs after the flood event.

17.1.6.3 Flood Diversion Diversion structures route runoff in excess of base flow to storage facilities during wet periods, for later use during dry period. Flood diversion structures, such as dikes, are also useful methods for mitigating the adverse effect of torrential rains and at the same time capturing the excess water for later use.

17.1.6.4 Transverse Dikes Transverse dikes are built in sections along a river to store excessive runoff. These dikes can be built using material dredged from the river or transported from adjacent lands. The dike material, usually clay or silt, must be highly compacted and in many cases it is advisable to place riprap on the dike to increase its strength and protect it from erosion.

17.1.6.5 Water Traps Water traps are used to control the deleterious effects of runoff in a river basin and to facilitate water storage and the recharge of aquifers. They are built like an earth dam, usually 1- to 3-m high, using local materials. The walls are compacted in 20-cm layers using the same equipment as is used to build a dam.

17.1.6.6 Watershed and Afforestation Watershed management is the process of guiding and organizing the use of the land and other resources on a watershed to provide desired goods and services without harming soil and water resources. The interrelationships among land use, soil, and water, and the linkages between uplands and downstream areas such practices are changes in land use, vegetative cover, and other structural actions that are taken to achieve specific watershed

262  Sustainable Development Practices Using Geoinformatics management objectives [13–17]. Forests and trees affect the hydrologic behavior of a watershed, including the quantity and quality of stream flow, erosion, and sedimentation. In general, natural forests yield the highest quality of water of any ecosystem. The lowest erosion and sedimentation rates are usually associated with forested watersheds in natural conditions; stream flow from forested watersheds tends to be more uniform, with peak flows lower than those from watersheds with other vegetative cover. Given this background, the role of trees and forests can be viewed in terms of watershed protection enhancement of water resources and flood control activities. Such approach and practices are useful in flood control and integrated approach of resource management.

17.1.7 Non-Structural Measures of Flood Risk Management Non-structural measures strive to keep people away from flood waters. It contemplates the use of flood plains judiciously, simultaneously permitting vacating of the same for use by the river whenever the situation demands. The measures that alter the exposure of life and property to flooding floodplain, land use planning, flood forecasting and warning, flood proofing, and assistance. However, non-structural measures should always be considered conjunctively in the planning and use of structural measures because of the potential for synergistic enhancement of their effectiveness. Under some river basin conditions, the introduction of non-structural methods to limit flood damage may alone be more cost-effective than alternatives involving structural methods. Non-structural approaches to flood management fall naturally into two categories: 1. Those anticipatory measures which can be assessed, defined, and implemented in the flood plains to reduce the risk to property from identifiable potential floods. 2. Those planned emergency response measures which are applied when a damaging flood is forecast, imminent or underway, to help mitigate its damaging effects.

17.1.7.1 Non-Structural Measures 1. 2. 3. 4.

Flood Plain Zoning Flood Forecasting Control of Flood Plain Development Flood Insurance

Role of Geo-Informatics in Natural Resource Management  263 5. Flood Proofing 6. Catchment Management

17.1.7.2 Flood Plain Zoning Flood plain zoning is to regulate land use in the flood plains in order to restrict the damage due to floods, while deriving maximum benefits from the same.

17.1.7.3 Flood Forecasting Flood forecasting, and flood warning, may, in some instances, provide a realistic means to authorities and individuals to reduce the damage inflicted on persons and properties in areas exposed to flood risks [18, 19]. With longer or shorter lead times, depending on the drainage basins and hydro-meteorological parameters, flood forecasting can permit the prediction of the progress of floods, enabling the responsible authorities and involved populations to take personal, material and organizational decisions to reduce the detrimental consequences of the imminent flood. These decisions can range from routine responses (e.g., change of dam operation instructions, opening or closing of gates, anticipatory releases to enlarge storage capacity), to preventive instructions (barring navigation on the major flow channels, for example), up to emergency measures (announcing a generalized alert; mobilizing evacuation of and assistance for the population situated in high risk areas; or ordering planned breaches of flood dikes, for example). The data most frequently used in flood forecasting (input variables) are: ➢➢ antecedent precipitation in the drainage basin, ➢➢ soil saturation level in various points in the drainage basin, ➢➢ stream flow at points in the drainage basin upstream from the point of concern, ➢➢ the snow pack and the temperature for basins which have snow melt floods, ➢➢ water storage capacity and levels in any reservoirs in the basin. Additionally, where appropriate to the basin size and to the flood forecasting time window, meteorological forecasts of risks, locations, and levels of predicted precipitation may also be incorporated. Normally, flood forecasting is expressed with a prediction of the river “stage” or water level or of the discharge at some future time at the

264  Sustainable Development Practices Using Geoinformatics downstream point of concern. Additionally, it can also provide, if feasible, an indication of the maximum expected level and its timing. It is evident that the further ahead the forecasted event is the less precise will be its accuracy. Therefore, the period must inevitably be limited in order to avoid issuance of alarmist information, which in the end would tend to undermine the credibility of the forecasting amongst the population at risk.

17.1.7.4 Flood Plain Development Flood plain development has as its basis the notion that there is willingness by government agencies and the general public to control the location of development (i.e., land uses, buildings, infrastructure, etc.) on identified flood plains. Techniques associated with this measure tend to be less costly in capital expenditures but more so in terms of human commitment (social capital). To be successful, the control of flood plain development requires a collective social commitment to act. The objectives of such control measures are to: ➢➢ reduce future potential for flood damage and loss of life; ➢➢ determine and describe acceptable or compatible land uses within the designated floodplain, and (this is a key issue); ➢➢ increase public and institutional awareness of risks associated with flooding. The control of flood plain development cannot be fully effective on a national scale where the river basin extends across more than one country. Upstream developments can have a direct bearing on any flood risk management or control development works in the lower riparian countries. Effective and proactive cooperation between all riparian countries, when dealing with international rivers, is needed to optimize the potential for damage mitigation in the flood plains. There are a variety of techniques than can be employed to regulate or control the types of development and use within the floodplain. Some of these techniques include flood plain regulations; zoning and land use by-laws, subdivision regulations, building codes, development policies, and tax adjustments.

17.1.7.5 Flood Insurance The principal objective of flood insurance is to spread the costs of flood damages so that the society involved can manage those costs. This involves

Role of Geo-Informatics in Natural Resource Management  265 spreading the costs both in terms of time and population. It also entails establishing an equitable system with minimum external costs and with little or no extraneous adverse effects. Flood insurance differs from the other tools for managing flood losses: whereas other tools reduce the cost of flood damage from each flood, insurance distributes the losses over time and space. There is a definite connection between insurance and disaster relief. If a country is contemplating the establishment or enhancement of a flood insurance mechanism, then there must be some problem of availability, affordability, or low market penetration to be addressed. Although insurance does not reduce the long-term cost of flood damage, it is often complementary to those flood-plain management strategies, which do reduce losses, such as, flood mitigation works, forecasting, flood proofing, or changes in land-use to more flood-tolerant uses.

17.1.7.6 Flood Proofing Flood proofing measures help greatly in the mitigation of distress and provide immediate relief to the population in flood prone areas. It is essentially a combination of structural change and emergency action. Flood proofing is “the modification of buildings and structures and their immediate surroundings to reduce damage in flooding.” It limits flood proofing to physical measures in order to avoid or minimize exposure to floods [18–20, 23]. As such the construction of buildings on individual earth mounds raised to the level of ordinary high floods and use of easily dismantled materials for houses in flood prone areas can be considered as flood proofing.

17.1.7.7 Catchment Management Using catchments management in a river basin to control or alleviate the generation of floods consists of stepping in at the sources of floods and trying to modify the way or rate in which rainfall is transformed into stream flows. While, depending on basin characteristics, there is a limit to the effectiveness of good catchment management practices, their application, maintenance, or enhancement should always be carefully assessed; as noted below, they also correlate with erosion-control practices and thus generate additional benefits. The elements of the hydrologic cycle relevant to a drainage basin are: • precipitation, which is the principal cause of runoff; • interception by foliage, which slows down the fall of rain onto the soil and which also retains some rain subsequently transformed into evaporation;

266  Sustainable Development Practices Using Geoinformatics • evaporation and evapo-transpiration (from ponds, lakes, and other free water surfaces, from the soil surface, and from the basin’s vegetative growth), which will be insignificant during precipitation periods, but which can become significant later; • surface infiltration into the upper soil strata, which can store a significant amount of the precipitation and subsequently release it slowly in the form of evapo-transpiration or through modified surface discharge; • deep infiltration into ground water basins, when some water can emerge into the watercourse but after a much longer delay and often at a very distant point; • direct overland flows which, discharging along successive draws and streams can rapidly swell the flows of the main stream; • delayed overland flows, which include some of the intercepted precipitation and some of the surface and deep infiltrations. Catchment management thus consists of modifying the basin conditions to cause a change in the distribution or influences of these elements, particularly by increasing the amount of interception and of surface infiltration in order to reduce the amounts and rates of direct overland flow. Incidentally, such actions correlate with an alleviation of an associated significant river basin issue—soil erosion. On steeply sloping soils, the initial overland discharges can, before reaching the streambeds, flow with a velocity sufficient to dislodge, then carry away the soil constituent materials. Besides possibly being significant arable soils from the originating land, these materials become the major part of the river’s sediment discharge, either as suspended particles or as bed load, and contribute to the downstream sedimentation problems whether in reservoirs or in channels. Thus, direct overland flow and soil erosion are essentially the two factors that catchment management is aimed at treating.

17.2 Flood Preparedness Measures Activities and measures taken in advance to ensure effective response to the impact of hazards, including the issuance of timely and effective early warnings and the temporary evacuation of people and property from threatened locations [19]. Flood preparedness is about putting in place a set of appropriate arrangements in advance for an effective response to

Role of Geo-Informatics in Natural Resource Management  267 floods. Some of the commonly identified flood preparedness activities are: public awareness raising on flood preparedness, response, and mitigation measures; stockpiling of emergency relief materials, i.e., food, fodder for livestock, emergency medicines, materials for temporary shelter, etc; installation of community-based early warning system for issuance of timely and effective flood warnings; management of safe areas for temporary removal of people and property from a threatened location; transportation to safe areas/evacuation center; ensuring access to health and sanitation facilities; conducting drills and rehearsals. The key to flood preparedness is to have a clarity and agreement on the roles and responsibilities of relevant stakeholders such as the government agencies, disaster management organizations, Red Cross, voluntary groups as well as community members. Such an arrangement is possible by forming disaster management committee and teams at various levels to agree on set of standard operating procedures (SOPs) defining what actions to be taken before, during, and after floods. A. Before flooding occurs • The route to the nearest safe shelters is to be known. • The First Aid Kit is to be ready with extra medication for snake bite and diarrhea. Strong ropes should be available for tying things. • A radio, torch, and spare batteries are to be arranged. • Fresh water, dry food, candles, matchbox, kerosene, etc., are to be stocked. • Umbrellas and bamboo sticks are also necessary to protect from snakes). • Higher ground is to be selected for stay where people and animals can take shelter. After hearing a flood warning. • Flood warning and advice may be easily obtained through radio and television. • We must keep vigil of flood warning given by local authorities. • Dry food and drinking water and warm clothes are made to be ready. • Emergency kit must be checked. B. At the time of evacuation • Pack clothing, essential medication, valuables, personal papers, etc., in water proof bags to be taken to the safe shelter. • Raise furniture, appliances on beds and tables.

268  Sustainable Development Practices Using Geoinformatics • Put sandbags in the toilet bowl and cover all drain holes to prevent sewage backflow. • Do not get into water of unknown depth and current. • Lock your house and take the recommended or known evacuation routes for your area of safe shelter. C. During floods • Boiled water or use of halogen tablet to purify water must be used. • Food should be covered. • Children are not allowed to remain on empty stomach. • Bleaching powder and lime are to be used to disinfect the surroundings. • Entry in flood waters may be avoided. If one needs to enter then proper foot wear may be used. • Water over knee level may be avoided. D. After a flood • One has to be in touch with local radio. • Children may not be allowed to play in, or near, flood waters. • One has to be stay away from drains, culverts. • Electrical appliances should not be used. • Food of floodwaters must be avoided.

17.3 Flood Response Measures Management and control of the adverse consequences of floods will require coordinated and effective response systems at all levels, like state, district, taluka, and Local community. Response component of DRM plans will involve rapid deployment of supplies and logistics, along with the duration of potential deployment. These plans will prescribe appropriate coordination mechanism with other agencies working in the affected areas. Local community in the affected neighborhood is always the first responder after a disaster.

17.3.1 Components of Flood Response Major components of Flood Response are as follows: 1. Estimation of Severity of Flood 2. Emergency Search and Rescue

Role of Geo-Informatics in Natural Resource Management  269 3. 4. 5. 6. 7. 8. 9.

Emergency Relief Incident Response System Emergency Action Plan Community-Based Disaster Preparedness and Response Emergency Logistics Emergency Medical Response Specialized Teams for Response

17.3.1.1 Estimation of Severity of Flood The preliminary assessment of the severity of a flood should be based on water level and the estimate of the area flooded. The flood control cell works round the clock during the monsoon period. The flood control cell collects Gauge level of interstate rivers. The daily flood report, three hourly water levels of interstate basins and hourly water level of schemes during flood are updated by online data entry. The flood cell also collects information of other major/medium project. and informs officers of Narmada Water Resources, Water Supply & Kalpsar (NWRWS&K) and Revenue Department officers about the situation of flood in various rivers of state [21]. The Flood Control Cell, Gandhinagar also obtains the weather forecast and rainfall data, etc., from IMD the water level and forecast convey in morning after 8:00 am to all concerned the same can be assessed from satellite imageries.

17.3.1.2 Emergency Search and Rescue Community level teams are developed in each district with basic training in search and rescue. Training modules are developed for trainers of community level search and rescue teams by the NDRF. NDRF battalions impart training and assist the state government/district authorities in training communities. They are further assisted by the ATIs, CD, Home Guards, and NGOs. The state governments, through the ATIs, develop procedures for formally recognizing and certifying such trained search and rescue team members; they provide suitable indemnity to community level team members for their actions in the course of emergency response following a flood.

17.3.1.3 Emergency Relief The trained teams assist in planning and setting up emergency shelters, distributing relief among the affected people, identifying missing people,

270  Sustainable Development Practices Using Geoinformatics and addressing the needs of education, health care, water supply and sanitation, food, etc., of the affected community. Members of these teams are aware about the specific requirement of the disaster-affected communities. It will be ensured by the concerned authorities that the stockpiling of the essential commodities has been carried out. These teams will also assist the government in identifying the most vulnerable people who may need special assistance.

17.3.1.4 Incident Response System Meteorological Center and Flood Meteorological office stationed at Ahmedabad collects information regarding meteorological situation of the State. Meteorological center also issues heavy rainfall warnings to those officers of NWRWS&K Department and Revenue Department of Government of Gujarat. Government of India has set up two Divisions, Tapi Division and Mahi division for issuing flood warnings of six inter-state rivers, viz., (1)  Damanganga, (2) Tapi, (3) Narmada, (4) Mahi, (5)  Sabarmati, and (6) Banas. The inflow forecast and flood level forecast for the above basins are to be conveyed officer of the rank of Superintending Engineer or Collector of concerned districts or Municipal Commissioners, as Appropriate Authorities (Focal Officers) for various basins/regions during monsoon period. The Focal Officer can nominate any Executive Engineer/Officers in his area as his second in command who will act as Sub-Focal Officer for discharging duties of Focal Officer. Keeping constant watch over the flood situation, flood warning, monitoring flood discharges through concerned project authorities, formulating flood forecast as and when required conveying these warning including conveying inflow forecast and flood level forecast from C.W.C. or the case may be in advance to the concerned Revenue and Police authorities for alerting and evacuating people of the area likely to be affected by the incoming floods if necessary. On receipt of flood warning, the revenue authorities will, in turn, take necessary actions for alerting and evacuating the people likely to be affected in accordance with warning as per Flood Memorandum. Whenever heavy outflow is likely to be let off Focal Officer/Executive Engineer inform either to the Assistant Engineer of Railway or to the Station Master of the nearest railway station or Divisional Railway Managers. In case, advance warning is received by the Railway authority in time it will be possible to take preventive measures to regulate the running of trains and to protect the Railway property, staff, and passengers. All concerned Focal Officers should prepare a drill to be followed during monsoon at the time of various floods including catastrophic flood and fix

Role of Geo-Informatics in Natural Resource Management  271 duties of all concerned persons at that moment. The rehearsal of this drill should be made before the onset of monsoon. During emergency flood messages are also conveyed by Focal Officer or any officer authorized by him and Collector of the District to All India Radio/Doordarshan Kendra for necessary broadcast. There are many drains in the state. These drains are linked up with inter taluka or inter-districts. Several drains are long and having a large capacity. Due to heavy to very heavy rainfall in the catchment areas of drain, the drains causes damages to land, crops, property, cattle of the adjoining areas. The Executive Engineer, in charge of drain has to function as a “FOCAL OFFICER and Dy. EE as sub-focal officer have to take necessary action and make efforts to control the situation. Sub-Focal Officer in-charge of the drains has to intimate his higher authorities and revenue authorities like Mamlatdar, Prant Officer, Collectors, Police Authorities, and Home Guard Authorities regarding the situation. All authorities are requested to extend the help required by the sub-focal officer to overcome the situation.

17.3.1.5 Control Room Set-Up As a part of “Flood Warning Arrangement” a Flood Control Cell under the control of Superintending Engineer from 1st June to 31st October or up to one week after withdrawal of monsoon by I.M.D. The Flood Control Cell works round the clock during the monsoon period. The Flood Control Cell collects gauge levels of inter State rivers, viz., Damanganga, Tapi, Narmada, Mahi, Sabarmati and Banas from Tapi and Mahi Divisions of C.W.C. and maintains up-to-date records of three hourly water levels during monsoon and hourly water levels during floods. The cell also collects information of other Major/Medium Projects and informs the officers of the NWRWS&K Department and Revenue Department. In the event of any news items appearing in the newspapers regarding flood damages including inundation, etc., in any area, the concerned Superintending Engineer should immediately take stock of situation and issue necessary press release clarifying the actual situation.

17.3.1.6 Model Action Plan 1. Identification of the flood prone blocks, talukas, tehsils and villages. 2. Setting up operation control center and ensure duties to run it round the clock.

272  Sustainable Development Practices Using Geoinformatics 3. Maintaining a log book to keep data about rise of flood waters at regular intervals of the rivers in the State, ensuring coordination committee for relief. 4. Flood warning communicated through mobile units and microphone in the flood prone sub-division and blocks to issue warning. 5. Readiness for being mobilized at short notice. 6. Flood prone blocks connectivity with the telephones and police.

17.3.1.7 Community-Based Disaster Preparedness and Response NGOs, self-help groups, CBOs, youth organizations such as NCC, NYKS, NSS, etc., women’s groups, volunteer agencies, CD, Home Guards, etc., normally volunteer their services in the aftermath of any disaster. Village level task forces can also be constituted on voluntary basis for better preparedness of the community. The state governments/SDMAs and DDMAs coordinate the allocation of these human resources for performing various response activities [22].

17.3.1.8 Emergency Logistics and Equipment Motor launches, country boats, inflatable rubber boats, life jackets, life buoys, and other equipment’s will be required immediately after floods to carry out search and rescue of trapped people. State governments compile a list of such equipment, identify suppliers thereof and enter into longterm agreement for their quick mobilization and deployment in the event of floods. The IDRN, which is a web-based resource inventory of information on emergency equipment and response personnel available in every district, will also be used for this purpose. The information on IDRN is revised and updated frequently. The state governments may avail of CRF for this purpose to the extent of 10% as provided in the existing rules and guidelines for disbursement.

17.3.1.9 Emergency Medical Response Prompt and efficient emergency medical response provide by Quick Reaction Medical Teams (QRMTs), mobile field hospitals, including floating hospitals for riverine islands and areas inaccessible by roads, Accident Relief Medical Vans (ARMVs), and heli-ambulances become activated to reach the flood-affected areas immediately, along with dressing material,

Role of Geo-Informatics in Natural Resource Management  273 splints, portable X-ray machines, mobile operation theatres, resuscitation equipment, and life-saving drugs, etc. Resuscitation, triage, and medical evacuation of victims and admission who require hospitalization [18].

17.3.1.10 State Disaster Response Force To augment the capacities of the states, all state governments have constituted, from within their armed police force, adequate strength of personnel for the SDRF with appropriate disaster response capabilities. In addition, the police, fire and emergency services, Home Guards, and CD are being strengthened and upgraded to have adequate capacity to respond effectively to disasters. Deployment of the Indian Armed Forces for post-flood response work will be resorted to only as the last option.

17.3.1.10.1 Fire and Emergency Services in the Urban Local Bodies

The fire and emergency services in the ULBs of state may be used as an emergency-cum-fire services force. The fire and emergency services in the flood prone areas will develop adequate capacity to respond to serious flood situations, in addition to managing fires.

17.3.1.10.2 Police Force

The police plays an important role in the aftermath of floods in maintaining law and order, assisting in search and rescue, and in the transportation and certification of casualties. It is equally important that the police forces are properly equipped and trained.

17.3.1.10.3 Home Guards

The Home Guards serve as an auxiliary arm of the police force and support the district administration in various tasks. They will be trained for carrying out search, rescue, and relief operations on occurrence of floods.

17.3.1.10.4 Civil Defence

The community has a major role to play both as a victim and necessarily as a first responder. Integration of the CD organization into disaster management can work as a great catalyst for organizing community capacity building. CD recruited and trained personnel. CD has its coverage to all the districts in the country and assigning it an important role in DM framework, the primary role of CD will be community capacity building

274  Sustainable Development Practices Using Geoinformatics and creating public awareness in pre-disaster phase. The proposal envisages converting the town specific setup of CD to a district specific set up.

17.3.1.10.5 Relief Camps

The setting up of relief camps for the people whose houses have been damaged by floods and the provision of basic amenities in such camps involves complex logistics of mobilizing relief supplies, tents, water supply and sanitation systems, transport and communication systems, and medical supplies. The panchayat buildings in the villages in flood prone areas will be made flood proof as by raising their plinth level at least 0.6 m above the drainage/flood submergence line and making them at least double storeyed or constructing ring bunds around them. Wherever the panchayat building is single storey, a stairway will invariably be provided to the roof so that people can take shelter there temporarily.

17.4 Gujarat Flood Case Study 2017 Unprecedented rains lashed major parts of Gujarat in the 3rd and 4th weeks of July, 2017 with the development of low-pressure zone. The districts of North Gujarat along with Banaskantha, Patan, and Surendranagar were the worst affected. Majority of the area of Gujarat is vulnerable to floods due to the presence of seventeen rivers majority of which are on flat beds, longest coast line, and a wide variation in rainfall pattern. Flooding in the zone extending from Saurashtra to upper basins impacts both residential areas and agriculture. The flood risk in Saurashtra is lower than that of the South Gujarat plains. The relatively flat plains in the lower basic areas with hilly catchments in upper parts of South Gujarat accentuate flood risks. The 1998 flood was one of the most severe in remembered history and caused medium to heavy damage across the State. Heavy rains and consequent flooding in 2004 also caused widespread damage particularly in the Surat, Navsari, and Bharuch districts. Flood 2006 affected 20 districts, 132 talukas and more than 8,000 villages while the 2013 flood affected 14 districts and around 1568 villages. In 2015, heavy rain and flood also caused widespread damage particularly in Amreli and Banaskantha districts. In 2017, the monsoon commenced early with heavy rainfall. By July 21st 2017, the State had received almost half (45.90%) of the entire season’s average rainfall. In the next seven days, it received a quarter of the season’s rainfall (26.57%) Surendranagar was first hit by heavy downpour that started

Role of Geo-Informatics in Natural Resource Management  275 from July 14. The district received over 110 mm of rainfall—nearly 20% of its annual average in nearly 24 hours on July 21–22, 2017. Banaskantha too recorded a whopping 257 mm of rainfall, nearly 40% of the annual monsoon rainfall in nearly 24 hours on July 24–25, 2017. The flood situation compounded further with a major breach in the Narmada canal near Khariya village, Kankrejtaluka, Banaskantha. State reservoirs, both natural and man-made, were already full. Unprecedented rainfall during July 2017 resulted into very heavy inflow into the dams such as Sipu, Dantiwada, Machhu, Dharoi across rivers such as Banas, Bhogavo, Sipu, Sabarmati, etc. In 2019, Vadodara city received 424-mm rainfall pouring within a very short span of six hours from 2 pm to 8 pm on 31st July 2019. The floodwaters inundated city Vadodara and washed away villages, damaged/ destroyed houses caused loss of life and extensive damage to property. The heavy rains have left many of the major dams full while the medium and small dams are overflowing, the worst being the case Vishwamitri river, which flows through Vadodara, was rising dramatically from 23 ft toward its danger levels of 26 ft, authorities said. The nearby Ajwa dam’s water level also increased to 209.45 ft against its warning level of 215 ft. Much of the overflowing water was been drained into the Vishwamitri river flowing through the Baroda city. Flood Vulnerability Assessment takes into account socio-economic vulnerability, vulnerability analysis of slums, hospitals, schools, and industries. Socio-economic vulnerability is determined by measuring the vulnerability of different sections of the society in different parts of the regions affected by floods. Field investigation is done with respect to basic information about the flood prone or flood affected area in terms of financial management infrastructure, water supply and sanitation, past flood experiences, flood warning and adaptation measures, and effect of flood on various parameters specific to sector. During the event of floods, the schools experience educational (disruption in studying curricula, delaying of session, reduced quality of education due to time constraint to finish the syllabus), physical (loss of touch with learning protocols and enhanced uncertainty regarding future prospects), and economic (loss of income and additional expenditure for repairing damaged infrastructure) losses. Slums with minimum provisions of basic facilities like drinking water, roads, lights, sanitation, etc., are highly susceptible to the impacts of disasters like floods. Floods damage the houses and household items incurring expenditure in repairing the damaged houses. There is spurt in medical emergencies like chicken guinea, fever, cholera, etc., as sanitation is a pressing concern in the slums. Hospitals are vulnerable in terms of level of submergence and damage to boundary walls, doors, windows, wiring which, in turn, result

Figure 17.1  Gujarat Flood Hazard Risk Zonation: Settlement-wise Flood Frequency. Source: GSDMP, 2017-17, Part-I.

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Figure 17.2  Flood Hazard Map Gujarat (BMTPC, 2019).

GUJARAT 20

40

60

80

100 km

Total No. of House : 1,75,24,030* Population : 6,04,39,692

0

Flood Hazard Map

Role of Geo-Informatics in Natural Resource Management  277

278  Sustainable Development Practices Using Geoinformatics in short circuit of electric and communication system which hamper the networking among the hospitals. The industries are not just vulnerable in terms of production disruption but also post-flood disruption in transportation and communication. For determining flood risk rainfall records, regional information, botanical evidence such as scars on trees, site-specific data such as stream gaging records, historic information—flood marks on buildings and other structures, areas flooded (discuss with long-time residents) are required. Watershed modeling, regional methods, empirical equations, transfer methods, and statistical analysis of stream-flow records are the most commonly applied techniques used to define flood risk.

17.5 Preparedness Measures by State Government a. Updation of District Disaster Management Plans. b. Government of Gujarat has constituted a Weather Watch Group which meets every week during the monsoon season. This group collects information, interprets it, and subsequently disseminates it. c. State government was also in continuous touch with the India Meteorological Department (IMD) to keep a watch on weather conditions and rainfall. d. Based on the IMD forecast, State Government prepositioned teams of NDRF, SDRF, Fire and Emergency Services, etc., in the districts as a precautionary measure. State Government also requested Army, BSF, and Air Force to be ready for deployment in case of need.

17.6 Media Handling a. The government published a combined list of these Twitter accounts to enable people to reach out to the district administration in disaster situations. b. Four WhatsApp groups—Revenue Department, Crisis Management, Crisis Core, and Gujarat Rescue (Gujarat Government and Defence Forces)—were formed. c. Two Press briefings were held daily—one in the morning and other in the evening at the State Emergency Operation Centre.

Role of Geo-Informatics in Natural Resource Management  279

17.7 Rescue Operation a. Gujarat Government also appreciated the work done by NDMA on social media. “Do’s and Don’ts” for preparedness and response to various disasters is a good initiative for awareness generation. b. On the very first day, a meeting of senior-level officers of all agencies that were to be engaged in rescue operations (e.g., Army, Air Force, NDRF, SDRF, and Coast Guard) was organized. c. Army setup base camp hospitals in Dharah, Kankrej with the help of State administration. d. Ten NDRF teams were normally stationed in Gujarat (five each in Ahmedabad and Baroda). Eight teams reached via rail from Pune Battalion. e. As the situation became even more grave, 14 more NDRF teams were flown in from Delhi, Mundli, and Arakonam. f. Two deliveries were managed with the help of Air Force. g. Lack of equipment was a concern. Equipment made available to Municipal Corporations through GSDMA and additional boats received from NDRF were provided to SDRF personnel. They did a commendable job despite shortage of equipment. h. Rescue operations were phenomenal. At least 18,000 people were rescued. Of these, 7,000 were saved by NDRF followed by Army and Air Force. i. Volunteers also acted as first responders. However, there is no trained volunteer network available in the State for organized response. j. Several deaths from Banaskantha and Patan were reported during these floods, due to the fact that these districts received almost a 100 per cent of their annual rainfall within these 4–5 days.

17.8 Relief Work a. Normally, relief operations begin only after the floods recede as the entire administrative machinery is involved in rescue operations.

280  Sustainable Development Practices Using Geoinformatics b. Both rescue and relief operations were conducted simultaneously (immediate relief included moving people into safe shelters, arrangements for essentials like food, water, medicines, etc.). c. The cash doles were deposited in the bank accounts of the beneficiaries under the DBT (Direct Benefit Transfer) scheme. d. Free distribution of fodder for cattle was also undertaken, Cattle assistance was enhanced from Rs. 30,000 to Rs. 40,000 for each cattle loss. e. Hardly 10–15% farmers had crop insurance, all insurance companies to streamline the process of settling, insurance claims. f. Separately, crop assistance was provided under NDRF/ SDRF norms. g. Electricity bill for farmers was also waived off for a period of three months. h. State Government declared special relief package of Rs. 1,500 Crores for severely affected Banaskantha and Patan districts.

17.9 Speedy Restoration of Essential Services a. 836 roads were restored out of 952 roads for smooth transportation; total length of damage roads 15,048 km. b. Restoration of electricity supply in 609 villages of Banaskantha district and 208 villages of Patan district. c. Water supply started in 568 villages out of 712 affected villages of Banaskantha district. Water was provided in 144 villages through water tankers. Similarly, water supply started in 205 villages out of 328 villages of Patan district. Water has been provided to 108 villages of Patan district through water tankers. d. All 2,281 trips have been started in Banaskantha district and 2,277 trips of Patan district by State Road Transport Corporation. e. Sanitation: Disposal of stored water and removal of mud, disposal of dead animals, complete cleanliness, and preventive measures to prevent epidemic.

Role of Geo-Informatics in Natural Resource Management  281

17.10 Use of Drones—New Initiative Adopted a. Some people were stranded at Surendranagar for about 24 hours and rescue agencies were also not able to rescue them. Drones were used to supply food packets, water, blankets, and a mobile phone with SIM to keep communication open.

References 1. Oliver, John E. (Ed.), (2005), Encyclopedia of World Climatology, Springer, Netherland. 2. The Global Facility for Disaster Reduction and Recovery (GFDRR) Report; https://www.gfdrr.org/en 3. Sinha, D.K., (2006), Towards Basics of Natural Disaster Reduction, Research co. Book Centre, New Delhi. 4. Carter, W. Nick, 1992, Disaster Management: A Disaster Manager’s Handbook, AsianDevelopment Bank, Manila. 5. Taori, 2005, Disaster Management through Panchyati Raj, Concept, New Delhi. 6. Singh, Tej, 2006, Disaster Management Approaches and Strategies, Akanksha Publishing House, New Delhi. 7. Carter, W. Nick, 1991, Disaster Management: A Disaster Manager’s Handbook, Asian Development Bank, Manila. 8. Government of India, 1997, Vulnerability Atlas of India. 9. Hall, J., &Solomatine, D. (2010). A framework for uncertainty analysisin flood risk management decisions.International Journal of RiverBasin Management,6(2), 85–98. 10. Schanze, J. (2016). Resilience in flood risk management–Exploringits added value for science and practice. E3S Web of Conferences, 7(08003), 1–9. 11. Winsemius, H. C., Aerts, J. C. J. H., van Beek, L. P. H., Bierkens, M. F. P., Bouwman, A., Jongmann, B.,...Ard, P. J.(2016). Global drivers of future river flood risk.Nature ClimateChange,6, 381–385. 12. Zischg, A. P., Hofer, P., Mosimann, M., Röthlisberger, V., Ramirez, J. A., Keiler, M., &Weingartner, R. (2018). Flood riskevolution: Disentangling key drivers of flood risk change with aretro-model experiment. Science of the Total Environment,639,195–207. 13. De Bruijn, K.M., M. Mens& F. Klijn (2009). A method for developing longterm strategies for flood risk management. In: Samuels, P., S. Huntingdon, W. Allsop& J. Harrop (eds., 2009). Flood Risk Management: Research and Practice. Taylor & Francis Group, London. pp.793–80.

282  Sustainable Development Practices Using Geoinformatics 14. Klijn, F., van Buuren, M., & van Rooij, S. A. (2004). Flood-risk management strategies for an uncertain future: living with Rhine River floods in the Netherlands?AMBIO: A Journal of the Human Environment, 33(3), 141–147. 15. Schanze, J., (2007). Flood risk management research – from extreme events to citizens involvement. IOER, Dresden. pp. 112–121. 16. Tulane University, Dept. Earth & Environmental Sciences, Natural Disasters; https://www.tulane.edu/~sanelson/Natural_Disasters/introduction.htm. 17. United Nation – Space based information for Disaster Management and Emergency Response;http://www.un-spider.org/about/what-is-un-spider 18. United Nations Office for Disaster Risk Reduction Report 2017; https://www. unisdr.org/. 19. Tripathi, G., Parida, B. R., & Pandey, A. C. (2019). Spatio-Temporal Rainfall Variability and Flood Prognosis Analysis Using Satellite Data Over North Bihar during the August 2017 Flood Event. Hydrology, 6(2), 38. 20. Tripathi, G., Pandey, A. C., Parida, B. R., & Kumar, A. (2020). Flood Inundation Mapping and Impact Assessment Using Multi-Temporal Optical and SAR Satellite Data: A Case Study of 2017 Flood in Darbhanga District, Bihar, India. Water Resources Management, 1–22. 21. Bhatt, T.A. (2014). Analysis of Demand and Supply of Water in India. Journal of Environment and Earth Science, Vol., 04 No. 11. pp.67–73. 22. Gujrat State Disaster Management Authority Flood Report, 2017; http:// gsdma.org/Content/flood-4220. 23. Rawat, Mukesh. 2019. “When skies rained death & destruction: India suffered Rs 3,78,247.047 cr loss due to floods in 65 yrs” India Today, August 27, 2019. 24. NIDM, GSDMA: A case study of Gujrat Flood 2017; https://ndma.gov.in/ images/guidelines/gujrat-flood-study-2017.pdf.

18 Environmental Impacts by the Clustering of Rice Mills, Ernakulam District, Kerala State L. Vineetha and T.S. Lancelet* Department of Geography, Sree Sankaracharya University of Sanskrit, Kalady, India

Abstract

Food processing industries play a major role in the economic development of a country. Due to rapid growth of population and industrialization, the nature of operation of these industries, especially of paddy processing industries, underwent a gradual change. This, in turn, affected the stability of environment leading to serious damage to the ecology. However, the impact is not sudden, but in due course of time, it has devastating effects on our environment. The main issue of pollution had turned up after the modernization of mills which, in turn, had changed the mode of operation in the rice mills. The method “parboiling” which is commonly used is one of the major reasons for the serious impacts created in the environment. However, in developing countries majority of the rice processing units have installed effluent treatment plants for treating out waste water, many of them still neglect to systematically function it due to its high cost. In Kerala, Ernakulam district stands first with maximum number of modern rice mills. This study analyzes the locational factors how favored in rice mill clustering in Ernakulam. The environmental problems were identified through field survey and house hold survey in the select panchayats of Kalady, Okkal, and Koovappady. The physio-chemical analysis of waste water effluent carried out revealed the organic and inorganic presence of the pollutants and its extent. Keywords:  Husk, parboiling, hand pounding, soaking, rice mills

*Corresponding author: [email protected] Shruti Kanga, Varun Narayan Mishra, and Suraj Kumar Singh (eds.) Sustainable Development Practices Using Geoinformatics, (283–300) © 2021 Scrivener Publishing LLC

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284  Sustainable Development Practices Using Geoinformatics

18.1 Introduction The economic development of any nation depends upon the growth of industries. It increases national income and thereby per capita income also hikes. Moreover, it creates employment opportunities and especially contributes to the growth of cottage and small-scale industries in rural areas. In countries which are agrarian in nature, industrial development boosts the growth of agro-based industries, thus encouraging more production in agriculture. The tertiary sector, i.e., trade, transport and communication, insurance, banking, etc., will develop with the growth of industries. Furthermore, industrial development will lead to the expansion of markets and emergence of new industrial areas. Rice processing industries are one of the biggest agro processing industries in India. Many centuries ago different processing equipment existed in Indian homes. The oldest method of rice processing was “hand pounding method” and was a group of different types of traditional equipment used. The hand pounding method of milling resulted in medium polished rice having high concentration of thiamine content. The only disadvantage of this method was the breakage of grains that was more compared to machine milling. The real need of modernization of mills started in India during late 50s when our country faced acute food shortage. Machines began to be imported by Indian government, and since, it was a capital intensive procedure, and the government encouraged the private millers by providing them with credit facilities. With the increased production of paddy, there was a tremendous growth of modern rice mills in the country.

18.2 Environmental Pollution and Rice Processing Industries Environmental pollution is one of the burning issues in today’s world, affecting a major part of the world population. According to World Health Organization (WHO), around 25% of deaths in developing countries are due to various environmental factors. Due to rapid industrialization and urbanization large-scale ecological impacts have been noticed in our country and it is increasing at an alarming rate. There is an immediate requirement of taking up adequate policy measures by the stake holders to combat pollution on various global scales. They not only pollute the environment but also pose serious health issues to the population. Bio systems are totally affected by various organic and inorganic pollutants which are emitted by industries based on their on their categories.

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18.3 Study Area Ernakulam district is located in the central part of Kerala extending latitudinally between 9° 47’13’’ and 10° 10’44’’N and longitudinally between 76° 10’05’’ and 77° 05’24’’E (Figure 18.1). The district is divided into two revenue divisions 76°27'10"E

76°29'11"E

76°31'12"E

76°33'13"E 10°13'42"N

10°13'42"N

76°25'9"E

LOCATION MAP KALADY, KOOVAPPADY, OKKAL PANCHAYATS N E

Kalady

10°11'41"N

10°11'41"N

W

Neeleshwaram Panchayat

S

10°9'40"N

10°9'40"N

Koovappady

Mudakuzha Panchayat Ockal

Roads Periyar River ockkal Panchayat

Arabian sea

du il Na Tam

10°27'10"N

W

E S

0 20 40 74°38'10"E

80

120

75°48'15"E

9°17'5"N 9°46'10"N

160 Miles 76°58'20"E

78°8'25"E

N W

Thrissur

E S

Idukki

n sea Arabia

Ernakulam District

ERNAKULAM DISTRICT

10°6'30"N 10°16'40"N 10°26'50"N

N

a

9°56'20"N

tak

9°46'10"N

11°37'15"N

rna

koovappady Panchayat

Alappuzha 0 5

Kottayam 10

20

30

40 Miles

9°36'0"N

KERALA

Ka

kalady Panchayat

76°29'11"E 76°31'12"E 76°33'13"E 78°8'25"E 76°12'0"E 76°22'10"E 76°32'20"E 76°42'30"E 76°52'40"E 77°2'50"E 10°27'10"N 11°37'15"N 12°47'20"N 9°56'20"N 10°6'30"N 10°16'40"N 10°26'50"N

76°27'10"E 76°58'20"E

76°25'9"E 75°48'15"E 74°38'10"E

Rayamangalam Panchayat

9°36'0"N

12°47'20"N

Perumbavoor Municipality

9°17'5"N

10°7'39"N

10°7'39"N

Kanjoor Panchayat

76°12'0"E 76°22'10"E 76°32'20"E 76°42'30"E 76°52'40"E 77°2'50"E

Figure 18.1  Location map. Prepared by authors using toposheets, Kerala 1:50,000. Source: Toposheet by Survey of India (1968).

286  Sustainable Development Practices Using Geoinformatics and seven taluks. The Kochi division has its headquarters at Kochi, and it includes Aluva, Paravur, Kochi, and Kanayannurtaluks with 71 villages. Three panchayaths are selected for the detailed study of the research problem. The panchayaths selected are Kalady, Koovappady, and Okkalpanchayaths. The Kalady Panchayath lies in Aluvataluk, and Koovappady and Okkalpanchayaths lie in Kunnathunad Taluk of Ernakulam district. Considering block wise administration, Koovappady and Okkalpanchayaths come under Koovappady block, and Kalady Panchayath comes under Angamaly block.

18.4 Methodology and Review of Literature The Industrial Development Report 2016 titled, “Industrial Development Report 2016”, addresses about the conditions under which technology and innovation achieves industrial development. From a social point of view, industrialization truly indicates the Human Development Index’. M. Sokol (2011) in his book, Economic Geography details about the different approaches to economic geography, its relevance in the local, regional, and national authorities, international organizations or any planning-related issues. The limitations in the physic-chemical methods and biological methods make Pooja Vaishnav Shrivastava et al. (2011), in their research paper, “Treatment of rice mill effluent for pollution control by Electrocoagulation”, reveals that electrocoagulation is a favorable alternatives for the removal of pollutants from the industrial effluents. The maximum removal of COD (chemical oxygen demand), oil and grease, and turbidity and TSSs (total suspended solids) has been considered in the present investigation. The process variables are pH, current density, and inter space between electrodes. As a part of study, this paper presents effect of pH on removal of COD, oil and grease, turbidity and TSS. Chia-Lin Chang (2008), in his paper, “Industrial agglomeration, geographic innovation and total factor productivity: The case of Taiwan”, analyzes the impact of geographic innovation on total factor productivity (TFP) in Taiwan in 2001 using 242 fourdigit standard industrial classification (SIC) industries. The study started off with a pilot survey and interaction with the local folks. Both primary and secondary sources were used for the survey. The historical growth of rice mills were clearly understood by the secondary data sources available from 1990 when the mills turned modernized. The locational factors were analyzed, and thus, the factors favoring the concentration were identified. The environmental problems were identified through field survey and house hold survey. The physio-chemical analysis of waste water effluent carried out revealed the organic and inorganic presence of the pollutants and its extent.

Environmental Impacts by the Clustering of Rice Mills  287

18.5 Spatial Distribution of Rice Mill Clustering Rice is the staple food of more than half of the world’s population and its demand had always increased in demand with the increase in population. The estimated rice requirement of Kerala is about 7,500 tons per day while production is totally insufficient to meet its daily requirement. The rest of the paddy is being imported from states like Tamil Nadu, Andhra Pradesh, and Karnataka. In Kerala, Palakkad, and Alappuzha districts are considered as the “rice bowls”, but much of the rice mills are concentrated in Ernakulam district which is located at the central part of the state. The District wise distribution of rice mills in Kerala is shown by Figure 18.2. There are no mills in Kollam, Pathanamthitta, Idukki, Kozhikode, Malappuram, Kannur, and Kasargode districts. District wise number of modern rice mills in Kerala is given in Table 18.1. In Alappuzha district, the major reason for the absence of mills is the non-availability of saline free water which is the prime requisite for setting up of the rice processing units. Palakkad and Ernakulam have the maximum concentration of mills due to several factors which includes availability of fresh water and leveled terrain. Both the districts are situated in two wide river basins of Bharathapuzha and Periyar rivers respectively. Demographically, Ernakulam district turns to be a feeder for central and south districts where population is much higher and Palakkad district acts as the feeder for the northern districts of Kerala. Taluk wise distribution of rice mills in Ernakulam is given by Table 18.2. Ernakulam district stands first with maximum number of modern rice mills in Kerala. There are seven taluks in the district, namely, Aluva, Kanayannur, Kochi, Kunnathunadu, Kothamangalam, Muvattupuzha, and Paravur. Aluva and Kunnathunadtaluks have the most concentration of mills as Periyar river flows through its boundary so maximum availability of fresh water source for paddy processing. Some mills even though modern, practice traditional sun drying method and this require around 80,000 ft2 of leveled land. Along with that, space has to be allotted for storing of paddy, installation of machinery including place for boiler and chimney. The flat river basin provided by the Periyarriver offers excellent terrain for the processing of paddy. This is one of the main reasons why the rice mills clustered around the stretch of River Periyar. The study area includes three panchayaths, namely, Koovappady, Kalady, and Okkalpanchayaths, where the mills are more concentrated. The numbers of mills are more aligned toward the eastern and south eastern part of the area, since together, the two panchayats offer a broad land area for the setting up of rice mills (Figure 18.3). Moreover, as Periyar river

75°24'40"E

75°54'50"E

76°25'0"E

76°55'10"E

77°25'20"E

77°55'30"E

Kasaragod

N W

12°3'50"N

12°34'0"N

KERALA DISTRICT WISE DISTRIBUTION OF RICEMILLS

E S

Kannur

11°33'40"N

11°33'40"N

Wayanad

11°3'30"N

Kozhikode

11°3'30"N

13°4'10"N

74°54'30"E

12°3'50"N

12°34'0"N

13°4'10"N

288  Sustainable Development Practices Using Geoinformatics

Malappuram

10°33'20"N

10°33'20"N

Palakkad

Thrissur

Tamilnadu 10°3'10"N

10°3'10"N

Arabian Sea Ernakulam

9°33'0"N

Alappuzha Pathanamthitta

No. of ricemills

9°2'50"N

9°2'50"N

9°33'0"N

Idukki Kottayam

0 Kollam

1 3 30

0 10 20

40

60 74°54'30"E

75°24'40"E

75°54'50"E

60

80 Miles 76°25'0"E

8°32'40"N

8°32'40"N

2 Thiruvananthapuram

76°55'10"E

77°25'20"E

77°55'30"E

Figure 18.2  District wise distribution of rice mills in Kerala. Prepared by the authors.

borders the panchayaths, it is much more evident that water also plays an important determinant factor for the clustering of the rice mills. The quality of land also determines the location of rice mills. A flat, undulating slope is always preferred for its concentration. One of the main processes is drying of paddy which is done both manually and

Environmental Impacts by the Clustering of Rice Mills  289 Table 18.1  District wise number of modern rice mills in Kerala (2016). District

Area (km2)

Population

No. of Mills

Thiruvananthapuram

2,192

3,307,284

2

Kollam

2,491

2,629,703

-

Pathanamthitta

2,637

1,195,537

-

Alappuzha

1,414

2,121,943

2

Kottayam

2,208

1,979,384

2

Idukki

4,358

1,107,453

-

Ernakulam

3,068

3,279,860

60

Thrissur

3,032

3,110,327

3

Palakkad

4,480

2,810,892

30

Malappuram

3,550

4,110,956

-

Kozhikode

2,344

3,089543

-

Wayanad

2,131

816,558

1

Kannur

2,966

2,525637

-

Kasargode

1,992

1,302,600

-

Total

38,863

33,387,677

100

Source: Rice Mill Owners Association, 2016.

Table 18.2  Taluk wise distribution of rice mills in Ernakulam (2015). Taluk

Number of Rice Mills

Aluva

45

Kanayannur

2

Kochi

1

Kunnathunad

20

Kothamangalam

4

Muvattupuzha

5

Paravur

3

Source: RMO (Rice Mill Owner’s Association), 2015.

290  Sustainable Development Practices Using Geoinformatics mechanically. Sun drying is the most common process which is traditionally practiced, and it requires a well leveled terrain. Along with that, space has to be allotted for storing of paddy, installation of machinery, etc. The flat river basin of Periyar provides excellent terrain for the processing. The

76°12'0"E

76°22'10"E

76°32'20"E

76°42'30"E

76°52'40"E

77°2'50"E

10°26'50"N

10°26'50"N

TALUK WISE DISTRIBUTION OF RICE MILLS IN ERNAKULAM DISTRICT

N

W

E

10°16'40"N

S

10°16'40"N

Thrissur Thrissur

10°6'30"N

Aluva 10°6'30"N

Kothamangalam Paravur

9°56'20"N

Idukki

Kochi

Alappuzha

Muvattupuzha

9°46'10"N

Kanayannur

9°36'0"N

Kottayam

No. of ricemills 1

9°36'0"N

Arabian sea

9°46'10"N

9°56'20"N

Kunnathunad

0 10 20

40

60

3

Periyar River

80 Miles

Taluk boundary

9°27'50"N

9°27'50"N

2 4 5

9°17'40"N

45

76°12'0"E

76°22'10"E

76°32'20"E

76°42'30"E

76°52'40"E

77°2'50"E

9°17'40"N

20

Figure 18.3  Taluk wise distribution of rice mills in Ernakulam (2015). Prepared by the Authors based of the data by industrial development corporation.

Environmental Impacts by the Clustering of Rice Mills  291 study area includes three panchayaths where the concentration of mills is maximum (Figure 18.4). The numbers of mills are more aligned toward the eastern and south eastern part of the area, since two panchayaths offer a broad land area for the setting up of mills. Moreover, Periyar river borders the three panchayaths which favors the setting up of mills.

76°26'14"E

76°26'16"E

76°30'18"E

76°32'20"E

KOOVAPPADY, KALADY AND OKKAL PANCHAYATS LOCATION OF RICE MILLS

10°14'36"N

10°14'36"N

76°24'12"E

N

E S

10°12'34"N

10°12'34"N

W

Koovappady

Mudakuzha Panchayat

Ockal

10°6'30"N

10°10'32"N

Kalady

10°4'26"N

Rayamangalam Panchayat

10°6'26"N

Perumbavoor Municipality

10°4'26"N

10°6'26"N

kanjoor Panchayat

kalady Panchayat 0 0.5 1

2

3

4 Miles

10°6'30"N

10°10'32"N

Neeleshwaram Panchayat

ockal Panchayat koovapady Panchayat Rice mills

76°24'12"E

76°26'14"E

76°26'16"E

76°30'18"E

10°2'24"N

10°2'24"N

Drainage

76°32'20"E

Figure 18.4  Locations of rice mills. Prepared by the Authors on the basis of GPS data.

292  Sustainable Development Practices Using Geoinformatics

18.6 Parboiling Process and Characteristics of Rice Mill Effluents As the demand for rice increased as a result of increase in population, many mills have been converted to modern or high tech mills which, in turn, increased the environmental pollution. The main process done in the mills is parboiling process. It is a three-tier process namely soaking, steaming, and drying. When paddy grains undergo parboiling, the starch gets gelatinized which improves the hardness of the rice there by increasing the milling quality and minimize the grain breakage. The pollution rate is comparatively much higher in parboiling process. Over long duration of time, the effluent cause irreversible changes in the soil and underground water sources. However, it does not contain any toxic elements but contains a high concentration of organic and inorganic substances causing pollution. Moreover, the parboiling process requires enormous quantity of water which ultimately leads to the exploitation of underground water resources.

18.7 Description of Rice Mills Taken for Assessing the Impact on Environment For assessing the impact on environment, two major clusters were identified and studied. The largest rice mill cluster was identified in Koovappady Panchayath, in Kunnathunad Taluk, and another cluster in Okkal Panchayath in Aluva Taluk of the district.

18.8 First Model Cluster This is the largest rice mill cluster identified in terms of production capacity and area. The cluster consist of seven mills including the most modern sophisticated one and it spreads over an area of 62 acres of land in the panchayath. The cluster has a production capacity of 650 tons of paddy per day and two shifts of parboiling are carried out every day. Over 2,000 families are directly affected by the pollution problems from rice mill clusters.

18.9 Overutilization of Groundwater Resources Parboiling and soaking require enormous quantity of water and for running a huge cluster of rice mills it requires approximately 3 lakh liters of water per day. As stated above the mill process about 650 tons of paddy and two sets of

Environmental Impacts by the Clustering of Rice Mills  293 Table 18.3  Figure showing requirement of water and the amount of effluent generated for a single shift of parboiling. Amount of Paddy Processed (metric ton)

Water Requirement (approx.) (in liters)

Effluent Generated (approx.) (in liters)

650

300,000

65,000

Table 18.4  Anticipatory figure showing requirement of water and amount of effluent generated for four shift of parboiling process. Amount of Paddy Processed (metric ton)

Water Requirement (approx.) (in liters)

Effluent generated (approx.) (in liters)

650 × 4

300,000 × 4

65,000 × 4

parboiling are carried out. There is clearly an over utilization of underground water resources. The people residing in the close vicinity of the mill faced acute water scarcity and they complained about their wells getting dried up. It was revealed from the rice mill owners that a single set of parboiling generates a huge amount of effluent and also water requirement is more (Table 18.3). This is the case of the biggest cluster where two shifts of parboiling are done. From the above approximate values given by the mill owners clearly reveal about the demand of water requirement for the paddy processing and also about the amount of effluent generated. There is an over exploitation of the ground water sources in the area, especially it should be noted that four shifts of parboiling is being done together in both the clusters and an anticipatory figures have been calculated (Table 18.4) and so the need of water requirement is higher than it is given above and the effluent is also more being generated.

18.10 Physio-Chemical Analysis of Rice Mill Effluent From Second Model Cluster The second model cluster is situated in the Okkal panchayath of Aluvataluk and it spreads over 5 acres of land. It is bordered by Periyar river in the north and Koovappady panchayath to its east. The major issue identified here is the draining of effluents into a nearby which opens up at Periyar river at its far end (Figure 18.5). The wells get polluted by the mixing of effluent emitting a foul smell. Henceforth, a sample of 1-liter effluent was collected to analyze about the extent of pollution it caused. The effluent collection points and the test

294  Sustainable Development Practices Using Geoinformatics 76°27'10"E

76°29'11"E

76°31'12"E

76°33'13"E

EFFLUENT COLLECTED POINT IN OKKAL PANCHAYAT

10°13'42"N

10°13'42"N

76°25'9"E

N E

10°11'41"N

10°11'41"N

W S

Neeleshwaram Panchayat

5

6 7

kalady Panchayat

2

4 3 16

11 19

7 10

16

15

18 17

12

Kanjoor Panchayat 14

15 14

13

10°5'38"N

Perumbavoor Municipality

10°7'39"N

11

10°7'39"N

Mudakuzha Panchayat

12

8

13

Rayamangalam Panchayat

Effluent Collected point

1.6

2.4

Rice mills Rice mill Clusters

Miles 3.2

Drainage Koovappady panchayat Okkal Panchayat

10°3'37"N

0 0.4 0.8

76°25'9"E

76°27'10"E

10°5'38"N

15

20

6 5

2

9

10

10°9'40"N

1

76°29'11"E

76°31'12"E

10°3'37"N

10°9'40"N

3

1 Ockal

8

4

76°33'13"E

Figure 18.5  Effluent collected points in Okkal Panchayath (field survey).

analysis details are examined in Table 18.5. It is being compared with the ISI standard limits for the discharge of effluent, respectively.

18.10.1 pH Value It is one of the important tests used as the quality of the water and the nature of treatment that should be given all depends on the pH value.

Environmental Impacts by the Clustering of Rice Mills  295 Table 18.5  Physio-chemical analysis of rice mill effluent from second model cluster. ISI Limit for Discharge of Industrial Effluent On Land for Irrigation

Test result (Laboratory Analysis)

Sl. No

Parameters

Inland Surface Water

1

pH

5.5–9.0

5.5–9.0

3.92

2

Color, Hazen

5–25

5–25

90

3

Odor

Absent

Absent

Objectionable

4

Total Dissolved Solids, mg/L

2100

2,100

1,300

5

Chloride, mg/L

1000

600

282.87

6

Sulphate, mg/L

1000

1,000

96

7

Calcium, mg/L

75–200

75–200

66.56

8

Potassium, mg/L

–

–

550

9

Sodium, mg/L

60

60

65

10

TSS

100

100

450

11

BOD

30

100

1,519

12

COD

250

–

1,843

Source: Physio-chemical analysis done by the authors, 2017.

pH value (hydrogen ion concentration) indicates the acidic or basic nature of the sample tested at a given temperature. It is largely determined by the equilibrium state of bicarbonate, carbonate, or carbon dioxide. The pH value from 0 to 7 are likely to be acidic, 7 to 14 alkaline and 7 indicates neutral. The sample of effluent tested shows pH value shows 3.92, indicating the highly acidic nature of the water. It is because of a higher concentration of hydronium ions than hydroxide ions. Any kind of organic or inorganic concentration affects the pH value range and affects the hydrogen ion concentration in the solution.

296  Sustainable Development Practices Using Geoinformatics

18.10.2 Color (Hazen) Color is another important indicator of water quality and is done with the help of a comparator. Pure water usually show light blue color when light is transmitted at a depth and can get disturbed due to the presence of any organic matter. In that case, it exhibits green, greenish blue, brown, or yellow. Industrial waste often shows very unusual colors. It is the standard visual color scale recommended by APHA. It is also referred as “PtCo” (Platinum Cobalt Color) as it is based on chloroplatinate solutions. It ranges from 0 (water white) to 500 (parts per million of platinum cobalt to water). Hazen is used for the same color scale as it was first defined by the chemist Allen Hazen. When referred as “Hazen color”, the range is often above 500 units. It basically indicates the yellow tintness in water and in the test conducted it shows 90 units.

18.10.3 Total Dissolved Solids/TSSs The filterable or non-filterable residue left over after evaporation and drying at a given temperature is referred as “solids”. It includes “total suspended solids” and “total dissolved solids”. It affects the water quality in a number of ways. Mainly, it includes inorganic and traces of organic salts either in ionized or non-ionized form. The inorganic materials include calcium, magnesium, sodium, and potassium, which is in the form of nitrates, carbonates, chlorides, bicarbonates, and sulphates. When the dissolved solids in water are more than 500 ppm, palatability of water decreases and may cause gastro intestinal irrigation (Park and Park, 1980). In the water sample tested, the result shows 1,300 mg/L which is under permissible level. TSSs include both organic and inorganic matters larger than two microns and it may be any kinds of sediment or silt. In the case of industrial effluent, it can be chemical precipitate as well. In the effluent sample, the TSS shows a higher reading of 450 which is much above the permissible level of 100 as per ISI limits. This will lead to an increase in water temperatures and decrease dissolved oxygen levels as these particles tend to absorb more heat. The surface temperature of water naturally increase and it doesn’t get mixed up with lower layers. Since respiration and decomposition process occurs mainly at lower layers, it becomes hypoxic and makes it almost impossible for the organisms to survive.

Environmental Impacts by the Clustering of Rice Mills  297

18.10.4 Chloride and Sulphate Chloride content in water can be attributed to the inclusion of salt deposits or effluent from industrial units. Mainly high levels of chlorine content are found in coastal areas. In the effluent, the chloride levels are under permissible levels of 282.87 mg/L as against 600 mg/L for irrigation and 1,000 mg/L for surface water. High concentration of sulphate indicates higher rate of organic pollution. It results from sludge from industries and mainly results from anaerobic concentration of organic matter. The sulphate concentration of analysed effluent is under tolerance level of 96.0 mg/L.

18.10.5 Potassium Potassium is an important component in plant and human nutrition. It is present in groundwater due to mineral dissolution. It is widely used as a distillery effluent and is used widely used for irrigation. Here, in the analysis, the limit is maximum (550 mg/L). As such increased levels do not pose much serious problems.

18.10.6 Bio-Chemical Oxygen Demand Biological oxygen demand indicates the presence of organic matters. It is the food source of bacteria which break down it into still less complex organic substances such as carbon dioxide and water. The bacteria will multiply and grow making use of dissolved oxygen in the water for the breakdown and the level of dissolved oxygen level considerably decreases and water will turn anaerobic. Aquatic organisms face great threat when BOD levels are high. In the effluent analyzed, the BOD rates shows considerably higher levels (1,519 mg/L) when the permissible limit where 30 in surface water and 100 for irrigational purposes.

18.10.7 Chemical Oxygen Demand Chemical oxygen demand is another important water quality parameter. It is required to break down and oxidize organic matter in water. It actually determines the impact created in the environment by the discharged effluent. If the COD level is greater, it indicates higher amount of oxidizable organic matter which will reduce dissolved oxygen levels. The levels of COD show higher levels in the effluent analysis (1,843 mg/L) when the permissible level is 250 mg/L.

298  Sustainable Development Practices Using Geoinformatics From the physico-chemical analysis carried out of the rice mill effluent, it is evident that it is highly acidic in nature with higher amounts of COD and BOD which reveals the presence of huge amounts of organic matters. The untreated effluent therefore pollutes the water sources in the area, leading to many kinds of diseases to the residents in the vicinity of the mills.

18.11 Conclusion Food processing industries contribute to the overall development of the countries globally. Due to the over population, many countries are forced to increase the production and generally overlook the serious impacts on environment. Based on the study carried out, it is clearly evident that the rice mills are one of the most polluting sectors among other industries in terms of volume of discharge and effluent composition. They can be considered as the “silent polluters” of our environment. Being the staple food of the population shutting down of such industries is not at all a practical solution for the problem but finding alternative ways to tackle the environmental issues is the need of the hour as slowly and gradually it can cause severe damage to our environment and livelihood.

References Alagh Y. K., Regional Aspects of Indian Industrialisation, Bombay University Press, 1972. Ambily CB, Rajathy S, John S, “Impact of rice mills on ground water quality at Koovappady, Ernakulam, Kerala, India”, Journal of Zoology Studies.; 3(3):37– 40, 2016. APHA. “Standard Methods for the Examination of Water and Wastewater”, 21st edition, American Public Health, 2005. Bath KS, Kaur H. “Crustacean population in relation to certain physic chemical factors at Harike reservoir”, Punjab J.Engtl Ecology;15(7) :345-347,1997 BaiswarajKumnoar, Industrial Location and Regional Development I in Backward Areas, Oxford Book Company; 2007. Carson, D., Cromie, S., McGowan, P. and Hill, J, “Marketing and Entrepreneurship in SMEs: An Innovative Approach”, Hemel Hempstead: Prentice Hall, 1995. Lin Chang, “Industrial agglomeration, geographic innovation and total factor productivity”, 2008. M. Sokol, Economic Geography, Economic management, Finance and Social Sciences, University of London, 2011.

Environmental Impacts by the Clustering of Rice Mills  299 World Health Organization (WHO), “Guideline for Drinking Water Quality”, Geneva. 2004. World Health Organization (WHO), “Guideline for Drinking Water Quality”, Geneva. 2004. Weber, Translated by Friedrick C., Theory of Location of Industries, University of Chicago, P.139. 1929.

19 GIS-Based Investigation of Topography, Watershed, and Hydrological Parameters of Wainganga River Basin, Central India Nanabhau Santujee Kudnar

*

C. J. Patel College Tirora, Gondia, Maharashtra, India

Abstract

The present study highlights the importance of the Digital Elevation Model (DEM) and satellite images for assessment of drainage and extraction of their relative parameters for the Wainganga River watershed area. The hydrological parameters such as drainage analysis, topographic parameters, and land-use patterns were evaluated and interpreted for watershed management of the area. This has been done using the software of the Arc map module in Arc GIS 9.3 and ERDAS Imagine 9.2 satellite image analysis. In the topographical study of the Wainganga River Basin (WRB), the total calculated mountain area is 10.56%, the plateau region is 33.92%, and the plain region covers 55.51%. While, the area height break-up indicates that the 2.77% of the area is above 880 m, 28.51% of the area is the range 480 to 281 m, while 46.61% of the area is below 280-m height. The watershed area analysis of the WRB shows that watershed area is about 49,949.48 km2. The Wainganga River has 26 tributaries out of which 14 are on left while 12 are on its right bank. The hydrological investigation shows that the climate of the basin is characterized by hot summer from March to May followed by a rainy season from June to September. The post-monsoon season is also observed in the month of October. The annual mean rainfall range varies from 1,000 to 1,400 mm. However, a maximum of annual mean rainfall is found to be 1,830.50 mm at Shivni and a minimum of 1,000.07 mm at Sitekasa. Keywords:  Wainganga River Basin, DEM, topography, watershed area, river hydrology

E * mail: [email protected] Shruti Kanga, Varun Narayan Mishra, and Suraj Kumar Singh (eds.) Sustainable Development Practices Using Geoinformatics, (301–318) © 2021 Scrivener Publishing LLC

301

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19.1 Introduction Watershed is defined as an area that drains water into a river or other body of water and considered as a major ingredient in managing water resources. To carry out management strategies related to water resources the relevant systems in the watershed must be considered. Modeling has become one of the most powerful tools for watershed management in the last decades (Zhang et al., 2016; Ahmad and Pandey, 2018; Aslam et al., 2020; Rajasekhar et al., 2020; Pathare and Pathare, 2020; Nassimand Munjed, 2008). A drainage basin is an area of land where precipitation collects and drains off into a common outlet, such as into a river, bay, or other body of water. The drainage basin includes all the surface water from rain runoff, snowmelt, and nearby streams that run downslope toward the shared outlet, as well as the groundwater underneath the earth’s surface. Other terms used interchangeably with drainage basin are catchment area, catchment basin, drainage area, river basin, and water basin (Xu et al., 2020; Walega et al., 2016; Rajasekhar et al., 2019; Yadav et al., 2016). The catchment is the most significant factor determining the amount or likelihood of flooding. Catchment factors are topography, shape, size, soil type, and land use (paved or roofed areas). Catchment topography and shape determine the time taken for the rain to reach the river, while catchment size, soil type, and development determine the amount of water to reach the rivers (Rogers, 1982; Kudnar, 2020: Kudnar and Rajashekhar, 2020). Generally, topography plays a big part in how fast runoff will reach a river. The rain that falls in steep mountainous areas will reach the primary river in the drainage basin faster than flat or lightly sloping areas (e.g., >1% gradient). Land use can contribute to the volume of water reaching the river in a similar way to as that of clay soils (Barrow, 1998: Al-Abed et al., 2005; Al-Abed and Al-Sharif, 2008; Elmahdy et al., 2016). For example, rainfall on roofs, pavements, and roads will be collected by rivers with almost no absorption into the groundwater. Such water management studies are important for protecting the limited water resources because at most of the places, surface water resources are rare, and at some places, it is totally absent (Sreedevi et al., 2009). Recently, researchers are using automatic terrain analysis based on an SRTM-DEM for geomorphological research (Ehsani et al., 2010; Bisen and Kudnar, 2013 a, b, 2019). They have highlighted the importance of SRTM-DEM resolution on terrain, landscape, and soil analysis. While, hydrological simulation models interfaced with Geographical Information Systems (GIS) were examined by Texak

GIS-Based Investigation  303 et al., (2014) and Al-Abed et al. (2005). According to them, GIS interfaced hydrological models were considered as a major tool for surface water management at a watershed scale because they are capable of presenting the relationship between the spatial and hydrological features of the watershed in an efficient way. The present study is focused on the analysis of the topography, watershed, and hydrological investigation of the Wainganga River. To achieve this, we have prepared different thematic layers by using GIS with the aid of ArcGIS software. This includes Physical Map, Contour Map, Digital Elevation Map, Geological Map, Watershed Map, Land Use Map, and Average Annual mean rainfall. The current study assessment of various morphological and hydrological parameters of the Wainganga Basin in India by applying geoprocessing methods such as the Arc map module in ARC GIS 9.3 and ERDAS imagine 9.2., all parameters were computed mathematically to analyze the characteristics of different morphological and hydrological parameters for sustainable development and planning of the river basin.

19.2 Study Area The Wainganga River is one of the major tributaries of the Godavari River. The Wainganga River rises at El 640.0 m near village Partabpur (21°57’N and 79°34’E) about 20 km from the town of Satpura plateau and flows in a wide half-circle, bending and winding among the spurs of the hills from the west to the east of the Seoni District of Madhya Pradesh. (Figure 19.1). The total river basin area is 49949.48 km2. while the latitude extension is 19°30’N to 22°30 N’ and the longitude extension is 79°00’E to 80°30 E’. The total length of the Wainganga River is 638.91 km, of which 270.2 km lies in Madhya Pradesh. It then travels 32 km along the border between Madhya Pradesh and Maharashtra, while the remaining 336.17 km lies in Maharashtra (Kudnar, 2015 a, b, 2017; Paranjpye, 2013).

19.3 Methodology The topographical data is obtained from a one-inch topographic map of Survey of India (1:63,360 or 1:250,000) with the help of toposheets no. 55J, 55K, 55N, 55O, 55P,56M, 64B, 64C, 64D, and 65A. Using these sheets, we

304  Sustainable Development Practices Using Geoinformatics 30°0'0"N

GODAVARI BASIN Ba gh

d ar W ha

10°0'0"N

90°0'0"E

220

0

jra

ri ba

an

Indravati ava ri

Sa

njra

God

220 Km

74°12'0"E

78°18'0"E

82°24'0"E

20°0'0"N

20°40'0"N

21°20'0"N

22°0'0"N

WAINGANGA BASIN

16°36'0"N

80°0'0"E

980 KM

M

22°40'0"N

70°0'0"E

0

Penganga

a

ava ri

rn Pu

God

Ma

980

20°42'0"N

Wainganga

20°0'0"N

INDIA

Legend: Elevation in meter High : 1139 20

Low : 73 78°0'0"E

78°40'0"E

79°20'0"E

80°0'0"E

0

20 Km

80°40'0"E

Figure 19.1  Wainganga study area.

have carried out various classification and analysis which includes sorting of data, digitization of various layers, preparation of maps, statistical analysis, and other GIS/RS techniques. Using WGS 84 datum, Universal Transverse Mercator (UTM) help of SOI topographic maps were georeferenced zone 44N projection in ArcGIS desktop 9.3. In this study, the

GIS-Based Investigation  305 Wainganga River Basin (WRB) was delineated and the drainage network was extracted using Cartosat-DEM (1 arcsec) in conjunction with SOI toposheets, GPS location, river hydrology including inflows, R-R Model, Regression Equation, sediment load material are calculated. After completion of DEM, the flow direction was calculated for each pixel, to generate a drainage network, the flow accumulation was taken into account, based on the flow direction of each cell.

19.4 Results and Discussions 19.4.1 Physiographical Regions Area The Wainganga basin’s total calculated Mountain Region is in 10.56% and it is expanded in 5,276.01 area km2. In mountain areas including the south part of Mandala district, Chhindwara and Seoni District occupies the southeastern portion of the Satpura Range and the upper valley of the Wainganga River. The Tamia hills are the highest 1,148 m (3,765 ft) height above the Mean Sea Level point in the study area. Another second-highest point the Khamla is 1,137 man msl in the entire country and forms the part of Gwagarh hills. The Vindhyan Range up to Katangi, Kaimur Range, northern and western portions include the plateaus of Lakhnadon, the eastern section consists of the watershed and elevated basin of the Wainganga, and in the southwest is a narrow strip of rocky land known as Dongartal (Figure 19.2). The Wainganga basin’s total calculated plateau region is 33.92%, and it is expanded in 16,944.26 km2 area. Physiographically, the plateau region has been divided broadly into two main geomorphic units. The Wainganga River’s basin total calculated plain region covers 55.51% area, i.e., 27729.22 km2. The south lowlands and slightly undulating plain are comparatively well cultivated and drained by the Wainganga River and its tributaries. A number of subrivers and Tributary in the piedmont plateau region have developed narrow cultivated land (Radecki-Pawlik et al., 2017; Bridge, 2003; Kudnar, 2018, 2019).

19.4.2 Absolute Relief It is a maximum elevation of a unit area. Commonly, the absolute relief is used in the delineation of terrain morphology, which throws light on the structural and erosional characteristics of the region. The Tamia hills are the highest 1,148 m (3,765 ft) height above the Mean Sea Level point in the

306  Sustainable Development Practices Using Geoinformatics

20°40'0"N

21°20'0"N

22°0'0"N

22°40'0"N

Physiographic Regions

Legend: 20°0'0"N

Wainganga Basin Region’s Categories Mountain Region [Above 600m] Platue Region [300 to 600m] 20 10 0

Plain Region [Below 300m]

78°0'0"E

78°40'0"E

79°20'0"E

80°0'0"E

20 Km

80°40'0"E

Source: 1) Cartosat DEM 2009 SR 2.5 meter 2) Survey of India [SOI] Scale 1:250000

Figure 19.2  Physiographical regions.

study area. In the phase of the morphometric analysis, the area has been divided into five altitudinal zones. In a topographical analysis of the WRB, the area height break-up indicates that the 2.77% of the area is above 880 m, 28.51% of the area is the range 480 to 281 m while 46.61% of the area is below 280 m (Figure 19.3). The absolute relief analysis comprises of profiles and area height relations, for which all types of profiles, hypsographic curves, altimetric frequency, and spot-height frequency histograms have been separately drawn.

19.4.3 Digital Elevation Model Relief is a base of landscape and is one of the main factors in its development. Topography (Figure 19.4) influences the migration and

20°40'0"N

21°20'0"N

22°0'0"N

22°40'0"N

GIS-Based Investigation  307

Legend: Wainganga Basin Contour in meter

20°0'0"N

Above 880 681 – 880 481 – 680 281 – 480 Below 280 78°0'0"E

78°40'0"E

20 10 0

79°20'0"E

80°0'0"E

20 Km

80°40'0"E

Source: 1) Survey of India [SOI] Toposheets Scale 1:250000

Figure 19.3  Contour map.

accumulation of substances moved by gravity along the land surface and in the soil, microclimatic and hydrological characteristics, soil formation, and vegetation cover properties. Topography is an indicator of geological structures, particularly faults, which can control mineral deposits, seismic foci, and may affect soil and plant characteristics. In this connection, during the last two decades, the use of digital terrain modeling has become an important trend in geosciences. Currently, digital terrain modeling is widely used to solve various multiscale problems of geomorphology, hydrology, remote sensing, soil science, geology (Figure 19.5), geophysics, geobotany, glaciology, oceanology, climatology, planetology, and other disciplines. Digital terrain modeling is the science of quantitative modeling and analysis of the topographic surface and relationships between topography and other natural and artificial

20°40'0"N

21°20'0"N

22°0'0"N

22°40'0"N

308  Sustainable Development Practices Using Geoinformatics

Legend: 20°0'0"N

Wainganga Basin Elevation in meter High : 1139 Low : 73 78°0'0"E

78°40'0"E

20 10 0

79°20'0"E

80°0'0"E

20 Km

80°40'0"E

Source: 1) Cartosat DEM 2009 SR 2.5 meter 1) Survey of India [SOI] Scale 1:250000

Figure 19.4  Digital Elevation Models (DEM).

components of geosystems. Methods of digital terrain modeling are scale independent. They can be used at a broad range of spatial scales (detailed, field, catchment, regional, continental, and global scales). By digital terrain models (DTMs) we mean digital representations of topographic (morphometric) variables describing the topographic surface. The following types of morphometric variables are used in landscape investigations.

19.4.4 The WRB Catchment Area The Wainganga has the main 26 tributaries, of which 14 are on its left bank and 12 are on the right bank. Among these rivers, left Bank Rivers are Sagar, Nahar, Deo, Son join the Wainganga in Madhya Pradesh as it flows through Seoni and Balaghat district. While, Bagh, Chulband, Gadhavi, Satti, Tipagsrhi, Khobragarhi, Pal, Kathani, Phuar, and

22°40'0"N

GIS-Based Investigation  309

Czl

γPt1 KI

Czl

Czl

γPt1m

Pt1s

βKPgd

λPt1n γPt1d Pt12k

βK3Pgd C?P1gt Czl

Pt12c αPt1n

Czl Q KI Pt3pg KI Pt3pg PTgk C?P1gt Q Pt3pg λPt1n Pt3pg PTgk

λPt1nb

20°40'0"N

Pt1s

βK3Pgd

γPt1d

Agn 20 10 0

78°40'0"E

79°20'0"E

80°0'0"E

20 Km

80°40'0"E

Source: 1) Geological Survey of India [GSI] Scale 1:2000000

Legend: Fault Lithology

21°20'0"N

Agn

Pt1s Pt1sa

78°0'0"E

22°0'0"N

βKPgd

20°0'0"N

Pmo

Pmo

λPt1nb

λPt1n

Agn

Pt12c

αPt1n

C?P1gt Czl

Pt12k Pt1s

βK3Pgd βKPgd

KI

Pt1sa

γPt1

PTgk

Pt3pg

γPt1d

Pmo

Q

γPt1m

Figure 19.5  Wainganga River geological map.

Pohar join the Wainganga in Maharashtra as it flows through Gondia, Bhandara, and Gadchiroli district. Furthermore, Bagh (at Birsola, 283 m above MSL), Chandan, and Bawanthadi (at Bapera, 275 m above MSL) join the Wainganga on the borders of Madhya Pradesh and Maharashtra. Right bank rivers which are 12 in number are named Hira, Pench, Kanhan, Chandan, Bawanthadi, Sur, Ambi, Mari, Haman, Pathari, Mal, and Andhari. These rivers join the Wainganga in Madhya Pradesh as it flows (Figure 19.6) through Betul, Chhidwarah, Seoni district, and Maharashtra, as it flows through Nagpur, Bhandara, and Chandrapur districts (Gosainand Rao, 2004; Charlton, 2007; Robert, 2014) (Table 19.1).

22°40'0"N

310  Sustainable Development Practices Using Geoinformatics

Sagar

Pench

Hira

22°0'0"N

Wainganga

Nahar

Chandan

Deo

Bawanthadi

Son

Sur

21°20'0"N

Kanhan Bagh

20°40'0"N

Chulbund Ambi

Gadhavi

Maru

Satti Tipahsrhi Khobragarhi Pal Pathari Kathani

Andhari Mal

Phuar Pohar

20 10 0

78°0'0"E

78°40'0"E

79°20'0"E

20°0'0"N

Haman

80°0'0"E

20 Km

80°40'0"E

Figure 19.6  Wainganga River sub-basin.

19.4.5 Land Use Pattern The major land use categories in the Wainganga River’s basin include buildup land (1.80%) and agricultural land (17.77) that comprises of generally Kharif, rabi, and double-crop system in the region. Forest cover (62.07%) comprises of Dense forest 16.05%), Space vegetation (20.01%), Open Scrub (26.01%), and recent plantations (Table 19.2). Deciduous or Dense forest largely spreads out in the region in the east of the all WRB area. Water Bodies (1.62%), Barren Land (8.65%), Fallow Land (5.36%), Gravel Land (0.34%), and Rocky Land or Open Space (2.38%) can also be found in the region (Figure 19.7).

GIS-Based Investigation  311 Table 19.1  Wainganga River Basin watershed area. Sr. No.

River Basin

Area (km2)

Sr. No.

River Basin

Area (km2)

1

Ambi

830.37

15

Maru

727.90

2

Andhari

1,223.88

16

Nahar

877.37

3

Bagh

2,938.72

17

Pal

276.23

4

Bawanthadi

2,161.79

18

Pathari

514.29

5

Chandan

1,145.29

19

Pench

4717.86

6

Chulbund

2,537.22

20

Phuar

429.91

7

Deo

840.32

21

Pohar

874.55

8

Gadhavi

1,557.23

22

Sagar

1,065.46

9

Haman

2,078.26

23

Satti

830.85

10

Hira

1,017.77

24

Son

1,428.96

11

Kanhan

7,640.26

25

Sur

1,004.24

12

Kathani

932.73

26

Tipagsrhi

796.49

13

Khobragarhi

200.52

27

Wainganga

1,1160.66

14

Mal

140.39

Grand Total

49,949.48

19.4.6 Hydrology Most of the rainfall occurs between Junes to October. The remaining months are usually dry. The annual mean rainfall ranges from a maximum of 1,830.50 mm at Shivni to a minimum of 1,000.07 mm at Sitekasa. However, it mostly varies from 1,000 to 1,400 mm. The 95%, 90%, 75%, 60%, 50%, and mean rainfall at these stations are near about 850, 950, 1,100, 1,250, 1,300, and 1,331 mm, respectively. The areas receiving 75% dependable precipitation below 600 mm is classified as Drought prone for irrigation purposes as per Central Water Commission (CWC) New Delhi

312  Sustainable Development Practices Using Geoinformatics Table 19.2  The land use pattern of the Wainganga sub-basin. Sr. No.

Land use/Land Cover Category

Area (km2)

Total Geographical Area (%)

1

Dense Forest

8,018.55

16.05

2

Space Vegetation

9,994.97

20.01

3

Open Scrub

12,995.75

26.01

4

Agriculture

8,876.70

17.77

5

Settlement

897.19

1.80

6

Water Bodies

809.10

1.62

7

Barren Land

4,318.96

8.65

8

Fallow Land

2,676.20

5.36

9

Gravel Land

171.09

0.34

10

Rocky Land/Open Space

1,190.96

2.38

49,949.48

100

Grand Total

guide lines and accordingly the Wainganga sub basin is not drought-prone. The isohyets of 1,500 mm pass the river parallel where the Dam-toe Power Station is proposed. Gridded rainfall data of 0.5 × 0.5° and 1 × 1 resolution was analyzed to study long term temporal and spatial trends on annual and seasonal scales in WRB located in Central India during 1901–2013. The amount, intensity, and areal distribution of precipitation are essential in many hydrological studies. The total amount of precipitation, which reaches the ground in a stated period, is expressed as the depth to which it would cover in liquid form on a horizontal projection of the earth’s surface (WMO, 1983). All observations of rain are taken in India at 08:30 hours IST to ensure standardization and inter-comparison of rainfall from different rain gauge stations. The rainfall measured at 08:30 hours on any particular date is entered against that date and it is understood that the rainfall so registered has been received 24 hours proceeding 08:30 hours of the day of observation (Leopold et al., 1964; Thorndycraft et al., 2008).

20°0'0"N

20°40'0"N

21°20'0"N

22°0'0"N

22°40'0"N

GIS-Based Investigation  313

Legend: Dense Forest

Water Bodies

Sparce vegetation

Barren Land

Open Scrub

Fallow Land

Agriculture

Gravel Land

Settlement

Rocky land/Open space

78°0'0"E

78°40'0"E

20 10 0

79°20'0"E

80°0'0"E

20 Km

80°40'0"E

Source: Landsat-8 OLI & TIRS SR 30 meter [Dec. 2013]

Figure 19.7  Land use map of Wainganga sub-basin.

19.4.6.1 Inflows The received stage gauge and discharge data at Kardha, Pawani, Lakhandur, Bamni, Bhimkund, and Ashti have been analyzed every month for the rainy season June and October. The summary of such analysis is presented in Table 19.3. Surface water is available through flowing streams and rivers and used directly or through constructed storages or natural lakes.

19.4.6.2 Rainfall-Runoff Modeling The inflow can also refer to the average volume of incoming water in unit time. It is contrasted with the outflow. Inflow is mostly used when referring to rivers and the amount of water in units that enter the country. The monsoon run-off in the Wainganga Rivers sub-basin is given by Table 19.4.

314  Sustainable Development Practices Using Geoinformatics Table 19.3  Inflows. Inflows (mm3) Station

Parameters

June

July

August

September

October

Kardha GDS

Maximum Minimum Mean STD Cv

128.85 25.85 69.30 41.70 0.60

2,189.98 28.96 367.87 526.96 1.43

1826.61 234.44 584.64 428.95 0.73

1,058.40 37.71 407.64 336.56 0.83

297.11 58.83 135.74 86.10 0.63

Pawani GDS

Maximum Minimum Mean STD Cv

54.33 2.63 22.25 17.56 0.79

234.72 15.13 111.87 68.01 0.61

351.02 111.17 203.11 69.25 0.34

612.03 20.68 184.56 164.32 0.89

125.39 12.98 44.75 35.79 0.80

Bamni GDS

Maximum Minimum Mean STD Cv

108.27 53.95 74.72 41.54 0.56

455.15 70.41 197.58 244.44 1.24

329.59 178.05 247.49 200.27 0.81

149.19 25.23 80.60 181.10 2.25

77.15 59.01 68.21 120.56 1.77

Lakhandur GDS

Maximum Minimum Mean STD Cv

147.06 2.71 48.17 66.93 1.39

1219.30 21.33 323.55 332.31 1.03

1275.46 106.91 473.32 301.44 0.64

1239.64 16.30 316.59 299.31 0.95

204.99 4.80 69.37 60.86 0.88

Asti GDS

Maximum Minimum Mean STD Cv

237.29 3.11 62.36 81.04 1.30

761.89 38.91 355.62 239.64 0.67

771.71 346.62 539.98 161.34 0.30

815.20 65.63 357.39 265.09 0.74

166.72 27.96 95.85 60.16 0.63

Bhimkund GDS

Maximum Minimum Mean STD Cv

666.76 13.32 143.03 257.39 1.80

264.43 61.98 153.39 92.24 0.60

470.93 79.10 270.99 150.12 0.55

298.17 56.58 161.64 98.72 0.61

63.22 24.12 45.04 19.69 0.44

GIS-Based Investigation  315 Table 19.4  Average observed (monsoon) runoff at CWC sites in the Wainganga Rivers sub-basin (1996–2001). Sr. No.

Name of the Site

Name of the River

Catchment Area (km2)

Runoff (Cu. Km.) June–Nov

1 2 3 4 5 6 7 8 9

Satrapur Ramkona Rajagaon Kumhari Keolari Rajoli Wairagarh Salebardi Pauni

Kanhan Kanhan Bagh Wainganga Wainganga Mul Khobragarthi Chulband Wainganga

11,100 2,500 5,380 8,070 2,960 0.97 1,900 2,600 1,800 35,520

2.23 0.83 2.27 3.12 0.97 0.65 0.72 0.54 9.53

Source: Central Water Commission (CWC), Information System Organization (ISO) (Hydrology Data Directorate)

19.5 Conclusion The Wainganga River is one of the most important tributaries of the Godavari River. From the topographical study of the WRB, it can be concluded that the total calculated mountain area is 10.56%, the plateau region is 33.92%, and the plain region covers 55.51%. The watershed area analysis reveals that watershed area is about 49,949.48 km2 and has 26 tributaries, while the hydrological investigation shows that the climate of the basin is characterized by hot summer from March to May followed by a rainy season from June to September. The annual mean rainfall range varies from 1,000 to 1,400 mm. These topographical, watershed, and hydrological parameters are of great importance for the water management of the WRB. These parameters may also provide a platform for transferring water resources for creating irrigation facilities. These parameters may also be used for various purposes like constructing damps, canals, forest management, and eco-tourism.

Abbreviations WRB: Wainganga River Basin RS: Remote Sensing GIS: Geographic Information System DEM: Digital Elevation Model

316  Sustainable Development Practices Using Geoinformatics

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GIS-Based Investigation  317 L. B. Leopold, M. G. Wolman, J. P. Miller. Fluvial Processes in Geomorphology. San Francisco: Freeman, pp. 39–56, 1964. M. Rajasekhar, S. R. Gadhiraju, A. Kadam, et al., Identification of groundwater recharge-based potential rainwater harvesting sites for sustainable development of a semiarid region of southern India using geospatial, AHP, and SCS-CN approach. Arab J Geosci, pp. 13–24, 2020. M. Rajasekhar, R. G. SudarsanaR. SiddiRaju, Assessment of groundwater potential zones in parts of the semi-arid region of Anantapur District, Andhra Pradesh, India using GIS and AHP approach. Model. Earth Syst. Environ. 5, pp.1303–1317, 2019. M. Zhang, F. Yang, J. X. Wu, Z. W. Fan, Y. Y. Wang, Application of minimum reward risk model in reservoir generation scheduling. Water Resources Management, 30(4), pp. 1345–1355, 2016. N. Ahmad, P. Pandey, Assessment and monitoring of land degradation using geospatial technology in Bathinda district, Punjab, India. Solid Earth 9(1): pp. 75–90, 2018. N. Al-Abed, F. Abdullah, A. Abu Khyarah, GIS-hydrological models for managing water resources in the Zarqa River basin. Environ Geol 47: pp. 405–411, 2005. N. S. Kudnar, Linear aspects of the Wainganga river basin morphometry using geographical information system. Mon Multidiscip Online Res J Rev Res 5(2): pp. 1–9, 2015. N. S. Kudnar, Morphometric analysis and planning for water resource development of the Wainganga river basin using traditional & GIS techniques. University Grants Commission (Delhi), pp. 11–110, 2015. N. S. Kudnar, Morphometric analysis of the Wainganga river basin using traditional & GIS techniques. Ph.D. thesis, RashtrasantTukadojiMaharaj Nagpur University, Nagpur, pp 40–90, 2017. N. S. Kudnar, Water pollution a major issue in urban areas: a case study of the Wainganga river basin. VidyawartaIntMultidiscip Res J 2: pp. 78–84, 2018. N. S. Kudnar, Impacts of GPS-based mobile application for tourism: A case study of Gondia district, VidyawartaIntMultidiscip Res J 1: pp.19–22, 2019. N. S. Kudnar, GIS-based assessment of morphological and hydrological parameters of Wainganga river basin, Central India. Model. Earth Syst. Environ, pp. 1–18. 2020. N. S. Kudnar, M. Rajasekhar, A study of the morphometric analysis and cycle of erosion in Waingangā Basin, India. Model. Earth Syst. Environ. 6, pp. 311– 327, 2020. N. Al-Abed & M. Al-Sharif, Hydrological modeling of zarqa river basin – jordan using the hydrological simulation program – fortran (HSPF) Model Water Resources Management Volume 22, Issue 9, pp. 1203–1220, 2008. P. D. Sreedevi, P. D. Sreekanth, H. H. Khan, S. Ahmed, Drainage morphometryand its influence on hydrology in a semi-arid region: using SRTM data and GIS. Environ Earth Sci 70, pp. 839–848, 2013.

318  Sustainable Development Practices Using Geoinformatics R. O. Charlton, Fundamentals of Fluvial Geomorphology, Routledge, pp. 22–280, 2007. S. K. Yadav,  A. Dubey,  S.  Szilard, S. K. Singh, Prioritization of sub watersheds based on earth observation data of agricultural dominated northern river basin of India. GeocartoInternational. 33(4), pp. 339–356, 2016 S. I. Elmahdy,  M. M.  Marghany,  M. M. Mohamed, Application of a weighted spatial probability model in GIS to analyse landslides in Penang Island, Malaysia. Geomat Nat Hazards Risk, 7, pp. 345–359. 2016. V. Paranjpye, A Master Plan for Integrated Development and Management of Water Resources of Wainganga Sub- Basin. pp. 10–37, 2013. V. R. Thorndycraft, Gerardo Benito, and K. J. Gregory, Fluvial Geomorphology: A Perspective on Current Status and Methods, Geomorphology 98.1–2; pp. 2–12, 2008. W. F. Rogers, (1982): Some characteristics and implications of drainage basin linearity and non-linearity Journal of Hydrology Volume 55, Issues 1–4, pp. 247–265, 1982.

Index 0.5 × 0.5° and 1 × 1 resolution, 312 Agro horticulture, 99 Agroforestry, 100 Analytical Hierarchy Process (AHP), 112 ANFIS, 51 AOT, 23 ArcGIS, 304 Ariel aspect, 65 Barasat, Kolkata, 4 Bharathapuzha and Periyar rivers, 287 Bifurcation ratio, 63 Biodegradable, non-biodegradable, 103 Bistatic scatterometer system, 49 Build Back Better, 250 Cartosat and LISS 4, 89 Cartosat-DEM, 132, 305 Chemical oxygen, 297 Classification, 232 Climate change, 73, 206 Climate resilience, 243 Climate resilient housing, 250 CO2 and CH4, 75 Coal mining, 24, 142 Component, 210 Component of flood response, 266 Concentric development, 10 Confusion matrix, 6, 236 Correlation, 12 Cyclone, 244

Desiltation, waste water management, 101 Digital Elevation Model (DEM), 156 Digital Terrain Models (DTMs), 308 Disaster, Epidemic, COVID-19, 254 Disaster risk management, 257 Disaster risk reduction, 255 Dissolved solids, 295 Distribution of LST, NDVI, 12 Drainage density, 58 Drainage network, 156 Drones, 274 Dry mole fractions, 80 Effluent, 283 Environmental pollution, 284 Environmental stress, 16 Fani, 244 Flood response measures, 266 Forest health, 145 Form factor, 69 Geographic Information System (GIS), 23, 113 Geographical Information System (GIS), 129 Geology features, 220 Geotagging of utilities facilities, 90 GI Science, 153 GIS/RS techniques, 304 Gomati river watershed, 155 GPS, 305 Gravimetric moisture content, 50

319

320  Index Greenhouse gases, 74 Greenhouse Gases Observing Satellite (GOSAT), 74 Grid partition–based adaptive neuro-fuzzy inference system (G-ANFIS), 49 Ground water, 216 Ground water label, 218 Ground Water Prospect Map, Rajiv Gandhi National Drinking Water Mission, 94 Groundwater Potential Index (GWPI), 223 Hand pounding, 284 Hazard, 25, 134 Incidence angle, 51 Indicators, 209 Installation, 287 Intergovernmental Panel on Climate Change (IPCC), 206 Kappa, 236

Kappa statistics, 6 Land capability classification, geotagging of village amenities, 96 Land Surface Temperature, 3 Land use and land cover (LULC), 185, 232 Land use/land cover, 23 Landsat 5, 189 Landsat 5, Landsat 8, 4 Landsat 8, 189 Landsat 8 OLI, 115 Landslide vulnerability zonation (LVZ), 134 Landslides, 128 Linear aspect, 60 Linear pattern, 10 Literacy rate, 36 Livelihood, 206

Livelihood vulnerability, 206 Livelihood vulnerability index (LVI), 211 LST, 7 LU/LC mapping, 219 LULC, 8 LULC change analysis, 190 LULC classes, 190 LULC statistics, 192 Maximum of 1,830.50 mm at Shivni, 311

Maximum Likelihood, 6

Membership function (MF), 51 Micro level planning, 85 Minimum of 1,000.07 mm at Sitekasa, 311 Morphometric analysis, 59, 154 Morphometric parameters, 156 Multi-criteria decision-making (MCDM), 111 Multi-Criteria Evaluation (MCE), 113 Multi-hazard resistant, 251 Multi-temporal, 186 Native floras, 145 NDVI, 7 Non structural measures, 260 Paddy processing, 283 Pairwise Comparison Matrix (PWCM), 119 Parboiling, 283 Pardhan Mantri Gram Sadak Yojana, NREGA, 107 Partabpur, 303 Particulate matter, 24 Physio-chemical, 283 Planck’s Function, 7 Polarizations, 49 Poor planning, 16 Population density, 23 Post-monsoon, 190

Index  321 Potato crop, 112 Pre-monsoon, 190 Producer’s accuracy, 236 Profile, 210 PWV, 25 Rain water harvesting, 170 Relief aspect, 64 Remote sensing, 25 Remote sensing and GIS, 2, 216 Resourcesat-1, 132 Rice mill clustering, 297 Risk, 23 Root Mean Squared Error (RMSE), 53 R-R model, 305 RS & GIS, structural measures, 258 Saline free water, 287 Sansad Adarsh gram, 87 Satellite images, 187 Satpura plateau, 303 Scattering coefficient, 51 Slope, 217 Smart village, GIS and GPS techniques, 86 Socio-economic, 39 Soil, 147, 221 Soil moisture (SM), 48 Subsidence, 142 Suitability, 116

Tamia hills, 305 Tapi basin, 57 Temperature, 25 Thematic layer, 135 Tolerance level, 297 Total worker, 29 Transition matrix, 236 Trend delineation, 16

Tri-junction, 6

UHI, 12 Underground mining, 142 Urban Heat Island, 3 Urban sprawl, 2 Urban Sprawl pattern, 10 User’s accuracy, 236 USGS, 5 Vegetation cover, 10 Vulnerability, 23, 207 Vulnerability assessment, 208 Wainganga river, 303 Water, 169 Water audit, 169 Water demand assessment, 175 Water resources, 170 Water storage, 178 Water supply department, 172 Watershed, 153, 302 Weightage Index, 135 Western Ghats, 130

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