Geo-intelligence for Sustainable Development (Advances in Geographical and Environmental Sciences) 9811647674, 9789811647673

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Advances in Geographical and Environmental Sciences

T. P. Singh Dharmaveer Singh R. B. Singh   Editors

Geo-intelligence for Sustainable Development

Advances in Geographical and Environmental Sciences Series Editor R. B. Singh, University of Delhi, Delhi, India

Advances in Geographical and Environmental Sciences synthesizes series diagnostigation and prognostication of earth environment, incorporating challenging interactive areas within ecological envelope of geosphere, biosphere, hydrosphere, atmosphere and cryosphere. It deals with land use land cover change (LUCC), urbanization, energy flux, land-ocean fluxes, climate, food security, ecohydrology, biodiversity, natural hazards and disasters, human health and their mutual interaction and feedback mechanism in order to contribute towards sustainable future. The geosciences methods range from traditional field techniques and conventional data collection, use of remote sensing and geographical information system, computer aided technique to advance geostatistical and dynamic modeling. The series integrate past, present and future of geospheric attributes incorporating biophysical and human dimensions in spatio-temporal perspectives. The geosciences, encompassing land-ocean-atmosphere interaction is considered as a vital component in the context of environmental issues, especially in observation and prediction of air and water pollution, global warming and urban heat islands. It is important to communicate the advances in geosciences to increase resilience of society through capacity building for mitigating the impact of natural hazards and disasters. Sustainability of human society depends strongly on the earth environment, and thus the development of geosciences is critical for a better understanding of our living environment, and its sustainable development. Geoscience also has the responsibility to not confine itself to addressing current problems but it is also developing a framework to address future issues. In order to build a ’Future Earth Model’ for understanding and predicting the functioning of the whole climatic system, collaboration of experts in the traditional earth disciplines as well as in ecology, information technology, instrumentation and complex system is essential, through initiatives from human geoscientists. Thus human geosceince is emerging as key policy science for contributing towards sustainability/survivality science together with future earth initiative. Advances in Geographical and Environmental Sciences series publishes books that contain novel approaches in tackling issues of human geoscience in its broadest sense—books in the series should focus on true progress in a particular area or region. The series includes monographs and edited volumes without any limitations in the page numbers.

More information about this series at http://www.springer.com/series/13113

T. P. Singh · Dharmaveer Singh · R. B. Singh Editors

Geo-intelligence for Sustainable Development

Editors T. P. Singh Symbiosis Institute of Geoinformatics Symbiosis International (Deemed University) Pune, India

Dharmaveer Singh Symbiosis Institute of Geoinformatics Symbiosis International (Deemed University) Pune, India

R. B. Singh Department of Geography University of Delhi New Delhi, India

ISSN 2198-3542 ISSN 2198-3550 (electronic) Advances in Geographical and Environmental Sciences ISBN 978-981-16-4767-3 ISBN 978-981-16-4768-0 (eBook) https://doi.org/10.1007/978-981-16-4768-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Earth has witnessed tremendous transformation in its physical climate systems, hydrological cycles and biogeochemical cycles over recent decades due to the anthropogenic activities. This has disturbed the natural balance of the earth and its ecosystems. Anthropogenic activities are the main source of greenhouse gases, which led to global warming and eventually caused global climate change. Human-induced climate change has altered patterns of the rainfall and increased the risk of extreme events, e.g. heat waves, storms, drought, flood, etc. They cause huge loss of lives and extensive damages to properties. Additionally, new developments as a result of population growth are putting pressures on the urban environment, which contribute to the proliferation of slums, water scarcity and pollution of air, soil and water resources. This has brought adverse effects on ecological resources and disturbs the socio-cultural ethos and affects the psychological wellbeing. This presents a difficult challenge to society and the environment. Finding appropriate solutions to these threats/challenges are not simple as these are generally complex and require state-of-the-art technology for the collection and analysis of data. The advent of space-based remote sensing technology has revolutionised the ways of spatial data collection. Satellite data offers a synoptic and global overview of the land, water and air. This promotes an interdisciplinary approach to studying the Earth as a whole. However, with the scientific and technological advancements, a surge has been observed in the data collection instruments/tools. It has increased volume and heterogeneity of the data, and raised concerns for the proper handling and storages of datasets. For the monitoring and assessment of Earth’s related processes, socio-economic development and hazards, the fast retrieval of the data collected from these instruments is required. But, heterogeneity in data, e.g. lack of integration and interoperability has limited our ability to interpret the processes. The issues of integration and interoperability amongst the datasets of the different origins are resolved with the advent of Geo-Intelligence (GI), which encourages the use and assimilation of intricate, multidisciplinary data for providing resolutions to earth science and social sciences based challenges. Geo-intelligence is one of the data-driven high-end geo-computation intelligence, which includes high-end mathematical and statistical algorithms such as Artificial Learning, Machine learning and Deeplearning techniques. This offers intensive v

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pattern recognition, dimensionality reduction, feature engineering, classification and regression from the big datasets, especially remote sensing and GIS datasets. The proposed book ‘Geo-intelligence for Sustainable Development’ explores different dimensions of GI technology in developing a computing framework for locationbased, data integrating earth observation and predictive modelling to address above mentioned issues at all levels and scales for attaining the goals of sustainable development as defined under the Sustainable Development Agenda, 2030. This book provides an insight into the applications of GI technology in different fields of spatial and social sciences and attempts to bridge the gap between them. Pune, India Pune, India New Delhi, India

Dr. T. P. Singh Dr. Dharmaveer Singh Dr. R. B. Singh

Acknowledgements

Journey is easier when you travel together, certainly togetherness is more important than individualism, especially when you are planning to write a book as an editor. Book could never have been edited or written without the colossal help of chapter authors and colleagues. We would like to thank each of the chapter co-authors who contributed their original work given their time and efforts and modified chapters according to the suggestion and discussion during interaction. The book was inspired by the copious, benignities of Hon’ble Dr. SB Mujumdar, Chancellor Symbiosis International (Deemed University) to uplift society through technology and innovation. Editors are indebted to Dr. Vidya Yeravedkar, Prochancellor Symbiosis International (Deemed University), who has always been motivated to the quality work, which can translate towards societal development has made a deep impression on me. She could not even realise how much I have learned from her. It is our privilege to express the deep sense of gratitude to Dr. Rajani Gupte, Vice Chancellor, Symbiosis International (Deemed University). I owe her lots of gratitude to provide support and guidance whenever required during editing and compilation of book. We also thank each and every person of Symbiosis Institute of Geoinformatics, we are greatly indebted to Dr. Navendu Choudhry, Dr. Vidya Patkar, Dr. Sandipan Das, Ms. Darshna Pathak, Dr. Binaya Patnaik, Lt. Col. B. K. Pradhan, who put their effort to provide support during every steps of writing this book and provides invaluable suggestions. We have also benefited greatly from the personal discussion with Dr. P. K. Joshi, Professor JNU, New Delhi, who always ready to respond promptly and generously every time when we require his suggestion and help. I extend my thanks to the publisher, who accepted the contents and topic of the book in a very first interaction and provided support in making this book to publish.

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And now finally we come to our family members who have assisted us in a variety of ways. They have given enthusiastic support during this journey. We have received invaluable help from them, no words will be enough to convey our gratitude to them. T. P. Singh Dharmaveer Singh R. B. Singh

Contents

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Geo-intelligence Role in Sustainable City Missions of the Global South: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sk. Mustak and Sudhir Kumar Singh Cloud-Based Geospatial Mapping and Analysis of Prayagraj Kumbh Mela of India: The UNESCO Intangible Cultural Heritage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sonam Agrawal and Khairnar Gaurav Bapurao Geo-intelligence-Based Approach for Sustainable Development of Peri-Urban Areas: A Case Study of Kozhikode City, Kerala (India) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. P. Nishara, V. Sruthi Krishnan, and C. Mohammed Firoz

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Smart City: Artificial Intelligence in the City of the Future . . . . . . . . Arti Chandani, Om Prakash, Prakrit Prakash, and Mita Mehta

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Geo-Intelligence for Ecosystem Services in Poverty Alleviation and Food Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Faith Njoki Karanja

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Geo-intelligence for Pandemic Prevention and Control . . . . . . . . . . . . Fenzhen Su, Fengqin Yan, and Han Xiao

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Geo-intelligence in Public Health: A Panoptical to COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prasad Pathak, Sharvari Shukla, Sakshi Nigam, and Pranav Pandya

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Use of Remote Sensing Data to Identify Air Pollution Signatures in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 K. N. Sivaramakrishnan, Lipika Deka, and Manik Gupta

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Urban Growth Impact on Cauvery River: A Geospatial Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 J. Brema, Shivam Trivedi, Monica Sherin, Dnyanadev S. Dhotrad, K. Ganesha Raj, and Dipak Samal ix

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10 Artificial Neural Network (ANN)-Based Predictions of Bulk Permittivity of CO2 -Water-Porous Media System . . . . . . . . . . . . . . . . 149 Kazeem O. Rabiu, Luqman K. Abidoye, Lipika Deka, and Diganta B. Das 11 Long-Term Satellite Data Time Series Analysis for Land Degradation Mapping to Support Sustainable Land Management in Ukraine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Mykhailo Popov, Sergey Stankevich, Anna Kozlova, Iryna Piestova, Mykola Lubskiy, Olga Titarenko, Mykhailo Svideniuk, Artem Andreiev, Artur Lysenko, and Sudhir Kumar Singh 12 Modeling of the Mass Balance of Glaciers with Debris Cover . . . . . . 191 Yong Zhang and Shiyin Liu 13 Geo-Intelligence-Based Approach to Investigate Temporal Changes in the Length and Surface Area and Ice Velocity of Sakchum Glacier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 Rakesh Sahu, Dharmaveer Singh, A. S. Gagnon, and P. K. Singh

Editors and Contributors

About the Editors Dr. T. P. Singh is a Professor and Director at Symbiosis Institute of Geoinformatics, Symbiosis International (Deemed University), Pune (India). Prof. Singh has obtained his Master degree in Remote Sensing from Pierre and Marrie Curie University (France) and Ph.D. from Indian Institute of Remote Sensing (India). He has a teaching and research experience of over 20 years in the field of the Geospatial Technology. He has executed several research projects funded by the research organisations of the National and International reputes such as the Indian Space Research Organisation (ISRO), Department of Science and Technology (DST), Ministry of Environment and Forest (MoEF) and Japan International Cooperation Agency (JICA). He has visited several countries as a Geospatial expert and published more than 50 research papers in peer-reviewed journals/conference proceedings. Prof. Singh is a chairman of Indian Society of Geomatics (ISG) Pune Chapter and also a member of the several boards of societies/research agencies and university committees. Dr. Dharmaveer Singh is an Assistant Professor at Symbiosis Institute of Geoinformatics, Symbiosis International (Deemed University), Pune (India). He served in the capacity of a Research Scientist-C at National Institute of Hydrology, Roorkee (India) from 2016 to 2018. Dr. Singh, a Ph.D. in Geoinformatics (2015) from Motilal Nehru National Institute of Technology Allahabad, has been conferred by several academic awards: UGC Early Career Research Grant in 2019; Post-Doctoral Fellowship from Cold and Arid Regions Environmental & Engineering Research Institute, Chinese Academy of Sciences in 2016; and CSIR JRF (NET) from Government of India in 2009 & 2010. His main research interests include water resources management, water security & economics and climate change & modelling. He has published 30 research papers in peer-reviewed journals (SCI & Scopus Indexed) and conference proceedings. He is currently heading couple of research projects sponsored by UGC and is a reviewer for the several scientific journals of the International reputes.

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Dr. R. B. Singh (Ex. Professor & Head of Department of Geography, Delhi School of Economics, Delhi University) is the Secretary-General and Treasurer of the International Geographical Union (IGU); Chair, Research Council, CSIR-Central Food Technological Research Institute, Mysore; Member-Research Council-CSIRCentral Institute of Medicinal and Aromatic Plants, Lucknow; Member of International Science Council, Prestigious Scientific Committee-Health and Wellbeing in Changing Urban Environment-System. He was awarded the prestigious Japan Society for the Promotion of Scientific Research Fellowship and has presented papers and chaired sessions in more than 40 countries. He has published 14 books, 35 edited research volumes, and more than 215 research papers. He has supervised 34 Ph.D. and 79 M.Phil. Students. In 1988 the UNSCO/the International Social Science Council awarded him to research and study grants in social and human sciences.

Contributors Luqman K. Abidoye Chemical Engineering Department, Loughborough University, Loughborough, UK; Civil Engineering Department, Osun State University, Osogbo, Nigeria Sonam Agrawal GIS Cell, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, Uttar Pradesh, India Artem Andreiev Scientific Centre for Aerospace Research of the Earth IGS NAS of Ukraine, Kyiv, Ukraine Khairnar Gaurav Bapurao GIS Cell, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, Uttar Pradesh, India J. Brema Karunya Institute of Technology and Sciences, Coimbatore, India Arti Chandani Symbiosis Institute of Management Studies, Symbiosis International (Deemed University), Khadki, Pune, India Diganta B. Das Chemical Engineering Department, Loughborough University, Loughborough, UK Lipika Deka Faculty of Computing, Engineering and Media, De Montfort University, Leicester, UK Dnyanadev S. Dhotrad Centre for Environmental Planning & Technology (CEPT) University, Ahmedabad, India C. Mohammed Firoz Department of Architecture and Planning, National Institute of Technology Calicut, Kozhikode, India A. S. Gagnon School of Biological and Environmental Sciences, Liverpool John Moores University, Liverpool, UK

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Manik Gupta Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad, India Faith Njoki Karanja Department of Geospatial and Space Technology, University of Nairobi, Nairobi, Kenya Anna Kozlova Scientific Centre for Aerospace Research of the Earth IGS NAS of Ukraine, Kyiv, Ukraine Shiyin Liu Institute of International Rivers and Eco-Security, Yunnan University, Kunming, China Mykola Lubskiy Scientific Centre for Aerospace Research of the Earth IGS NAS of Ukraine, Kyiv, Ukraine Artur Lysenko Scientific Centre for Aerospace Research of the Earth IGS NAS of Ukraine, Kyiv, Ukraine Mita Mehta Symbiosis Institute of Management Studies, Symbiosis International (Deemed University), Khadki, Pune, India Sk. Mustak Department of Geography, Central University of Punjab, Bathinda, India Sakshi Nigam Symbiosis Statistical Institute (SSI), Symbiosis International (Deemed University), Pune, India V. P. Nishara Department of Architecture and Planning, National Institute of Technology Calicut, Kozhikode, India Pranav Pandya Symbiosis Institute of Geo-Informatics, Symbiosis International (Deemed University), Pune, India Prasad Pathak Department of Physical and Natural Sciences, FLAME School of Liberal Education, FLAME University, Pune, India Iryna Piestova Scientific Centre for Aerospace Research of the Earth IGS NAS of Ukraine, Kyiv, Ukraine Mykhailo Popov Scientific Centre for Aerospace Research of the Earth IGS NAS of Ukraine, Kyiv, Ukraine Om Prakash Symbiosis Institute of Computer Studies and Research, Symbiosis International (Deemed University), Pune, India Prakrit Prakash Manipal Institute of Technology, Manipal, Udupi, India Kazeem O. Rabiu Chemical Engineering Department, Loughborough University, Loughborough, UK; Civil Engineering Department, Osun State University, Osogbo, Nigeria K. Ganesha Raj Regional Remote Sensing Centre-South/NRSC/ISRO, Bengaluru, India

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Rakesh Sahu GIS Cell, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India Dipak Samal Centre for Environmental Planning & Technology (CEPT) University, Ahmedabad, India Monica Sherin Karunya Institute of Technology and Sciences, Coimbatore, India Sharvari Shukla Symbiosis Statistical Institute (SSI), Symbiosis International (Deemed University), Pune, India Dharmaveer Singh Symbiosis Institute of Geo-Informatics, Symbiosis International, Pune, India P. K. Singh Water Resources Systems Division, National Institute of Hydrology, Roorkee, India Sudhir Kumar Singh K. Banerjee Centre of Atmospheric and Ocean Studies, IIDS, University of Allahabad, Prayagraj, Uttar Pradesh, India Sudhir Kumar Singh K. Banerjee Centre of Atmospheric and Ocean Studies (KBCAOS), University of Allahabad, Prayagraj, India K. N. Sivaramakrishnan Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad, India V. Sruthi Krishnan Department of Architecture and Planning, National Institute of Technology Calicut, Kozhikode, India Sergey Stankevich Scientific Centre for Aerospace Research of the Earth IGS NAS of Ukraine, Kyiv, Ukraine Fenzhen Su State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China; Commission on Geographical Information Science, International Geographic Union, Beijing, China; Collaborative Innovation Center for the South China Sea Studies, Nanjing University, Nanjing, China; Innovation Academy of South China Sea Ecology and Environmental Engineering, Chinese Academy of Sciences, Guangzhou, China Mykhailo Svideniuk Scientific Centre for Aerospace Research of the Earth IGS NAS of Ukraine, Kyiv, Ukraine Olga Titarenko Scientific Centre for Aerospace Research of the Earth IGS NAS of Ukraine, Kyiv, Ukraine Shivam Trivedi Regional Remote Sensing Centre-South/NRSC/ISRO, Bengaluru, India

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Han Xiao State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China; Collaborative Innovation Center for the South China Sea Studies, Nanjing University, Nanjing, China; Innovation Academy of South China Sea Ecology and Environmental Engineering, Chinese Academy of Sciences, Guangzhou, China Fengqin Yan State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China; Collaborative Innovation Center for the South China Sea Studies, Nanjing University, Nanjing, China; Innovation Academy of South China Sea Ecology and Environmental Engineering, Chinese Academy of Sciences, Guangzhou, China Yong Zhang School of Resource, Environment and Safety Engineering, Hunan University of Science and Technology, Xiangtan, China

Chapter 1

Geo-intelligence Role in Sustainable City Missions of the Global South: A Review Sk. Mustak and Sudhir Kumar Singh

Abstract Sustainable development is the development meeting the needs of the present generation without compromising the ability of future generations to meet their own needs. Essentially, it includes economic, social and environmental components, e.g. health, education, climate change, infrastructure, etc. In order to quantify and measure the progress in terms of sustainable development, 17 Sustainable Development Goals (SDGs) were identified by the United Nations in 2015 under the Sustainable Development Agenda, 2030. These goals have specific sets of measurable indicators and targets. Conventional data are static, outdated and often lack in providing real-time information of these indicators. These are, thus, insufficient and need to be complemented by information gathered via geospatial intelligence for real-time decision-making and sustainable policy interventions. Geo-intelligence is one of the data-driven high-end geo-computation intelligence approached which has recently been increasingly used in urban planning and management. This is because geo-intelligence is sophisticated, with high-end mathematical and statistical algorithms such as machine learning and deep learning. This offers intensive pattern recognition, dimensionality reduction, feature engineering, classification and regression from the big datasets, especially remote sensing and GIS datasets. The objective of this study is to develop a comprehensive literature survey on the use of geo-intelligence in the sustainable city missions in the Global South. This is because the cities in the Global South are confronted with several socio-economic and developing challenges where updated information and high-end technology have rarely been used for sustainable development. This study provides a review of existing background on the use of geo-intelligence in the sustainable city missions, on one hand, and recommends guidelines for geo-intelligence-based sustainable city missions in the Global South on the other.

Sk. Mustak (B) Department of Geography, Central University of Punjab, Bathinda 151001, India S. K. Singh K. Banerjee Centre of Atmospheric and Ocean Studies (KBCAOS), University of Allahabad, Prayagraj 211002, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 T. P. Singh et al. (eds.), Geo-intelligence for Sustainable Development, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-16-4768-0_1

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Keywords Geo-intelligence · Sustainable development · Remote sensing · GIS · Machine learning

1.1 Introduction Sustainable development (SD) is ‘the development meeting the needs of the present generation without compromising the ability of future generations to meet their own needs’ (UN-WCED 1987). It is based on the concept of earth’s carrying capacity and its ability to provide natural resources for ecosystem services for development while maintaining the availability of the same for coming generations. Sustainability and development, individually, are complex concepts with no uniformly accepted definitions. However, both have various common components that are used as a benchmark to measure changes in level of SD. These are economic, social and environmental components such as health, education, climate change, infrastructure, etc. A general consensus is that ‘sustainability’ is humanity’s aim to achieve equilibrium between physical and socio-economic environment, while ‘sustainable development’ is the processes leading to it (Robertson 2012). In order to minimize this ambiguity in development and planning studies, in 2015, the United Nations identified 17 Sustainable Development Goals (SDGs) under the Sustainable Development Agenda, 2030. These goals have specific sets of measurable indicators and targets in order to quantify and measure the progress in terms of SDGs. These provide significant roadmaps to create sustainable cities with respect to different components of city functionality and have even listed sustainable cities’ creation as an individual goal in the form of SDG 11.

1.1.1 Sustainable Cities in Global South The global urban tipping point (more than 50% of world’s population in urban area) was crossed more than a decade ago. At present, urban areas are home to 55% of population and contribute to 80% of GDP globally. This number is expected to reach 60% by 2030 and 70% of the world is expected to be urban by 2050. Since the 1970s, the urban population in Global South has experienced a faster growth rate when compared to Global North. It not only boasts of nearly three-quarters of the world’s urban population, but is also home to most of the world’s largest and fastest-growing cities like Singapore, Mumbai, Dhaka, Rio de Janeiro, etc. (UNPD 2006). Urban areas are the centres of economic growth, social integration and human concentration. But the rate at which these cities are growing has led to the rise of several serious issues like overcrowding, population pressure on land, water and other natural resources, and environmental pollution. These, in turn, have exacerbated environmental and habitatrelated problems (shortage of food and water supply; inadequate sanitation, drainage

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and waste collection services; health challenges and poor access to affordable healthcare) at local, regional and global scale (Robertson 2012; Satterthwaite 2003). As majority of population and economic assets are concentrated in major urban centres, risks and vulnerabilities of people living there increase manifold. In Global South, it is an even bigger concern as it comprises mainly of developing nations where urban development is haphazard and coping capacity is way less. For example, regionally, more than 30% of residents in South Asia and 60% in sub-Saharan Africa live in slums. It is due to these reasons that SD of cities matters, particularly for poor and marginalized sections because of their low capacity to survive generally, which falls down even lower in the face of disasters (UNPD 2006).

1.1.2 Geo-intelligence in Sustainable City Missions Geo-intelligence is a type of the data-driven, high-end geo-computation intelligence which has recently been increasingly used in urban planning and management. This is because it is sophisticated, with high-end mathematical and statistical algorithms such as artificial intelligence, machine learning and deep learning. It offers features such as intensive pattern recognition, dimensionality reduction, feature engineering and classification and regression from the big datasets, especially remote sensing and GIS datasets. Conventional methods of data collection (personal interviews, questionnaire, enumeration, etc.) are static, outdated and often lack in providing real-time information of indicators to sustainable development. These are, thus, insufficient and need to be complemented by information gathered via geospatial intelligence for real-time decision-making and sustainable policy interventions. Hence, this study attempts to develop a comprehensive literature survey on the use of geo-intelligence in the sustainable city missions in the Global South. This is because the cities in the Global South are confronted with several socio-economic and developing challenges where updated information and high-end technology have rarely been used. Also, majority of the research in field of sustainability and its compiled reviews are carried out with respect to requirements of cities and regions of Global North (Nagendra et al. 2018). Thus, this study will provide a review of research carried out in sustainable city development in Global South to provide a comprehensive database on the background on the use of geo-intelligence in the sustainable city missions on one hand, and will attempt to recommend guidelines for geo-intelligence-based sustainable city missions in the Global South on the other.

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1.2 Materials and Methods 1.2.1 Datasets For the purpose of this study, secondary database has been used in the form of official reports (of international organizations like UN-Habitat, United Nations Development Programme, Asian Development Bank, etc.), peer-reviewed research papers (published and unpublished, available in open domain), and news and educational articles. The papers and articles reviewed were selected keeping in mind two criteria, i.e. reviewing papers published after 2000 (except World Commission on Environment and Development’s report titled ‘Our Common Future’, 1987) to ensure that no obsolete information is mentioned in the paper, and that articles and papers with keywords sustainable cities, sustainable development, Global South, geo-intelligence, deep learning, machine learning, artificial intelligence, geospatial intelligence, GIS, GPS and remote sensing are all considered for an elaborate and comprehensive review. Also, an attempt has been made to review the components in isolation as well as their applicability in an integrated form through various case studies.

1.2.2 Study Area The chapter focuses on studying sustainable cities in the Global South and the work carried out in them via using geo-intelligence tools. Although the term ‘Global South’ has been defined in a number of ways since 1970s, it is mainly a geopolitical term referring to the group of developing countries in Latin America, Africa, Oceania and developing part of Asia. Also, it does not represent the geographical southern hemisphere of earth. Used by Carl Oglesby in 1969 for the first time, it became popular in twenty-first century and since then has been used as an alternative to ‘third world’, developing world, newly industrializing, colonized countries and so on. Being a dynamic, non-demarcated region, it is difficult to correctly delineate the study area considering a wide variety of parameters. So, for the purpose of this study, the area identified by UNDP in South–South Cooperation has been studied under the umbrella term of Global South. The countries forming this region aren’t as economically and technologically developed as their northern counterparts, and thus often face issues such as political turmoil, poverty, wars, corruption, conflict, etc. But these have gained significance in geopolitical, scientific and socio-economic studies in twenty-first century due to rapid rate of economic and technological development as a result of South–South Cooperation and North–South Cooperation (Kaul 2013).

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1.2.3 Methods This study attempts to develop a comprehensive review of literature based on geointelligence in the sustainable city missions in the Global South. For this, the chapter has been divided into five sections, namely, introduction, methodology, results and discussions, recommendations, and conclusion. After methodology, section three starts with a brief introduction and background of the concepts of sustainable city and geo-intelligence. Thereafter, different components of geo-intelligence and their applicability in sustainable city development with respect to global south have been studied one by one. It is followed by few case studies from Global South and also applicability of geo-intelligence in disaster response and warning systems. The next segment of the chapter provides a few recommendations that can help enhance the efficiency of geo-intelligence in building sustainability, followed by concluding remarks.

1.3 Results and Discussions 1.3.1 Sustainable City: The Concept Sustainability has been a key concept in regional, national and international discussions for urban planning since the advent of Club of Rome’s Limits to Growth theory and Brundtland commission’s ‘Our Common Future’ report (UN-World Commission on Environment and Development 1987). Thus, from 1970 onwards, the concept of sustainable city was raised in light of growing environmental concerns. It gained prominence post Rio Earth Summit’s Agenda 21 (1992), and UN-Habitat’s ‘New Urban Agenda’ (Habitat-III, 2016) cemented its significance in global discussions on conscious developmental policy-making and debates on sustainability (Lima et al. 2020; Waldrop 2019; Yazdani and Dola 2013). With growing scientific focus on reducing carbon emissions, controlling environmental pollution, creating sustainable living and inclusive cities, sustainable cities are growing in both Global North and South. Some examples of sustainable cities in Global South are Education City in Qatar, Masdar City in Abu Dhabi, New Songdo City in South Korea, etc. Although sustainability and sustainable cities have been discussed by researchers, planners, scientists and governments around the globe even before the term sustainable development was officially defined by Brundtland Commission, till date there’s no globally accepted definition of the same. From the definitions given in the latter part of this section, it is evident that sustainability, in general, and sustainable city, in particular, are broad concepts dealing not only with issues of environmental protection, resource consumption and human development but also with the questions of ‘inter- and intra-generational equity’ (Shaker 2015). Thus, for a city to be categorized as sustainable, certain elements of development must be visible in it. These are energy efficiency and focus on renewable

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energy, sustainable transportation and inclusive mobility, sustainable education and healthcare infrastructure, waste management, sustainable buildings, etc. (Ermolaeva 2017; Sodiq et al. 2019). Sustainable city can be defined as: • Sustainable city as a city balancing economic, ecological and social development with citizen participation. • It is a city where people meet their needs without harming the environment or needs of other living beings, now or in future. • A city where inflow of resources/materials and their disposal or outflow does not exceed the capacity of surrounding environment. • A city attempting to achieve sustainability principles as a sustainable city. Definition of sustainable city by countries/institution: • A sustainable city is one ‘where achievements in social, economic and physical development are made to last’. • According to World Economic and Social Survey (2013), it is city ‘which integrates social development, economic development, environmental management and urban governance’. • European Union (2018) has defined it as a city where urban consumption levels are equal or below what the natural environment can provide without experiencing stress on its resources. • According to World Bank (2020), a city ‘capable of adapting to, mitigating, and promoting economic, social, and environment change’ can be called a sustainable city.

1.3.2 Geo-intelligence: The Concept Geo-intelligence, often used interchangeably with imagery intelligence, is a broad concept incorporating components like imagery intelligence, geographical information system, terrain elevation data, infrared and vector, and even artificial intelligence, machine learning, big data, etc. It is a high-end information system with sophisticated tools that builds on simpler datasets by providing pattern computation, correlation and regression, modelling, forecasting, prediction, and decision-making (Balogan et al. 2019; Dold and Groopman 2017). Sustainability being a qualitative concept was quantified into 17 Sustainable Development Goals (SDGs) by transforming the qualitative norms into 169 quantifiable targets, such as poverty, housing, carbon emissions, mobility, land use, clean water, sanitation, etc. This quantification has resulted in increased significance of geospatial tools and role of geo-intelligence in analysis and policy-building for sustainability.

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Definition of geo-intelligence: • National Geospatial Intelligence Agency, USA defines geo-intelligence as a knowledge base ‘consisting of imagery and spatial information for assessment, description and visual depiction of geo-referenced activities on earth’. • According to Hannay and Baatard (2011), geo-intelligence is a collection of techniques that lead to aggregation, assessment and deduction of information from geo-tagged data by mining and study of its locational aspects.

1.3.3 Components and Role in Sustainable City Development For easier demarcation of tools and techniques, geo-intelligence can be broadly categorized into geospatial techniques and data science techniques. Geospatial techniques are that part of geo-intelligence that provides location based or spatial information with respect to earth. These include remote sensing, Global Positioning System (GPS) and Geographical Information System (GIS). Whereas data science typically focuses on description, prediction and prescription for efficient decision-making, i.e. aggregation of data and its description, prediction of future trends using statistical tools and provides recommendations for the future course of action. Although components of geospatial techniques are also included in it, it is more commonly known as the use of Artificial Intelligence (AI), Machine learning (ML) and Deep Learning (DL). A brief discussion of these techniques and how they work and their role in sustainable city planning is given below.

1.3.3.1

Remote Sensing

Remote sensing refers to collecting information of an object, target or phenomenon from a distance (air-borne, space-borne or ground-borne platforms) using electromagnetic energy sources. It processes, enhances and classifies the collected image for further extraction of information through GIS and is especially useful in tracking temporal changes as it acquires images on a regular basis. Thus, it creates a resource base for potential policy-making, creating a neutral information source for assessing and balancing policies (Acharya and Lee 2019; Kadhim et al. 2016; Wellmann et al. 2020). For example, using SDG 11.1 dealing with urban housing, slums and informal settlements, information can be gathered by using Earth Observation satellites like Landsat-8 and IRS satellites. This information when studied in isolation can be used for real-time observation of study area, comparative study of the area over a period of time and the changes witnesses therein, and when studied in conjunction of machine and data learning tools, it can be used to create a comprehensive dataset for delineating hotspots of various activity types, early warning systems, model building, future estimations, etc. These tools can in turn be used to build and maintain urban climatic, economic and social sustainability (Pfeffer and Georgiadou 2019). For example, similar idea was adopted by climate researchers in ‘Maputo, Mozambique’

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who have built a ‘digital dashboard’ which collects satellite data on weather and climate change. It is being used to acquire data pertaining to various features on the earth and to identify hotspots to disasters, to provide early warning, real-time information of region’s status and help in governance and policy intervention to minimize the impact of disasters (Balogun et al. 2019).

1.3.3.2

Global Navigation Satellite Systems (GNNS)

GNSS is a group of navigation satellites that provides location of users, places, etc. with unparalleled accuracy. Nowadays, a number of navigation satellite systems have been developed by countries like USA, China, India, Russia, etc. (GPS, BeiDou, NAVIC, GLONASS, respectively). These systems provide facilities of navigation, surveying, data capture for further processing and analysis by GIS and are used for geo-tagging, geo-fencing, cellular telephony and several such services for sustainable planning (Acharya and Lee 2019). In case of sustainable city development, GNSS is commonly used to ensure smooth mobility and information of traffic flows as a result of location services provided in form of professional tools as well as via appbased location services in mobile phones, watches, vehicles, etc. For example, its role in maintaining urban sustainability has become clear in 2020 with the advent of COVID-19 and its spread. GNSS-based applications like ‘Aarogya Setu’ have been widely used for getting location of hospitals, contact-tracing, travel information of people, etc. to minimize the spread of the disease. Moreover, food delivery (Zomato, Swiggy, Food panda, etc.) and cab services (Uber, Ola, etc.) have ensured a near seamless shift to the isolation-based new normal.

1.3.3.3

Geographic Information System (GIS)

GIS is a system composed of hardware, software and human component that collects, retrieves, processes, transforms, interprets and stores information gathered from the real world to map and monitor scenarios in real time and if needed builds models for future projections and estimations (Acharya and Lee 2019). GIS is redefined as ‘an organized activity that measures, represents, and transforms geographic phenomenon while interacting with social structures’ (Pfeffer and Georgiadou 2019). GIS has been known to help in the process of scientific research, in general, and sustainable city planning, in particular, in the form of conceptualization of the components to be observed, and their measurements and comparison for a clear picture. For example, SDG 11.1.1 dealing with urban housing, slums and informal settlements can be conceptualized as the proportion of population living in informal housing, categorization of urban population into groups based on the size of house, etc. This contributes to infrastructural and cultural sustainability in the city (Pfeffer and Georgiadou 2019). Similarly, it can be used to study all indicators of city sustainability like transportation, inclusive infrastructure, water and sanitation, sustainable buildings, disaster preparedness, governance and policy-making and others.

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Artificial Intelligence (AI)

AI is defined as any software technology capable of perception, decision-making, prediction, automatic knowledge extraction and recognition of data patterns, interactive communication and logical reasoning. It has a large number of sub-fields including machine learning (ML) and deep learning (Vinuesa et al. 2020). The challenge of large-scale geospatial data fusion to get an elaborate picture of past, present and future geographical scenarios as well as building projections for future changes in case of alternating growth experiences is carried out using artificial intelligence. It helps in building enable smart, low-carbon cities having autonomous electrical vehicles, smart appliances, integrate renewable energy sources, build smart grids, etc. that work across different SDGs such as SDGs 7, 11 and 13 on climate action (Vinuesa et al. 2020). For example, by integrating data science and artificial intelligence with geospatial tools, Israel has developed ‘Neighbourhood 360º’ programme and measuring index that analyses approximately 50 criteria of sustainability including infrastructure, housing, health, education, transportation, resource use, public spaces, etc. It works towards efficient and inclusive planning of cities in Israel to achieve targets of SDG 11 and to provide to its inhabitants prosperous, clean, healthy and good quality neighbourhoods (National Review-Israel 2019).

1.3.3.5

Machine Learning (ML)

Geospatial Artificial Intelligence (Geo-AI) uses machine learning to extract knowledge from spatial data by processing big samples, detection of even minor changes and their classification, providing high-quality information that makes up for missing data, etc. It thus helps in feature extraction, image recognition and data processing. It is being constantly re-evaluated and improved by increasing speed and accuracy of data generation and dissemination of information, especially for emergency response scenarios like climatic disasters. Machine learning tools can be applied to areas such as autonomous mobility, smart city management, augmented building, green buildings and energy management for sustainable city development (Walter et al. 2020). In case of sustainable infrastructure, in particular, machine learning helps in better planning for renewal and re-development, in increasing infrastructure’s operational efficiency, and thereby in reducing its environmental impact. This may be carried out using 3D visualization and BIM software which provide different design alternatives based on the requirement of the region, people and local climatic conditions (D’Amico et al. 2018; Murray 2019). For example, in Indonesia, Ministry of Health is using available satellite communication systems to implement ‘e-health’, i.e. a health management information system to improve healthcare services and maintain efficient workflows. It is a holistic programming covering medical records, surveillance systems, telemedicine, medical research and learning, and consumer grievance redressal. Similarly, India started telemedicine programme in 2001 that provides medical diagnosis and care to patients in remote areas using satellite TVs, Internet and smartphone facilities.

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Deep Learning (DL)

In recent years, machine learning developed significantly to the effect that the problems in modelling that need to be fixed by programmer are now rectified by the machine itself. The technology enabling a machine to train itself and to complete its tasks by developing its own computational methods and new algorithms is known as deep learning. It can be used for a wide range of problems ranging from imagery or object recognition to development of automatic or self-driving systems (Chanda 2019; Li et al. 2016; Madu et al. 2016; Walter et al. 2020). In sustainable city development, developing autonomous vehicles can increase fuel efficiency, reduce emissions, reduce accidents and improve road safety, and reduce traffic congestion; air quality monitoring can provide real-time information for better policy development (UNIDO 2016). For example, Japan is working on autonomous metro rail projects to tackle the decreasing working population size. Tesla has made significant stride in Automatic Vehicles (AVs) and is working on integrating electrical vehicles with AVs to make transportation environmentally sustainable (Chehri and Mouftah 2019). Additionally, Dairi et al. (2019) have also used deep learning methods to study wastewater treatment plants in Saudi Arabia. They found that coupled with machine learning and statistical techniques, deep learning was able to efficiently work towards reducing water scarcity even in cases of abnormal events and provided sustainable solutions. New Songdo IBD City in S. Korea has developed four systems (i.e. KOPSS- Korea Planning Support System, UPIS-Urban Planning Information System, U-City Project and SBDS-Spatial Big Data System) to collect information and provide solutions for sustainable city development under SDG 11, and to provide analytics for urban policies and big data activities (UN-GGIM 2016). In both Global South and North, geospatial techniques coupled with data science have helped in identification and measurement of several indicators of sustainable development. This becomes clear from given examples based on experiences from countries of Global South. In planning and eventual growth and development of their cities towards sustainability, geo-intelligence has been used to map and assess poverty levels, i.e. SDG 1, using remote sensing and machine learning (e.g. Multidimensional Poverty Index and Report, Human Development Index and Report of United Nations). To assess the state of forests and environment by using GIS and deep learning (e.g. State of Forest Report, National Tiger Census, National Bird Census, India); to make projections of food security and crop production levels, i.e. SDG 2, using geospatial tools and artificial intelligence (Agricultural Census and Projections, India; Global Hunger Report, Welt Hunger Hilfe). To predict disasters and build early warning systems, i.e. SDG 13, using data science and geospatial techniques (e.g. Indian Ocean Tsunami Warning and Mitigation System) and to identify hotspots and spreads of diseases, i.e. SDG 3 (e.g. Ebola spread in Africa, COVID pandemic, SARS spread, etc.), build climate models, i.e. SDG 13 (e.g. IPCC AR4 Climate Models; Air Quality Forecasts, India), predict flows of transport and plan for better mobility systems, i.e. SDG 11 (e.g. Transit oriented development, Brazil; Mass Rapid Transit, Japan; etc.) and many more.

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1.3.4 Case Studies To emphasize the role of geo-intelligence in sustainable city missions in Global South, few case studies are discussed below in two different segments. The purpose of first discussion is to give examples of some sustainable cities in Global South and provide information regarding their approach towards sustainability. The second segment focuses on sustainability of a few cities with respect to disasters and gives a brief description of how these cities developed early warning systems specific to their needs and built resilience as well as reduced damages associated with disasters.

1.3.4.1 (a)

(b)

(c)

(d)

(e)

Sustainable Cities

Auroville (Tamil Nadu, India): A sustainable city built on the premise of ecofriendly, organic techniques of production and community participation. It has not only done commendable research in organic farming and sustainable, low waste living but has also uplifted the living standards of locals living in surrounding areas by providing cheap modern and traditional healthcare, vocational training, education, self-employment, etc. (Bhatia 2014). Masdar City (Abu Dhabi, UAE): This city aims to be sustainable in areas of energy consumption and efficiency, renewable energy resources, sustainable technology and sustainable transportation. To achieve these goals, work is being carried out in research and development to reduce waste, reduce carbon footprint, complete pedestrianization of the city, etc. (BBC Bitesize). Singapore is one of the most well-recognized sustainable green cities of Asia. The city has fixed sustainability targets to improve energy efficiency, constructing green buildings, extensive public transit systems by 2030. These are reviewed regularly via sustainable development blueprint developed by the state for this specific purpose. Also, Singapore has started mapping wind energy hotspots, for energy efficiency and reducing carbon emissions, using LIDAR (Sodiq et al. 2019; The climate reality project 2017). Tianjin Eco-city (China) is a Sino-Singapore city with the aim to develop as a ‘socially harmonious, environmentally friendly, and resource-efficient thriving city’. Its objective is to reduce the adverse impact of climate change and thereby focuses on energy efficiency, use of clean, renewable energy, green buildings and transportation, eco-friendly water and waste management, etc. (Koh et al. 2010; Nanjing School 2012). Additionally, China has also developed China Urban Sustainability Index (2011) and measures various sustainability indicators of around 200 cities regularly (European Union 2018). Bogota (Columbia) used GIS and remote sensing to monitor urban transportation and via overlaying it with ML built a supply-oriented model mobility plan (Avtar et al. 2020).

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1.3.4.2

Disaster Management and Early Warning System

Kerala (India) experienced one of the worst floods in the last century in 2018. It not only affected nearly 7 lakh people but also caused a financial damage of more than 20,000 crore to the state. In the immediate aftermath, i.e. for relief and response measures, people and local authorities coordinated using Google Maps (for re-routing transportation, locating hospitals, relief camps, etc.) and social media (sharing videos of damage, flooding events, people stuck in their homes, etc.). UAVs were used to capture and assess the extent of damage caused, and to assist in providing relief packages. These also helped in spreading the news far and wide and attracted massive donations from around the globe. Post-relief and response measures focused on mitigation measures and developing resilience in case of similar events in future. For this, GeoSpoc (a geoanalytics company) collected satellite imagery using Sentinel 1A and 1B satellites carrying Synthetic Aperture Radar (SAR) instruments. This imagery was used to map the extent and magnitude of flooding by building Digital Elevation Models (DEMs) and interactive maps of the affected region. Furthermore, the state government of Kerala also authorized further data collection to mapping zones of flood risks in Kerala and providing an open access to these maps to the public. Using these geo-intelligence techniques, two pillars of early warning system (i.e. knowledge of risk and risk monitoring and warning) have been built in Kerala. The third and fourth pillars (dissemination of information and response capability) are being strengthened by creating an integrated and interactive information dissemination system using data collected by various agencies like IMD, INCOIS, NDMA and TERI and action plan on how to handle a disaster event is being formulated by Kerala State DMA in conjunction with NDMA. Additionally, to build resilience to floods, the government of Kerala is also looking into adoption eco-friendly building strategies that focus on living with and giving space to rivers (Agrawal 2018; Varghese 2019). Similar techniques have been used throughout the world to build resilience to disasters. A few are briefly discussed below: (a)

(b)

(c)

(d)

Surat (India) has developed hydrological model in collaboration with ACCCRN. This is an early warning system for urban floods which provides information 5 days in advance. It is being used since 2013 to predict floods in river Tapi and to minimize the resultant effects. It has been successful in minimizing damage by also initiating community participation and has led to the flood resilient city status of Surat (NIUA and TERI 2020). Taiwan has developed a community-based, flood inundation early warning system. It is an integrated geospatial, web-based intelligent hydroinformatics system that provides online forecast of flood depth in the region (Yekeen et al. 2019). Mexico City (Mexico) has developed an earthquake early warning system for its west coast region. It provides warning to regions lying 320 km inland before the shaking (Yekeen et al. 2019). The Japan Aerospace Exploration Agency (JAXA) has developed the ‘JAXA Realtime Rainfall Watch’ and ‘GSMaP_NOW’ that give real-time rainfall

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(f)

13

information. It also provides a comprehensive weather forecast in neighbouring countries that do not have their own weather forecasting systems (Sheldon 2018). Bangladesh has been using SPARRSO (GIS and remote sensing data) to monitor coastal afforestation in order to prevent excessive changes that can induce disasters. Phetchaburi Province (Thailand), along with the Geo-Informatics and Space Technology Development Agency (GISTDA), is utilizing geospatial and earth observation data for flood mitigation, better drainage system and improved drinking water quality. Also, Thailand is using geo-intelligence for deforestation detection and maritime, oil and coastal management (UN-ESCAP 2018).

It is evident that in addition to planning for sustainable city development, geointelligence has also been used to build resilience to disasters in developing countries of the Global South. However, most of these plans have focused on building early warning systems, risk and vulnerability assessments, identification of hotspots, postdisaster response and community involvement.

1.4 Recommendations (1)

(2)

(3)

(4)

In addition to forecasting for modelling and future estimates, developing backcasting models, using big data through AI, ML, DL and programming, for urban planning can provide a more accurate and elaborate guidelines for urban planning and helps in making sustainable decisions (Avtar et al. 2020). Sustainability requirements of cities in Global North revolve mainly around ecological concerns (Green Agenda) whereas of those in Global South are facing more socio-economic concerns (Brown Agenda). Making planning and policy decisions while keeping this in mind can help to achieve holistic development of cities (Yazdani and Dola 2013). Looking beyond the traditional components of sustainable development and recognizing the overlapping boundaries of smart cities, eco-cities, resilient cities, green cities, etc. can help in integrated and intelligent development across different planning spheres and different generations. Experiences from successful sustainability plans stress upon a bottom-up approach in planning with effective collaboration between government and stakeholders, i.e. recognizing significance of local community and local government’s involvement and creating enabling conditions for their effective participation are key to ensure fulfillment of SDGs and targets (Hohmann and Reudenbach 2015).

In addition to the above mentioned, we can also follow guidelines and recommendations provided by several scientific research organizations like World Bank’s Sustainable Cities Initiative, UN-Habitat’s Sustainable Cities Programme, etc.

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1.5 Conclusion Sustainable city as a concept has evolved over time. From focusing on livability, basic needs and environment protection in 1990s to an inclusive socio-economic, ecological, and community involvement and knowledge-based concept of today that involves ideas of circular economy, functionality and ICT, it is a broad and complex concept. It not only embraces adaptation to climate change but also resilience to it, covers a multitude of problems and provides an equally wide range of solutions if approached in a scientific, open-minded and holistic manner. In order to ensure maximum applicability and benefits to the communities from the concept, it becomes imperative to focus on three aspects, i.e. (a) integrating statistical and geospatial data, (b) identifying problems with respect to one another and not in isolation—to build an integrated plan and (c) recognizing that there is ‘no one size that fits all’. The plans used in developed nations can’t be implemented as it is in developing nations. However, data sharing retains its value, i.e. to get required information from everywhere possible but to tweak it as per region’s own needs for sustainable, smart, inclusive and resilient cities in the Global South and elsewhere.

References Acharya T, Lee H (2019) Remote sensing and geospatial technologies for sustainable development: a review of applications. Sens Mater 31. https://doi.org/10.18494/SAM.2019.2706 Agrawal N (2018) How GIS helped Kerala: flood response and disaster management. https://geospoc.com/blog/2018/11/13/how-gis-helped-kerala-flood-response-and-disastermanagement/. Accessed 26 Dec 2020 Avtar R et al (2020) Utilizing geospatial information to implement SDGs and monitor their progress. Environ Monit Assess 192(21). https://doi.org/10.1007/s10661-019-7996-9 Balogun et al (2019) Assessing the potentials of digitalization as a tool for climate change adaptation and sustainable development in urban centres. Sustain Cities Soc. https://doi.org/10.1016/j.scs. 2019.101888 Bhatia B (2014) Auroville: a Utopian paradox. Columbia University Libraries. https://doi.org/10. 7916/D8RR1X4S Chanda K (2019) Artificial intelligence vs. machine learning vs. deep learning. www.geeksforgeek s.org. Accessed 27 July 2020 Chehri A, Mouftah H (2019) Autonomous vehicles in the sustainable cities, the beginning of a green adventure. Sustain Cities Soc. https://doi.org/10.1016/j.scs.2019.101751 Dairi A et al (2019) Deep learning approach for sustainable WWTP operation: a case study on data-driven influent conditions monitoring. Sustain Cities Soc. https://doi.org/10.1016/j.scs.2019. 101670 D’Amico B et al (2018) Machine learning for sustainable structures: a call for data. Structures. https://doi.org/10.1016/j.istruc.2018.11.013 Dold J, Groopman J (2017) The future of geospatial intelligence. Geo-Spatial Inf Sci 20. https:// doi.org/10.1080/10095020.2017.1337318 Ermolaeva P (2017) In the labyrinths of the “sustainable city” concepts: the meta-analysis of contemporary studies. Turkish Online J Des Art Commun. https://doi.org/10.7456/1070DSE/104 European Union (2018) In depth report: indicators for sustainable cities. Sci Environ Policy. https:// doi.org/10.2779/121865

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Hannay P, Baatard G (2011) Geointelligence: data mining locational social media content for profiling and information gathering. https://www.researchgate.net/publication/254592132_ GeoIntelligence_Data_Mining_Locational_Social_Media_Content_for_Profiling_and_Inform ation_Gathering. Accessed 1 Oct 2020 Hohmann R, Reudenbach L (2015) Sustainable development goals and habitat III: opportunities for a successful new urban agenda. Cities Alliance Discuss 3 Kadhim N et al (2016) Advances in remote sensing applications for urban sustainability. EuroMediterr J Environ Integr. https://doi.org/10.1007/s41207-016-0007-4 Kaul I (2013) The rise of Global South: implications for the provisioning of global public goods. United Nations Development Program. http://hdr.undp.org/en/content/rise-global-south. Accessed 12 June 2020. Koh KL et al (2010) “Eco-cities” and “sustainable cities”—whither? Social space. https://ink.lib rary.smu.edu.sg/lien_research/58. Accessed 7 Aug 2020 Li X et al (2016) Deep learning architecture for air quality predictions. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-016-7812-9 Lima EG et al (2020) Smart and sustainable cities: the main guidelines of city statute for increasing the intelligence of Brazilian cities. Sustainability. https://doi.org/10.3390/su12031025 Madu C et al (2016) Urban sustainability management: a deep learning perspective. Sustain Cities Soc. https://doi.org/10.1016/j.scs.2016.12.012 Murray S (2019) The critical role of infrastructure for the sustainable development goals. The Economist Intelligence Unit Nagendra H et al (2018) The urban south and the predicament of global sustainability. Nat Sustain. https://doi.org/10.1038/s41893-018-0101-5 National Review-Israel (2019) Implementation of the sustainable development goals Nanjing School (2012) https://www.google.com/url?sa=t&source=web&rct=j&url=http://share. nanjingschool.com/dpgeography/files/2012/10/TainjinCaseStudy1aao2f4.pdf&ved=2ahUKE wii_o_z_qfsAhW6zTgGHQliC_wQFjABegQIAAB&usg=AOvVaw0dWlA6bVrEZaTBKYeB r8ql. Accessed 8 Sept 2020 NIUA & TERI (2020) Main streaming urban resilience: lessons from Indian cities Pfeffer K, Georgiadou Y (2019) Global ambitions, local contexts: alternative ways of knowing the world. https://doi.org/10.3390/ijgi8110516 Robertson M (2012) Sustainable cities: local solutions in the Global South. Practical Action Publishing Ltd Satterthwaite D (2003) The links between poverty and the environment in urban areas of Africa, Asia, and Latin America. https://doi.org/10.1177/0002716203257095 Shaker RR (2015) The spatial distribution of development in Europe and its underlying sustainability correlation. Appl Geogr 63. https://doi.org/10.1016/j.apgeog.2015.07.009 Sheldon A (2018) Good practices and emerging trends on geospatial technology and information applications for the sustainable development goals in Asia and the Pacific. Staff working paper series. United Nations Economic and Social Commission for Asia and the Pacific (ESCAP) Sodiq A et al (2019) Towards modern sustainable cities: review of sustainability principles and trends. J Clean Prod. https://doi.org/10.1016/j.jclepro.2019.04.106 The Climate Reality Project (2017) Five sustainable cities making a difference for the planet. https:// climaterealityproject.org/blog/five-sustainable-cities-making-difference-planet. Accessed 18 Aug 2020 United Nations Population Division (2006) World urbanization prospects: 2007 revision UN-GGIM (2016) Transforming our world: geospatial information key to achieving the 2030 agenda for sustainable development UNIDO (2016) Sustainable cities: platforms for low carbon industrialization and turning today’s cities into livable economic powerhouses Varghese NE (2019) Capacities and gaps in early warning systems: Kerala floods. https://www.res earchgate.net/publication/337649105_CAPACITIES_AND_GAPS_IN_EARLY_WARNING_ SYSTEMS_KERALA_FLOODS. Accessed 26 Dec 2020

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Vinuesa R et al (2020) The role of artificial intelligence in achieving the sustainable development goals. Nat Commun. https://doi.org/10.1038/s41467-019-14108-y Waldrop M (2019) The quest for the sustainable city. Proc Natl Acad Sci. https://doi.org/10.1073/ pnas.1912802116 Walter C et al (2020) Future trends in geospatial information management: the five to ten year vision. UN-GGIM Wellmann T et al (2020) Remote sensing in urban planning: contributions towards ecologically sound policies? Landsc Urban Plan. https://doi.org/10.1016/j.landurbplan.2020.103921 World Bank (2020) Sustainable cities initiative World Commission on Environment and Development (1987) Our common future. Oxford University Press World Economic and Social Survey (2013) Towards sustainable cities Yazdani S, Dola K (2013) Sustainable city priorities in Global North versus Global South. J Sustain Dev 6. https://doi.org/10.5539/jsd.v6n7p38 Yekeen S et al (2019) Early warning systems and geospatial tools: managing disasters for urban sustainability. Sustain Cities Communities. https://doi.org/10.1007/978-3-319-71061-7_103-1

Chapter 2

Cloud-Based Geospatial Mapping and Analysis of Prayagraj Kumbh Mela of India: The UNESCO Intangible Cultural Heritage Sonam Agrawal and Khairnar Gaurav Bapurao Abstract Kumbh Mela of Prayagraj is an important religious and historical event which involves large congregation of people. Millions of people gather at the bank of the river Ganga. This study demonstrates the use of Geographic Information System (GIS) and remote sensing for the mapping and management of Prayagraj Kumbh Mela of the year 2019. The facilities like police station, fire station, cultural pandals, accommodation, etc., were mapped from the maps and Google Earth images through GIS. The Land Use Land Cover (LULC) classes of this region were identified from Sentinel 2 images. Six LULC classes, namely vegetation, built-up, sand, water, open ground, and tents, were generated through supervised image classification. The spatiotemporal LULC pattern changes that occurred during the Kumbh Mela were analyzed. A cloud-based web GIS platform was developed through which anyone could view the work related to the Kumbh Mela. A web interface was created using Apache Tomcat, GeoServer, and OpenLayers. It was uploaded on the cloud platform by using the Amazon Web Services. This study depicts how novel techniques like GIS, remote sensing, and cloud computing can be used to analyze the world’s largest pilgrim gathering and disseminate the related information to the public. Keywords Kumbh Mela · GIS · Cloud computing · LULC · Image classification

2.1 Introduction Kumbh Mela took place in four cities of India, namely, Prayagraj (Uttar Pradesh), Haridwar (Uttarakhand), Nasik (Maharashtra), and Ujjain (Madhya Pradesh). Kumbh Mela is a mass religious congregation and pilgrimage. Sacred rituals are performed during this event, including the dip in holy rivers. More details about it can be found in research articles where the socio-political background of Kumbh Mela is discussed (Arya et al. 2018; Lochtefeld 2004; Maclean 2001; Verma and Sarangi S. Agrawal (B) · K. G. Bapurao GIS Cell, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, Uttar Pradesh 211004, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 T. P. Singh et al. (eds.), Geo-intelligence for Sustainable Development, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-16-4768-0_2

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2019). United Nations Educational, Scientific and Cultural Organization (UNESCO) has declared Kumbh Mela as an intangible cultural heritage as it is the largest peaceful congregation of pilgrims on the earth. Kumbh Mela (previously Ardh Kumbh) of 2019 took place in Prayagraj at the confluence of the Ganga, Yamuna, and invisible Saraswati rivers. It was a grand-scale event of 55 days (~8 weeks) from mid-January to March. It was scheduled according to the Hindu calendar from Makar Sankranti (January 15, 2019) to Mahashivratri (March 04, 2019). As per the newspaper reports, about 240 million footfall was recorded in this period. A temporary city was built along the river banks for this event, as shown in Fig. 2.1. It included tents, check plate roads, pontoon bridges, toilets, changing rooms, drinking water facility, food stalls, cultural pandals, etc. Temporary roads were constructed using the check plates so that pilgrims could easily walk or take the shuttle busses. About 22 pontoon bridges were constructed across the rivers. These floating bridges helped in providing mobility across the rivers. The mapping of utilities and other spatial features such as road, fire station, and public accommodation will be useful in information dissemination and management of this mega event. The absence of a digitized database and non-unified representation of utilities affect the situation assessment and decision-making (Wang et al. 2019). Geographic Information System (GIS) technology can be used in utility mapping (Manonmani et al. 2012). GIS can be defined as “a computer-based system to aid

Fig. 2.1 Temporary structures built for the Kumbh Mela in 2019

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in the collection, maintenance, storage, analysis, output, and distribution of spatial data and information” (Bolstad 2012). Kumbh Mela event made significant changes on the ground. Its preparation starts to appear on the field, somewhere from November. Spatiotemporal study on Land Use Land Cover (LULC) can give precise information on the changes caused by human activities (Kumar and Agrawal 2019). Remote sensing data, such as satellite images, is required for this purpose. The LULC maps can be generated by image classification, which is based on spectral pattern recognition. Supervised or unsupervised classification algorithms are used for this purpose whose selection depends upon several factors like a priori knowledge of LULC classes, number of desired LULC classes, purpose of classification, etc. A web GIS application can be developed using Open Geospatial Consortium (OGC) web services to disseminate all the Kumbh Mela related information to the general public. These geospatial web services can be made accessible across the globe by hosting them on cloud platforms (Agrawal and Gupta 2017, 2020). Cloud computing has cut down the responsibility of developers. The hardware and software infrastructure maintenance task is shifted to cloud providers who use the virtualization technique to allocate the data center capabilities (Tripathi et al. 2020). The US National Institute of Standards and Technology (NIST) defines cloud computing as “a model for enabling ubiquitous, convenient, ondemand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction” (Mell and Grance 2011). It is a metered service to provide computing resources. Cloud computing provides the Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Geospatial technologies are found very useful in the Kumbh Mela related works. Some studies that are done on Kumbh Mela using geospatial technologies are on crowd management (Trivedi and Pandey 2020), pedestrian trip planning (Verma et al. 2018, 2019), urban planning (Chadha and Onkar 2016), healthcare (Balsari et al. 2016), the impact of mass bathing on water quality, etc. There is a need for a work that gives the holistic picture of Kumbh Mela and accessible to all the people. This work aimed to perform the utility mapping of Kumbh Mela, study the spatiotemporal LULC changes that occur during Kumbh Mela, and create a cloud-based geospatial mapping platform for Kumbh Mela.

2.2 Study Area and Data Used The study area of the work is Kumbh Mela Kshetra and the nearby region, as shown in Fig. 2.4. It is located in the Prayagraj district of Uttar Pradesh state in India. The geographic extent of the study area is from 81° 50 42 E to 81° 55 12 E longitude and from 25° 30 25 N to 25° 22 48 N latitude. This area was divided into 21 sectors. Sentinel 2 Multispectral Imager (MSI) Level 1C data were used for the spatiotemporal LULC analysis of Kumbh Mela. Images of four months, i.e.,

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from November 2018 to February 2019, were downloaded from the European Space Agency (ESA) website. Table 2.1 provides the detail of these images. Additionally, Survey of India (SOI) topographic maps (G44P14 and G44P15) available at the scale of 1:50,000 with WGS 84 (EPSG 4326) datum and UTM projection, and the Kumbh mela map published by state government were used for creating utility maps of the area.

2.3 Methodology The overall methodology is given in Fig. 2.2 and discussed under following subheadings.

2.3.1 Data Preparation Data from different sources were collected, as mentioned in the previous section. All the data were registered on the common datum and projection system. SOI toposheet No. G44P14 and G44P15 were georeferenced first by using the first order affine transformation and nearest neighbor resampling technique. The study area was clipped from both the maps, and then stitched together using mosaic operation. This gave the topographical basemap of the entire study area. Sentinel images that were downloaded from the ESA website were already rectified. Each of the images was in band sequential form and each band was in JPEG2000 format. There were 12 bands in the Sentinel 2 image, among which four bands were used, which were band 2 (438–532 nm), band 3 (536–582 nm), band 4 (646–685 nm) and band 8 (774–907 nm). All the layers were stacked together to create the composite image. Later, this image was clipped by the minimum bounding box of the Kumbh Mela area. However, the rectification of the Kumbh Mela map was a challenging task as graticules were not marked on it. The Ground Control Points (GCPs) were identified for it. Only man-made immovable objects with high contrast were considered for GCPs. The topographical map, satellite image, and Google Earth image were used for GCP marking. The twenty well-distributed GCPs were marked on the Kumbh Mela map and transformation was calculated. The resampling process was then carried out using the nearest neighbor method.

2.3.2 Utility Mapping The digitization process was carried out on the Kumbh Mela map. The shapefiles was first created in ArcCatalogue for sector boundary, river, island, road, sector offices, police stations, cultural pandals, and public accommodations. In ArcMap,

Name of satellite

Sentinel 2

Sentinel 2

Sentinel 2

Sentinel 2

S. No

1

2

3

4

13.02.2019

14.01.2019

15.12.2018

15.11.2018

Date of acquisition

Table 2.1 Satellite data details

S2A_MSIL1C_20190213T050921_N0207_R019_T44RNP_20190213T085756.SAFE

S2A_MSIL1C_20190114T051151_N0207_R019_T44RNP_20190114T070424.SAFE

S2A_MSIL1C_20181215T051211_N0207_R019_T44RNP_20181215T070327.SAFE

S2A_MSIL1C_20181115T051051_N0207_R019_T44RNP_20181115T070855.SAFE

Image URI

10

10

10

10

Spatial resolution (m)

2, 3, 4, 8

2, 3, 4, 8

2, 3, 4, 8

2, 3, 4, 8

Bands

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Fig. 2.2 Methodology adopted for this study

digitization of boundary and utilities was performed with the help of the editor tool. When the features were not clear or missing on the map, then Google Earth was used. After digitization, the utility maps were generated.

2.3.3 LULC Change Analysis Data of Sentinel-2 were selected to detect the changes occurred during Kumbh Mela. First of all, image stacking was performed in ERDAS IMAGINE. As mentioned in a dataset, four bands—band 2, band 3, band 4, and band 8—which indicate blue, green, red, and near-infrared bands, respectively, were selected for stacking operation. Subsetting is performed on a stacked image to clip the study area. LULC maps were created using supervised classification for four months, i.e., from November 2018 to February 2019. Gaussian maximum likelihood method was selected for supervised

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classification. The study area was divided into six main land use categories namely vegetation, built-up, sand, water, ground/open area, and tents. Training samples of these classes were collected with the help of ground truth information. Accuracy assessment was performed on classified images to check the correctness of the classification. Kappa coefficient, which is a statistical measure of this accuracy, was calculated. If the Kappa coefficient’s value is more than 0.75, then classification is considered a good classification (Monserud and Leemans 1992).

2.3.4 Hosting on Cloud Platform A cloud-based web GIS interface was developed that could be accessed by the users through any web browser. Standards defined by the OGC consortium were followed to construct the cloud-based web interface. The Web Map Services (WMSs) were created, which rendered the map in the image format. Different parameters were mentioned in the WMS request, including bounding box, style, coordinate reference system, width, height, etc. Amazon Web Services (AWS) was selected as a cloud platform. A virtual computing environment was created through Elastic Compute Cloud (EC2) instance. Figure 2.3 is showing the architecture of cloud-based web GIS mapping platform. Its development steps are discussed below.

2.3.4.1

Virtual Machine Creation

First of all, an account was created on AWS. Then t2.micro EC2 instance was created through AWS Management Console. For this Ubuntu Server 18.04 LTS (HVM), SSD Volume Type [64-bit (×86)] was selected as Amazon Machine Image (AMI), which is a pre-configured instance template. 8 GB persistent storage was assigned to the instance using Elastic Block Store (EBS). 1 GB RAM was set as instance memory. The instance access rules were decided through security group settings. It was configured to give the SSH and TCP access to the host machine. The secure connection between the local computer and EC2 instance was provided by the generation of public and private keys. The AWS stores the public key while the private key is kept by the developer. Finally, EC2 instance was launched.

2.3.4.2

Software Installation

PuTTY was used for connecting with the EC2 instance using the SSH protocol. PuTTY was first downloaded and installed. PuTTY private key file was generated with the help of the PuTTY Key Generator (puttygen.exe). In this private key file (.pem), which was generated during the EC2 instance configuration, was used. Now PuTTY (putty.exe) was opened and the local computer got connected to the EC2

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Fig. 2.3 Architecture of cloud-based web GIS mapping platform

S. Agrawal and K. G. Bapurao

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instance with the public DNS of the EC2 instance and private key file of PuTTY. As a result, the EC2 instance window appeared on the screen. Apache Tomcat is an open-source Java-based web server. Its setup was downloaded from the website. It was installed on the EC2 instance by using the commands. User role permissions and Java heap memory size were set. Since Apache Tomcat works on the port number 8080, therefore this port was opened during the configuration of the EC2 instance. GeoServer was used as the GIS server. It is the OGC compliant server. GeoServer was deployed on the Apache Tomcat server by the web archive (war) file.

2.3.4.3

File Transfer

WinSCP was used to transfer the data files from the local computer to the EC2 instance. It was a graphical user interface for windows. For this purpose, the WinSCP installation package was downloaded and installed. WinSCP and EC2 instance was connected by providing the public DNS of the EC2 instance. Once the connection was established, files were transferred by the simple drag and drop operation.

2.3.4.4

Web Interface Creation

All shapefiles of Kumbh Mela were upload on the Geoserver. Firstly, a workspace was created in Geoserver. Then a store was formed in this workspace. All shapefiles were uploaded in that store. The coordinate reference frame was set for these shapefiles. All shapefiles get merged with the help of the layer group tab. WMS services were created to serve the Kumbh Mela maps and related analysis works to the clients. WMS renders the map in the image format. Different parameters are mentioned in the service request, which includes the extent of display in the form of bounding box parameter, style, coordinate reference system, width, height, etc. Customized styles were used to display the layers. uDig software was used in the SLD file generation. The web interface was designed through JSP and OpenLayers. JSP is the server-side scripting language, while OpenLayers is the JavaScript library for preparing web services mash-up. All the JSP files were placed inside the webapps folder of Apache Tomcat.

2.4 Results and Discussion 2.4.1 Utility Maps The utility map prepared for the Kumbh Mela is shown in Fig. 2.4. This map shows six different utilities and facilities that are road, sector office, entry/exit points, cultural

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Fig. 2.4 Utility map of Kumbh Mela

pandals, emergency service, and public accommodation. The utility map was created in such a manner that it could help anyone in detecting and locating the desired utility. GIS analysis could be further applied on the spatial entities, e.g., buffer can be created around pandals for proximity analysis. Similarly, network analysis can be performed on the roads for finding the shortest path, etc.

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2.4.2 Spatiotemporal Change Analysis Four satellite images of Kumbh Mela duration were selected for the spatiotemporal LULC analysis within study area. All the images were pre-processed as described in the data preparation section. After this, supervised classification was performed. In this classification, a priori knowledge of the LULC types is required. Five LULC classes were identified in the study area, which are vegetation, built-up, sand, water, ground, and tents. Training samples were selected for this purpose, whose interpretation marks are listed in Table 2.2. Samples were selected from the well-distributed sites to include the within-class variability. The quality of samples was checked by the histogram plots. It was verified that the histogram was unimodal for each band. Classification was then performed using the Maximum Likelihood classifier. Overall accuracy and Kappa coefficient were generated from classified images. The result showed that the Kappa coefficient was greater than 0.75, suggesting good relation between ground truth and classified images. Classification results are shown in Fig. 2.5. In these thematic maps, vegetation cover was given by yellow color, built-up area was represent as red color, sand was given in dark brown color, water bodies were indicated by blue color, open ground represented by light sienna color, and tents were shown by purple color. Table 2.2 Samples of training data LULC type Color

Texture

Vegetation

Shades of red

Smooth

Built-up

Mixed shades of cadet blue

Very rough

Sand

White to cyan

Quite smooth

Water

Normal to dark shades Smooth of mazarine

Ground

Mixed shades of grey

Quite rough

Tents

Mixed shades of cyan

Rough

Interpretation sign

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Fig. 2.5 Spatiotemporal LULC maps of Kumbh Mela generated through supervised classification

The area of each LULC class was computed from per-pixel based classified images. The percentage of each LULC class was also calculated using the following formula:

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LULC classi % =

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Area of LULC classi Total study area

(2.1)

LULC statistics are given in Table 2.3. Further, the change in each LULC class is calculated by Ai = Ain − Aim

(2.2)

where Ai is the change in area of LULC class i, n is the current month, and m is the previous month. These statistics are given in the form of a graph in Fig. 2.6. LULC Table 2.3 LULC statistics for Kumbh Mela LULC classes

November Area (km2 )

%

Area (km2 )

%

Area (km2 )

%

Area (km2 )

%

Vegetation

40.80

18.34

37.70

16.95

48.28

21.70

57.37

25.79

Built-up

52.13

23.43

52.40

23.56

65.31

29.36

67.89

30.52

Sand

25.97

11.68

31.81

14.30

25.30

11.37

22.57

10.14

Water

20.58

9.25

17.31

7.78

17.93

8.06

17.05

7.66

Ground

82.79

37.22

82.72

37.19

62.81

28.23

51.59

23.19

0.18

0.08

1.31

0.59

3.63

1.63

6.79

3.05

Tents

December

January

February

Fig. 2.6 Temporal changes in the area of LULC classes (all the values are in km2 )

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maps revealed that during the Kumbh Mela, tent area had a steep rise, whereas the sand and ground areas tended to decrease. The analysis shows that the tent class mostly evolved either by the transition of sand to tent or by transition of ground into a tent. Built-up area was increased from 52.13 to 67.89 km2 in the period of just four months. This rise in built-up has occurred due to large scale construction activities that had taken place nearly at the start of the Kumbh Mela.

2.4.3 Cloud-Based Web GIS Application Geovisualization and concurrent access to Kumbh Mela related data were provided using web mapping. Infrastructure as Service (IaaS) and Software as a Service (SaaS) of cloud computing was used to create the web interface and on-demand services. All the data and software were deployed on the virtual machine or EC2 instance. GeoServer was used to publish the OGC WMS services. The WMS server provides three services, viz getCapabilitites, getMap, and getFeatureInfo. The metadata of the WMS is provided through the getCapabilitites request. The getMap request renders the spatial data in the form of a map image. The attributes of the spatial features are displayed by getFeatureInfo request. Figures 2.7 and 2.8 are showing the web interface through which a user can access the data and services related to Kumbh Mela. It is developed using JSP, JavaScript, and OpenLayers. The user has to give the URL of the home page, which is http://34. 208.136.216/:8080/Kumbh/.

Fig. 2.7 Snapshot of cloud-based web GIS interface developed for Kumbh Mela. The Google Earth image is used as the basemap

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Fig. 2.8 Snapshot of cloud-based web GIS interface developed for Kumbh Mela. Open Street Map is used as the basemap. On the right side, thematic layers are listed which can be selected by the users

The interface is kept very simple so that anyone can easily deal with it. It is enabled by interactive tools like zoom, pan, selection, etc. Google Earth and OpenStreetMap (OSM) images are used as the basemaps. The upper layer provides the sector boundary, facilities, roads, etc. On the right side, all the thematic layers and base maps are listed. Users can select the required utilities and maps by clicking on the checkbox. The selected layer will be displayed on the screen by fetching the data from WMS provider using HTTP getMap request. If the user want to know the coordinates of any place then a click is required at that point. The coordinates of that point will appear in a pop-up window. A zoom tool is given on the left side so that the user can set the appropriate zoom level. If a user wants to know the attribute data of any spatial feature, they just need to click on that feature. All the attributes of that feature will be listed below the map. It is provided by geFeatureInfo request of WMS.

2.5 Conclusion The sharing and exchange of facilities information are necessary for Kumbh Mela. Many times available utilities and facilities remain unknown to citizens. The present chapter demonstrates application of Geo-intelligence in mapping of existing utilities/facilities for situation assessment and decision-making during the Kumbh Mela of 2019. A cloud-based web GIS application for Kumbh Mela using the OGC standards, Geoserver, Apache Tomcat, OpenLayers library, and AWS was developed. It provides a web GIS interface where users, from tourists to decision-makers, can

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view the geospatial raster and vector layers along with their attributes. Multiple users can simultaneously access web services at any time and from anywhere by using the web browser only. However, the utility database must be continuously updated and enriched in the future by adding data promptly.

References Agrawal S, Gupta RD (2017) Web GIS and its architecture: a review. Arab J Geosci 10(23):1–13. https://doi.org/10.1007/s12517-017-3296-2 Agrawal S, Gupta RD (2020) Development of SOA-based WebGIS framework for education sector. Arab J Geosci 13(13):1–20. https://doi.org/10.1007/s12517-020-05490-9 Arya V, Sharma S, Sethi D, Verma H, Shiva A (2018) Ties that bind tourists: embedding destination motivators to destination attachment: a study in the context of Kumbh Fair, India. Asia Pacific J Tour Res 23(12):1160–1172. https://doi.org/10.1080/10941665.2018.1528992 Balsari S, Greenough PG, Kazi D, Heerboth A, Dwivedi S, Leaning J (2016) Public health aspects of the world’s largest mass gathering: the 2013 Kumbh Mela in Allahabad, India. J Public Health Policy 37(4):411–427. https://doi.org/10.1057/s41271-016-0034-z Bolstad P (2012) GIS fundamentals, 4th ed. Eider Press Chadha H, Onkar P (2016) Changing cities in the perspective of religious tourism—a case of Allahabad. Procedia Technol 24:1706–1713. https://doi.org/10.1016/j.protcy.2016.05.200 Kumar V, Agrawal S (2019) Agricultural land use change analysis using remote sensing and GIS: a case study of Allahabad, India. In: ISPRS-GEOGLAM-ISRS joint international workshop on “Earth observations for agricultural monitoring”, vol XLII-3/W6. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, New Delhi, India, pp 397–402. https://doi.org/10.5194/isprs-archives-XLII-3-W6-397-2019 Lochtefeld JG (2004) The construction of the Kumbha Mela. South Asian Pop Culture 2(2):103–126. https://doi.org/10.1080/1474668042000275707 Maclean K (2001) Conflicting spaces: the Kumbh Mela and the fort of Allahabad. South Asia: J South Asian Stud 24(2):135–159. https://doi.org/10.1080/00856400108723455 Manonmani R, Prabaharan S, Vidhya R, Ramalingam M (2012) Application of GIS in urban utility mapping using image processing techniques. Geo-Spatial Inf Sci 15(4):271–275. https://doi.org/ 10.1080/10095020.2012.714660 Mell P, Grance T (2011) The NIST definition of cloud computing. Nist Special Publication 800-145. http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf Monserud RA, Leemans R (1992) Comparing global vegetation maps with the Kappa statistic. Ecol Model 62(4):275–293. https://doi.org/10.1016/0304-3800(92)90003-W Tripathi AK, Agrawal S, Gupta RD (2020) Cloud enabled SDI architecture: a review. Earth Sci Inf 13(2):211–231. https://doi.org/10.1007/s12145-020-00446-9 Trivedi A, Pandey M (2020) Agent based modelling and simulation to estimate movement time of pilgrims from one place to another at Allahabad Jn. Railway Station during Kumbh Mela-2019. Auton Agents Multi-Agent Syst 34(1):30. https://doi.org/10.1007/s10458-020-09454-x Verma M, Sarangi P (2019) Modeling attributes of religious tourism: a study of Kumbh Mela, India. J Conv Event Tour 20(4):296–324. https://doi.org/10.1080/15470148.2019.1652124 Verma A, Verma M, Rahul TM, Khurana S, Rai A (2018) Acceptable trip distance for walking in mass religious gatherings—a case study of world’s largest human gathering Kumbh Mela in Ujjain, India. Sustain Cities Soc 41(June):505–512. https://doi.org/10.1016/j.scs.2018.06.010 Verma A, Verma M, Rahul TM, Khurana S, Rai A (2019) Measuring accessibility of various facilities by walking in world’s largest mass religious gathering—Kumbh Mela. Sustain Cities Soc 45(August 2018):79–86. https://doi.org/10.1016/j.scs.2018.11.038

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Wang M, Deng Y, Won J, Cheng JCP (2019) An integrated underground utility management and decision support based on BIM and GIS. Autom Constr 107(July):102931. https://doi.org/10. 1016/j.autcon.2019.102931

Chapter 3

Geo-intelligence-Based Approach for Sustainable Development of Peri-Urban Areas: A Case Study of Kozhikode City, Kerala (India) V. P. Nishara, V. Sruthi Krishnan, and C. Mohammed Firoz Abstract The land use and land cover is changing in different parts of the world, the root cause of which is the increasing urbanization rate. The peri-urban areas are transforming due to this pressure, leading to urban expansion and resulting in major changes in land use along the highway. Such peri-urban areas are largely neglected in policy and practice because they are mostly included in the rural category and come as a region beyond the urban administration. Hence, the present study is focused on the analysis of the impact of land use land cover change on the urbanizing region along a highway using geospatial technology. Seven typical nodes were carefully chosen along the corridor and a growth node was identified. An undeveloped node having an area of 5 km2 was chosen as a case study for further analysis and proposal. The entire study area was zoned into three categories (emerging zone, agricultural and tourism zone, and residential zone) based on the prominent land uses for the suggestion of the proposals. The present study can be beneficial to planners, administrators, and policymakers as a stepping stone in promoting the sustainability of peri-urban areas. Keywords Sustainability · Urbanization · Peri-urban areas · Transit corridor

3.1 Introduction Urbanization is an indicator of modernization, growth, economic progress, and prosperity of society (Das and Laya 2016). Today, 55% of the world’s population lives in urban areas, a proportion that is expected to increase by 68% (2/3rd) by 2050 (UNDESA 2018). Urbanization in India is taking place at a rapid and a higher rate than other cities in the world, in which the population in urban areas is increasing faster than the total population of the country. It has raised from 17.29 to 31.6% from 1951 to 2011 time period and it is expected to grow from 377 to 600 million

V. P. Nishara · V. Sruthi Krishnan (B) · C. M. Firoz Department of Architecture and Planning, National Institute of Technology Calicut, Kozhikode 673601, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 T. P. Singh et al. (eds.), Geo-intelligence for Sustainable Development, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-16-4768-0_3

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by 2030 (Ministry of Housing & Urban Affairs 2017). Urbanization leads to positive as well as negative impacts. It directs to better access to education, health care, employment, etc. On the other hand, there are some major challenges caused due to rapid urbanization (Cohen 2006). Urbanization without proper planning can lead to urban sprawl, which causes adverse impacts like segregation of land use leading to higher expenditure for infrastructure, lack of housing choices for all categories of income group, loss of vegetation, high dependence on private vehicles, and other related problems (Downs 2005). These can cause negative impacts on the environmental quality of an area (Sruthi Krishnan and Firoz 2020). Quantification of land use and land cover (LULC) helps to detect urban sprawl, and for that, past land use needs to be analyzed. Modern planning tools like Remote Sensing (RS) and Geographic Information System (GIS) are efficient to detect and analyze land-use changes at different scales and provide timely and accurate information about the land. These tools are increasingly used in environmental and sustainability-related studies for better assessment, representation, zonation, and database creation (Sruthi Krishnan et al. 2016). Moreover, the spatial assessment of peri-urban areas can ease the planning process for the future. If land use and transportation projects are planned and coordinated properly, more community and environmentally sensitive plans and policies can be developed (AASHTO 2005). Kerala is one of the fastest growing states in India and has a rural–urban continuum composite settlement (Downs 2005). The share of the urban population in Kerala as per Census report 2011 is 47.7% which is much higher than that of India (31.1%). The state shows sporadic growth spotted along the major roads, especially highways, leading to a ribbon development (Firoz et al. 2014), and it is estimated that by 2021, the urban areas would be mainly located along the major roads namely the national highway (NH) and state highway (SH). The construction of a highway along a rural area provides regional accessibility. Therefore, new commercial and real estate projects get attracted causing a boom in economy and population and thus leading to peri-urbanization (Song et al. 2016). The main problem that the peri-urban areas are facing is that there are no proper development planning strategies because these areas neither come under the rural or urban zones, which leads to haphazard urban development. The construction of the transit corridor brought excessive LULC changes without proper planning and management, causing a lot of social and environmental problems in the fringe areas (Müller et al. 2010; Ratner and Goetz 2013). Many people wish to move to cities for economic activities and for pursuing other interests, but they are unable to do so because of the high cost of living in the cities. So, a more affordable and reasonable option is to reside in rural areas that are situated just outside the city limits (Aijaz 2019). The rationale of this study is to assess the land use and land cover pattern changes over the past 20 years along NH 66 Bypass in Kozhikode, Kerala. For this purpose, seven typical nodes were carefully chosen along the corridor and a growth node was identified using the LULC study. The land use and land cover study help in understanding where the change has occurred, type of transformation, rate of change, driving forces, and causes of change (Drummond and Loveland 2010). Further, an

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undeveloped node (Erangikkal node) having an area of 5 km2 was chosen as a case study for a detailed explanation.

3.2 Study Area Kerala, one of the fastest growing states of India (Fig. 3.1), constitutes 1.58% (area) and 3% (population) of the country (Census of India 2011). It has the highest human development index (HDI) in the country due to the social, economic status, health, etc. (Census of India 2011). Kozhikode, one of the 14 districts in Kerala, has a population of 2.5 million which accounts for the 2nd and 3rd largest urban agglomeration in 2015 and urban population in 2011, respectively. As per the survey conducted by the Economist Intelligence Unit, it is the 4th fastest growing city in the world due to its progress in social service and potential development (Economist Intelligence Unit survey 2020). It is also enlisted as one of the livable cities and dream destination because of highquality work and life, advancement in social service, and development potential (Augustine 2016). The main attraction of this district is the presence of reputed colleges like NIT Calicut, IIM Kozhikode, and IT sectors, which attracts highly educated workgroup. Major projects like Cyber parks, Integrated townships, Birla

Fig. 3.1 Study area

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V. P. Nishara et al.

Institute, and Monorail projects and some other ventures which are proposed would boost the real estate market along this road. Urbanization is rapidly happening in contiguous LSGs. The urban population of Kozhikode has increased and also the city is expanding to its rural and peripheral areas. As per studies conducted earlier, the population of the core has not grown in the last 20 years, but there is a shift of people from the core to new areas of the periphery, which means the core itself is shifting outwards (T&CP Department Kozhikode 2015).

3.3 Methodology The overall methodology adopted in the study is depicted in the form of a flowchart in Fig. 3.2.

Fig. 3.2 Methodology adopted for the present study

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3.3.1 Nodes Identification NH 66 bypass in Kozhikode of length 29.4 km is a newly constructed bypass that helps in decongesting the city’s traffic. This corridor is passing through undeveloped areas around the periphery of the city which increased accessibility and thereby improved human activities along the corridor, which means it would boom the economy by attracting new buildings, especially commercial and real estate projects. Along the transportation corridor, seven nodes were selected based on the popularity or the junction. The seven nodes are needed to be analyzed further for understanding which would be the areas where the future growth might be desired or not be desired. Hence, a buffer area of 500 m from the center of the node was demarcated to understand the trends of land use within the study area. The whole highway effect zone was divided into seven segments ranging from 0 to 29.1 km. Each zone was divided based on natural feature boundaries like river or road and also taking into consideration the importance of junction/area or buildings.

3.3.2 LULC Analysis LULC analysis of the seven nodes was done through a spatial and quantitative analysis. For understanding baseline land use conditions, it is essential to detect the pattern of change. Satellite images give an overall view of the desired areas at regular intervals. The study areas were taken based on a 500 m radius with an area of 0.785 km2 . The digitizing was done using google earth data for the years 2000, 2010, and 2020. GPS points were also collected for ground truth and analysis. The satellite images were classified into mainly five classes, namely, agriculture, vegetation, residential, commercial, and vacant land (Munthali et al. 2019; Tagore and Shah 2013). Key informant interviews and focus group discussions (FGD) were also conducted to collect information about the locality. The results obtained from the comparison of seven nodes were used for delineating the final study area.

3.3.3 Study Area Delineation For the demarcation of the study area, a buffer of 500 m was taken to a maximum of 1000 m as per the standards, from both sides of the corridor, where 500 m is a highly accessible area and 1000 m is a less accessible area from the road. Later, a border with a natural boundary was drawn to study the land-use changes in that area. The land use of the study area was prepared using satellite images and GIS software.

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Fig. 3.3 Proposed land use zones in the study area

3.3.4 Proposals The entire study area is planned to be zoned into three categories, namely, emerging zone, agriculture and tourism zone, and residential zone based on the prominent land use, leaving the existing character of the study area undisturbed (Fig. 3.3). The area around the major transportation corridor (NH66 bypass) is the emerging zone. The agriculture and tourism zone is a mix of agriculture and recreational activities along the river coast. The residential zone consists of affordable housing for low and middle-income groups with basic facilities.

3.4 Results 3.4.1 Nodes Identification Kozhikode city has expanded mainly along the major highways, which has bought many landlocked areas in the periphery of the city. NH66 bypass is a newly

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Fig. 3.4 Nodes selected along NH66 bypass

constructed road at the periphery of the city, and it is expected that the urbanization would spread to outer rural areas through the NH66 bypass. For further study, the area within 500 m on either side of the corridor was taken as the buffer and was divided into seven zones (Fig. 3.4). This distance represents a 5 to 6 min walk to the major transit. Along the 29 km long transportation corridor, seven nodes were selected based on their significance in each zone.

3.4.2 LULC Analysis The land use land cover analysis for the seven major nodes was done for the years 2000, 2010, and 2020, to analyze the major changes that took place because of the development of the NH66 bypass (Table 3.1). The trend of residential land use in all the nodes revealed an increasing pattern. The highest percentage is observed for

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Table 3.1 Quantitative analysis of LULC for different nodes LULC classification

Year

Node 1 (km2 )

Node 2 (km2 )

Node 3 (km2 )

Node 4 (km2 )

Node 5 (km2 )

Node 6 (km2 )

Node 7 (km2 )

Residential

2000

0.017

0.035

0.034

0.136

0.044

0.015

0.006

2010

0.05

0.085

0.208

0.226

0.079

0.044

0.074

2020

0.248

0.208

0.282

0.308

0.237

0.085

0.143

2000

0.57

0.53

0.64

0.528

0.553

0.521

0.575

2010

0.542

0.456

0.284

0.405

0.529

0.521

0.436

2020

0.298

0.358

0.108

0.29

0.409

0.513

0.371

2000

0.094

0.042

0.059

0.013

0.067

0.11

0

2010

0.082

0.031

0.083

0.017

0

0

0

2020

0.06

0.031

0.043

0.005

0

0

0

2000

0.015

0.03

0.002

0.008

0.009

0.0004

0.009

2010

0.015

0.066

0.002

0.016

0.016

0.002

0.026

2020

0.04

0.099

0.084

0.074

0.043

0.014

0.039

2000

0.059

0.046

0.045

0

0.022

0.003

0

2010

0.042

0.042

0.063

0.004

0.058

0.02

0.006

2020

0.063

0.034

0.024

0.018

0.04

0.024

0.022

Green space

Agriculture

Commercial

Vacant land

the node 3 that has expanded to six times of its value in the year 2000. Almost all the nodes have a large share of residential areas except node 6 and node 7. This signifies the dramatic change in residential land cover change which puts pressure on vegetation, especially agricultural land. Further, the rate of change of green space in node 6 has not much changed and maintained almost the same value for the last two decades. So, it can be concluded that, even though the residential area increased, it has not much encroached the green space. In nodes 3 and 4, agriculture area has increased during 2010 in small percentage, but dramatically declined in the next 10 years. This may be related to the construction of the NH66 bypass through the undeveloped area. Also, the lack of Development Control Regulations (DCR) in peri-urban areas, made them easier to acquire land with minimum legal procedures. Nodes 1 and 2 show a gradual decline in agriculture activities. But in the case of nodes 5, 6, and 7, the agricultural areas completely shrank (Fig. 3.5). In some cases due to lack of availability of farmers, the agricultural areas turned into a forest because of the unavailability of care it needed. Similar to the residential areas, commercial activities also have increased in each node. The highest rise in area is experienced at node 2, i.e., from 0.03 to 0.099 km2 . The smallest percentage of change in commercial land use is seen at node 6. So, altogether, built-up land use is very much low in node 6, which depicts that the peri-urbanization has limited effect here compared to the other nodes. Combining the land cover of the residential, commercial, and residential areas along with road networks and other impervious surfaces, led to the expansion of the built-up area in the periphery of the city. The change results shows a continuous

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Fig. 3.5 LULC map of the nodes (5, 6, and 7) for the year 2000, 2010, and 2020

decline in agriculture and green space in every node and an increase in the built-up area. Nodes 1, 4, 6, and 7 show the increase, and nodes 2, 3, and 5 show the reduction in the open land. Due to the fewer developments in the former nodes, people tend to settle in an area where there are more activities.

3.4.3 Study Area Delineation For detailed analysis, Node 6 has been selected as the final study area because opting for an undeveloped area is better than an already developed area that is congested

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Table 3.2 Present land use percentage in comparison with URDPFI Guidelines (T&CP 2015; Student Report 2020) Land use type

Area (km2 )

Percentage area (%)

URDPFI standards %

Residential

1.63

33.1

45–50

Vegetation

1.48

30

–

Waterbody

1.72

34.9

Balance

Commercial

0.023

0.5

10–13

Public/semi-public

0.05

1

6–8

Recreational space

0.008

0.2

12–14

Vacant land

0.018

0.4

–

Total area

4.93

100

and no more densification is possible. Presently, Node 6 has a less built-up area. Also, this undeveloped area is nearer to bypass in comparison to the other nodes, which makes it confirmed that the accessibility factor itself is sure and this area would surely be developed in near future. So, it would be better to select an area in which development is desired and to plan proactively rather than reacting after the urbanization has taken place. Erangikkal (node 6) is a new growth node in a peri-urban or urban fringe area where it acts as a hinterland around the city. The two highways, i.e., state highway and national highway bypass, are intersecting in this area. The railway facilities are also available in a walk of 10 min from the junction. The above-mentioned facts make this node the most ideal model as a growth node. The major issue in the study area is its poor connectivity when compared to the city center and has a large number of low-rise residential, commercial, and several other undeveloped properties along the road. Erangikkal is a flood-prone area during monsoon season, and also adding upon this, the area is surrounded by a river that roots more waterlogging. The present LULC map of the study area is mapped using the GIS manual method, and the present land use percentage is given in Table 3.2. Analysis of existing land use reveals that the planning area has only less than half the required share of commercial, recreational, and public/semi-public facilities when compared to the URDPFI guidelines. The residential area is almost nearer to the required share. All the other land uses do not make-up even half of the requirement. From the land use percentage (Table), vegetation or green space is only 30%, which has less productive agricultural activities. The residential land use is classified into high-density and low-density residential. High-density residential is mainly concentrated along the major transportation corridor and its share is also small. The low-density residential is in a spread manner showing the urban sprawl in the study area. The major share of land use is water bodies, i.e., about 34.9%.

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3.5 Proposals For putting forward the proposals, the entire area was classified into three zones as explained in Sect. 3.3.4.

3.5.1 Emerging Zone The emerging zone is proposed along the transit corridor with high-density compact development. The sprawl from the core city area to the periphery could be controlled by compact development along the highway and a better infrastructure could be provided with less expenditure. The total area allotted for the emerging zone is 1.75 km2 . The emerging zone can be further explained based on compact mix-use zone, energy-efficient green and smart infrastructure, and streetscape. The compact mix-use zone is a node having high density around the transit corridor, and this area would be highly accessible to public services and jobs and provide greater sustainable development causing lesser impact to nature (OECD 2012). A section in the emerging zone is delineated according to the proposed Town Planning (TP) Scheme (Jain 2019) and can be proposed to other areas too (Sanyal and Deuskar 2012). TP schemes help in organizing development in a planned manner and can provide better accessibility. The initial plot boundary of the area was acquired from the (T&CP Department Kozhikode 2015). The TP scheme is classified into different land uses, namely, residential, industrial, commercial, public/semi-public, open space, green space, and social infrastructure (Fig. 3.6). Energy-efficient green and smart infrastructure explain the elements that are needed to promote vegetation in that area and other basic facilities needed in a high-density zone. Landscaping an area has a lot of benefits like it enhances the aesthetics and act as a screening which maximizes privacy. Open and green space planning in a mix-use zone helps in protecting present vegetation and making it compulsory for corridors or houses to have some portion of plants with the help of regulations or policies. Open space is planned in such a way that it is visible and accessible from various locations, mainly residential blocks, and it must be 10 min walk from the majority of residential blocks (Fig. 3.7). The major transit corridor is 45 m wide having only facilities for the vehicles. So, if streetscape is planned in a manner that includes all elements such as a Bus Rapid Transit System (BRTS), pathways, cycle lanes, on-street parking, and median, along with compact development, it would provide a better corridor (Fig. 3.8). The percentage share of each land use after proposing the TP scheme is tabulated in Table 3.3.

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Fig. 3.6 Town planning (TP) schemed area

Fig. 3.7 Location of open space

3.5.2 Agriculture and Tourism Zone For the agriculture and tourism zone, the focus is given to farm and resort tourism. It would showcase the heritage of Kerala, along with natural walkways along the river coast, where waterways activities can be promoted. This zone can ensure food security and also promote tourism-related activities. The total area allotted for this zone is 1.857 km2 . The proposed agriculture zone can be utilized by encouraging

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Fig. 3.8 Right of way design of Transit corridor

Table 3.3 Percentage of land use for TP schemed area

Sl. No.

Land use

Area (m2 )

1

Residential

0.177

47

2

Commercial

0.064

17.25

3

Public/semi

0.02

5.4

4

Open space/recreational

0.014

3.7

5

Greenspace

0.014

3.7

7

Social infrastructure

0.017

46

8

Transportation

0.048

13

Percentage

farming which would help to increase the economy in a small proportion. Currently, most of the major nodes lack vibrant walkable spaces. Recreational space along the river bank is designed to encourage walkability and to relax, especially for residents (Fig. 3.9). This space makes residents less dependent on city parks. This area is restricted to pedestrians and cyclists only to a width of 10 m so that people can walk safely without any fear. In the tourism zone, Kerala style traditional houses facing the river can be planned, where the foreigners learn about the culture and enjoy their vacation. Trees must be planted along the walkway and around the resort. This would allow some privacy and shading for the residents. Trees and other utilities must be planned in conjunction, so that tree canopies do not obstruct lightings to users. A model of the resort in the tourism zone is shown in Fig. 3.10. Overall, the agriculture and tourism zone, both, can increase the economy, and these productivities can be much more if these zones are connected. Since the tourism zone and agriculture zone are connected through a bridge, both these areas can be used together for promoting tourism. And also, water-related sports activities would have high demands, and proximity to the river gives better opportunities.

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Fig. 3.9 A walkway along the river bank near to agriculture area

Fig. 3.10 Model of resort in the tourism zone

3.5.3 Residential Zone The residential zone is only focusing on housing for all categories of families along with the other basic facilities. The proposals in residential zones are explained in three parts: open and green space, building materials, and disaster management. Open and green space is proposed in every zone as it plays an important role in urban sustainability. Planting trees would allow some privacy for the residents. A strip of the landscape must be provided along the residential property lines. At least 50% of the area must be unpaved and trees must be planted. If the plot area exceeds 0.5 ha, about 20% green space should be left and must be unpaved. Farming can be promoted by group farming, kitchen gardens, and vegetable gardens. Building materials that are affordable and sustainable is planned for affordable housing for the lower-income group who cannot afford to stay in city core areas. Laterite would be locally available for construction. Since Kerala is an agrarian state, the waste products such as rice straw or husk can be used as cement replacement (straw bale construction) which would be low-cost material. It helps in significant savings in energy with rapid construction and only needs minimum infrastructure. Since the study area is surrounded by water, a stormwater management proposal is

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also included. Unfortunately, some portions of areas are within a flood plain which would discourage or limit the development opportunities in the affected area. The “no development zone” would include a 25 m radius from the riverside (Government of Kerala 2019). Finally, all the proposals in each zone were combined, and a final land use map was prepared under the GIS environment (Fig. 3.11). The percentage of land use in the study area is given in Table 3.4. The percentage area of each class in future land use showed that the water body has the largest share of 35% (1.754 km2 ) of the total land-use group. High density along

Fig. 3.11 Proposed land use map of the study area

Table 3.4 Future land use percentage of Erangikkal Sl. No.

LULC

Area (km2 )

Percentage

1

High-density area in the emerging zone

0.784

15.68

2

Medium-density area in the emerging zone

0.437

8.74

3

Residential

0.596

11.92

4

Agriculture

0.768

15.36

5

Waterbody

1.754

35

6

Recreational

0.155

3.1

7

Tourism-related activities

0.158

3.16

8

Other land uses

0.348

6.96

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V. P. Nishara et al.

the corridor for compact development has almost 16% among the class assigned, so that the problems of urban sprawl can be reduced to a limit. The medium density area is mainly for residential purposes and summing up other residential land uses in another zone; it accounts for almost 20%. Even though the percentage is less than the present, we can protect the environment that has been deteriorating day by day.

3.6 Implementation and Planning Recommendations The proposals in Erangikkal can be implemented through special land reorganization techniques. Since the settlement pattern of Kerala is of a segregated manner, the land acquisition would be a difficult task. To settle the dislikes of locals, there should be proper remedies that make the people agree. The undertaking agents can provide incentives for residents, businesses, and other development for taking place within the specified node by the following methods. Development rights would allow the landowner to transfer their land to the government and instead of that, the government should provide land in some other area where more growth is anticipated. In the case of agricultural land, farmers can sell the development value of the land, which allows them to continue with agricultural activities and they get money to keep the land undeveloped. At the same time, the buyer of the development’s rights could use the amount of development elsewhere in the area. There may be small areas with buildings that may act as an obstacle to developing high-density construction. Assembling small land parcels and combining them, would give new development opportunities. The separate land in the study area can be pooled using different techniques like the Plot reconstitution scheme or land pooling scheme. Land assembly creates an opportunity to create a large property in the surrounding area of the transportation corridor. Limiting new infrastructure can reduce development pressure by making it more costly for the developer to build in rural or agricultural areas. In the case of funds, the local self-government can develop the area with the help of real estate developers who are interested in opportunities for higher intensity development. LSG funds and other participants or in convergence with any Central/state/LSG schemes under Private Public partnerships funds can be used for the sustainable development in Erangikkal. There is a need to further investigate the consequence of land-use change due to the transportation corridor in terms of economic, social, and political aspects so that land-use management strategies and proposals can be made based on a scientific basis. This study is concentrated on a node along the corridor. Once the node is fully developed, additional developments can begin their way out of the node along the corridor. This would ensure a pedestrian-oriented development that would be created over time. Through this, the corridor and node would make a development pattern that would strengthen the transit system. The proposal of walkways along the river needs a separate study about the implications of that project on the environment and community.

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3.7 Conclusion This study analyzed the land-use changes that had occurred as a result of the development of the transportation corridor along the NH66 Bypass in Kozhikode district of Kerala, India. Major growth nodes along the highway were carefully chosen, and the land-use change was studied in the influence area, between the period 2000 and 2020. A growth node in the zone was selected to identify future growth and intensification potential. To control unplanned urban sprawl and loss of green spaces, sustainable land-use policies were proposed. The integration of remote sensing and GIS was successfully used as a rational tool to study the impact of highway construction. The present study covered a diverse range of topics related to urban growth, sustainability, compact, and green development which examines the existing situation and looks into an effective and viable approach for the assessment of landscape change to develop sustainable landscape management strategy in the outskirts of the city. It involved protection, enhancement, and promoting compact green development and management of urban sprawl as well as providing sustainable urban management in the urban fringe area through densifying the area along the transportation corridor.

References (AASHTO), AA of SH and TO (2005) Handbook on integrating land use considerations into transportation projects to address induced growth Aijaz R (2019) India’s peri-urban regions: the need for policy and the challenges of governance. ORF Issue Brief Augustine A (2016) Predicting the unpredictable-a case analysis of Kozhikode City. Procedia Technol 24:1726–1733 Census of India (2011) 2011 census data [WWW document]. Govt. of India. https://censusindia. gov.in/2011-common/censusdata2011.html Cohen B (2006) Urbanization in developing countries: current trends, future projections, and key challenges for sustainability. Technol Soc 28:63–80 Das DS, Laya K (2016) Urbanization and development in Kerala. Int J Appl Res 2:586–590 Downs A (2005) Smart growth: why we discuss it more than we do it. J Am Plan Assoc 71:367–378 Drummond MA, Loveland TR (2010) Land-use pressure and a transition to forest-cover loss in the Eastern United States. Bioscience 60:286–298 Economist Intelligence Unit Survey (2020) The Hindustan Times Government of Kerala (2019) Kerala municipality building rules, Government of Kerala Jain V (2019) Examining the town planning scheme of India and lessons from land readjustment in Japan Ministry of Housing & Urban Affairs (2017) National Transit Oriented Development (TOD) Policy Firoz CM, Banerji H, Sen J (2014) A methodology to define the typology of rural-urban continuum settlements in Kerala. J Reg Dev Plan 3:49–60 Müller K, Steinmeier C, Küchler M (2010) Urban growth along motorways in Switzerland. Landsc Urban Plan 98:3–12 Munthali MG, Davis N, Adeola AM, Botai JO, Kamwi JM, Chisale HLW, Orimoogunje OOI (2019) Local perception of drivers of land-use and land-cover change dynamics across Dedza district, Central Malawi region. Sustain 11

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OECD (2012) OECD green growth studies compact city policies : a comparative assessment [WWW document]. https://www.oecd.org/regional/greening-cities-regions/compact-city.htm Ratner KA, Goetz AR (2013) The reshaping of land use and urban form in Denver through transitoriented development. Cities 30:31–46 Sanyal B, Deuskar C (2012) A better way to grow? Town planning schemes as a hybrid land readjustment process in Ahmedabad, India. Value capture and land policies 149:182 Song J, Ye J, Zhu E, Deng J, Wang K (2016) Analyzing the impact of highways associated with farmland loss under rapid urbanization. ISPRS Int J Geo-Inf 5 Sruthi Krishnan V, Firoz CM (2020) Regional urban environmental quality assessment and spatial analysis. J Urban Manag 9:191–204 Sruthi Krishnan V, Ravibabu Mandla V, Rama Mohan RP (2016) A geospatial approach for the development of hazardous building zonation mapping. Arab J Sci Eng 41:1329–1341 Student Report MP (2020) Sustainable urban development along a transit corridor. M. Plan Student Rep. National Institute of Technology Calicut T&CP (2015) Urban and regional development plans formulation and implementation (URDPFI) guidelines T&CP Department Kozhikode (2015) Master plan for Kozhikode urban area—2035 Tagore GS, Shah SK (2013) Land use and land cover change detection using remote sensing and GIS. Int J Agric Environ Biotechnol 6:447 UNDESA (2018) World urbanization prospects. Department of Economic and Social Affairs. United Nations

Chapter 4

Smart City: Artificial Intelligence in the City of the Future Arti Chandani, Om Prakash, Prakrit Prakash, and Mita Mehta

Abstract In the digital world, as the companies and society are ushering towards Industry 5.0, there is a unique moment in history in which science and technology are enormously progressing. This chapter evidences the profound changes in people’s lives, with significant variations experienced from one generation to other. There are technological implications as well as the social implications affecting the configuration of modern cities. Importance of artificial intelligence (AI) is paramount and utilizing this power of AI, smart city systems are creating modern municipal services and processes. Disruptive technologies, such as broadband, 5G (which is expected to be 100 times faster than 4G), Big Data, the Cloud, the Internet of Things or Artificial Intelligence—all of them with a very high growth in recent years—are the catalysts for this transformation. The chapter evidences on the development of AI that opens the door to unprecedented technological developments and many applications that entail improvements in quality of life and well-being of citizens. Keywords AI · Digital sensors · Virtual buying · Big data · Privacy · Smart city

4.1 Introduction The evolution of various new technologies has led to the rise and development of smart cities worldwide to simplify people’s lives using the latest technology. The adoption of smart cities was welcomed and embraced by many governments worldwide, and this has led to the emergence of a new concept known as intelligent living. A. Chandani (B) · M. Mehta Symbiosis Institute of Management Studies, Symbiosis International (Deemed University), Range Hills Road, Khadki, Pune 411020, India e-mail: [email protected] O. Prakash Symbiosis Institute of Computer Studies and Research, Symbiosis International (Deemed University), Pune 411016, India P. Prakash Manipal Institute of Technology, Manipal, Udupi 576104, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 T. P. Singh et al. (eds.), Geo-intelligence for Sustainable Development, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-16-4768-0_4

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It is a concept where tourists and visitors get to enjoy various products driven by new technology. Smart technology implies the use of integrated methods to offer resolutions. Here, a smart city implies a community where the residents, businesses, academic institutions, and government agencies work together to bring about integrated systems and achieve efficiency, thereby improving the quality of life (Shapiro 2006). Further, smart living refers to the establishment of intelligent destinations as a differentiator to make the visitors enjoy a unique experience. Achieving the status of intelligent destination involves extensive use of data, technology, control, and coordination as the ingredients to create improved efficiencies, commercial development, and enriched standards of living in those cities. This idea of smart cities and smart living is better exemplified in Dubai than any other city across the globe (Farsi et al. 2020). Dubai has established itself as a financial and living hub in the Middle East. Over the last decade, it has undertaken massive infrastructural development that is both unique and exemplary. Other countries are imitating the development record set by Dubai. These developments have led to an enormous increase in the number of tourists arriving in the city. This has further propelled the growth of the city to an unprecedented level. The smart city initiative has brought about smart living that has led to an emergence of a new initiative—the happiness vision—to market Dubai as the Disney World of the Middle East (Singh 2015).

4.1.1 Literature Review It has already been pointed out that smart cities refer to the strategy deployed by businesses as they aim to meet particular goals by integrating innovating systems such as artificial intelligence and block chain to simplify processes with a stakeholder focus (Kundu 2019). Accomplishing this goal involves extensive integration of innovative technologies in all facets of daily life. The rise of smart living is a result of the advancement of smart cities. The idea of smart city was to solve the problem of urbanization. Viability is at the heart of the proliferation of smart living, and many vacation destinations are prioritizing it as a deliberate goal in the process of projecting. Examples of these destinations include Barcelona, Bangkok, New York, and Singapore. Technology has propelled the growth of these cities because it has penetrated almost all the daily life events such as smart hotels, smart airports, smart transportation such as railway system, waterways, and surface transportation. This integration of technology has significantly improved tourist’s experiences through increasing efficiency, automating processes, and focusing on customer satisfaction (Farsi et al. 2020). The fundamental principle behind smart living in these cities is to provide a link between the destination and the tourists by solving their specific needs. Dubai, for example, is characterized by the use of highly innovative devices and technologies that amazes the visitors. These technologies optimize the use of resources while providing the visitors and tourists with a lifetime experience. The technologies

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comprise cloud services, end-user internet services, and Internet of Things among others. They seamlessly integrate these technologies to provide tourists with a unique and premium service that is uncommon in many destinations. These experiences depend on the seamless conversion of the user’s data into resolutions that meet precise needs (Bessis and Dobre 2014). Smart cities have distinguished as a technology hub through seamless digitization of systems and processes, high level of interaction between the tourist and the destination, and a robust branding campaign. The infrastructural development in smart cities uniquely blends leisure, trade, entertainment, and luxury. The skyscrapers, the malls, and beaches give tourists a lifetime experience (Singh 2015). The smart infrastructure capabilities also let the smart cities to provide for one of the most lavish hotels in the world. They attract many tourists who would want to dine and experience some of the most innovative IT solutions in the world. All these developments have led to an increase in the number of multinational companies making smart cities as their regional hubs (Kirwan and Zhiyong 2020). The deployment of technology-dependent solutions in key living products, investment in massive and unique infrastructure, and marketing of the cities as the Happiness Center in the world. Some of the most expressed feelings among visitors after seeing smart cities include ‘excitement,’ ‘great,’ ‘beautiful,’ and ‘wow.’ In view of this, one may conclude that this concept has sold the Happiness Vision to its visitors and tourists alike. To achieve this dream, smart cities embark upon an aggressive development and deployment of extensive innovative technologies aimed at simplifying the visitor’s experience. The smart projects, innovative technologies that provide tailored solutions to tourists, and the pioneering developments in integration of AI in daily life have cemented their position as a market leader in innovation and one of the most preferred destinations (Kirwan and Zhiyong 2020). Whenever a tourist visits a smart city, he/she is provided with a range of IT-based solutions to solve his/her all needs such as transport, retail systems, and healthcare. There are specifically dedicated applications to help the tourist navigate the city. Tourists have access to information kiosks and entertainment centers as well as sprawling malls. These applications provide navigation services around the malls and city to make sure that the visitors feel comfortable (Souza et al. 2019). Smart cities have continued to capture the imagination of many people around the world as seen from the statistics. For example, in Dubai, there were 15.8 million visitors in the year 2017, 15.93 million in 2018, and 16.73 million in 2019. The number of tourists and visitors were projected to hit 20 million in the year 2020 (Shapiro 2006), but this is not likely to happen due to the corona virus pandemic that has halted global travel. Given the above statistics, smart cities are becoming tourist hubs driven by smart technology and massive infrastructural development, which has not been in vain. Barcelona and Singapore continue to experience an upward trend in the number of visitors and tourists. For example, in Singapore, there were 17.4 million visitors in 2017, 18.5 million in 2018, and 19.11 million in 2019. In Barcelona, there were 19 million visitors in 2017, 20 million in 2018, and 22 million in 2019 (Farsi et al. 2020). Despite the achievements attributable to the ‘Smart’ concept, there are challenges that have affected its attraction to international tourists. Even though the smart living

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has increased the number of visitors annually, the lack of its integration with the overall system may limit the number of visitors. This is a competitive edge that countries such as Singapore and the United Kingdom and France have perfected, and this is reflected in the number of visitors and tourists visiting these countries annually. For example, in 2019, there were 40.9 million visitors in London, in the United Kingdom; 35.4 million in Paris, France; 22.7 million in Bangkok, Thailand; and 19.1 million in Singapore. A critical challenge to smart living is a rapid increase in non-repeat visitors and tourists. It is expected that the rapid development of infrastructure and scenic attractions would sustain and that there would be continuous increase in the number of visitors. However, with the problem of non-repeat customers growing, it is likely to cause a scenario where the supply exceeds the demand. Another crucial challenge that smart cities face is its dependence on oil for the high profile living and infrastructural developments. The cities need to invest billions of dollars accrued as profits from oil production into developing the infrastructure with the aim of reducing its reliance on oil-driven economy. With the development of other smart cities around the globe, the competition could only get stiffer (Visvizi and Lytras 2019).

4.2 From Digital City to Smart City Smart Cities have attracted worldwide attention in recent years as they may provide solutions to the problems that urbanization bring to global society. Smart Cities aim to develop systems for intelligent management of population, industry, space, land, environment, social life, public service, etc. The concept itself is still emerging, and the work of defining it is in progress (Kundu 2019). Various cases also contain different approaches. Some similar terms are: digital community, smart community, digital city, information city, e-city. However, Digital City and Smart City refer to entirely different definitions. The demarcation of Digital City and Smart City occurred in 2008 when the idea of a Smart Planet was proposed by IBM. IBM defined its view of a Smarter Planet system through three IT characteristics or dimensions: Instrumented, Interconnected, and Intelligent.

4.2.1 Digital City Digital City integrates urban information and creates public space to be meant for public with the help of digital information, network, and visual techniques so as to obtain data about population, resource, environment, economics, and social statistics. Digital Cities refer to the huge information systems of an entire city, which is managed by computer databases and communication networks through digital processing. It is on the foundation of a broadband metropolitan area network and geo-spatial data, supported by a global positioning system, remote sensing, a geographic information

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system, virtual reality, data fusion, dynamic interoperability, and other technologies (Picon 2015). Digital Cities constitute data fusion from multiple sources of various levels with multimedia and virtual reality technology. Prime examples are the Digital City Amsterdam (1996), Helsinki Arena 2000 Project (1996), and Digital City Kyoto (1998; Eiza et al. 2020).

4.2.2 Smart City The concept of Smart Cities was born from IBM’s Smarter Planet. A Smart City makes full use of the IT technology in almost every domain, consolidating into Digital City, Internet, and the Internet of Things (IoT), which embeds sensors into all corners of the city through power grids, railways, bridges, tunnels, highways, buildings, water supply systems, dams, oil and gas pipelines, etc. Then, through supercomputing and cloud computing, an urban system integrates data transfer and information, enabling smart management and service (Kaur et al. 2020). In short, ‘Smart Cities = Internet of Things + Internet’ (Bessi and Dobre 2014).

The unique feature lies in the ‘system of system’ breaking the ‘information island’ barriers which results from unbalanced regional development and department interest divisions. Breaking these barriers would result in functional subsystems sharing resources as well as collaborative operations among every region or industry. Typical projects of Smart Cities include the ‘I-Japan 2015’ in Japan, the West Orange project, the Geuzenveld project, the Energy Dock project, the ITO tower project in Netherlands, the Stockholm Intelligent Transportation System in Sweden, the U-Korea and Seoul IPTV government in Korea, the ‘iN2015’ in Singapore, the Multimedia Super Corridor in Malaysia, and the T-city in Germany (Eiza et al. 2020).

4.3 General Concepts: Blueprint and Perspective Considering the Internet of Things is the basis of a Smart City, Smart Cities will enhance the urban comprehensive management level while improving the ability of reducing and preventing disasters and handling emergencies. A Smart City operates with the interaction of water, energy, communication, transport, business, and people through integration systems. Extensive application of smart technology in these core areas maximizes the benefits of the resources. It is a new city mode full of intercommunication, integration, collaboration, and innovation (Bessis and Dobre 2014). Smart Cities will promote city-wide public resource sharing and enhance the flowability of people, materials, information, and funds. Smart Cities show us a blueprint of how to make the government operation more efficient, make industries more advanced, provide higher quality of life for citizens, and making the public service and information infrastructure near perfect. Through further mining, integrating,

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and allocating relevant tangible and intangible resources of political and economic society, deep integration will be achieved (Souza et al. 2019).

4.3.1 Key Technology, Model, and Framework A Smart City can be described as an ISGBP model (Souza et al. 2019). • It consists of five parts: Infrastructure (I), Service (S), Government (G), Business (B), and Public (P). • Among them, the G, B, and P compose the main body, and the intelligent service is emphasized and is divided into three parts: data service, function service, and model service. • Between the substance element of I and the people element of G, B, P, there exists a variety of related relationships: G-I, B-I, P-I, G-B, G-P, B-P, B-B, P-P relations, etc., which associate through the intelligent service. • Each relationship extends to a series of specific services that combine with each other to make up a higher level of services and a more complex relationship structure, finally forming a stereoscopic-cross intelligent service system. Smart City involves a vast array of technologies such as cloud computing, communication technologies, networking, GIS, satellite positioning, high performance computing, artificial intelligence, software engineering, system engineering, information security technology modeling and simulation, etc. For the moment, IoT and cloud technologies are emphasized as the key technologies (Bessis and Dobre 2014).

4.3.1.1

Internet of Things (IoT)

Internet of Things (IoT) normally gathers real-time information on objects using sensing equipment such as RFIDs. It then propagates collected information to various processing centers using internet, wireless network, or even the optical fiber technology. Post the exhaustive analyses of humongous information it gathers, an intelligent control and management is realized and it improves efficiency of urban operations. IoT integrates contact relationships of people as an object. These ‘objects’ can then ‘communicate’ with other objects in the ambit to allow the smart city to display and use the intelligent characteristics. Research and Development of IOT majorly focuses on wireless sensor network node technologies, WSN Gateways, system miniaturization, UHF/RFID, intelligent wireless technologies, communications and heterogeneous networks, network planning and deployments, comprehensive perception and information processing, the middleware platform, code resolution services, searches, tracking, and information dissemination.

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Cloud Computing

Cloud computing is developed based on parallel computing, distributed computing, and grid computing. It is a mixed bag of technologies, which evolves from virtualization, utility computing, SaaS, SOA, and more related technologies. Cloud storage refers to a system that, through cluster application, grid technology, and the distributed file system, interconnects and facilitates collaboration among different storage equipment of networks, providing data storage and transaction access servers. Cloud storage has unlimited expansion ability which can support mass data of hundreds of TB to PB. It uses parallel computing to achieve massive data computing ability. It employs high-availability, guaranteeing the system that normal operating conditions are available at any point of time, even with hardware malfunction. The cloud storage systems architecture mainly consists of four layers: the storage layer, basic management layer, application interface layer, and access layer. The solution of cloud computing provides public cloud, private cloud, and mixed cloud (public–private) computing environments with three-level services at the same time throughout. SaaS (Software as a Service), PaaS (Platform as a Service), and IaaS (Infrastructure as a Service) (Bessis and Dobre 2014).

4.3.1.3

Smart Network

Smart network is an important and integral part of a Smart City. CPSM is developing to help realize smart management of city pipelines, which carry out the webintegration of urban network resources. It also offers application sharing and supports assistant decision-making. It links the intelligent sensors which are implanted in network facilities, realizes full perception of pipelines and associated networks, and processes intelligent analysis on perceived information with cloud computing technology. The key technology of CPSM covers portal integration, web service, data warehouse, data acquisition, network communication, intelligent software, and sensor technology (Eiza et al. 2020).

4.3.1.4

Architecture of a Smart City

The architecture of a Smart City basically follows the ‘3 I’ features proposed by IBM: Instrumented, Interconnected, and Intelligent. Therefore, it is logically divided into a Sensing Layer, Network Layer, and Application Layer. In fact, putting the information transmission and processing at the same layer or the processing and application at the same layer effectively limits the scope of information processing. This converges in to the closed loop application. It is not conducive to sharing and reusing resources among different applications or different items. Therefore, information transmission and processing both tend to be separated (Picon 2015). The general and public information processing are independent on IoT infrastructure, and can be commonly used by multiple applications. Thus, the architecture is divided into four

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levels: A Sensing Layer, Transmission Layer, Processing Layer, and Application Layer (Elmangoush et al. 2013). In this architecture, the Sensing Layer is majorly responsible for object identifications and information collection with IoT as its core. It has two main components: a basic identifier or sensor (such as RFID tags, readerwriters, multiple kinds of sensors, cameras, GPS, two-dimensional barcode labels, etc.), as well as a fusion network with inductors (such as sensor network; Elmangoush et al. 2013). The Sensing Layer involves the technologies of electronic radio frequency, emerging sensors, wireless networking, and field bus control. It also has a part in the products of sensors, electronic tagging, sensor nodes, wireless routers, wireless gateways, and more Internet of Things. The Transmission Layer undertakes all kinds of access and transport options. It is the path of information exchange and data transmission, including the Transmission Network and Access Network. The Transmission Network includes a telecommunication network, broadcast and television network, the Internet, electric power telecommunication network, private network, and a digital trunked system. The Access Network comprises of fiber access, wireless access, Ethernet access, satellite access, and various different accesses, completing sensor networks as well as all aspects of accessing of RFID networks (Eiza et al. 2020). The Processing Layer plays an intelligent function of processing and controlling perceived information as well as utilizing decision making. The Processing Layer comprises of the business support platforms, middleware platforms, network management platforms (like M2M management platforms), information processing platforms, information security platforms, service support platforms, and others. It implements coordination, management, computing, storage, analyses, and data mining and offers service and other functions for industries and the public user. Technologies and the service model include middleware technologies, virtual technologies, hi-fidelity technologies, cloud computing service models, and the SOA systems architecture design (Souza et al. 2019). However, the Application Layer provides the solution set of wide intelligence application, combining the network and industry fields. Smart Cities may eventually realize depth of fusion amongst information technologies and industry-specialized technologies through the Application Layer. It has an extensive effect on the national economy as well as social development. This layer’s key problems are socialization of information resources sharing, and safeguarding information security (Picon 2015).

4.4 Application Range and Scope There are various applications of Smart Cities as mentioned hereunder. Smart meshing regionalized management is the application where the city can be more effective in management and service. Smart food application, which ensures the food is safe and free from contamination from the source to the consumer through the smart safety management system and tracking control system, production evaluating system, and emergency system. Citizens just need to press the keys of their mobile

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phones, learning about food origin, growth condition, nutrition facts, even cooking methods, and recipe suggestions. Smart water resource application manages water resources. With the help of management systems, relevant organizations can bring the water situation under real-time monitoring, providing fast response times to water pollution emergencies. Smart water resources also ensure intelligent deployment of limited water reserves, thus providing proper utilization (Kirwan and Zhiyong 2020). Smart traffic application is needed as people, vehicles, roads, and the environment mutually exchange the information on traffic, flow, noise, accidents, weather, temperature, etc. Smart traffic improves the efficiency aspect of mobility and accessibility as well as reduces the energy consumption and protects the environment. The transportation system predicts traffic flow and gets dynamic control of road conditions in advance so that diversions appear before congestion, allowing drivers alternative routes to alleviate traffic congestion. Smart medical application ensured that all kinds of medical information and resources integrate through the electronic medical record and medical information integration platform, so doctors can refer to patients’ history records in which they find out symptomatic regularity. This helps ensure that patients get fast, consistent, and accurate health care in different hospitals. Smart electric power application ensures that there is not only the current flow, but also the information flow in the power grid. Through effective information retrieval, the smart electric power system can be constructed by clean power transmission, dynamic distribution, and reasonable usage, thus allowing on-demand electric use without a risk of overload. Smart public security service application ensures that the urban security monitoring is established in public places, residential districts, transport facilities, campuses, construction sites, etc. (Singh 2015). Smart home application supports takes care that the inter-communication comes into effect when data is exchanged among various sources of home appliances through different communicative methods. Every family enjoys more convenient style of living and a higher quality of life through these aspects. Smart education, smart parks, and smart enterprises provides for the embedding, connecting, sensing, and mass information processing technologies and applies to all aspects of work and life. They enable cities to step into a new stage of management and development of urban infrastructure, education, scientific and technological activities, public safety, community service, etc. (Souza et al. 2019). Other typical applications and practices include outdoor streaming of media and emergency response linkage systems. Smart Cities attempt to achieve the comprehensive intelligence that eliminates the gap between urban and rural areas, as well as promote economic development, and improve government efficiency and public security (Picon 2015).

4.5 Privacy Issues and Challenges Data privacy should be the key concern for the smart cities and IoT-driven technologies. The various types of sensitive and personal data that can be collected

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in a smart city include financial transactions, medical information, social interactions, transport usage, personal security, and movement. The most important privacy issues are related to data security, regulations on collection, storage, and use of data, transparency for citizens to know how this is all done, and future usage of data collected—may appear innocent now but might have potential for other, purposes in future, smarter computer systems that analyze all this data without human input and make decisions (Visvizi and Lytras 2019). However, there is a positive side that building smart cities like Dubai and Barcelona would induce change in job markets. Jobs that will need to be created or re-skilled include Chief Information Officer/s at the Government level and at an organizational level, Chief Technology Officer/s at the Government level and at an organizational level, Chief Security Officer/s at the Government level and at an organizational level, Chief Privacy Officer/s at the Government level and at an organizational level, Chief Knowledge Officer/s at the Government level and at an organizational level, General IT repair and installation technicians, Specialized IT repair and installation technicians, and Lawyers that specialize in IT. These jobs will need to be created to cope with the increased demand of Internet usage and the ethical, legal, and social concerns that accompany it (Farsi et al. 2020). Whilst all over the world there is discernible move to build smart city, there has been a point well highlighted that future of smart cities would lead to ‘Big Brother is watching’ situation. This can rather negatively impact people’s lives. Even without these ‘smart cities,’ people are already being watched. Most businesses have cameras installed as well as on streets, at houses, grocery stores, and bars. The ‘smart cities’ idea is already being introduced with the introduction of finger printing and facial recognition in bars/clubs after a certain time of the night. When talking about the recent crime activity, the use of cameras in businesses has helped police immensely to identify these suspects either through the communities help or facial recognition from these different images captured on the cameras. A constant consciousness that the Big Brother watches your every move is actually burdening, just as one gets conscious when being watched on camera. When it comes to the technology side of it, big brother can potentially use our phone to see what we are interested in and what we are doing at all times. It can potentially lead to the hacking of the personal details as everyone is connected through the cloud of through multiple devices. Though the ‘Big Brother is watching’ can either be a way to help one feel safer or more vulnerable to the connected world to be a target for a hacker or victim to an online attacker where they will have access to all of the personal data (Visvizi and Lytras 2019).

4.6 Discussion and Suggestion Smart Cities involve technology and systems which may result into panic, once they temporarily get beyond control, and cause disaster. This may affect at the national level dramatically. Data is heavily dependent on the system. How the data will be used

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for other systems in the future is a question on account of close correlation between data designs and software system. The more the data exists, the greater the risk will run. On the decision of Smart City upgrade, tremendous cost would be difficult to bear considering as presently many technology schemes have not been clearly defined. An example is the design scheme of cloud computing. The construction of Smart Cities is the complicated system engineering with difficulties which persist for a long period and demand large capitals. Any problem in any part will bring huge losses. Therefore, overall planning and gradual development are necessary (Eiza et al. 2020). There are some suggestions about how to form the Smart City strategy suited for local situations, making reference to the experience of cases and lessons we learned. Policy guidance and top design at the national level should come earlier. National standard on the Internet of Things and other information management system should come earlier. Urban and regional planning should be more far-seeing and coherent, while also paying attention to actual local conditions. The national broadband plan and infrastructure construction should be set up and strengthened as soon as possible. Information security should be considered at all times. The implementation of Smart Cities should be people-oriented, stage-by-stage, and step-by-step. Great care needs to be taken so as to foster and manage skilled professionals (Shapiro 2006). The processes to construct a Smart City is under constant evolution of the institutional systems and technological standard on massive scale involvement of capital, technologies, talent, the city, industries, academia, organizations, and society. Some organizations such as the City Protocol are making effort for the development of better cities worldwide, which is an international group of cities and technology companies that will develop standards for Smart Cities using methods similar to those used to develop the Internet Protocols (Visvizi and Lytras 2019).

4.7 Conclusion and Prospects Smart Cities change the interactive way among the government, enterprise, and people, making early intelligible responses to every kind of demand in terms of livelihood, environment protection, public security, urban service, and industrial and commercial activity. Smart Cities improve urban efficiency, and outline a vision for a better urban life. They will make a difference in the future. Smart cities provide comprehensive perception. The IoT, as an important part of Smart Cities, will interconnect urban public facilities, as well as Home Appliances, office Supplies, etc., and make a real-time sensing method for urban operation system (Kaur et al. 2020). There is integration in full, with respect to all applications. The IoT and the Internet will be completely connected and combined, integrating data into the full view of core urban operation system, and providing the foundation of smart facilities. The smart city offers collaborative operation. Each operation system, platform, and participant cooperates harmoniously and efficiently. The city will be in the best cooperating condition (Kaur et al. 2020). Clean energy will become the major use of urban energy.

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Solar photovoltaic power, wind energy, biomass energy, tidal energy, and other new energy of advanced technologies will be applied on a large-scale. Knowledge-based service industry will become the main form of future urban industry, as a whole industry around the Knowledge-intensive services industry will flourish in future cities, such as financial service, modern logistics, information service, education and research service, and idea and product design. Megalopolis, urban agglomeration, and city group will boom. Convenient information and traffic network will close cities which develop cooperatively and complement each other with respective characteristics. London Metropolitan Area, North American Great Lakes cities group, German Ruhr cities group, Chinese Yangtze River Delta, Pearl River Delta, etc., have been formed. Active and personalized service will be provided and prepared in advance, as finding out the demands of enterprises and the public is automatic (Visvizi and Lytras 2019).

References Bessis N, Dobre C (eds) (2014) Big data and internet of things: a roadmap for smart environments, vol 546. Springer International Publishing, Basel, Switzerland Eiza MH, Cao Y, Xu L (eds) (2020) Toward sustainable and economic smart mobility: shaping the future of smart cities. World Scientific Elmangoush A, Coskun H, Wahle S, Magedanz T (2013) Design aspects for a reference M2M communication platform for smart cities. In: 2013 9th international conference on innovations in information technology (IIT). IEEE, pp 204–209 Farsi M, Daneshkhah A, Hosseinian-Far A, Jahankhani H (eds) (2020) Digital twin technologies and smart cities. Springer Kaur MJ, Mishra VP, Maheshwari P (2020) The convergence of digital twin, IoT, and machine learning: transforming data into action. In: Digital twin technologies and smart cities. Springer, Cham, pp 3–17 Kirwan CG, Zhiyong F (2020) Smart cities and artificial intelligence. Elsevier Kundu D (2019) Blockchain and trust in a smart city. Environ Urban ASIA 10(1):31–43 Picon A (2015) Smart cities: a spatialized intelligence. Wiley Shapiro JM (2006) Smart cities: quality of life, productivity, and the growth effects of human capital. Rev Econ Stat 88(2):324–335 Singh B (2015) Smart city-smart life: Dubai expo 2020. Middle East J Bus 55(2473):1–4 Souza JTD, Francisco ACD, Piekarski CM, Prado GFD (2019) Data mining and machine learning to promote smart cities: a systematic review from 2000 to 2018. Sustainability 11(4):1077 Visvizi A, Lytras M (eds) (2019) Smart cities: issues and challenges: mapping political, social and economic risks and threats. Elsevier

Chapter 5

Geo-Intelligence for Ecosystem Services in Poverty Alleviation and Food Security Faith Njoki Karanja

Abstract According to the World Bank, 9.2% of the global population is poor and surviving on $1.90 or less a day, a metric obtained based on a person’s income and ability to meet basic needs. However, considering other dimensions like health, education, and standards of living provide a robust estimate namely the Multidimensional Poverty Index. The Sustainable Development Goal Number 1 is on ending poverty in all its forms everywhere. On the other hand, achieving food security, improved nutrition, and promoting sustainable agriculture is captured in goal number 2. Although reports indicate significant progress in mitigating hunger, extreme hunger and malnutrition remain an impediment to development in many countries. Indeed, in 2017 an estimated 821 million people was found to be severely undernourished. The dimensions of food security are namely availability, access, utilization, and stability. Poverty and food insecurity are interrelated, for instance, economically challenged people tend to be more vulnerable to food prices shocks and access. Human beings rely on terrestrial ecosystem for survival from where it is estimated that over 90% of food comes from. It is also viewed as a source of economic activities by providing energy, water, and materials for construction, among many others. Consequently a sustainable approach in dealing with poverty and food insecurity cannot ignore ecosystem services. Ecosystem services in poverty reduction and food security is complex in nature due to the multi-dimensional data needs. This therefore necessitates a paradigm shift from the traditional geospatial data handling to the Geointelligence approach. This book chapter focuses on a framework of Geo-intelligence of ecosystem services in poverty reduction and food security with five sections: An Introduction; A review on poverty reduction and food security; Ecosystem Services in poverty reduction and food security; Geo-intelligence for Ecosystem Services in Poverty Alleviation and Food Security; and a Summary. Keywords Geo-intelligence · Ecosystem services · Poverty alleviation · Food security F. N. Karanja (B) Department of Geospatial and Space Technology, University of Nairobi, Nairobi 30197 00100, Kenya e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 T. P. Singh et al. (eds.), Geo-intelligence for Sustainable Development, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-16-4768-0_5

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5.1 Introduction The blueprint toward achieving a sustainable future for all is espoused by the Sustainable Development Goals (17 goals) which were preceded by the Millennium Development Goals and meant to be realized by 2030 (UNDP 2020). The focus is on tackling the global challenges with the first goal aimed at ending poverty in all its form everywhere whereas the second goal is set to end hunger by achieving food security, improving nutrition and promoting sustainable agriculture. Statistics reveal that extreme global poverty rate dropped to 9.2% in 2017, from 10.1% in 2015. That is equivalent to 689 million people living on less than $1.90 a day (World Bank 2020a). This population is unable to meet basic needs like food, education, health even access to clean water and sanitation. The poverty levels in the rural areas are estimated to be three times more compared to the urban areas at 17.2%. In addition it has also been established that 8% of the global working population live in extreme poverty (2018) implying that having a job does not necessarily translate to poverty alleviation (UN 2019). However, it is important to recognize that poverty is more than income, and so considering other indicators like health, education, and standards of living provides a robust way of assessment through the Multidimensional Poverty Index (MPI). An estimated 1.3 billion people spread across 107 developing countries were found to be multidimensionally poor based on the MPI according to a report by UNDP in 2020. Although a lot of progress has been made in addressing food insecurity as a result of rapid economic growth and increased agricultural productivity, extreme hunger and malnutrition remain a huge barrier to development in many countries. Further, in 2017, an estimate of 821 million people was found to be severely undernourished. This has been attributed to environmental degradation, drought, and biodiversity loss (FAO 2018). The causes of food insecurity are diverse and multifaceted. These may include political instability, war, macroeconomic imbalances, and trade dislocations to environmental degradation, poverty, population growth, gender inequality, inadequate education, and poor health. According to a report by the leading organizations on status of food security and nutrition in the world namely Food and Agriculture Organization (FAO), International Fund for Agricultural Development (IFAD), United Nations International Children’s Emergency Fund (UNICEF), World Food Program (WFP), and World Health Organization (WHO) the progress to ending hunger and improving nutrition is not encouraging (FAO 2020). Countries need to do more by understanding their unique challenges so that appropriate solutions are found. Poverty reduction and food security are like two sides of a coin. Empowering people economically goes a long way in ensuring that people have access to food in the right quantity and quality. Therefore strategies designed to reduce poverty in the long run end up addressing issues of food security. It has been shown that economically challenged people use more of their incomes on food and since they have no control of shocks like variations in food prices this makes them vulnerable.

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This is more critical in developing countries where most people are found in the informal sector (World Bank 1986). Human beings benefit from ecosystem services in various ways both directly and indirectly and by extension support the survival and quality of life (MEA 2005). Therefore understanding the role of ecosystem services in poverty reduction and food security is important. The global dynamics in terms of poverty and food insecurity levels imply that the benefits accrued from ecosystem services will of necessity vary. This therefore demands an innovative way of dealing with poverty and food insecurity where data has to be gathered, organized, and displayed logically to determine the magnitude of the problem and scope. This is where location intelligence comes in. There are different schools of thought with regard to Geospatial Intelligence necessitated by area of focus and application. Bacastow and Bellafiore (2009) define Geointelligence through the academic lens as knowledge that is actionable; it is also viewed as a process as well as a profession. It endeavors to provide information that can support decision making. On the other hand, the definition of Geo-intelligence through the industry lens is the profession that provides a platform of relevant Geospatial Data integration with an aim of generating intelligent solutions that explain the historical developments and the forecasts (Murdock and Clark 2016). From a security perspective, the interest is in military, defense, and intelligent applications. In some applications like humanitarian crisis (hunger and pandemic) and crime management that require in some instances real time and localized information, the success lies in the ability to identify appropriate data, collect, and manipulate geospatial data to support decision making and programming. For instance, addressing humanitarian crisis like hunger occasioned by economic downturn, prolonged drought, and flood disasters especially in areas where poverty levels are high requires more than traditional geospatial data handling approach. In such instances, the Geo-intelligence approach is able to addresses questions with regard to what, when, where, who, which, and how (Stack 2017). The overarching goal of the Geo-intelligence approach is to effectively solve users’ needs by combining multi-faceted data sets to reveal the invisible.

5.2 Poverty Reduction and Food Security Poverty reduction and food insecurity are both multi-dimensional in nature. Poverty reduction and eradicating food insecurity requires an enabling environment that does not only depend on good policies, programs, legal frameworks, good governance, and partnership, but should be evidence based.

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5.2.1 Poverty Reduction Poverty has been an elusive phenomena with many initiatives aimed at minimizing its effects across the entire world. It has universally been accepted that lack of access to basic necessities, e.g., food, shelter, health, and education constitute poverty of some kind. The severity and vulnerability of poverty varies from one place to another. For instance, one of the goals of the World Bank Group is to end extreme poverty and promote shared prosperity. Progress has been observed to this end with statistics showing a decrease from 36% to 10% of people leaving on less than $1.90 a day in 1990 to 2015 (World Bank 2018). However unprecedented global shocks like COVID 19, climate change, and natural disasters reverse the gains made in eradicating poverty. Poverty metrics are dynamic and vary from one geographic location to another with majority of the poor people living in the rural areas. The rural populace also tend to be poorly educated and largely work in the agricultural sector. Another factor that has been found to contribute to high poverty rates is conflict (Justino and Verwimp 2013). Indeed, it has been established that 43 countries with the highest poverty rates are as a result of conflict with poverty levels estimated at over 40% for the past decade, World Bank (2020b). In contrast countries with no evidence of conflict have had their poverty rates reduced by half. However, lack of data makes it difficult to accurately assess the extent of the population experiencing poverty. A number of indicators are required in estimating poverty for instance illiteracy, health, security, and access to basic utilities, other than just focus on financial aspects. In terms of strategies used to deal with poverty, it is recognized that there is no universal one. Consequently, each country has to contextualize their approach by making use of own data and the users’ needs to come up with country driven programs. Quality economic growth that ensures inclusiveness, focus on human capital and taking care of the vulnerable in society, will ensure poverty is dealt with in a sustainable way. In middle income countries, 60% of the populations live in extreme poverty. Under the multidimensional poverty index (MPI) model, three dimensions are considered namely health, education, and living standards (Alkire et al. 2020). The index is computed as shown in Eq. 5.1. MPI = H × A,

(5.1)

where H is multidimensional headcount ratio (H) given by Eq. 5.2 H=

q , n

(5.2)

q is the number of people who are multidimensionally poor and n is the total population. A is intensity of poverty calculated as shown in Eq. 5.3

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Table 5.1 Multi-dimensional poverty dimensions and indicators Poverty dimensions

Indicators

SDG target

Weight

Health

Child mortality

3

1/6

Nutrition

2

1/6

Education

School attendance

4

1/6

Year of schooling

4

1/6

Assets

1

1/18

Housing

11

1/18

Electricity

7

1/18

Drinking water

6

1/18

Sanitation

6

1/18

Cooking fuel

7

1/18

Living standards

n A=

i=1 ci (k)

q

(5.3)

where ci (k) is the censored deprivation score of individual i and q is the number of people who are multidimensionally poor. A total of ten indicators are used to measure poverty where for health (child mortality and nutrition); education (school attendance and years of schooling) and living standards (assets, housing, electricity, drinking water, sanitation, and cooking oil). Table 5.1 shows the poverty dimensions, indicators, the associated SDG Target as well as the weight assigned to the indicators. From the table, it is evident that the indicators cut across different SDG Targets, for instance, in addition to the poverty target, others are Zero Hunger (2), Good Health and Well Being (3), Quality Education (4), Clean Water and Sanitation (6), Affordable and Clean Energy (7), and Sustainable Cities and Communities (11). Each of the poverty dimensions is assigned equal weight and the interpretation is that one is declared poor if they are denied a third (1/3) of the weighted indicators. A comparison between Kenya and South Africa between 2010 and 2018 based on the Multi dimensional poverty index is shown in Fig. 5.1a, b. It is evident from the figures that efforts to alleviate poverty have been succeeding as the years progress with a reported MPI being less in 2018 compared to 2010 for both Kenya and South Africa (Fig. 5.1a, b). A comparison of the two countries on the Health dimension whose indicators are Nutrition and Mortality between the years 2010 to 2019 shows some improvements in addressing child mortality with South Africa making remarkable progress. In terms of nutrition, South Africa is generally consistent whereas the situation in Kenya appears erratic as depicted in (Fig. 5.2a, b). Figure 5.3a, b represent two indicators used for the education poverty dimension namely years of schooling and school attendance between 2010 and 2019 for both Kenya and South Africa. Evidently, there are some variations between the two case

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a

b

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studies with general percentages being less in South Africa compared to Kenya. With regard to the living standards poverty dimension, a marked difference between the two case studies namely Kenya and South Africa is noted, as clearly illustrated in Fig. 5.4a, b. South Africa generally demonstrates better standards in all the indicators.

5.2.2 Food Security Food security is a multidimensional concept that involves a whole range of different factors such as social inequalities and environmentally sustainable food systems. Food Security as a concept has been evolving over time starting in the mid-1970s during the global food crisis. As a result a number of definitions exist with Maxwell and Smith (1992) through their review identified 200 definitions. The different approaches to technical and policy issues have resulted in the many perceptions of food security. Notable definitions are for instance by the World Food Summit (WFS) (United Nations 2020) as “availability at all times of adequate world food supplies of basic foodstuffs to sustain a steady expansion of food consumption and to offset fluctuations in production and prices” and redefined in 1996. FAO and World Bank have had their own definitions in the mid-1980s. FAO food security concept encompassed access by vulnerable people both physical and economic access in 1983 (FAO 1983). According to the World Bank Report on poverty and hunger, the recognition of the nexus between poverty and food insecurity is explained (World Bank 1986; Watts and Bohle 1993). In the mid 1990, the definition was expanded to include food safety and nutrition. In 1996, the World Food Summit had an expanded definition of food security at all levels in terms of physical, economic access to sufficient, safe, nutritious food to meet their dietary needs, and food preferences for an active and healthy life (FAO 1996). In 2001, the State of Food Insecurity fine-tuned the definition as “Food security is a situation that exists when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food that

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Stability Availability

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meets their dietary needs and food preferences for an active and healthy life (FAO 2002). The focus of food security on individuals and households as a right has been reported by studies like (Sen 1981). However, as much as these definitions are encompassing in terms of broad statements of common goals and implied responsibilities from an implementation point of view, actionable objectives are necessary. The idea is therefore to have narrower, simpler objectives to base international, and national public action on. To this end, the 1996 WFS exemplified this direction of policy by making the primary objective of international action on food security halving of the number of hungry or undernourished people by 2015 according to the Millennium Development Goals whose focus was to deal with indignity associated with poverty at a global scale. This initiative started in 2000. Despite the strides achieved by the MDG initiative, a lot needed to be done. It is evident from Fig. 5.5 that food dimension indicators respond to various SDG targets in addition to zero hunger namely Good Health and Well Being (3); Clean Water and Sanitation (6); Industry, Innovation and Infrastructure (9), and Responsible Consumption and Production (12). For sustainable food security, there is need for stability in availability, access as well as utilization. Therefore the dimension of food security on stability cuts across the other three. A demonstration of the global comparison and Africa on the number of undernourished people as an indicator of food security dimension between the years 2005 and 2017 is shown in Fig. 5.6. The number of undernourished people was generally the same between 2005 and 2012, after which the numbers started going up. This calls for further investigation to establish this trend. Further comparison between different regions namely Africa, Asia, Latin America, and the Caribbean as well as Oceania is shown in Fig. 5.7 between the years 2005 and 2017. From Fig. 5.7, Asia tends to be consistent in terms of population having undernourished people globally but showing a decrease in the number as the years progress. A comparison of global severe food insecurity in different regions from 2014 to 2017 is shown in Fig. 5.8. It is evident that Africa tops the other regions in terms of severe food insecurity followed by Central America, with Northern America doing better in combating food insecurity.

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5.2.3 Balancing Poverty Reduction and Food Security Tackling poverty reduction and food security is a balancing act as shown in Fig. 5.9. Addressing one will have an effect on the other. Indeed inequalities in income are a recipe for food insecurity.

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5.3 Ecosystem Services for Poverty Reduction and Food Security Different environments are used by various ecosystems for coexistence. On one hand, we have the physical environment that provide suitable environment in terms of climate, temperature, humidity, and nutrients, and on the other, there are living organisms like plants, animals, and micro-organisms that depend on each other (Millennium Ecosystem Assessment 2005). In this regard, ecosystems can be said to be balanced systems and they are constantly evolving on the basis of the dynamic processes at play. Human beings rely on terrestrial ecosystem for survival from where it is estimated over 90% of food comes from Orradóttir and Aegisdóttir 2015. In addition, terrestrial ecosystem is also a source of energy, construction materials, clothes, water, air, and medicine just to name a few. There are many types of ecosystems, but in general they can be classified into two namely artificial and natural (Costanza et al. 1997; De Groot et al. 2003; Millennium Ecosystem Assessment 2005). The

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artificial ecosystems are associated with human interference on the natural landscape for instance urban cities whereas natural ecosystems refer to aquatic and terrestrial (Millennium Ecosystem Assessment, 2005). The ability of ecosystems to provide a range of services relevant to the human well-being, e.g., health, livelihoods, and survival has been reported by Costanza et al. (1997), Millennium Ecosystem Assessment (2005), TEEB Synthesis (2009), and Burkhard et al. (2012 and 2017). Simply ecosystem services are the benefits which can be direct or indirect obtained by people from ecosystems (MEA 2005; TEEB 2009). The concept of ecosystem services has evolved over time since 1970s as reported in the Study of Critical Environmental Problems (SCEP). Subsequently, policy makers recognized the value of a sustainable ecosystem after a publication by the United Nations on Millennium Ecosystem Assessment (2005). The rationale used to classify the ecosystem services is informed by the need to quantify and map to support decision making (Burkhard and Maes 2017). Different criteria have been explored in the classification of the ecosystem services which include spatial dimension; service flow and beneficiary; benefit type; competing interest among the beneficiaries. From review, four main categories of ecosystem services have been reported, namely, provisioning; regulating; cultural, and supporting. Disruption of ecosystem services that lead to environmental degradation can be traced to household adaptation mechanisms designed to deal with poverty and food insecurity (Costanza et al. 1997; Godfray et al. 2010).

5.3.1 Geospatial Intelligence (GEOINT) for Ecosystem Services in Poverty Alleviation and Food Security Geospatial Intelligence (GEOINT) integrates geospatial data with social economic, environmental, and political to generate new knowledge thus informing policy, programs, and decision making. Ecosystems continue to evolve and for them to be exploited to benefit human beings within the context of poverty alleviation and food security requires a paradigm shift in the way data is managed. This entails identifying, collecting, collating, and corroborating data sets in form of images, GIS data, economic, and environmental data which when analyzed provide an insight into the vulnerability with regard to poverty and food in security in areas of interest. The need for GEOINT approach is the complexity associated with poverty alleviation and food insecurity given their multi-dimensional nature. These two SDGs are such that addressing poverty will have ramifications on food security. Reducing poverty means empowering people thus improving their buying capacity and enhancing the entire value chain. Dealing with poverty alleviation and food insecurity in a sustainable manner requires that this is done within the framework of ecosystem services with GEOINT as an enabler.

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5.3.2 Conceptual Framework for GEOINT for Ecosystem Services in Poverty Alleviation and Food Security The vulnerability and severity of poverty and food insecurity can be perceived through different lenses given their multi-dimensional nature. For instance, one can use the living standards a lens to assess poverty levels in one region and in another education as an indicator. For food insecurity, one may employ availability in one region whereas access may work better in another. The implication therefore is that different dimensions and indicators can be used to inform poverty alleviation and food insecurity policies, programs, and implementation considering each region uniquely. There is therefore need to rethink the approaches used in terms of how these two phenomena are handled with respect to datasets, integration, and dissemination. Issues to do with data disaggregation, data granularity, historical data, and time series become critical especially for generating what if scenarios to inform the methods to deploy to respond to poverty alleviation and food insecurity. The unit of analysis is another important consideration for instance whether at macro or micro level. From the analysis of different parts of different indicators in various parts of the world, it is evident that poverty and food insecurity present unique challenges which calls for specific lens for appropriate strategic response to be designed and employed. From the onset, it is important to conceptualize the GEOINT system and Fig. 5.10 shows the nexus among Poverty alleviation, food security, ecosystem services, and GEOINT components.

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Outcome: Improved and Sustainable Livelihoods Fig. 5.10 GEOINT system concept

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Appropriate response on poverty alleviation and food security issues goes beyond spatial location. Local knowledge about what underpins poverty and food security and their linkage is of vital importance. This calls for a different approach in dealing with these two elusive phenomena that have challenged the world over the years. Different parts of the world are at different levels in alleviating poverty and food security. They use different strategies because the factors/variables that contribute to poverty and food insecurity vary from one region to another. GEOINT revolutionizes the approaches in terms of data types, analysis procedures and information visualization to inform policy, programs and actions on poverty alleviation and food security temporal and spatial scales notwithstanding. Indeed appropriate strategies Policies in dealing with these two complex and multidimensional global challenges demand having the right information, at the right time and for the right location which calls for the GEOINT approach.

5.3.3 A GEOINT System for Ecosystem Services in Poverty Reduction and Food Security Different countries and regions in the world are at different levels in responding and combating poverty and food insecurity. There is need for specific location based insights if appropriate decisions and responses are to be made. The first two SDGs focus is to eliminate poverty and food insecurity by 2030. However, a review at the current situation raises concerns on whether the world is still on course with regard to achieving the goals (United Nations Foundation 2020). Natural shocks like climate change, unprecedented pandemics like COVID 19 in addition to man-made shocks like conflicts tend to complicate the realization of these goals. Figure 5.11 shows the generic components of a GEOINT system for ecosystem services in poverty alleviation and food security. Taking advantage of location intelligence including activities, culture it is possible to come up with tailored solutions in terms of analytical tools and methods that can highlight vulnerabilities in terms of poverty and food insecurity thereby enabling appropriate strategies and responses to be initiated and executed. Poverty alleviation and food security requires multi-dimensional data sets that of necessity demand a shift in the way the data is analyzed and visualized. They both require humanitarian response where different data sets are integrated to provide real time information on where response is required, who requires response, what response, how will the response be packaged, when is the response required. The data requirements for such a system will vary from one region to the other depending on the dynamics. The availability of the right data in terms of granularity and spatial coverage will ensure that the right information is generated and add value to the decision and policy makers. Core dimensional datasets like poverty index maps, food insecurity hotspots would be vital. In addition, data on population

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Fig. 5.11 GEOINT system of ecosystem services for poverty reduction and food security

density, water and sanitation, health facilities, housing, employment, food Availability, accessibility, utilization, stability, employment among many other would support information on poverty and food security prevalence. Further, other data sets that can be explored can leverage on methods like crowd sourcing and social media platforms to provide real time localized information to support appropriate and timely response. The uniqueness pointed out necessitates the use of local knowledge and intelligence so as to provide policy and decision makers with information that can respond to areas that require intervention in terms of poverty alleviation or food insecurity. In this regard, efforts should be made to have all the necessary data/information and use an appropriate platform to process and transform the data into actionable information. Generation of different scenarios would also help in placing appropriate action plans in place for preparedness.

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5.4 Conclusion In the present chapter, poverty dimensions namely health, education, and living standards as well as food security dimensions that include availability, access, utilization, and stability are clearly defined with associated indicators. Poverty alleviation and food security are very fundamental in as far as the human wellbeing is concerned. It is therefore important to have mechanisms for estimating the extent of the vulnerability and severity of poverty and food insecurity to facilitate in the design of the appropriate policies, strategies, and programs. However the uncertainties associated with poverty and food insecurity incidences as a result of unprecedented natural and man-made shocks necessitates a different approach in tackling these complex global challenges. In addition, in order to ensure this is done sustainably, it is important to take into consideration the ecosystem services because they are a source of livelihoods and survival. Assessing and monitoring actual poverty and hunger depends on individual countries. This is despite the fact that there are international and regional organizations that support poverty alleviation and food security initiatives both on short and long term; the success depends on the commitment of the individual countries. A review of poverty and food security trends in different countries and regions revealed the uniqueness of the vulnerability impacts. The need therefore to use a different approach with a view of introducing intelligent information to support appropriate decision making. Geo-intelligence provides the framework that goes beyond traditional geospatial information by incorporating a myriad of data sets including crowd sourced data that introduces the unique and value added local knowledge that changes the way decisions are made. This chapter has provided a framework that is supposed to improvise the conventional way of doing things by leveraging on local location intelligence.

References Alkire S, Kanagaratnam U, Suppa N (2020) The global multidimensional poverty index (MPI) 2020. OPHI MPI Methodological Notes 49, Oxford Poverty and Human Development Bacastow T, Bellafiore D (2009) Redefining geospatial intelligence. Am Intell J 27(1):38–40 Burkhard B, de Groot R, Costanza R, Seppelt R, Jørgensen SE, Potschin M (2012) Solutions for sustaining natural capital and ecosystem services. Ecol Ind 21:1–6 Burkhard B, Maes J (Eds) (2017) Mapping ecosystem services. Pensoft Publishers, Sofia, 374 pp. Available at: http://ab.pensoft.net/articles.php?id=12837 Cardoso A, Somma F, Petersen J (2016) An indicator framework for assessing ecosystem services in support of the EU Biodiversity Strategy to 2020. Ecosyst Serv 17:14–23 Costanza R, d’Arge R, de Groot R, Farber S, Grasso M, Hannon B, Limburg K, Naeem S, O’Neill RV, Paruelo J, Raskin RG, Sutton P, van den Belt M (1997) The value of the world’s ecosystem services and natural capital. Nature 1997(387):253–260 De Groot RS, Wilson MA, Boumans RMJ (2002) A typology for the classification, description and valuation of ecosystem functions, goods and services. Ecol Econ 41:393–408

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FAO (1983) World food security: a reappraisal of the concepts and approaches. Director Generals Report, Rome FAO (1996) Food, agriculture and food security: developments since the World Food Conference and prospects for the future. World Food Summit technical background document No. 1, Rome FAO (2002) The state of food and agriculture 2002. FAO, Rome FAO (2020) http://www.fao.org/publications/sofi/en/. Accessed 22nd Oct 2020 FAO, IFAD, UNICEF, WFP and WHO (2018) The state of food security and nutrition in the world 2018. Building climate resilience for food security and nutrition. Rome, FAO. Licence: CC BY-NC-SA 3.0 IGO Fisher B, Turner RK, Morling P (2009) Defining and classifying ecosystem services for decision making. Ecol Econ 68(3):643–653 Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D, Muir JF, Pretty J, Robinson S, Thomas SM, Toulmin C (2010) Food security: the challenge of feeding 9 billion people. Science 2010(327):812–818 Haines-Young R, Potschin M (2010) The links between biodiversity, ecosystem services and human well-being. In: Raffaelli DG, Frid CLJ (eds) Ecosystem ecology: a new synthesis. Cambridge University Press, British Ecological Society, pp 110–139 Haines-Young R, Potschin M (2013) Common international classification of ecosystem services (CICES): consultation on version 4, August–December 2012. EEA Framework Contract No EEA/IEA/09/003. Pieejams: http://cices.eu/ Justino P, Verwimp P (2013) Review of income and wealth. Series 59(1). https://doi.org/10.1111/j. 1475-4991.2012.00528.x Maes J, Teller A, Erhard M (2014) Mapping and assessment of ecosystems and their services. Indicators for ecosystem assessments under action 5 of the EU biodiversity strategy to 2020. Publications office of the European Union, Luxembourg Maes J, Liquete C, Teller A, Erhard M, Paracchini ML, Barredo JI, Grizzetti B, European Union (2014) Mapping and assessment of ecosystems and their services. Indicators for ecosystem assessments under Action 5 of the EU Biodiversity Strategy to 2020 Maxwell S (1996) Food security: a post-modern perspective. Food Policy 21(2):155–170 Maxwell S, Smith M (1992) Household food security: a conceptual review. In: Maxwell S, Frankenberger TR (eds) Household food security: concepts, indicators, measurements: a technical review. UNICEF and IFAD, New York and Rome MEA (2005) Millennium ecosystem assessment: ecosystems and human wellbeing: synthesis. Washington D.C Millennium Ecosystem Assessment (2005) Ecosystems and human wellbeing: synthesis. Island Press, Washington, DC, p 137 Murdock D, Clark R (2016) “Chapter 5—Geospatial Intelligence” in the five disciplines of intelligence collection. CQ Press, Washington DC ODI (1997) Global hunger and food security after the world food summit. ODI Briefing Paper 1997 (1) February. Overseas Development Institute, London Orradóttir B, Aegisdóttir HH (2015) Healthy ecosystems, healthy earth, healthy people, this article is part of UNU’s “17 Days, 17 Goals” series, featuring research and commentary in support of the United Nations Sustainable Development Summit, 25–27 Sept 2015 in New York City Potschin M, Haines-Young R (2016) Defining and measuring ecosystem services. In: Potschin M, Haines-Young R, Fish R, Turner RK (eds) Routledge handbook of ecosystem services. Routledge, London and ä New York, pp 25–44 Rodríguez JP, Jr. Beard TD, Bennett EM, Cumming GS, Cork S, Agard J, Dobson AP, Peterson GD (2006) Trade-offs across space, time, and ecosystem services. Ecol Soc 11(1):28. http://www.eco logyandsociety.org/vol11/iss1/art28/ Sen A (1981) Poverty and famines: an essay on entitlement and deprivation. Clarendon Press, Oxford Stack J (2017) Community data: providing clarity on the situation in South Sudan. NGA Pathfinder 15(2):24–26

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TEEB (2009) TEEB—the economics of ecosystems and biodiversity for national and international policy makers—summary: responding to the value of nature, p 40 UKNEA (2011) The UK national ecosystem assessment: synthesis of the key findings UN (2019) https://unstats.un.org/sdgs/report/2019/goal-01 UNDP (2020) https://www.undp.org/content/undp/en/home/sustainable-development-goals.html United Nations Foundation (2020) Takeaways from the sustainable development goals report in 2020 Watts MJ, Bohle HG (1993) The space of vulnerability: the causal structure of hunger and famine. Prog Hum Geogr 17:43–67 World Bank (1986) Poverty and hunger: issues and options for food security in developing countries. Washington DC World Bank (2018) Decline of global extreme poverty continues but has slowed: world bank. https:// www.worldbank.org/en/news/press-release/2018/09/19/ World Bank (2020a). https://www.worldbank.org/en/topic/poverty/overview. (https://gistbok.ucgis. org/bok-topics/geospatial-intelligence-and-national-security) World Bank (2020b). https://www.worldbank.org/en/topic/poverty/publication/fragility-conflicton-the-front-lines-fight-against-poverty

Chapter 6

Geo-intelligence for Pandemic Prevention and Control Fenzhen Su, Fengqin Yan, and Han Xiao

Abstract Geographic Information System (GIS) technology is developing at an unprecedented speed, which also greatly promoted the development of Geointelligence. Geo-intelligence has already acted an important role in the pandemic prevention and control, such as the rapid integration of multisource data related to pandemic, rapid visualization of pandemic information, and prediction of regional transmission. Taking the 2019 novel coronavirus disease (COVID-19) prevention and control as an example, this study summarizes the important role of Geo-intelligence in the pandemic prevention and control from nine areas. This study introduced the important applications of Geo-intelligence in the fight against COVID-19, taking into account our previous work on spatial tracking and trajectory analysis, spatial transmission speed and scale prediction, spatial risk and epidemic control level classification, and cross-scale dynamic representation of epidemic maps in the fight against the epidemic. Our study provided solid support for government policy-making and effectiveness evaluation of the pandemic prevention and control during the outbreak of COVID-19. This study indicated that Geo-intelligence acted an important role in preventing and controlling pandemic. At the same time, the paper discussed the challenges and future developments of geographic intelligence. F. Su (B) · F. Yan · H. Xiao State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China e-mail: [email protected] College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China F. Su Commission on Geographical Information Science, International Geographic Union, Beijing 100101, China F. Su · F. Yan · H. Xiao Collaborative Innovation Center for the South China Sea Studies, Nanjing University, Nanjing 210023, China Innovation Academy of South China Sea Ecology and Environmental Engineering, Chinese Academy of Sciences, Guangzhou 510301, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 T. P. Singh et al. (eds.), Geo-intelligence for Sustainable Development, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-16-4768-0_6

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Keywords Geo-intelligence · Big data · Pandemic · COVID-19 · Spatial transmission

6.1 Introduction 6.1.1 The Development of Geo-intelligence Geographic Information System (GIS) is characterized by the spatial distribution, structure, pattern, and evolution of the function and interaction of the natural and human environment (Choi et al. 2020; McKercher et al. 2012). GIS and various sub-disciplines are gradually being deeply integrated, which has been used in many aspects of population migration, public health, and smart cities, etc., to help solving natural and social problems. With the rapid development of modern technologies such as the Internet of Things, smart sensing, big data, artificial intelligence, and automatic control, social, and technological undertakings have put forward new demands for GIS (Wang et al. 2019). Smart sensors are attached to or moved in the natural and social space, building a three-dimensional, multi-angle, multi-level, multi-element observation system and sensor network of the earth, and allowing quickly, accurately, and carefully acquiring High-dimensional dynamic information of the earth (Su et al. 2020). With the integration of technologies such as big data, cloud computing, machine learning, and artificial intelligence, massive amounts of heterogeneous realtime dynamic information enter the information space constructed by humans in an orderly manner, and efficient, precise, and on-demand management is driving GIS gradually Intelligent transformation (Wu et al. 2019; Zhang et al. 2018). Modern GIS has thus realized the transformation to Geo-intelligence.

6.1.2 The Support of Geo-intelligence in the Pandemic Prevention and Control The outbreak of the novel coronavirus disease (COVID-19) has caused serious social health and economic consequences by October 15, 2020, with more than 38,400,000 infections and 1.09 million deaths in more than 100 countries (https://news.google. com/covid19/). The uncertain detection of COVID-19, strong viral infections, long incubation periods, coupled with the background of large population flows, led to the urgent need for scientific assistance to control the spread of the epidemic (Chen 2020; Elie et al. 2020; Ivorra et al. 2020; Mbuvha and Marwala 2020; Petr et al. 2020). In the process of scientists adopting scientific methods helping COVID-19 controlling, we have the following experiences: the big data support is the key for the decisions and actions of preventing and controlling the widespread epidemic. The implementation of geographical intelligence will contribute to a rapid identification of the spatial–temporal process of epidemic development and preventing efficacy.

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Geo-intelligence allows rapid spatial information supply, analysis, and aggregation about the epidemic dynamics and epidemic development rules. It has performed excellent scientific support in the spatial prevention and control of epidemics, identification of the spatial spread of epidemics, resource allocation, and social emotion detection (Zhou et al. 2017, 2020). Geo-intelligence has thus provided timely support for government to accurately judge the epidemic situation and make prevention and control decisions in the fight against COVID-19, and it has been proved that it has achieved an excellent epidemic prevention result. For example, Zhou et al. (2020) put Geo-intelligence into practice in the fight against COVID-19 and provide decision support to COVID-19 prevention and control. In this study, we summarized the main applications of Geo-intelligence in pandemic prevention and control in nine aspects, including the construction of a big data information system for COVID-19, multi-scale dynamic mapping of epidemics, spatial tracking of cases and comparison of spatiotemporal trajectories, spatiotemporal predictions of epidemic spreading, spatial segmentation of epidemic risk and prevention level by estimation of population flow and distribution, spatial assessment of medical resource supply and demand, assessment of the impact epidemics on regional economic operations.

6.2 Geo-intelligence for Decision Support in the Pandemic Prevention and Control 6.2.1 Big Data Information System for COVID-19 With the development of geographic intelligence, we can now quickly build GIS based on big data for different aims. Considering that epidemic prevention and control requires rapid analysis of temporal and spatial dynamics, and comprehensive consideration of multiple geographic scales, the epidemic geographic information system should have the following capabilities: • Connect the health departments to internet to establish epidemic data spatiotemporal awareness network from multiple sources, and build a real-time dynamic geographic intelligence system with hourly or even minute-per-minute time scale; • Establish a spatiotemporal model of big data about epidemics to realize multisource heterogeneity of different spatial references, time, and scales. And establish a unified storage format and semantics standardized data modeling for multisource heterogeneous data management; • Establish a calculation engine for epidemic description, diagnosis, prediction and decision-making, and develop the system with only “visual query” to an integrated system with “visual query and analysis” functions; • Develop multi-scale comprehensive spatiotemporal dynamic visualization technology, and visualize epidemics at multi-scales of “country, province, city,

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county, community, and individual” in order to achieve a unified visual analysis benchmarks of temporal and spatial conditions; • Adopting a new generation of cloud architecture technology, taking the infrastructure as the background, taking the spatiotemporal big data management platform as the middle, and epidemics as the application prospects, developing a three-tier structure system to meet the needs of rapid system construction in emergency situations.

6.2.2 Multiscale Dynamic Mapping for Epidemics Geo-intelligence offers various designed type of mapping solutions. Since the outbreak of COVID-19, epidemic information has spread through social platforms such as Weibo, WeChat, in addition to government-issued news. The vast amount of information from multiple sources presents epidemic mapping with significant challenges. We have achieved real-time map generation in more than 34 states and 300 cities and realized data-driven multi-scale mapping templates. By pre-creating thematic map templates, we quickly created maps and realized large-scale publish. We have developed several color schemes, which consider the understanding of colors of general people and can also provide more intuitive emotional information. It also uses a knowledge map combined epidemiology and emergency knowledge related to emergency measures. From February 1, 2020, the daily report “Epidemic Map Story” on the WeChat platform has also been released. Daily animated maps are used to represent the temporal and spatial characteristics of epidemics. We release more than 10 updates to the public every day through WeChat, including the global COVID-19 trend, the spatial distribution of the COVID-19 epidemic in China, and the evolution and spatial distribution of the epidemic situation in important cities (Zhou et al. 2020).

6.2.3 Spatial Tracking and Spatiotemporal Trajectory of Big Data Geo-intelligence can quickly and automatically extract spatiotemporal trajectories of patients from text data, create spatiotemporal comparison methods, find potential spatiotemporal links for patients, and provides a way to automatically detect epidemics between regions. The comparison of human activity tracking between patients and populations provides an important scientific basis for separating potentially infected populations to identify the epidemic transmission centers. In previous study: (1) we developed a reconstruction technology to analyze and discover spatiotemporal events in patient tracking data, which can automatically convert the tracking text into quantitative spatiotemporal events. (2) We have set up a spatiotemporal event database containing more than 70,000 patient tracking texts for

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the entire country. (3) We have created a “patient-node-patient” model. The model first analyzes and integrates the similarity between location and text and displays the exposure index of the general population relative to the case to assess Individual Risk of Illness.

6.2.4 Spatiotemporal Prediction of Spreading Speed and Magnitude of COVID-19 Geo-intelligence provided ideas and solutions for performing spatial simulations in relation to the geographic environment and social space. The COVID-19 epidemic broke out in China and during the Spring Festival, the number of population migration is large, and there is a strong spatial unevenness. The spatiotemporal transmission of infection is a very large and complex system, which poses a considerable challenge to mathematical modeling (Grassly and Fraser 2008; Riley 2007). We considered a spatiotemporal diffusion model centered on Wuhan (hypersensitivity-exposureinfection-removal-death-cumulative model, multi-SEIRDC model). The model has been used to track and predict the COVID-19 epidemic in different regions of China. According to the survey, by February 2, 2020, the effective number of copies outside Hubei will fall below the threshold of 1 and reach a critical point. Within a month or so before the end of the COVID-19 epidemic, the number of new cases per day fluctuated between less than 10 cases. If no infected cases are imported from abroad, the COVID-19 epidemic outside Hubei is expected to end in mid-March (Zhou et al. 2020).

6.2.5 Spatial Evaluation of the Epidemic Risk and Prevention Level Evaluating the epidemic and spreading risks in different regions is of great significance for decision-making and adjustment of prevention and control work. Geointelligence could provide scientific solutions for quantitative evaluation of epidemic risk and prevention level. We examined the correlation between the number of confirmed cases in each province and the population movement from Wuhan to each province. Based on the number of confirmed cases and the spatial distribution of population migration, a risk assessment model was constructed. Three variables were included in the prediction, namely, the number of cases, population migration, and transportation network. For cities with high epidemic risk such as Beijing and Shenzhen, we outline three risk-level areas of regional scale and city scale.

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6.2.6 Spatial Distribution of Supply–Demand for Medical Resources Since the outbreak of COVID-19, medical resources have been under unprecedented strain. Spatial distribution of supply–demand for medical resources is the basis of the epidemic prevention and control. How to monitor the use of basic medical protective supplies in hospitals across the country in real time by means of geographic big data analysis is the key to optimizing the deployment of supplies and ensuring epidemic prevention and control. According to hospital help information, open source spatiotemporal big data, etc., an analysis of the current national shortage of medical protective equipment was conducted by cross-validation and sample verification (telephone and web queries) (Zhou et al. 2020). Results indicated that there were 462 hospitals nationwide with shortages of medical protection supplies, including 336 hospitals in Hubei Province. Combining the number of cases with the number of hospitals with material shortages, we classified the lack of medical protective equipment into four levels: level 1 is the critical shortage zone, with a large number of shortage hospitals and high case density; level 2 is the shortage zone, with a large number of shortage hospitals and medium case density; level 3 is the potential shortage zone, with a small number of shortage hospitals and high case density; level 4 is the non-shortage zone, with a shortage of The number of hospitals is small and case density is low. Results indicated that the first level was mainly in Hubei Province. The second level was mainly in Guangzhou, Kunming, Chengdu, Hangzhou, Xinyang, Hefei, and Anqing, while the third level was mainly in Nanchang, Beijing, Shenzhen, Shanghai, Chongqing, Jiujiang, Changsha, Xinyu, and Wenzhou (Zhou et al. 2020).

6.2.7 Transportation Risk Assessment Keeping the transport system for materials stable and efficient is a key guarantee in the fight against the epidemic. At the same time, the transport of materials is also a high-risk part where epidemic prevention is extremely weak and can easily become an important route for virus transmission. Therefore, epidemic prevention and control presents new demands and challenges to Geo-intelligence—the analysis of transport track monitoring data, early warning, prevention and control, and traceability of possible risks of “person-to-person” and “person-to-material” transmission of the virus along the transport of materials. Using provincial epidemic information, online shopping data as well as postal operations data, we analyzed provincial changes in the supply–demand situation and prices of essential household goods such as vegetable and meat during the outbreak of epidemic, and regional changes in volume of postal and courier express delivery to identify the area and type of material supply and transportation risk. These analyses provided decision support for the rapid deployment of supplies. Additionally, the nodes that may cause COVID-19 transmission during material transportation can be identified by tracking the trajectory of material

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delivery. Results indicated that risk indices of COVID-19 transmission related to the transportation process in Zhejiang, Guangdong, Hubei et al. were higher on January 31st, 2020. At present, there are still some challenges in the estimation of transportation risk. Firstly, it is difficult to obtain and integrate real-time big data information including residents’ online shopping big data and classified product consumption data and so on. Secondly, technologies and efficiency of real-time docking, and integrated computing between GIS system and various types of big data need to be improved. Thirdly, terminal classified material transportation monitoring data covering major cities across the country and a platform for national data integration and computing are lacking. With the development of Geo-intelligence, it will act a more important role in assessing the material supply and transportation risk.

6.2.8 Rapid Assessment of Population Flow and Pattern Population flow is important information for the prediction of spatial spread, the division of risk areas, and the decision-making of pandemic control measures (Wang et al. 2019). Previous study showed that there was a relationship between the number of cases and the population flow (Zhao et al. 2020). The population movement during the Spring Festival travel period in China has greatly increased the spread rate and spatial spread of infectious diseases, which makes it difficult to predict the epidemic. For example, there were more than five million people moved from Wuhan to other places before January 23, 2020 (News China 2020). Rapid and accurate estimation of the number and spatial distribution of people flowing from Wuhan to other parts of Hubei can provide a scientific basis for making epidemic prevention decisions. Our study obtained the spatial pattern of the population flowing from Wuhan to other parts of Hubei by integrating multi-source data such as Tencent location request data, and land use and land cover data. The results showed that more than half of the migrant population from Wuhan flows into rural areas. Therefore, epidemic prevention in the rural areas around Wuhan needs to be taken seriously especially in these regions: Huanggang, Huangshi, Xiantao, Tianmen, Qianjiang, Suizhou, Xiangyang, and parts of Xiaogan, Jingzhou, Jingmen, and Xianning (Zhou et al. 2020). Baidu Migration Data provides daily population flows between 365 cities across China, including four municipalities, 293 prefecture-level cities, some county-level cities, and Hong Kong and Macao Special Administrative Regions. Although Baidu Migration Big Data only provides the movement data of the top 50 inflow and outflow populations of each city, by combining the inflow and outflow data of all cities together to build an inter-city population movement network, the maximum inflow and outflow of nodes in the network can reach over 300 due to the mutual compensation of inflow and outflow data. Therefore, the top 50 flows can basically characterize the population mobility, and the constructed network can be used to analyze the population mobility characteristics (Xu et al. 2017). We used Baidu Migration Data to predict the risks associated with the post-Chinese New Year flow of people returning to work. Results indicated that after the Spring Festival in 2020, population mobility

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was low as a result of the quarantine measures. However, since 17 February, there has been a gradual increase in population mobility, with some cities experiencing an inflow of people that exceeds that of the same period in 2019. We calculated the speed of recovery of urban population mobility based on population mobilities during February 17–23, 2020. Results indicated that cities with high speed of recovery of population mobility mostly located in economically developed provinces in southern China.

6.2.9 Monitoring and Evaluation of the Impact of the Epidemic on Regional Economic Operations COVID-19 has disrupted the normal economic operation order and affected the normal rhythm of macroeconomic operations in various countries and regions. On the one hand, rapid monitoring and evaluation can be achieved through Internet commercial big data. On the other hand, monitoring and evaluation can be carried out based on satellite remote sensing big data. For example, NO2 is the main polluting gas emitted by industrial facilities, urban cars, etc. By comparing the concentration of atmospheric NO2 , it is possible to infer the true operating level of the national and regional economies. On March 2, 2020, the National Aeronautics and Space Administration (NASA) compared China’s NO2 value in 2020 with the multi-year average value of NO2 in 2005–2019 and found that the NO2 in eastern and central China in 2020 has dropped by 10–30% compared to previous years. Using Total vertical column of Nitrogen Dioxide (NO2 ) data from Sentinel-5 Precursor (https://sentinel.esa.int/ web/sentinel/user-guides/sentinel-5p-tropomi), the NO2 changes was calculated. The three 10-day period with extremely low values of National TvcNO2 (periods from January 21 to February 19) were diagnosed responding to the most stringent stage of epidemic prevention and control in 2020. The average TvcNO2 was reduced by 37.77% relative to the beginning of 2020 (January 01–10) and gradually picked up from February 20 to March 20. Compared with the same period on Lunar Calendar in 2019, the average decline of NO2 from January 21 to February 19 in 2020 is 36.22%. The particularly obvious declined area of NO2 are the densely populated areas and developed regions, such as the North China region, Shandong Peninsula, and the Yangtze River Delta region. This significant decline indicates that COVID-19 has significantly disrupted the economic operations of various provinces and regions in China, while normal economic operations in Hubei Province, Wuhan City, and other places have almost completely stalled. In China, the key issues that affect the implementation of the above monitoring are mainly reflected in the free and convenient sharing mechanism of data (Internet commercial big data, satellite remote sensing big data, etc.), and there are still major shortcomings in the construction of sharing platforms. At the same time, there are also shortcomings in the research and development of special

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sensors with high-precision for atmosphere environment, water environment, luminous and thermal imaging. In addition, how to use Internet big data and satellite remote sensing big data to realize qualitative judgments and quantitative estimates of economic operations requires closer integration of natural sciences with economic and social research to form more reliable models and algorithms.

6.3 Discussion The unprecedented development of GIS technology has greatly promoted the development of Geo-intelligence, making it plays an increasing important role (Wu et al. 2019; Su et al. 2020). Su et al. (2020) applied Geo-intelligence to design an intelligent system for the South Island Reef. The outbreak of COVID-19 poses a great threat to society, economy, and human health (Li et al. 2020a, b; Tian et al. 2020; Chinazzi et al. 2020; Wang et al. 2020a, b, c), and it is of great significance to grasp its transmission information in time and carry out relevant prediction work (Pinter et al. 2020; Santosh 2020; Wang et al. 2020a, b, c; Yawney and Gadsden 2020). Geo-intelligence played an important role for providing scientific information to support COVID-19 prevention and control. Taking COVID-19 as an example, this study illustrates the important applications of Geo-intelligence in the pandemic prevention and control. The outbreak and rapid spread of the COVID-19 have brought great difficulties to prevention and control, and seriously threatened human life and health (Wang et al. 2020a, b, c; Zhou et al. 2020). This situation requires Geo-intelligence to collect and analyze relevant data rapidly, so as to provide scientific information for decisionmaking. We made efforts to improve the capabilities of geographic intelligence in epidemic prevention and control. For example, we quickly constructed the analysis platform by an innovative construction technology system to provide the technical platform for timely data analysis (Zhou et al. 2020). This study summarizes the main applications of Geo-intelligence on pandemic prevention and control in nine areas, and there are still other applications of Geointelligence for supporting pandemic control such as detecting social sentiment of pandemic. For example, Geo-intelligence can also provide data and technical support for the impact of the spatial barrier caused by the epidemic on the operation of society. The main idea for the prevention and control of COVID-19 is cutting off the path of infection, but this has also led to the spatial barrier of the whole society. The spatial barrier caused by the COVID-19 epidemic also affected the operation of society. First, the prevention of the epidemic and the resumption of production have become dilemmas faced by enterprises. On the macro level, the new crown pneumonia epidemic will cause greater losses to China’s economy in the first quarter; and at the micro level, enterprises, especially small and micro enterprises, will be severely affected due to factors such as economic benefits and contract breaches. Second, the livelihood difficulties of stranded migrant workers in China’s rural areas, especially in Hubei Province, have increased. All foreign migrant workers are unable to obtain livelihoods due to traffic restrictions. Among them, in Hubei Province alone, about

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6–8 million migrant workers are forced to stay in the province, unable to return to the city to work, and their income has suffered a great loss. Third, space barriers have a serious impact on China’s education industry. Affected by the epidemic, universities, middle schools, and primary schools across the country have suspended classes and have to adopt online teaching. During this period, the entrance examinations for doctoral degrees, civil service examinations, and even college entrance examinations and high school entrance examinations will be affected. In addition, there are 8.74 million college graduates nationwide in 2020. Due to home isolation and travel restrictions, college graduates in some regions (especially Hubei Province) will face the dual pressure of graduation delays and employment difficulties. Fourth, the impact of the epidemic will cause substantial layoffs of employees in some industries. The epidemic has caused varying degrees of losses to most industries. Among them, the tertiary industry, especially catering, tourism, retail, and housekeeping, has suffered heavy losses; in addition to the missed work caused by the epidemic, some enterprises, especially export-oriented companies may lose overseas markets. In contrast, e-commerce, online games, online education, telecommunications, and other technology companies that do not require centralized offices have ushered in development opportunities. Due to lack of relevant data, we did not analyze the impact of the spatial barrier caused by the epidemic on the operation of society in detail. More research is needed to study how to fully use Geo-intelligence to prevent and control pandemic in the future. In the fight against the epidemic, Geo-intelligence also faces challenges at multiple levels. At the data level, the challenges mainly include the rapid collection of massive epidemic data, accurate identification, online rapid analysis, and multi-source data integration; at the model level, the challenges are mainly due to the large-scale population movement during the Spring Festival transport period that brings greater uncertainty to the time and space prediction of the epidemic; at the map expression level, the challenges mainly include the identification of the true and false of massive multi-source information and the rational use of map projection and hierarchical statistical maps. With the development of Geo-intelligence, after these challenges are solved, it will play a more important role in epidemic prevention and control in the future.

6.4 Conclusion This study shows the role of Geo-intelligence in the big data information system construction for COVID-19, quick aggregation of multisource big data related to COVID-19, rapid visualization of COVID-19 pandemic information, assessment of population flow and pattern, prediction of regional transmission, monitoring and evaluation of the impact of the epidemic on regional economic operations, and so on, indicating that Geo-intelligence plays an important role in the pandemic prevention and control. Geo-intelligence provided solid support for decision-making and effectiveness estimation of pandemic prevention and control during the outbreak of

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COVID-19. In supporting the pandemic prevention and control, Geo-intelligence still faces many challenges such as the restrictions on data access. With the rapid development of Geo-intelligence, it will act a more important role in the pandemic prevention and control in the future.

References Chen XG (2020) Infectious disease modeling and epidemic response measures analysis considering asymptomatic infection. IEEE Access 8:149652–149660 Chinazzi M et al (2020) The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science 368:395–400 Choi Y, Baek J, Park S (2020) Review of GIS-based applications for mining: planning, operation, and environmental management. Appl Sci 10:2266 Elie R, Hubert E, Turinici G (2020) Contact rate epidemic control of COVID-19: an equilibrium view. Math Model Nat Phenomena 15 Grassly N, Fraser C (2008) Mathematical models of infectious disease transmission. Nat Rev Microbiol 6:477–487 Ivorra B, Ferrandez MR, Vela-Perez M, Ramos AM (2020) Mathematical modeling of the spread of the coronavirus disease 2019 (COVID-19) taking into account the undetected infections, the case of China. Commun Nonlinear Sci Numer Simul 88:105303 Li Q et al (2020a) Early transmission dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. N Engl J Med 382:1199–1207 Li R et al (2020b) Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2). Science 368:489–493 Mbuvha R, Marwala T (2020) Bayesian inference of COVID-19 spreading rates in South Africa. Plos One 15(8):e0237126 McKercher B, Shoval N, Ng E, Birenboim A (2012) First and repeat visitor behaviour: GPS tracking and GIS analysis in Hong Kong. Tour Geogr 14:147–161 News China (2020) Mayor of Wuhan: more than 5 million people left Wuhan. http://news.china. com.cn/2020-01/26/content_75650784.htm/. Accessed 26 Jan 2020 (In Chinese) Petr K, Georgios A, Ivica K et al (2020) Data-driven inference of the reproduction number for COVID-19 before and after interventions for 51 European countries. Swiss Medical Weekly 150:w20313 Pinter G, Felde I, Mosavi A et al (2020) COVID-19 pandemic prediction for hungary; a hybrid machine learning approach. Mathematics 8:890 Riley S (2007) Large-scale spatial-transmission models of infectious disease. Science 316:1298– 1301 Santosh KC (2020) COVID-19 prediction models and unexploited data. J Med Syst 44:170 Su F, Wu W, Zhang Y et al (2020) From geographic information system to intelligent geographic system. J Geo-Inf Sci 22(01):2–10 (In Chinese with English abstract) Tian H et al (2020) An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science 368:638–642 Wang Y, Yan J, Sun Q, Li J, Yang Z (2019) A MobileNets convolutional neural network for GIS partial discharge pattern recognition in the ubiquitous power internet of things context: optimization, comparison, and application. IEEE Access 7:150226–150236 Wang C, Horby P, Hayden F et al (2020a) A novel coronavirus outbreak of global health concern. Lancet 395:470–473 Wang C, Horby PW, Hayden FG et al (2020b) A novel coronavirus outbreak of global health concern. Lancet 395:470–473

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Wang XR, Zhou Q, He YK et al (2020c) Nosocomial outbreak of COVID-19 pneumonia in Wuhan, China. Eur Respir J 55:2000544 Wu W, Zhang Y, Su F et al (2019) The design and implementation of a virtual reality based geographical environment monitoring system for a remote island. Tropical Geogr 39(05):742–748 (In Chinese with English abstract) Xu J, Li A, Li D et al (2017) Difference of urban development in China from the perspective of passenger transport around Spring Festival. Appl Geogr 87:85–96 Yawney J, Gadsden SA (2020) A Study of the COVID-19 Impacts on the Canadian population. IEEE Access 8:128240–128249 Zhang Y, Wu W, Wang Q (2018) Functions design and implementation of South China Sea geographical information decision making simulation system based on service-oriented architecture. Mar Environ Sci 37(01):137–142 (In Chinese with English abstract) Zhao S, Zhuang Z, Ran J et al (2020) The association between domestic train transportation and novel coronavirus (2019-nCoV) outbreak in China from 2019 to 2020: a data-driven correlational report. Travel Med Infect Dis 33:101568 Zhou C, Su F, Harvey F et al (2017) Spatial data handling in big data era. Springer, Beijing Zhou C, Su F, Pei T et al (2020) COVID-19: Challenges to GIS with Big Data. Geogr Sustain 1(1):77–87

Chapter 7

Geo-intelligence in Public Health: A Panoptical to COVID-19 Pandemic Prasad Pathak, Sharvari Shukla, Sakshi Nigam, and Pranav Pandya

Abstract Public health is a measure of population well-being in reference to the spreading of diseases and fatalities arising from them. Public health focuses on vulnerabilities and measures to limit the spread of diseases within the population. Further, it provides measures to improve healthcare such that prevailing diseases are controlled. Pandemics like coronavirus disease 2019 (COVID-19) had no precedence, to learn and be prepared. Exploring underlying reasons for spread and using it for containment has been the key. Geo-intelligence provides an edge over statistical analyses where some of the geographical parameters such as crowding, urbanization, and other social factors together provide insights through the data. In this chapter, spatial visualization of the actual number of COVID-19 cases at the state level in India is analyzed along with demographic vulnerability indices, case fatality rate, and crowding index. Not individually but together, these indices highlighted extreme cases of COVID-19. States like Maharashtra in India were high in crowding index as well as demographic vulnerability index and hence, a greater number of deaths were observed. On the other hand, Mizoram showed that low population density expressed in low crowding index could have been behind no fatalities during the study period. Thus, Geo-intelligence would be useful to the government in controlling the spread and strategizing the vaccination program. Keywords Geo-intelligence · Demographic vulnerability · Crowding · Case fatality rate

P. Pathak Department of Physical and Natural Sciences, FLAME School of Liberal Education, FLAME University, Lavale, Pune 412115, India S. Shukla (B) · S. Nigam Symbiosis Statistical Institute (SSI), Symbiosis International (Deemed University), Senapati Bapat Road, Pune 411016, India e-mail: [email protected] P. Pandya Symbiosis Institute of Geo-Informatics, Symbiosis International (Deemed University), Pune 411016, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 T. P. Singh et al. (eds.), Geo-intelligence for Sustainable Development, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-16-4768-0_7

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7.1 Introduction Public health, at national level, targets protection and promotion of the well-being of residents and communities. It traces threatening disease outbreaks and works on their prevention. It also highlights the importance of the health of the nation as a whole to improve the quality of life of each individual. The major agenda of public health involves assessment of population health and disease diagnosis; policy development for prevention and strengthening the communities; and assurance in maintaining a strong health infrastructure with well-versed research and diverse workforce. The recent occurrence of the COVID-19 pandemic has posed unprecedented challenges to public health in the world. The pandemic has already led to India being the third largest country affected by the pandemic because of its high population. While researchers across the globe are trying to overcome the situation and revive health as well as the downfall in every aspect, the advancement in technology is seen as the risk mitigator. Geo-intelligence is considered as one of the most effective measures to predict and prevent a disease outbreak (Agrebi and Larbi 2020). The spatial standpoint is considered influential because the spatial markings of disease distribution have been the key in understanding the dynamics of transmission of the disease. In today’s data-driven world, it is important for a developing country like India to be prepared for any disease outbreak with a strong response system to plan disease prevention and avoid additional burden on the already inadequate public health system. It implies that the country should be completely aware of its infrastructure availability as well as demographic patterns for successful decision making. A system based on geospatial data can be a real game changer. The field of Geo-intelligence extends the use of geospatial technology and data for understanding not only the physical environment but also integrating demographic characteristics through various spatial scales to predict possibilities of future events. In this chapter, we highlight the importance of Geo-intelligence for public health by analyzing the situation of a disease outbreak in the leading developing economy, India. Using a combination of temporal and spatial variation in the governmentmandated intensity of lockdowns, we identify the demographic vulnerability of the states and union territories in India. Further, we understand the effect of various factors in increasing the number of cases in the country. We highlight the importance of Geo-intelligence in undertaking policy measures and future implications for enhancing the monitoring, warning, and mitigation of risks that such pandemics pose on the economy. These measures help in understanding the importance of Geointelligence for enhancing the Sustainable Development Goal 3 (SDG3) which is ‘good health and well-being’ which is important for the revival of the economy from this pandemic. The chapter is further divided into various sections. The first part majorly emphasizes on the literature on the importance of Geo-intelligence for public health by understanding the pandemic situation and public health profile of India. Later, the empirical approach is described in detail along with the results of the study. This is carried forward with a detailed discussion and concluding remarks on policy

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measures for intensifying the public health of the economy using Geo-intelligence as the technology advancement.

7.2 Importance of Geo-intelligence: The Conceptual Framework 7.2.1 Public Health in India and COVID-19 India, as a nation, has been standing strong as one of the leading developing economies with the highest growth rate of gross domestic product (GDP) (4.2%) till last year even in times of recession, though the growth rate of GDP fell to 3.1% in the previous year. One of the reasons cited for the deteriorating GDP of the country is the growing income inequality leading to poor health (Lynch et al. 2004). The birth rate of the country was 18.6 for every thousand people in 2018 with a decline in infant mortality rate witnessed in 2017. However, in 2017, India’s average life expectancy was 69 years, still below the global average of 72 years. In Asia-Pacific region, India still recorded the highest number of undernourished people (2018) (Sandhya 2020), malaria cases (2017), and high suicide rates in women (2016) (Sandhya 2020). Focusing on the health infrastructure of the country, there are 7,13,986 total government hospital beds available in India as per National Health Profile 2019, which means 0.55 beds per 1000 population. From the evidence, it is noted that almost twelve states lie below the specified range of 0.55 beds per thousand people, demonstrating poor condition of the health infrastructure (Chakraborty et al. 2020). COVID-19 has resulted in a huge disruption of the Indian economy. The lockdown and social distancing rules have had an adverse impact on the economy as there is an unavoidable trade-off between flattening the infection curve and steepening of the recession curve (Alam et al. 2020) resulting in severe demand and supply shocks across all economies. The strict lockdown imposition and the constantly widening spread of the infection, however, have pushed both the government and the private sector to incur unplanned health infrastructure-related expenditure on COVID-19related health care services. Despite this tremendous increase in health infrastructure, the effect of COVID-19 on Sustainable Development Goals (SDGs) is anticipated to be long lasting (UNDP 2020). While the pandemic poses a threat in progress of the SDGs, the emphasis on the highly affected regions in the country in terms of demography, socio-economic progress, hygiene conditions, and availability of health care can lead to better policy implementation and effective planning for India.

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7.2.2 Why Geo-intelligence? The term Geo-intelligence or geospatial intelligence was coined by the U.S. government in 2005 while focusing on defense applications. Geo-intelligence is defined as intelligence acquired from image research and data exploration identified for various locations (OMNISCI 2020). Geo-intelligence is not limited to analyzing images but it also integrates other geospatial data obtained from secondary sources and value-added products from it (Serban et al. 2019). In recent years, Geo-intelligence has been used in other sectors like automotive, agriculture, and climate change studies. It has also been used in varying health-related zones and national settings for improving the public health services which happens prior to the occurrence of a disease. Geo-intelligence applications include social media analytics for disease supervision (Boulos et al. 2019), predictive modeling to recognize high-risk regions affected by the disease (Rajkomar et al. 2018), and mobile health for healthcare services. It has also helped in recent times of the pandemic to identify risk-prone areas for maintaining social distancing norms. While the pandemic poses a threat in progress of the SDGs, the emphasis on the highly affected regions in the country in terms of demography, socio-economic progress, hygiene conditions, and availability of health care can lead to better policy implementation and effective planning for India. One of the most effective ways of achieving this is using GI to track the risk and vulnerability of coronavirus infection to a population, rather than the risk of infection (or susceptibility) itself. Understanding the work advancement in Geo-intelligence technology, we analyze the COVID-19 situation of India.

7.3 Materials and Methods 7.3.1 Data The data used was taken from the Lancet article for domain wise and overall vulnerability indices because the article used the most recent data available for the calculations (Acharya and Porwal 2020). It used the National Family Health Survey (NFHS), Rural Health Statistics 2018, Census of India 2011, and National Health Profile 2019. The number of confirmed and recovered cases along with the number of deaths was taken from COVID-19 India Org Data Operations Group (COVID19India API 2020). The urbanization rate was calculated using Census of India 2011 data, while the GMI was calculated from the Google Mobility report published by Google LLC. The crowding index was derived from the variables in NFHS-4 data, and the initial Red, Green, and Orange zone segregation for GMI was based on MOHW data released in May 2020 (Thever 2020). Further, detailed results and future implications and policy suggestions are discussed in the sections below.

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7.3.2 Method To understand the significance of Geo-intelligence in analyzing the pandemic, it is important to examine the factors that lead to an increase in the risk of the infection. We study the following factors: i.

ii.

iii. iv.

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COVID-19 confirmed cases: To assess the spread of the infection, we map the confirmed cases in the 30 Indian states and 6 union territories and mark the regions as Red, Orange, and Green zones based on the definition given by the Ministry of Health Affairs, Government of India. The comparison of these zones with the vulnerable regions helps in exploring the regions which would need immediate help. Demographic Vulnerability Index: According to The Lancet (Acharya and Porwal 2020), vulnerability is not limited to the risk of infection from a disease, while it is a more vital concept. As stated by the World Health Organization (WHO), it takes close to fourteen days to identify an infected COVID-19 individual, and that the disease can spread from one individual to another. In this paper, we consider the demographic vulnerability index developed by Acharya and Porwal in 2020 for COVID-19 cases till June 2020. The essence of the COVID-19 epidemic is such that both the speed of transmission and mortality due to infection depend on the demographic characteristics of the population; hence, demography should be part of a vulnerability index. It is calculated based on the percentage of population aged above 60, population proportion residing in urban areas, and population density. The demographic vulnerability index was calculated for 30 states and 6 union territories in India. Urbanization: Using the census 2011 data, demographic characteristics were studied in detail by understanding the rate of urbanization at the state level. Crowding Index: The crowding index is calculated as the number of individuals in a household by the total number of rooms in the household. During the pandemic, when social distancing is given utmost preference, an overcrowded household is considered to be highly vulnerable. Overcrowded housing in highdensity populations in the slums can be a breeding ground for any disease and can also lead to vector proliferation. The crowding index was reported as a percentage for each region in order to understand the effect of urbanization on the household and the possibilities of the disease spread in the regions. We identify states with high vulnerability due to overcrowdedness. Case Fatality Ratio (CFR): While highlighting the confirmed COVID-19 cases seems important, it is also important to understand the effect of increasing or decreasing the number of deaths and recoveries in a region. To understand the severity of COVID-19 cases better, we referred to the Case Fatality Ratio (CFR) proposed by Aristides Tsatsakis, 2020. However, we use another approach of calculating the CFR as suggested by Ghani et al. (2005). Accordingly, we estimate CFR as the ratio of current deaths to current deaths plus recoveries. This measure is free of any timeframe and can be estimated for any given date. With an increase in contact tracing and infection spread surveillance

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throughout the duration of the pandemic, CFR estimation suggests the importance of advanced healthcare systems and new treatments. Calculations were made for CFR considering the number of cases till October 10, 2020. Google Mobility Index: Mobility is defined at the state level in the Google Community Mobility Reports (Google, LLC, 2020) as the percentage change in the number of visitors to places of interest relative to the baseline 5-week period from January 3 to February 6, 2020. The state mean is taken over the percent change in the number of visitors (who have opted-in to share their Location History for their Google Account) to places of interest for each month to create the Google Mobility Index. The segregation of Red, Green, and Orange zone was cross verified using the Google Mobility Index (GMI).

7.4 Results There was a steady increase in the number of cumulative active cases of COVID19 reported from March till October 10, 2020. Looking at the cumulative total of COVID-19 cases (Fig. 7.1) by 10th of October, 2020, it is evident that the neighboring states, i.e., Karnataka, Andhra Pradesh, and Telangana in the southern part of the country were hit by the pandemic badly. In the central region of India, Madhya Pradesh was affected more while in the north, it was Uttaranchal. Within the northeast states, Manipur and Mizoram experienced a higher number of cases. Assam and Sikkim were also affected but less than Manipur and Mizoram. The national capital, Fig. 7.1 Cumulative confirmed COVID-19 cases as of October 10, 2020

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Fig. 7.2 a Demographic vulnerability in each state. b Urbanization in India

Delhi, also experienced a very high number of cases, and as expected, the state of Haryana was next to it. Punjab and Himachal Pradesh were of the lower magnitude. Tamil Nadu and Kerala were the least affected states as of 10 Oct 2020. The Demographic Vulnerability Index (Fig. 7.2a) (indicative of more vulnerable population or above the age of 60 years) highlighted Punjab, Maharashtra, Kerala, and Tamil Nadu being highly vulnerable. Gujarat, Haryana, and the southern states— Karnataka, Andhra Pradesh, and Telangana—were between 0.56 and 0.75 on the index. Most of the north-east states and Chhattisgarh were found to be the least vulnerable based on their demographic characteristic. Rajasthan, Uttaranchal, Uttar Pradesh, Bihar, and Jharkhand ranged between 0.36 and 0.55 on the vulnerability index. The proportion of urban population was also mapped (Fig. 7.2b) based on Census 2011 which indicated that Gujarat, Maharashtra, Telangana, Chhattisgarh, Kerala, and Tamil Nadu are highly urbanized states along with Delhi. Mizoram from the north-east was the only more urbanized state than the others. Punjab, Haryana, Uttaranchal, West Bengal, Karnataka, and Andhra Pradesh showed an urban population between 40 and 50%. Other states are less than 40% urbanized in terms of their population. As a semi-indicator of population density, the crowding index was analyzed. Uttar Pradesh was found to be the highest on the crowding index (13%), indicating that there are a greater number of people in the households per room in that state. Madhya Pradesh has a 9% crowding index while most of the states have lower crowding than these two. Chandigarh, Lakshadweep, Daman Diu, and Dadra Nagar Haveli have the least crowding (Table 7.1). Looking at CFR rates (Fig. 7.3), it can be evident that initially (May, 2020), most of the states experienced more fatalities than recoveries. Gujarat, Rajasthan, Telangana, and Andhra Pradesh remained lower along with the north-east states till early June, 2020. By the time, it was October of 2020, Maharashtra, Gujarat,

102 Table 7.1 Crowding index

P. Pathak et al. State or union territory

Crowding index (%)

State or union territory

Crowding index (%)

Andaman and Nicobar Islands

0

Madhya Pradesh

9

Andhra Pradesh 2

Maharashtra

5

Arunachal Pradesh

2

Manipur

2

Assam

4

Meghalaya

1

Bihar

6

Mizoram

2

Chandigarh

0

Nagaland

2

Chhattisgarh

3

Delhi

1

Dadra and Nagar Haveli

0

Odisha

5

Daman and Diu

0

Puducherry

1

Goa

0

Punjab

3

Gujarat

3

Rajasthan

6

Haryana

3

Sikkim

1

Himachal Pradesh

2

Tamil Nadu

4

Jammu and Kashmir

3

Tripura

1

Jharkhand

4

Uttar Pradesh

13

Karnataka

4

Uttarakhand

3

Kerala

2

West Bengal

3

Lakshadweep

0

Telangana

1

Madhya Pradesh, West Bengal, Haryana, and Delhi experienced higher CFR rates. Thus, Maharashtra and Madhya Pradesh continued to have higher deaths but other states showed fluctuation. From the GMI calculated using the data for May, 2020, the researchers identified three regions under the Red zone, 19 under the Green zone, and 14 under the Orange zone as given in Fig. 7.1. From the mobility charts above (Fig. 7.4), it is evident that only three states were in the red zone during May 2020. While only Maharashtra showed that 80% of its population experienced a −30% decrease in mobility, the other two states (Delhi and Chandigarh) showed less population with an overall decrease in mobility by −45%. The states in the orange zone indicated that less than 25% of the population showed a decline in mobility between −20 and −40%. Jammu and Kashmir on the other hand showed increased mobility (120%) but by only a fraction of its population. States in the green zone showed that a lower fraction of their population showed an overall decrease in mobility. Overall, this means that the mobility was not lower in most of the states even if it was strict lockdown applicable throughout the country.

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Fig. 7.3 CFR for deaths and recoveries due to COVID-19 as of October 10, 2020

Fig. 7.4 Representation of population and COVID-19 zones

7.5 Discussion Policy measures for an economy on public health largely depend upon the spread of the infection and factors based on location for a disease outcome. In this study, we explore a comparative approach using different measures to analyze the spatial effect of COVID-19 through GI. We review cases at the state level and investigate the vulnerability of each region and risk of deaths at the state level. This information is suggested to be used by decision makers for planning and strategizing the possible measures of reviving the economy’s public health. From all the variables mapped above, i.e., demographic vulnerability, CFR rate by October 2020, and changes in mobility, Delhi has been the prime target of the pandemic. The city recently reported the worst Air Quality Index of 304 (Sravasti

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2020) which would lead to a rise in respiratory diseases increasing the epidemiological vulnerability because this vulnerability index includes chronic health conditions of people in the region. Researchers also state urbanization as being one of the reasons for the increase in the number of cases which is also reflected by the high demographic vulnerability. The other union territories show less number of cases and deaths which can be attributed to lower demographic vulnerability. Maharashtra has experienced a high number of COVID-19 cases with a consistently high CFR rate since March till now. This is mainly due to demographic vulnerability. India has reported 62.8% of the infections from urban centers; in Maharashtra, this ratio is as high as higher 83.7% (Financial Express 2020). The mortality rate of the elderly group is reported to be above 23% during the pandemic because of existing comorbidities in the age group (Yadavar 2020). While the increasing number of cases in various states is mainly attributed to demographic vulnerability, Madhya Pradesh seems to be an outlier. It is one of the worst hit states in the central region due to the pandemic; however, its demographic vulnerability is lower and does not have a high urban population (30–40%) compared to its neighbors like Maharashtra and Chhattisgarh though it accounts for high vulnerability under all the other parameters. The state lacks quality health infrastructure where the requirement of HDU and ICU beds are not met (Ranjan 2020), and hence, it may have displayed higher CFR rates since the beginning. Chhattisgarh and most of the north-eastern states are highly vulnerable. However, these regions don’t have a high number of active COVID-19 cases. However, the CFR of the states, specifically Sikkim, is high even after low case counts which is possibly attributed to the high demographic vulnerability of the state. Sikkim currently has a CFR rate higher than that of India. The state attributes this to the existing comorbidities where the individuals who died were above the age of 55 years (Bismeen 2020). It is identified that Mizoram is the only state with zero deaths in the region. While researchers are still trying to identify the reason for this (Telegraph India 2020), based on the vulnerability index, we can identify one of the reasons for this as the least demographic vulnerability of the state. The population density of the state is comparatively lower (52 persons per square km) which is one of the reasons for the low vulnerability index. Public health researchers are observing the terrains marked by hills and jungles to identify the reason because the state’s condition in terms of health infrastructure is quite poor (Telegraph India 2020). Kerala and Karnataka are the two states and neighbors of each other which have higher urban populations and higher demographic vulnerability (like Maharashtra). Their CFR rate, however, decreased over time, and hence, the total number of cases is much lower. This can be attributed to better healthcare facilities and following precautionary measures to reduce the spread of the disease. From these spatial patterns, we can conclude that the urban population, demographic vulnerability index, CFR rate, and crowding index can give insights into spatial patterns of COVID-19. The mobility patterns also showed the lack of seriousness in following lockdown measures. More variables such as the availability of primary healthcare centers, special COVID-19 facilities, urban slums, and rural–urban connections can provide further awareness about the reasons for the spread of the disease and possible solutions.

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It is evident that the states with the more urban population, but less healthcare facilities may have created favorable grounds for the pandemic to spread. Cities across the globe have been the hot spots for COVID-19. Urban areas allow higher population density (indicated by crowding index in this study) which makes them susceptible to communicable diseases easily and following social distancing norms may prove difficult. Other risk factors in the urban environment such as poor housing, inadequate water supplies as well as sanitation and waste management along with overcrowdedness can increase the spread of virus (Ventura-Garcia et al. 2013). In a country like India, where 49% of the population resides in slums (Scroll.in 2020), better provision of public facilities to lower-income groups and better healthcare is necessary for good urban planning and better public health management. Otherwise, areas like Asia’s largest slum Dharavi would remain epicenters of the pandemic and impact the whole city and surrounding regions. The preliminary study carried out here showcases the usability of Geo-intelligence in identifying potential reasons behind the pandemic. With reliable and timely data for other variables, Geo-intelligence can be used under spatial causal factors critical for public health and pandemics like COVID-19 can be analyzed effectively. The local, regional, and national governments can use Geo-intelligence to its fullest to address public health issues and avoid future pandemics.

7.6 Conclusion The aim of the present study was to demonstrate the use of Geo-intelligence in public health using the COVID-19 scenario in India. In this preliminary research, Geo-intelligence was looked at as a tool to explore spatially varying public health parameters of India at the state level along with total cases and their fatality rate. The research looked at various aspects like demographic vulnerability, urbanization, and crowding index. It was observed that together these variables explain spatial variations in COVID cases. In most of the cases, higher urban population and higher demographic vulnerability were coherent with higher CFR rates and higher cumulative COVID-19 cases. This study can be further enhanced by adding a response through new medical facilities and existing healthcare system mapping. These findings indicate that demographic variables and their spatial variability are tightly linked with spatial patterns of diseases and hence, important for decision making in public health. Geo-intelligence can be the tool for integration in the healthcare system, and professional, decision makers can be trained and made aware of its use. This study has highlighted the use of Geo-intelligence in the public health domain and necessitates the need for further fine-grained analysis to model the spread of infectious diseases. A major limitation of the study has been the availability of reliable spatiotemporal COVID-19 data as well as a matching scale of causal variables. Further, the use of Geo-intelligence is recommended in gaining insights to COVID-19 with data at the district level and understanding local spread patterns such as urban to rural spread and

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impacts of labor migration. India now aiming for vaccine distribution for COVID19, Geo-intelligence will play a critical role in resource allocation using location optimization.

References Acharya R, Porwal A (2020) A vulnerability index for the management of and response to the COVID-19 epidemic in India: an ecological study. Lancet Glob Health 8(9):e1142–e1151 Alam MN, Alam MdS, Chavali K (2020) Stock market response during COVID-19 lockdown period in India: an event study. J Asian Finance Econ Bus 7:131–137. https://doi.org/10.13106/jafeb. 2020.vol7.no7.131 Agrebi S, Larbi A (2020) Use of artificial intelligence in infectious diseases. In: Artificial intelligence in precision health. Academic Press pp 415–438 Bismeen (2020) Why sikkim, which had no covid cases till May, now has third-highest case fatality rate. https://theprint.in/health/why-sikkim-which-had-no-covid-cases-till-may-now-hasthird-highest-case-fatality-rate/547510/. Accessed 2 Dec 2020 Boulos MNK, Peng G, VoPham T (2019) An overview of GeoAI applications in health and healthcare. Int J Health Geograph, 18(1):1–9 Chakraborty PS, Shamika Ravi, Sikim (2020) COVID-19 | Is India’s health infrastructure equipped to handle an epidemic? In: Brookings. https://www.brookings.edu/blog/up-front/2020/03/24/isindias-health-infrastructure-equipped-to-handle-an-epidemic/. Accessed 24 Oct 2020 COVID19-India API (2020) Case time series. https://api.covid19india.org/csv/latest/case_time_ser ies.csv Ghani AC (2005) Methods for estimating the case fatality ratio for a novel, emerging infectious disease. Am J Epidemiol. Oxford Academic. https://academic.oup.com/aje/article/162/5/479/ 82647. Accessed 2 Dec 2020 Ishaan G (2020) Covid-19 continues to surge in Maharashtra; Here’s why the state is recording more Coronavirus infections. In: Financ. Express. https://www.financialexpress.com/lifestyle/health/ covid-19-why-maharashtra-is-recording-more-infections/2082426/. Accessed 2 Dec 2020 Kadri R (2020) The vision of slum-free Indian cities needs to be viewed through the lens of inclusive development. https://scroll.in/article/972781/the-vision-of-slum-free-indian-citiesneeds-to-be-viewed-through-the-lens-of-inclusive-development. Accessed 2 Dec 2020 Lynch J, Smith GD, Harper SA, Hillemeier M, Ross N, Kaplan GA, Wolfson M (2004) Is income inequality a determinant of population health? Part 1. A systematic review. The Milbank Quart, 82(1):5–99 Mudur GS (2020) Coronavirus outbreak: why Mizoram has escaped Covid death so far—telegraph India. https://www.telegraphindia.com/north-east/coronavirus-outbreak-why-mizoramhas-escaped-covid-death-so-far/cid/1790783. Accessed 2 Dec 2020 OMNISCI (2020) What is geospatial intelligence (GEOINT)? Definition and FAQs. OmniSci. https://www.omnisci.com/technical-glossary/geoint. Accessed 24 Oct 2020 Rajkomar A, Oren E, Chen K et al (2018) Scalable and accurate deep learning with electronic health records. NPJ Digit Med 1:18. https://doi.org/10.1038/s41746-018-0029-1 Ranjan (2020) Madhya Pradesh needs more HDU, ICU beds as Covid-19 cases increase. In: Hindustan Times. https://www.hindustantimes.com/india-news/madhya-pradesh-needs-morehdu-icu-beds-as-covid-19-cases-increase/story-PLZgwlluYAjqJGh3czr6hL.html. Accessed 2 Dec 2020 Sandhya K (2020) State of health in India—statistics & facts. Statista. https://www.statista.com/top ics/5191/state-of-health-in-india/. Accessed 24 Oct 2020

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Serban O et al (2019) Real-time processing of social media with SENTINEL: a syndromic surveillance system incorporating deep learning for health classification. https://doi.org/10.1016/j.ipm. 2018.04.011 Sravasti D (2020) Doctors warn of Delhi covid spike as pollution levels rise & temperature begins to drop. https://theprint.in/health/doctors-warn-of-delhi-covid-spike-as-pollution-levelsrise-temperature-begins-to-drop/523721/. Accessed 2 Dec 2020 The Hindu (2020) GDP growth slows to a 11-year low of 4.2%, Q4 slumps to 3.1%. https://www.thehindu.com/business/Economy/gdp-growth-dips-to-31-in-january-march42-in-2019-20/article31703885.ece Thever Steffey (2020) Pune’s positivity rate goes up to 24.65%, but R naught stays low at 0.8. In: Hindustan Times. https://www.hindustantimes.com/pune-news/pune-s-positivity-rate-goesup-to-24-65-but-r-naught-stays-low-at-0-8/story-yCYauiQqt1w07fom8FhAoJ.html. Accessed 3 Oct 2020 UNDP (2020) Impact of COVID-19 on sustainable deveopmental goals. https://sdgintegration.undp. org/accelerating-development-progressduring-covid-19 Ventura-Garcia, Roura M, Pell C et al (2013) Socio-cultural aspects of Chagas disease: a systematic review of qualitative research. In: PLoS Negl. Trop. Dis. https://pubmed.ncbi.nlm.nih.gov/240 69473/. Accessed 2 Dec 2020 Yadavar S (2020) Why Maharashtra has India’s highest covid-19 mortality numbers. https://theprint. in/health/why-maharashtra-has-indias-highest-covid-19-mortality-numbers/398409/. Accessed 2 Dec 2020

Chapter 8

Use of Remote Sensing Data to Identify Air Pollution Signatures in India K. N. Sivaramakrishnan, Lipika Deka, and Manik Gupta

Abstract Air quality has a major impact on a country’s socio-economic position and identifying major air pollution sources is at the heart of tackling the issue. Spatially and temporally distributed air quality data acquisition across a country as varied as India has been a challenge to such analysis. The launch of the Sentinel-5P satellite has helped in the observation of a wider variety of air pollutants than measured before at a global scale on a daily basis. In this chapter, spatiotemporal multi-pollutant data retrieved from Sentinel-5P satellite is used to cluster states as well as districts in India, and the associated average monthly pollution signatures and trends depicted by each of the clusters are derived and presented. The clustering signatures can be used to identify states and districts based on the types of pollutants emitted by various pollution sources. Keywords Air quality · Pollution · Remote sensing · Sentinel-5P

8.1 Introduction Air pollution is one of the major health hazards in a developing country such as India. Hence, it is necessary to study the composition of air over the districts of our country to understand how to tackle pollutants at an individual level. It has been shown that the concentration of particulate matter less than 2.5 µm in diameter (PM2.5) has a direct positive correlation to the number of cases of lung diseases such as asthma in patients (Zheng et al. 2019). Other pollutants which directly affect the human respiratory system are nitrogen dioxide, sulfur dioxide, formaldehyde, and tropospheric ozone. High concentrations of carbon monoxide can cause dizziness, confusion, severe brain damage, or even death. Methane is another important gas K. N. Sivaramakrishnan · M. Gupta (B) Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad 500078, India e-mail: [email protected] L. Deka De Montfort University, Leicester L1 9BH, UK © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 T. P. Singh et al. (eds.), Geo-intelligence for Sustainable Development, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-16-4768-0_8

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which contributes to the greenhouse effect, and it is 80 times more harmful than CO2 if it sustains in the air for long periods of time. Hence, there is a dire need to equip policy-makers and policy enforcers with data driven knowledge of major pollutant sources and their geolocation, as well as increase the awareness about the ill-effects of these pollutants among the general public to ensure the well-being of society. Air-pollutant sources include road-traffic, industries such as brick kilns and portable power generators such as diesel power generators (Guttikunda 2017). The information presented in Primer on pollution source apportionment (2021) details currently used methods for identifying pollution sources. Methods can be expensive and may fail to detect all sources as they may be located in geographical wide areas. Recent efforts to identify pollution sources involved the use of low-cost sensors (Tracing the origins of air pollutants in India 2019). However, such methods need the additional overhead of sensor installation and maintenance on each site. The aim of this chapter is to present clustering methods on air pollution data derived from larger granularity satellite data and to identify the sources behind the pollution in each cluster. Grouping of the various states and districts in India has been done based on the pollution signatures emitted from different regions. It is cost effective to use satellite data since it does not involve additional installation overheads and provides a more holistic wider area picture. Such an approach can also be used to provide an insight for site selection for low-cost sensor installation for more accurate and localized studies if required. The outcome will aid the government in reforming policies to help alleviate the pollution levels effectively. Once it is determined which sources are responsible for emitting a particular pollutant, government bodies can take necessary steps to reduce the specific pollutant at the specific geolocation by placing restrictions targeting only the relevant sources. This will help the government to efficiently formulate targeted and more effective strategies to combat individual pollutants levels if there is an alarming spike in the levels of a specific pollutant. Rest of the chapter is arranged as follows: Sect. 8.2 touches upon the most notable work done in the area till date; Sect. 8.3 details the method employed to collect the required data used in this study, with Sect. 8.4 explaining how the data was prepared and preprocessed for further analysis; Sect. 8.5 presents the clustering algorithms employed with the discussion on the results achieved; and finally conclusions in Sect. 8.6 mentioning briefly now how the research can be taken ahead.

8.2 Literature Review Air pollution monitoring has been a major challenge faced by the Government of India, especially considering the fact that 26 of the 50 most polluted cities in the world are located in India as of 2019 (IQAir 2019). PM2.5, particulate matter of aerodynamic diameter less than 2.5 µm, was found to cause damage to the air passages, cardiovascular impairments, increase the likelihood of Diabetes Mellitus in humans

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and cause adverse effects in infancy (Feng et al. 2016). Thus, with the advent of lowcost sensors (Castell et al. 2017), it has become possible to evaluate the air quality at a given location by only taking into consideration the PM2.5 levels. But there are other pollutants as well that can cause adverse health effects, such as NO2 , SO2 , and CO, which can be measured by a high quality air pollution monitoring system, but which requires a higher installation cost and maintenance. As of 2020, there are only 804 pollution monitoring stations placed across 344 cities in India, as part of the National Air Quality Monitoring Program (NAMP) (CPCB 2020). This means that on average there is only 1 pollution monitoring station available per 1.6 million people in the country, the density varying from state to state with the North Eastern states having the least number of stations. In contrast, there are about 700 Air Quality Monitoring Areas in the UK, which translates to about 1 site per 100,000 individuals (DEFRA UK 2021). Even though the NAMP has been running since 1984, the data from the sensors is publicly available only from 2016. The number of stations covering rural areas is very sparse and even the data collected through the monitoring stations is patchy and prone to errors as some of them are manually collected and uploaded (Pant et al. 2019). There have been attempts to perform spatial interpolation of ground-based sensors to fill the gaps in the areas where pollution monitoring sites are not present. Various techniques have been employed to create these pollution maps such as the Kriging algorithm, Inverse Distance Weighting (Shukla et al. 2020) and more recently Artificial Neural Networks (ANNs) (Alimissis et al. 2018) and Long Short-Term Memory (LSTM) neural networks (Ma et al. 2019) have been used to increase the accuracy of these interpolations. But there are various drawbacks of these models, Inverse Distance Weighting cannot estimate values which fall outside the range of the training data, neural networks do not consider the spatiotemporal associations, and the LSTM requires a large set of tagged historical data and plenty of time for the training. Thus, the lack of finer resolution data points makes it hard to use spatial interpolation techniques to monitor air quality effectively in India. The alternative approach is to use remote sensing data to monitor the air quality over a region. The satellites measure the particulate matter present in the atmosphere by using spectroscopic retrieval methods. A significant positive correlation of 0.96 was observed between the Aerosol Optical Thickness (AOT) measured by the MODIS satellite and the PM2.5 measurements from the ground (Gupta et al. 2006). It was possible to get wider coverage of pollution measures as compared to ground-based sensors, but this came at a lower spatial resolution, which meant that the data was not adequate for pinpointing the source of the pollution. Spatial scaling techniques were employed to enhance the resolution of the AOT product from 10 to 1 km (Chudnovsky et al. 2013), which helped gain a finer insight. The temporal AOT data from Moderate Resolution Imaging Spectroradiometer (MODIS) has been used to perform back trajectory analysis by which the transportation of particulate matter across borders can be traced, and the source of the pollutant can be determined (Engel-Cox et al. 2005). The Global Ozone Monitoring Experiment (GOME) (Burrows et al. 1999) was launched in 1995 and it was able to measure tropospheric Ozone and NO2 on a global scale for the first time, but it had a very poor spatial

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resolution of 80 × 40 km2 per pixel. Apart from this, MODIS has a 1–2 day temporal resolution and GOME has a monthly temporal which means that data is not available as frequently as the 4 h or 8 h intervals as provided by ground-based sensors. The launch of Sentinel-5P by the European Space Agency (ESA) in October 2017 brought with it an increase in the spectral radius as well as spatial resolution. The onboard TROPOMI sensor can measure O3 , NO2 , SO2 , CH4 , CO, HCHO, and AER AI at a spatial resolution of [7 × 7 km2 ], which is about six times better as compared to GOME and also improve the sensitivity by order of magnitude (de Vries et al. 2016). Sentinel-5P also has a temporal resolution of 1, which means that it would cover the entire surface area of the earth once every day. Thus, there is now a wider spectrum of pollutants being measured at a very good spatial and temporal resolution, though not as fine granularity as a ground-based sensor. Since Sentinel-5P has a wider spectral range and a finer spatial resolution as compared to previous satellites, it helps study the air quality over an area in much finer details and is the preferred choice of data source for experiments carried out in this paper. Studies have been conducted to analyze the NO2 pollutant, in particular the one conducted by Kaplan et al. (2019) which correlates it with statistical indicators such as population density. In this study, multiple pollutants gathered from remote sensing data have been taken into consideration as well and used to perform a clustering of Indian states and districts based on their pollution signatures.

8.3 Data Collection This section will provide a brief description of how the remote sensing data was obtained. The images were retrieved using Google Earth Engine’s Level 3 products for Sentinel-5P. The method by which the Level 2 products, released by the ESA, are processed by Earth Engine are described in the next section. A yearly average of Level 3 NO2 , SO2 , CO, AER AI, O3 , and HCHO products was taken over the latitudinal and longitudinal extents of India from January 2019 to December 2019 and was then processed for further analysis. The following subsections describe the various Level-2 products that are released by the ESA and explain some of their retrieval methods and their significance in terms of their contribution to air pollution.

8.3.1 Nitrogen Dioxide (NO2 ) Sentinel-5P has two sub-products which are measured for NO2 , namely the tropospheric and the total column. In this study, the tropospheric NO2 column, which is the NO2 between the surface of the earth and the troposphere, is used as it plays a major role in determining the level of photochemical Ozone (Veefkind et al. 2012). It must be noted that the TROPOMI NO2 underestimates the NO2 level as compared to ground-based sensors but the correlation coefficient was found to be 0.84 and

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appropriately calibrated, which makes the product accurate enough to be used for analysis. The data measures trace gas concentrations in mol/m2 (Eskes et al. 2020).

8.3.2 Sulfur Dioxide (SO2 ) The sources of Sulfur Dioxide pollution in the atmosphere are both natural and man-made. The majority of pollution (70%) arises from coal power plants, smelting industries, and mines. Apart from incurring both long-term and short-term effects on climate, it affects vegetation and water quality when it washes down as acid rain (Romahn et al. 2020)

8.3.3 Aerosol UV Index (AER AI) This product is calculated based on the spectral contrast in the ultraviolet spectral range for the 354 and 388 mm wavelengths. It is a long established air quality index monitor and is useful in tracking the plumes released from dust, biomass burning, and volcanic ash (Apituley et al. 2018).

8.3.4 Carbon Monoxide (CO) Carbon monoxide is an important atmospheric trace gas for the understanding of tropospheric chemistry and in certain urban areas, it is a major atmospheric pollutant. In the 2.3 µm spectral range of the Shortwave Infrared (SWIR) part of the solar spectrum, TROPOMI clear sky observations provide CO total columns with sensitivity to the tropospheric boundary layer. This data has been validated against TCCON and NDACC ground-based network and the MOPITT satellite with a resulting bias of less than 10% (Apituley et al. 2018).

8.3.5 Formaldehyde (HCHO) Formaldehyde is an intermediate gas in almost all oxidation chains of Non-Methane Volatile Organic Compounds (NMVOC), leading eventually to CO2 . HCHO satellite observations are used in combination with tropospheric chemistry transport models to constrain NMVOC emission inventories in so-called top-down inversion approaches. This data has a mean bias of 50% with ground-based sensors and other satellites such as GOME-2 and OMI and is measured in mol/cm2 (Romahn et al. 2020).

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8.3.6 Ozone (O3 ) Ozone in the tropical troposphere plays various important roles. The intense UV radiation and high humidity in the tropics stimulate the formation of the hydroxyl radical (OH) by the photolysis of O3 . The O3 Tropospheric Column gives the measurement of tropospheric ozone between the surface and the 270 hPa pressure level. It is based on the Convective Cloud Differential (CCD) (Apituley et al. 2017).

8.4 Data Pre-processing The Earth Engine uses the Level 2 product, from the ESA, and filters the pixels based on the minimum pixel quality level corresponding to each scene. These images are then broken into tiles according to the orbit number to make it easier for ingestion and retrieval.

8.4.1 Quality Assurance Filtering The quality of the individual observations depends on many factors, including cloud cover, surface albedo, presence of snow-ice, saturation, geometry, etc. These observations are filtered in order to avoid misinterpretation of the data quality and to avoid the effects of sun glint. Each of the satellite products comes with a qa_value (quality assurance) band which can be used to filter out less accurate values. Different thresholds of qa_value are chosen for different products as defined in the Sentinel-5P Product User Manual. For making the Level-3 products, Google Earth Engine filters pixels associated with a qa_value below 0.8 for the AER AI product, 0.75 for Tropospheric NO2 and 0.5 for all other products, except O3 for which quality filtering is not done. This takes care of erroneous scenes and problematic retrievals. Figure 8.1a shows a Level-2 scene of Aerosol Index for a single day released by the ESA. In Fig. 8.1b, the missing pixels can be observed after filtering, which corresponds to those that had a qa_value of less than 0.8. A yearly average of these Level-3 products was taken in which these missing pixels were filled in with values from those scenes which had data over that region.

8.4.2 Regional Masking The district level and state level administrative boundaries for India were used to mask the Level-3 products. The CH4 column contained a lot of missing data as the

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(b) After QA Filtering

(a) Before QA Filtering Fig. 8.1 Level-2 AER AI index

retrieval of this pollutant was of low quality, therefore this pollutant was dropped in the subsequent analysis. The average pixel values of NO2 , SO2 , CO, AER AI, O3 , and HCHO was calculated for each of the masked districts and was stored in a tabular format. A total of 594 districts with six pollutant values was used to frame the monthly pollution data set. This was further cleansed, and the rows containing null values were removed. Similarly, the Sentinel-5P data was also masked using state border shape files to extract the state-wise average value of pollutants and converted into a tabular data set. The state-wise data had 33 rows and 6 features (Fig. 8.2, Table 8.1).

8.4.3 Standardization The dataset was then re-scaled by first removing the mean of each pollutant and scaling each column by the standard deviation as shown in Eq. 8.1. This ensures that the features are of comparable denominations before running the clustering algorithms on them. z=

x−x σ

(8.1)

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Fig. 8.2 Single level-3 scene masked with Telangana district boundaries

Table 8.1 A snippet of the district-wise pollution data District

NO2

SO2

CO

AER AI

O3

HCHO

Andaman Islands

4.28E−005

−4.73E−08

0.037036

−1.27958

0.117128

9.89E−05

Nicobar Islands

3.89E−05

−9.24E−06

0.033289

−1.23807

0.117677

8.95E−05

Anantapur

5.97E−05

4.64E−05

0.035942

−1.11059

0.116844

0.000145

Chittoor

5.81E−05

4.95E−05

0.035912

−1.17883

0.117122

0.000152

Cuddapah

6.20E−05

6.00E−05

0.037118

−1.14565

0.117214

0.000153

East Godavari

6.15E−05

8.09E−05

0.039594

−1.17801

0.11709

0.000174

Guntur

6.56E−05

8.16E−05

0.039045

−1.11898

0.117253

0.000156

8.5 Clustering Clustering is a method of grouping based on patterns in the data (Jain et al. 1999). This technique is primarily used to find clusters of data points with inherent similarity in unlabelled datasets. In this case, the unlabelled dataset consists of different pollutants emitted (NO2 , SO2 , CO, AER AI, O3 , and HCHO) for each state or district in India. The aim is to find clusters consisting of states or districts that emit a similar pollution signature which will help isolate pollution sources more easily.

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8.5.1 Clustering Methods Unsupervised clustering was performed on the pollution datasets using three different algorithms, namely—K-Means clustering, Agglomerative clustering, and DBSCAN (Patel and Thakral 2016). The distance measure which was used in all three methods was the L-2 norm, Euclidean Distance.

8.5.1.1

K-Means Clustering

Partitioning-based unsupervised clustering methods reallocate data points by moving them from one cluster to another, starting from an initial partitioning. This algorithm works by initializing K points as cluster centers. In each iteration, every point in the dataset is assigned to the cluster it is closest to (using the L2 norm distance). The cluster center is then reinitialized to the mean of the cluster set and the clustering iteration until convergence is achieved.

8.5.1.2

Ward Agglomerative Clustering

Hierarchical clustering constructs the clusters by recursively splitting or combining the data points. In agglomerative clustering (a method within the broader class of hierarchical clustering methods) (CMU Statistics 2009), the clusters are built by iteratively merging smaller clusters starting from individual data point up until the required number of clusters are reached. Ward’s distance minimizes the overall intercluster sum of squared distances within all clusters. (A, B) =



−

i∈A∪B



i∈B

 2 → → − x i −− m A∪B  −

2 → → − xi − − m B

i∈A

2 → → − x i −− m A

(8.2.)

→ Here, − x i represents the center of the ith cluster and (A, B) represents the cost of merging clusters A and B.

8.5.1.3

DBSCAN Clustering

Density-based clustering tries to associate each point to a set of probability distributions. This algorithm does not take the number of clusters as an input and uses two parameters min pts and epsilon to form clusters. Epsilon determines the maximum distance between two points up to which they can be grouped within the same cluster and the min pts determines the minimum number of points that must fall within a cluster for it to not be designated as a noise point. It is useful in detecting noise and outliers in the data.

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8.5.2 Optimal Number of Clusters The elbow method (Kodinariya and Makwana 2013) is a technique used to determine the optimal number of clusters to be chosen based on heuristics such as inter-cluster similarity and intra-cluster similarity. In the method presented here, the number of clusters is iteratively increased from 2 till 15 and the point at which the graph of the cost function has the highest curvature is taken as the elbow, or optimal number of clusters. The elbow method was performed using the distortion score as the cost function to find the optimal number of clusters for K-means clustering and the silhouette score was used to ensure that the intra-cluster similarity was optimal. Both scores are explained below. The same number of clusters as derived from the elbow method was used to slice the hierarchical clustering to compare the results of the two algorithms.

8.5.2.1

Distortion Score

This metric gives information about the overall cluster dissimilarity. It is calculated as the mean sum of squared distances to centers. S=

n i=1

(xi − x)

(8.3)

In Eq. 8.3, xi represents the ith row of the dataset and x represents the mean of the cluster it belongs to. The lower the value of S, lower the dissimilarity, the more optimal is the solution. Figure 8.3 represents the plot for the distortion scores as a result of fitting the K-Means model to the dataset, while varying the value of K from

Fig. 8.3 Distortion score elbow for K-means

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Fig. 8.4 Silhouette score elbow for K-means

2 to 15. The elbow method analysis results in the elbow line being drawn at K = 5 as this is the point of maximum curvature in the curve.

8.5.2.2

Silhouette Score

The silhouette score metric represents the intra-cluster similarity. It is calculated as the mean ratio of intra-cluster and next nearest-cluster distance. S=

b−a max(a, b)

(8.4)

In Eq. 8.4, a is the mean distance between a sample and all other points in the same class and b is the mean distance between a sample and all other points in the next nearest cluster. The score is higher when clusters are dense and well separated. Figure 8.4 represents the plot for silhouette scores as a result of fitting the KMeans model to the dataset while varying the value of K from 2 to 15. The elbow method analysis results in the elbow line being drawn at K = 6. The elbow method using distortion score resulted in 5 being the optimal number of clusters. From the silhouette score plot, it can be inferred that the intra-cluster similarity of K = 5 was 0.290 which was not too far away from the optimal silhouette score of 0.295. Hence, K = 5 was chosen as the optimal number of clusters to prevent over-fitting on the data and get well-rounded clusters.

8.5.3 Cluster Validation The silhouette method was used to determine the intra-cluster similarity of the clusters formed by the three algorithms mentioned in the previous section. It gives an idea

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Fig. 8.5 Silhouette score for K-means

of how closely related a state or district is to the cluster it has been assigned to by the respective method. Values close to 1 indicate a high affinity to that cluster and negative values imply that the data point might have been wrongly assigned to that cluster. Each color in Fig. 8.5 represents the distribution of the silhouette score of each state that falls within the cluster. The wider the graph for a cluster, the more the number of states that fall into it. The average silhouette score for K-Means was 0.290 and for Ward Agglomerative was 0.280. The lower polluting states and the higher polluting states had silhouette coefficients of almost 0.5, which indicates higher intra-cluster similarity. Both K-Means and Ward Agglomerative clustering resulted in a similar distribution of states across clusters. Only the state of Telangana was placed in cluster 1 in K-Means and cluster 0 in Ward Agglomerative. In the case of DBSCAN, it does not need to specify the number of clusters and instead the values of the parameter min pts was fixed to 3 and that of epsilon was 1.7. DBSCAN. Clustering resulted in a few states being classified as outliers since their pollution signatures did not match with any other state. But the remaining clusters that were formed were in correlation with K-Means and Ward Agglomerative clustering. The results from K-Means clustering have been presented and used for analysis in the following section.

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8.5.4 Analysis of Pollution Signatures and Clustering Results Across Indian States and District In this section, the various results from the state as well as district-wise clustering and the corresponding pollution signatures obtained for different clusters have been presented and analyzed in depth. The bar plots shown below in Fig. 8.6 represent the average pollution signatures for every cluster obtained as a clustering of pollution data across different states and districts. These pollution signatures serve as unique representation for each cluster. Each pollution signature shows the average pollutant magnitude for a given cluster. States and Districts which are part of Cluster 0, on average, have the lowest pollution profile and those which fall into Cluster 4 emit the highest level of pollutants as can be seen in Fig. 8.6. The varying trends for each pollutant across each cluster for both state-wise and district-wise clustering have also been shown in Fig. 8.7. Figure 8.8a shows the state-wise cluster map, and Fig. 8.8b shows the district-wise cluster map across India as obtained from K-means clustering algorithm. Each of the cluster behavior can be explained in terms of its corresponding pollution signature and trends as follows: Cluster 0—As can be seen in Fig. 8.6, cluster 0 has the least concentration of pollutants. States such as Jammu and Kashmir, Uttarakhand, Himachal Pradesh and Sikkim fall under this cluster. These states have

(a) State-wise Pollution Signatures

(b) District-wise Pollution Signatures

Fig. 8.6 Pollution signatures for K-means clustering

(a) State-wise Pollution Trend

(b) District-wise Pollution Trend

Fig. 8.7 Varying pollution trends across K-means clusters

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(a) State-wise Clusters

(b) District-wise Clusters

Fig. 8.8 Visualization of K-means clusters

a very low population density and consequently low vehicular traffic as they mainly comprise mountainous terrains. These states also have significantly lower industrial activity and hence do not contribute much to the air pollution as can be seen from lower pollutant levels. Cluster 1—On average, the regions which fall under this cluster have a higher level of emission of all pollutants when compared to cluster 0. This cluster comprises the southern states and a majority of the north eastern states. Cluster 2—The western and central states such as Telangana, Odisha, Gujarat, Madhya Pradesh, and Maharashtra fall under this cluster. It can be noted that in the district-wise clustering in Fig. 8.8b, the city of Chennai falls under this cluster 2, whereas it came under cluster 1 along with the state of Tamil Nadu in Fig. 8.8a. Cluster 3—This is the second-highest polluting cluster and most of the states which fall under this have a lot of industrial presence which is attributed to high pollutants and in particular high NO2 emissions. States such as Uttar Pradesh, Haryana, Rajasthan, and Punjab fall under this in the state-wise clustering. Cluster 4—This is the cluster which has the higher average percentage of pollutants like SO2 and CO as well as seen in Fig. 8.7. These states have some of the highest population densities in India and contain some of the worst polluting cities in the world. It is worth noting here that the results are consistent with the report from TERI—The Energy and Resources Institute (Sharma 2003), where the states belonging to cluster 3 and cluster 4 are those which have the highest PM10 emissions from brick kilns as well as from coal and iron ore mining. The biggest difference in terms of state vs. district level clustering can be noted from the regions which fall under this cluster. In Fig. 8.8a, it can be seen that only the state Delhi falls under this cluster 4, but in the district-wise clustering as shown in Fig. 8.8b, it can be seen that a majority of the districts from Uttar Pradesh, Bihar,

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Haryana, Punjab, Rajasthan, and Bihar as well now come under cluster 4, representing the highest overall pollution signature in these densely populated regions. Further comparing the two maps in Fig. 8.8a it can be seen that the mining intensive states of Jharkhand, West Bengal, parts of Chhattisgarh, and Odisha fall under cluster 2, whereas in Fig. 8.8b the districts from these states fall under cluster 3. Upon comparing the state-wise and district-wise clustering, we can see that cities like Mumbai, Chennai, and Hyderabad fall under a higher polluting cluster as compared to other districts within the same state. This is in line with expectations since these urban cities tend to emit higher levels of pollution. Thus based on this analysis, it is observed that a finer resolution regional pollution classification can be seen in district-wise clustering as compared to the state-wise clustering.

8.6 Conclusion and Future Work Acknowledging the importance of managing pollution levels so that it does not affect the socio-economic and health status of the general population, it is important to understand the precise pollutant signature over a certain area and the possible sources of the pollutants. The presented study explored three clustering algorithms on data retrieved from ESA’s Sentinel-5P satellite to address this issue. The clustering algorithms were used to assign unique pollutant signatures to states and districts across India. The results have been shown to be promising. To take this work further, it is planned to improve on the clustering algorithms to understand, if a similar or higher accuracy can be attained at an even finer granularity than a district level. In addition, studies need to be conducted that will help correlate pollution levels with socio-economic factors of the region. Furthermore, there is a need to study the effect of other variables such as wind, atmospheric pressure to understand the transport of pollutants from its source (Engel-Cox et al. 2005). This will help further identify pollution sources with higher accuracy.

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Chapter 9

Urban Growth Impact on Cauvery River: A Geospatial Perspective J. Brema, Shivam Trivedi, Monica Sherin, Dnyanadev S. Dhotrad, K. Ganesha Raj, and Dipak Samal

Abstract Rivers are essential for the survival of all living organisms and crucial for balancing the ecosystem as they are the lifeline of any civilization. Various anthropogenic activities are contaminating the river water through point and non-point sources of pollution, mainly from domestic sewage and industrial effluents. Cauvery River is one of the most important water sources for the states of Karnataka and Tamil Nadu. Geospatial technology offers great promise for generating spatial information on natural resources and assessing the dynamics through multi-temporal remote sensing data. This study is aimed at understanding the long-term land use/land cover changes in Cauvery basin for nearly last 2 decades (2001–2019), location of point and non-point sources of water pollution along the river, along with assessing impact of urbanization and industrialization. Satellite imageries from Landsat-5 TM (30 m), Landsat-8 OLI (30 m) and Resourcesat-2 LISS IV MX (5.8 m) were utilized to understand and assess the temporal changes in the study region. Stream network was derived from Open Street Maps (OSM)/India-Water Resources Information System (India-WRIS) using Digital Elevation Model (DEM). River course and major cities/towns were delineated in the buffer region. From the land use/land cover analysis, the conversion of cropland and vegetation into settlements was quite evident. To assess the river water quality for pollution parameters, available field datasets of National Water Quality Monitoring Programme (NWMP) from river water quality monitoring stations of Central Pollution Control Board (CPCB)/Karnataka State Pollution Control Board (KSPCB)/Tamil Nadu Pollution Control Board (TNPCB) were analysed for temperature, pH, Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Total coliform and Faecal coliform. Field survey, water sample collection and chemical analysis were also carried out for additional datasets during J. Brema (B) · M. Sherin Karunya Institute of Technology and Sciences, Coimbatore 641038, India e-mail: [email protected] S. Trivedi · K. G. Raj Regional Remote Sensing Centre-South/NRSC/ISRO, Bengaluru 560 037, India D. S. Dhotrad · D. Samal Centre for Environmental Planning & Technology (CEPT) University, Ahmedabad 380009, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 T. P. Singh et al. (eds.), Geo-intelligence for Sustainable Development, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-16-4768-0_9

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the study period in 2019. Based on the decadal population growth rate, the total current population (as in 2019) in the study region was also estimated. Based on the overall study, it was inferred that over the years, river water in selected stretches has become highly contaminated, especially in Srirangapatna (Karnataka) and in Bhavani, Pallipalayam, Trichy and Tiruppur (Tamil Nadu). This study helped to understand how the Cauvery River is getting polluted, identifying polluted stretches of river and possible point and non-point sources of pollution. The main reason for the deterioration in river water quality is attributed to increasing population in Cauvery River basin, which inturn generates increased waste (mainly sewage), making its way into the river in mostly untreated or partially treated form. Suitable remedial measures are also suggested to reduce river pollution and control its adverse impact on living organisms. Keywords Cauvery River · Urbanization · LULC · Water quality · Geospatial techniques

9.1 Introduction Globally, rivers are important resources of surface water, mainly for domestic consumption and irrigation purposes (IWRIS, 2014; Mihaela, 2018). River ecosystems are influenced by natural processes and anthropogenic impacts at various spatial and temporal scales (Kiesel et al., 2019). India is one of the top ten water rich countries of the world with 4% of world’s water resources. India is considered as the ‘Land of Rivers’ due to the presence of a large number of big and small rivers across the country. These rivers are considered very sacred and have played a vital role in the development of culture, religion, settlement of villages, towns and cities in India since time immemorial. Rivers provide water for various purposes like irrigation, bathing, washing, fishing, generation of hydroelectric power and used for several recreational purposes. The quality of water in several rivers has started deteriorating rapidly in the last few decades. Currently, the quality and quantity of river water is a matter of serious concern due to rapid increase in the population, urbanization, industrialization and deforestation (FAO, 2003; Ramasubramoniam et al., 2014; Limburg et al., 2013; Panigrahi & Pattanaik, 2019). Various anthropogenic activities are contaminating the river water through point and non-point sources of pollution, mainly from domestic sewage and industrial effluents (Singh et al., 2017a; Kumar et al., 2018; Sharma et al, 2019). Basically, water is used by all living beings for all sorts of needs and purposes. As per Ministry of Housing and Urban Affairs, India (2020), 135 L per capita per day (lpcd) has been suggested as the benchmark for urban water supply and 55 lpcd for rural areas, under Jal Jeevan Mission for basic needs such as drinking, cooking and washing. It is estimated that about 80–90% of this water is converted into waste water, i.e. it is not consumed and ends up in drainages and only the water that is used for cooking or drinking is actually consumed. Industrial wastes also contribute

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to pollution and contain several pollutants like lead, mercury, asbestos, sulphur and nitrates which are very harmful to both humans and aquatic life forms. If it is let into water body network in untreated or partially treated form, it will increase the pollutant concentration in the water, which when consumed by humans or animals cause serious health hazards and might even cause death. River water may be contaminated by various means, chemically or biologically and may become unfit for drinking and other uses. Entry of sewage in river water causing contamination causes increased Biochemical Oxygen Demand (BOD) and Chemical Oxygen Demand (COD) levels in water which makes the water unfit for drinking and other routine purposes. Understanding and identifying the pollution and preventing it should be the prime focus of research studies. World Health Organization (WHO) in 2017 stated that nearly 2.1 billion people (about 3 persons in 10 worldwide) lack access to safe, readily available water at home, which in turn becomes the main reason for health issues. Out of this number, about 844 million do not have even a basic drinking water service (WHO, 2017). During last several decades, the water quality of the Indian rivers has been deteriorating due to continuous discharge of industrial wastes and domestic sewage (Sivakumar et al., 2000; Raja et al., 2008; Abbasi & Abbasi, 2012; Susheela et al., 2014; Jafar & Loganthan, 2017; Singh et al., 2017b; Shivam et al., 2019; Johnson & Penaluna, 2019). A polluted river is harmful to both the environment as well as for all living beings, so understanding the causes of river pollution and providing a suitable solution to reduce the pollution is an essential requirement. With recent advances in spectral and spatial resolutions of satellite imageries, along with advanced computational tools in GIS environment, geospatial techniques constitute a potential tool for mapping and monitoring of natural resources as well as understanding the long-term changes and dynamics of any ecosystem (Ramasubamoniam et al., 2014; Sharma et al., 2019). Satellite data gives us the synoptic view of the study area facilitating in mapping the growth of cities/towns and the river course; and in understanding the pollution dimension along the selected river stretches. The present study was envisaged to understand and assess the environmental impact of urban growth in major part of Cauvery River Basin using geospatial techniques for optimal management of water resources, in the wake of development and growing need of population of India.

9.2 Urbanisation and River Water Pollution During last several decades, unprecedented population growth coupled with unplanned developmental activities has resulted in urbanization in many parts of the world. Rapid urbanization is quite alarming, particularly in developing countries like India. Population has a direct relationship with urban growth. Urbanization leads to irretrievable loss of productive agricultural lands, forests, surface water bodies and groundwater prospects. This is mainly due to uncontrolled growth in

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population resulting in serious problems related to food scarcity, informal settlements and environmental pollution along with destruction of ecological structures. With India’s rapidly growing population, accompanied by increasing hazards of domestic and industrial pollution to the inland waters of the country, scientists envision a rapid degradation of water quality unless concrete steps are taken immediately to reduce pollution of both natural and man-made water bodies. Contamination of the river due to anthropogenic activities such as discharging of untreated industrial wastes and sanitary wastes into the rivers, dumping of garbage, etc. leads to river pollution (Gaurav et al., 2019; Roy et al., 2020). The rivers are the vital component of the biosphere and down the ages river basins have been home to civilizations across the world and water has become a vital resource for economic growth and sustainable development. Water from the rivers is a basic natural resource, essential for various human activities. River water is used for multiple purposes. Rivers are important for the balance and maintenance of the ecosystem, as the rivers are major sources of potable and drinkable water. Due to pollution in the rivers, a large population suffers from various diseases caused due to consumption of contaminated water. Mostly pollution in the rivers occur due to dumping of domestic sewage, discharge of industrial effluents, improper solid waste management in urban areas and over-use of pesticides in agriculture fields (Narmada et al., 2015; Singh et al., 2017c).

9.3 Study Area The Cauvery River is one of the major rivers of India and among the seven sacred rivers of India including the Ganges, Yamuna, Godavari, Saraswathi, Narmadha and Indus. Also known as ‘Dakshina Ganga’, Cauvery River is considered as a very sacred river of South India. It originates in Tala Cauvery in the Brahmagiri hills of Western Ghats in Coorg (Kodagu) district of Karnataka at an elevation of 1,341 m from sea level. From there, the river continues for a distance of about 802 km, mainly through the states of Karnataka (461 km) and Tamil Nadu (421 km), before draining into the Bay of Bengal. It is the third largest river of South India and is a unique gift of Western Ghats to Southern India (Fig. 9.1). The Cauvery basin is surrounded by Western Ghats in the west and Eastern Ghats in the south and east and by the ridges separating the basin from the Tungabhadra and Pennar basins in the north. Cauvery River basin covers major part of peninsular India, spreading over the states of Tamil Nadu (54.05%), Karnataka (42.23%), Kerala (3.53%) and Union Territory of Puducherry (0.18%), which is nearly 2.7% of the total geographical area of the country. Total basin extends over an area of 81,155 km2 . It lies between 75° 27 to 79° 54 east longitudes and 10° 9 to 13° 30 north latitudes. The upper reach of Cauvery lies partly in Kerala and Karnataka, middle reach is covered partly by Karnataka and Tamil Nadu and the lower reach is covered by Tamil Nadu and the Union Territory of Puducherry. Physiographically, the Cauvery basin can be divided into three parts namely, the uplands in the Western Ghats, the

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Fig. 9.1 Study area location map showing Cauvery basin boundary (red) and selected river stretch within Karnataka state (black)

plateau region of Mysore and the delta in Tamil Nadu and Pondicherry. It can be further divide into five zones, based on relief, namely, the mountainous region, high plateau region (Mysore plateau), Transition Zone, Riverine Plain and the Deltaic region. The delta area is the most fertile tract of the basin. The soil characteristics vary considerably in the basin. The major soil types are red soil, black soil, lateritic soil, alluvial soil, mixed soil and forest soil. Red soil covers a major portion of the Cauvery basin. Coimbatore region has clayey red soil with high fertility but Mysore region has sandy red soil and the delta region has the capacity to retain moisture and shows a positive impact on groundwater potential. In the Karnataka region lying within the Cauvery basin, cropping pattern includes paddy, sugarcane, finger millet (Ragi) and some other irrigated and dry crops, while in the state of Tamil Nadu, the major crop is paddy. As a whole, majority of the cultivated area in the Cauvery basin is under paddy cultivation. The basin is covered with agricultural land for almost 66.21% and forest cover of 20.50%. The Cauvery River has a total of 12 tributaries, out of which 9 tributaries are situated in Karnataka state and remaining 3 tributaries are located in Tamil Nadu. Normally, Cauvery river basin experiences tropical monsoon climate. This can be further classified as cold weather period (January–February), hot weather period (March–May), south-west monsoon period (June–September) and north-east monsoon period (October–December). Rainfall pattern varies over the entire Cauvery river area. Rainfall from both south-west monsoon (June–September) and north-east monsoon (November–January) provides enough water to the Cauvery basin. The

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area under Western Ghats and Nilgiris receives high rainfall, the area under the rain shadow region of Western Ghats receives less rainfall and finally the delta region receives rainfall with an increasing order (moderate rainfall). The major river and its tributaries in Western Ghats, which lies within Karnataka region receives heavy rainfall every year during south-west monsoon. But the river stretch located in Tamil Nadu receives major rainfall during north-east monsoon. The Cauvery basin situated upstream of the Mettur dam entirely depends on South-west monsoon, whereas the downstream portion depends on north-east monsoon, which causes flood sometimes. Cauvery basin consists of 224 rain gauge stations which are widely distributed over the entire basin, providing information to India Meteorological Department (IMD). The highest rainfall is recorded in Kodagu district (Karnataka stretch) and in Nilgiris district (Tamil Nadu stretch) due to its altitude during south-west monsoon. According to the Central Water Commission (2002) and International Water Management Institute (2005), the Cauvery has total renewable surface runoff of 21.4 km3 . The potentially utilizable water in the basin (including surface and ground water) is about 27.8 km3 . The average annual water potential in the basin is 21.36 Billion Cubic Metre (BCM). The river basin encompasses a range of land use/land cover types, from dense forest areas and plantations in the Western Ghats hills to fertile agricultural land in the river valley. The cultivable area of the basin is about 5.8 M ha, which is about 3% of the cultivable area of the country. Along the Cauvery River, there are a number of ancient temples which were witness to its religious and cultural significances and still are a significant place of worship and rituals. The Cauvery River is one of the major sources of water for an extensive irrigation system, household consumption and for generation of hydroelectric power (Jomet et al., 2013; Hema & Subramani, 2019). The important industries in the river basin include cotton textile industry in Coimbatore and Mysore, cement factories in Coimbatore and Tiruchirappalli and several industries based on minerals and metals.

9.4 Cauvery River Water Quality in Karnataka In Karnataka, Cauvery river length can be divided into three stretches, (i) from the river origin to Krishna Raj Sagar (KRS) Dam, (ii) from KRS Dam to Sathyagala and (iii) from Sathyagala to Tamil Nadu Border. In the present study, a stretch of 60 km from KRS Dam to Sathyagala is considered for the Karnataka part of Cauvery (Fig. 9.1). This is the most polluted stretch of Cauvery river in Karnataka state and Central Pollution Control Board (CPCB) has reported a BOD level ranging from 3.1 to 6.7 for the stretch from Ranganathittu to Sathyagala, indicating priority 4 out of 5 (Shivam et al., 2019). The first river stretch (from origin to KRS dam) is relatively cleaner, while the last stretch of Cauvery in Karnataka (from Sathyagala to and along Tamil Nadu state border) flows majorly between dense forests. In this study, it was envisaged to assess the river water quality and identify possible sources of pollution using geospatial techniques across this stretch.

9 Urban Growth Impact on Cauvery River: A Geospatial Perspective Table 9.1 Change statistics for the study area

133

Classes

Area in km2 2001

2019

Water

281

208

−25.98 90.07

% change in area

Urban

151

287

Vegetation

1588

1639

3.21

Barren/sparse vegetation

2412

2298

−4.73

Total

4432

4432

The study is mainly focused on understanding the impact of urbanization and industrialization on river pollution and identifying the major cities and towns causing the pollution using temporal change study, in addition to assessment of the waste disposal mechanism of cities and towns in this selected study area. The study also attempted to identify the sources and causes of Cauvery river pollution, locate point and non-point sources of pollution for the study area and suggest possible remedial measures to reduce the river pollution. A 10 km buffer on either side was considered from the banks of the river, from which the spatiotemporal change would be assessed. From a study conducted by the CPCB (2018), BOD level in this Cauvery River stretch starting from Ranganathittu to Sathyagala is reported to range from 3.1 to 6.7 and it has been given a priority 4 (priority 1 being the highest, from a range of 1–5). The polluted river locations in a continuous sequence are defined as polluted river stretches. The area considered for the study is a stretch of Cauvery River from KRS Dam to Sathyagala. It is a 60 km stretch study area, the cities and towns with higher population than 10,000 are considered for the study. The methodology broadly consisted of spatial and non-spatial data analysis along with field survey and water sample collection. Satellite remote sensing data from two different satellites (Landsat 5 and IRS Resourcesat-2) were acquired for two time periods, i.e. for 2001 and 2019 (Table 9.1). These images were subjected to required corrections for further processing and analysis. Classification and spatiotemporal analysis was done on the study area, mainly to identify the impact of urbanization and industrialization on the study area and its impact on the pollution of the river. Ancillary data such as administrative boundaries, Survey of India (SOI) toposheets and DEM have also been used. DEM was used to extract the stream network of the Cauvery River and it was compared with the stream network obtained from the OSM and the India-WRIS and the stream network generated from the DEM was most suitable and hence considered for this study. Delineation of river course and mapping of major cities/towns was done using the satellite datasets. The nearby cities were also observed for the industrial and urban development in the study area. Cities/Towns/Villages above 10,000 population were only considered. Under National Water Quality Monitoring Programme (NWMP), CPCB has established water quality monitoring stations all over the country. Their present network comprises 2500 stations in 28 States and 6 Union Territories spread over the country. Along the selected river stretch of Cauvery River, the monthly average

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Fig. 9.2 Selected locations for water sample collection

pollution data was obtained from CPCB and Karnataka State Pollution Control Board (KSPCB) for four points in 2001 and six points in 2016. The sample collection by CPCB increased from four stations to six during these years. These locations were sparsely distributed. As part of this study, a field survey was carried out and river water samples were collected from the locations (Fig. 9.2) and they were tested for the following parameters—Faecal Coliform (mnp/100 ml), Total Coliform (MPN/100 ml), D.O. (mg/l), B.O.D (mg/l), pH and temperature. The points in orange colour indicate the locations where government has collected data for past couple of years and blue being additional data sample points for collection in this study. The satellite images were digitally classified using unsupervised method finally into four broad classes and change analysis was carried out for the years 2001–2019. This study has helped to identify how the urban growth has impacted the pollution of river (Table 9.1). With the help of the results, point and non-point sources of pollution were identified and the impact of change from 2001 to 19 on river pollution was also identified. The results helped to identify the level of pollution across the river for the study area and it helped to suggest appropriate and suitable measures to prevent the pollution. The cities and towns considered for the study were with a minimum population of 10,000 (Census 2011). The cities falling under the considered criteria for the study are nine (Kollegal, Malavalli, Mandya, Pandavapura, Srirangapatna, Krishanrajanagara, Mysore, Bannur and T. Narsipur). Mysore and Mandya are the most populated cities amongst these. The total population in 2001 was 11,33,368 as compared to 12,77,715 in 2011 and estimated to be 14,18,070 in 2019. The total population has increased since 2001 nearly 2.9 lakh has increased. All the cities have nominal changes but Mysore has huge increase in population (nearly 2 lakh).A detailed comparison was made for water pollution parameters from these datasets for 2001, 2011 and 2019. Based on the overall results, it was inferred that over the years, river water in this stretch has become highly contaminated. Values of pollution parameters are compared for years 2001, 2016 and 2019 in Table 9.2. BOD values ranged from

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Table 9.2 Comparison of Cauvery River water pollution parameters in Karnataka Temperature Station

2016

2019

27

N/A

N/A

26.23 26.30 26.53 27.33 26.35 25.73

N/A

KRS Dam Karekura Srirangapatna Sathyagala Bannur Ranganathittu

N/A

2001 7.9 9 8 9

N/A

N/A

N/A

N/A

6.80 7.05 6.10 6.84 7.94 6.44

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

28 29 29 N/A

Mahadevapura T Narasipura Dasanapura

B.O.D. (mg/l) Station

KRS Dam Karekura Srirangapatna Sathyagala Bannur Ranganathittu Mahadevapura T Narasipura Dasanapura

D.O. (mg/l)

2001

2001 1 1 1 1 N/A

2016 1.54 1.39 2.63 1.95 1.9 2.04

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A N/A

2016

pH 2019 5.5 6.2 4.5 6.5 3.4 5.5 0.5 4.5 5.9

Total Coliform (MPN/100ml) 2019 138 48 84 72 270 12 780 114 54

2001 (< 2500) 2016 (< 2500) 2019 (0.1 km2 , and 15% are covered by debris with an area of >1.0 km2 (Herreid and Pellicciotti 2020). High Mountain Asia (HMA) that generally includes Central Asia and South Asia West and East (Table 12.1) contains a great quantity of debris-covered glaciers. HMA has a total debris-covered surface of about 8,415 km2 (Scherler et al. 2018), representing about 9.0% of the total glacier area of the region. In the south part of central Himalaya, the debris-covered area represents 36% of the total studied glacier area, while it represents 19 and 21% in the north part of central Himalaya and western Himalaya, respectively; meanwhile, debris coverages in the Hindu Kush and Karakoram Mountains are respectively 22 and 18% (Scherler et al. 2011). In the Mount Gongga of the southeastern Tibetan Plateau, about 68.0% of these glaciers are covered by debris, in which about 13.5% of the total glacier area is debris-covered (Zhang et al. 2016). With glacier retreating, the debris-covered extent is likely to expand (Stokes et al. 2007; Kirkbride and Deline 2013; Tielidze et al. 2020; Xie et al. 2020). Such a trend in debris-covered expansion has been confirmed on different regions’ glaciers (Stokes et al. 2007; Kirkbride and Deline 2013; Tielidze et al. 2020; Xie et al. 2020). Glaciers in the Central Caucasus experienced significant expansion of the debriscovered surface with 3–6% during the period of 1985–2000 (Stokes et al. 2007) and 18.0% during 2000–2014 (Tielidze et al. 2020). In the Hunza basin of Karakoram, the total debris-covered surface on different glaciers experienced an expansion trend, increased by 8.1–21.3% during the period 1990–2019, which mainly concentrated the middle and upper parts of the glacier, near to the equilibrium line altitude (ELA; Xie et al. 2020).

12.2.2 Debris-Cover Effect Compared to snow or clean ice, debris layer has different physical properties, such as albedo, particle size, and color, leading to a unique thermal process, which alters the surface energy-balance process and imposes a barrier between the ice and the atmosphere. Consequently, ice melting processes beneath the debris layer are significantly different from those of clean ice and snow (Østrem 1959; Mattson et al. 1993; Nicholson et al. 2013). As shown in Fig. 12.2, a thin debris cover (~20–30 mm) can obtain extra energy due to increased absorption of shortwave radiation resulted from the lower albedo of debris-covered surface, which in turn efficiently transmits to the ice under the debris layer. As a result, the sub-debris ice melt rate is enhanced compared to that of bare ice. With debris thickness increasing with more than a few centimeters, the sub-debris ice melt rate is inhibited relative to that of bare ice (Østrem 1959; Nakawo and Young 1982; Mattson et al. 1993; Kayastha et al. 2000). Hereafter, the impact of debris cover on ice melting is called the debris-cover effect, including acceleration and insulation effects. Local debris properties and weather conditions have an influence on the energy processes of the debris-ice interface and

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Fig. 12.2 Variation in ice melt rate with increasing debris thickness on different glaciers (Reprinted from Mattson et al. (1993) by The Author(s) licensed under CC BY 4.0 (https://creativecommons. org/licenses/by/4.0/)

then may modify the ice melt process beneath the debris layer, but it has little influence on the general characteristics of the relation between ice melt rate and debris thickness as shown in Fig. 12.2. This further demonstrates the dominant influence of debris thickness and its spatial distribution on the melting processes beneath. Table 12.2 shows the debris-cover effect in different regions. Although debriscovered proportions are significantly different from region to region, the insulation effect is dominant, i.e., protecting mass loss from glaciers. However, in Mount Table 12.2 Debris-cover effect in different regions Region

Debris (%)

Debris-cover effect

References

South Alps, New Zealand

8.0

Insulation effect

Anderson and Mackintosh (2012)

Caucasus

8.1–23.0

Insulation effect

Lambrecht et al. (2011)

Altai

3.7–25.8

Insulation effect

Mayer et al. (2011)

Tuomur, Tien Shan

7.5–22.0

Insulation effect, and acceleration in 2% of ablation area

Su et al. (1985) Zhang et al. (2007)

Himalaya and Karakorum

2.0–36.0

Insulation effect

Scherler et al. (2011)

Langtang, Himalaya

19.0

Insulation effect

Immerzeel et al. (2012)

Mount Gongga

13.5

Acceleration effect in 10.2% of ablation area, and insulation in 40.8%

Zhang et al. (2016)

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Gongga of the southeastern Tibetan Plateau, about 10.2% of the total ablation area of these glaciers experiences an acceleration effect due to debris cover and its uneven distribution (Table 12.2), leading to about 25% of debris-covered glaciers with a more mass loss (Zhang et al. 2016). Although the debris cover inhibits ice melting in the Himalaya (Table 12.2), surface lowering rates on debris-covered ice are generally comparable to those of clean ice on average at a regional scale (Kääb et al. 2012; Nuimura et al. 2012; Pellicciotti et al. 2015), which is referred as the ‘debriscovered glacier anomaly’ (Pellicciotti et al. 2015). The distribution of debris cover significantly influences ice ablation gradient. The highest melt rate is found near the terminus of clean-ice glaciers where temperature is highest, while the highest melt rate is measured at the upper part of the ablation zone where the debris layer is thinner (Benn et al. 2012; Zhang et al. 2016). Furthermore, the uneven distribution of debris thickness leads to the differential ablation in the ablation zone, which easily form ice cliffs and supraglacial ponds for glaciers (Benn and Evans 2010). Supraglacial ponds and ice cliffs distributed in the ablation area contribute disproportionately to total ablation (Sakai et al. 2002; Benn et al. 2012; Miles et al. 2018). As a result of the insulation effect of the thick debris layer (Fig. 12.2), debris-covered glaciers can exist in lower elevations compared with clean-ice glaciers, and may persist through warmer climatic periods. Therefore, there are significant differences between debris-covered and clean glaciers under the same climatic conditions, which shows different responses to the same climatic forcing (Benn et al. 2012). In this case, we need to differently treat debris-covered glaciers and clean-ice glaciers when predicting mass balance and runoff, and assessing the risk of outburst floods.

12.2.3 Mass Balance of Debris-Covered Glacier The spatial distribution of debris cover leads to complicated ice melting processes through altering surface melt rates and associated spatial patterns, and then affects mass balance and its vertical structure of debris-covered glaciers. In general, the mass balance on debris-covered glaciers mainly considers processes of melting of clean ice and beneath debris layer, and snow accumulation. A simulated mass balance gradient on Ngozumpa Glacier, a debris-covered glacier in the Himalaya, indicated different mass balance gradients between debris-covered and clean-ice glaciers (Benn et al. 2012), on which the trend of downglacier increase in debris thickness inverts the ablation gradient on the lower part of the glacier. As mentioned above, mass loss rate on debris-covered glaciers in the Himalayas might be similar to that on clean-ice glaciers (e.g., Kääb et al. 2012; Nuimura et al. 2012; Pellicciotti et al. 2015). Pellicciotti et al. (2015) reconstructed mass changes of debris-covered glaciers in the upper Langtang valley of the Nepalese Himalaya, and their results found significant spatial differences in mass changes both between glaciers and within any single glacier, showing a very clear nonlinear profile of mass balance with elevation. Furthermore, the debris-cover effect leads to glacier termini with extensive responses

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to climate change (Scherler et al. 2011). In the Himalayas, most clean-ice glaciers experienced retreating (Bolch et al. 2012), while debris-covered glaciers showed different responses to climate change, although they experienced significant surface lowering and negative mass balance (Bolch et al. 2012; Benn et al. 2012; Kääb et al. 2012; Nuimura et al. 2012). Termini of debris-covered glaciers in the Himalayas show different states: some are retreating, others are stable, and others are advancing (Scherler et al. 2011; Bolch et al. 2012).

12.3 Glacier Mass Balance Modeling Till now, less than 1% of the world’s glaciers have observations of glacier mass balance (Zemp et al. 2015). Consequently, the assessment of glacier mass balance worldwide is largely affected by serious under sampling. Therefore, model simulation is one of the effective ways to solve this issue, which can calculate surface massbalance evolution through modeling ablation and accumulation. Note that a wide variety of glacier mass-balance models of different complexity and scope have been developed during recent decades, which ranges from energy-balance models which can assess the surface energy fluxes in detail (e.g., Hock and Holmgren 2005; Zhang et al. 2012) to degree-day models which use air temperature as the proxy of the melt energy (e.g., Braithwaite and Zhang 2000; Hock 2003; Zhang et al. 2017). Below, we will briefly introduce the methods for modeling glacier mass balance.

12.3.1 Degree-Day Mass-Balance Model Degree-day mass balance models can compute each component of the glacier mass budget, which commonly calculate ice ablation using degree-day model and estimate accumulation using a temperature threshold (e.g., Braithwaite and Zhang 2000; Marzeion et al. 2012; Zhang et al. 2017). For a glacier, the specific mass balance, b, is calculated following Eq. (12.1). In the model, a temperature threshold is generally used to distinguish between snow and rain, and then calculate surface accumulation. When the temperature is at or below the threshold temperature, precipitation is considered to be snow. We assume to be a mixture of rain and snow within the temperature range of 1 K above and below the threshold temperature, and then obtain the snow and rain proportions of total precipitation through linear interpolation. Therefore, accumulation is calculated by

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⎧ Ptot Tair ≥ Tl ⎪ ⎪ ⎪ ⎨ Tl − Tair PL = Ptot Ts < Tair < Tl ⎪ Tl − Ts ⎪ ⎪ ⎩ 0 Tair ≤ Ts

(12.2)

PS = Ptot − PL where PS and PL denote the amounts of snow and rain, Ptot is the total precipitation, T air is air temperature, T l and T s denote the temperature thresholds of rain and snow, respectively. Snow and ice melt is estimated using the degree-day model, which is an empirical relationship established between melt rate and air temperature (Braithwaite and Zhang 2000; Hock 2003; Zhang et al. 2006). The approach has become a widely used method for estimating glacier melt rate (e.g., Braithwaite and Zhang 2000; Hock 2003; Marzeion et al. 2012; Radi´c et al. 2014; Zhang et al. 2017), because air temperature is commonly the most easily existing data and is easily extrapolated and forecasted compare to other data. Although the model involves a simplification of the energy balance of the glacier-atmosphere interface, degree-day models generally obtain good model performance, which is often comparable with the performance of the energy balance model (WMO 1986). The basic formulation of the degree-day model is given by  M=

DDFsnow/ice Tair Tair > 0 Tair ≤ 0 0

(12.3)

where M is the amount of snow or ice melt, T air is air temperature, and DDFsnow/ice is the degree-day factors for snow and ice. Degree-day factors are different for snow and ice and show significant variation in space and time (Zhang et al. 2006, 2019). Compared to DDFs for snow, DDFs for ice are generally larger, because of the lower albedo of ice (Hock 2003; Zhang et al. 2006). Observed values on different glaciers vary between 2.5–11.6 mm d−1 K−1 for snow and between 6.6–20.0 mm d−1 K−1 for ice (Hock 2003). In the Tibetan Plateau and surroundings, DDFs for snow and ice range from 3.0 to 15.0 mm d−1 K−1 (Fig. 12.3) and from 1.5 to 10.0 mm d−1 K−1 (Fig. 12.4) (Zhang et al. 2019), respectively. In general, lower DDFs are very likely to be found in cold-dry areas of the Tibetan Plateau and surroundings, while higher values are found in warm-wet regions (Zhang et al. 2006). Note that all energy balance components can generally influence degree-day factors, several studies have attempted to incorporate more variables into the model to enhance the physical basis of the model, such as wind speed, vapor pressure and radiation components (Lang 1981; Zuzel and Cox 1975; Martinec 1989; Zhang et al. 2007). A widely quoted method has been proposed through incorporating shortwave or net radiation term to the general form (Eq. (12.4)) (e.g. Kustas and Rango 1994;

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Fig. 12.3 Spatial distribution of DDFs for ice in the Tibetan Plateau and surroundings. Data are from Zhang et al. (2019)

Fig. 12.4 Spatial distribution of DDFs for snow in the Tibetan Plateau and surroundings. Data are from Zhang et al. (2019)

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Kane and Gieck 1997; Hock 1999; Zhang et al. 2007), thus obtaining better performance at a daily or hourly resolution compared to the classical approach. Therefore, the modified degree-day model is commonly given by  M=

f m Tair + α R Tair > 0 Tair ≤ 0 0

(12.4)

where, f m is melt factor, α is a coefficient and R is the shortwave or net radiation balance. In addition, the model often divides the glacier into the elevation band or grid cell to consider the influence of changes in elevation and slope for considering spatial variability of melt rates (Hock 1999). To consider the influences of radiation heterogeneity and melt conditions in complex terrain, a model is proposed (Hock 1999), which incorporates the temporal and spatial variation of clear-sky shortwave radiation.

12.3.2 Surface Energy-Mass Balance Model Surface energy balance of glacier surface is an important method to understand and predict the response of glaciers to climate change, which is a physical link between glacier mass balance and runoff (Oerlemans and Fortuin 1992; Cuffey and Paterson 2010). When the surface temperature is 0 °C, we assume any extra energy at the glacier-atmosphere interface to be used immediately for melting. In general, an energy-mass balance model is widely used to estimate energy consumed by melting from the energy exchange at the glacier-atmosphere interface. Also, the model involves processes existing in the subsurface, where meltwater percolates in the underlying layers. According to components of the energy balance, the model is given as (1 − α)R S + R Ld + R Lu + Q S + Q L + Q R + Q G + Q M = 0

(12.5)

in which R S is downward shortwave radiation flux, R Ld is downward longwave radiation flux, R Lu is upward longwave radiation flux, Q S and Q L are net sensible and latent heat fluxes, respectively, Q R is heat flux supplied from the rain on the surface, Q G is conductive heat flux into the glacier surface, QM is the energy consumed by melt, and α is surface albedo. All terms in Eq. (12.5) are in units of W m−2 and defined as positive toward the surface. Melt rates, M, are then estimated from QM , which is given as M=

QM ρi L f

(12.6)

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where ρi is the density of ice, and L f is the latent heat of fusion. Most available energy consumed for ice melting is in turn provided by the solar radiation, the sensible and latent heat fluxes. The turbulent fluxes generally decrease with altitude because of vertical lapse rates of air temperature and vapor pressure. Consequently, the importance of net radiation increases with altitude compared to turbulent fluxes (Cuffey and Paterson 2010). The model does not consider the heat flux supplied from rain, which is not significant in the energy components. Turbulent heat fluxes were computed through the bulk method. Downward longwave radiation is estimated from air temperature, relative humidity and a ratio defined as downward shortwave radiation divided by that at the top of the atmosphere (Fujita and Ageta 2000). Assuming a black body for snow/ice surface, upward longwave radiation is obtained from Stefan–Boltzmann constant and surface temperature. Therefore, these terms can be given by R Lu = εσ (TS + 273.2)4 Q S = ca ρa CU (Ta − TS )

(12.7)

Q L = le ρa CU [r hq(Ta ) − q(TS )] where σ is the Stefan–Boltzmann constant, ε is glacier surface emissivity (assumed to be 1), Ta and TS are air and surface temperatures, ca and ρa are specific heat capacity and density of air, rh is relative humidity, C is bulk coefficient for sensible and latent heat, U is wind speed, q is the saturated specific humidity calculated as a function of air temperature (Kondo 1994), and le is latent heat of the evaporation of water. The absorbed shortwave radiation is estimated based on downward shortwave radiation and surface albedo. Albedo largely varies on glaciers in space and time, which is expressed as the averaged reflectivity over the spectrum (0.35–2.8 µm). Albedo values, varying between 0.1 (dirty ice) and >0.9 (fresh snow), influence the spatiotemporal distribution of meltwater production to a considerable degree. Therefore, albedo is an important parameter in modeling mass balance. In fact, albedo variation is influenced by surface conditions and incident shortwave radiation (Hock 2005; Cuffey and Paterson 2010). Consequently, albedo simulation is very complex, because it is largely difficult to quantify the relationship between albedo and its affected factors. In general, we treat separately snow and ice albedo on glaciers. Ice albedo is often assumed to be a constant in space and time. When all snow has melted, surface albedo becomes from a higher snow value to a lower ice value. In contrast to ice albedo, snow albedo is expressed by grain size of the snow, as well as atmospheric controls (Warren and Wiscombe 1980). A lot of parameterizations of snow albedo have been developed, which incorporate one or more variables related to surface conditions or incident shortwave radiation (summary in Brock et al. 2000). Aging curve approach is a common method for calculating the decreasing snow albedo, which is considered as a function of time after the last significant snowfall. The albedo scheme of Crops of Engineering (1956) has become a widely applied method, which is given as

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α = α0 + be−n d k

(12.8)

in which b and k are coefficients, α 0 and nd are minimum snow albedo and the number of days since the last significant snowfall, respectively. With the exception of the shortwave radiation term, all components of the energy balance are clearly determined by surface temperature. Hence, the iterative calculation is used to determine the surface temperature, in which the conductive heat flux is computed by changing the ice temperature profile. In addition, glacier mass loss is not equal to surface melting because of the refreezing process (Fujita et al. 1996; Wright et al. 2007; van Pelt et al. 2016). Therefore, glacier mass balance modeling should consider the refreezing, which is calculated by the heat conduction into snow layer and glacier ice, as well as the presence of water at the interface between the snow layer and glacier ice (Fujita and Ageta 2000). Snow accumulation is calculated using the same method shown in Eq. (12.3).

12.3.3 Modeling Glacier Mass Balance with Debris Cover As introduced above, debris layer alters the surface energy balance and imposes a barrier between the atmosphere and the ice due to its unique thermal processes compared to those of snow or clean ice. As a result, ice ablation beneath the debris layer is significantly different from those of clean ice and snow (Østrem 1959; Mattson et al. 1993; Nicholson et al. 2013). Therefore, in the case of debriscovered glaciers, the debris-cover effect should be taken into account in mass balance modeling, and needs to differently treat debris-covered glaciers and cleanice glaciers. However, there remains a challenge due to insufficient data of the extent and thickness of debris cover at a large scale. With the development of geospatial technology, geointelligence has revolutionized mass-balance modeling of debriscovered glaciers through providing satellite data and algorithms (e.g., Zhang et al. 2016; Scherler et al. 2018; Herreid and Pellicciotti 2020). For debris-covered glaciers, current studies have largely developed energy-mass balance approaches in recent decades (e.g., Nicholson and Benn 2006; Reid and Brock 2010; Reid et al. 2012; Zhang et al. 2012; Lejeune et al. 2013). According to components of the energy balance, the model is given by (1 − α  )R S + R Ld + R Lu + Q S + Q L + Q R + Q G = 0

(12.9)

where Q G is conductive heat flux into the debris layer, α  is debris-covered surface albedo. Components of energy balance and their calculation methods are identical to those mentioned above. As the energy flux through the debris layer is dominated by heat conduction down a vertical temperature gradient from the debris surface to the ice, the available energy

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for melt is calculated from the conductive heat flux across the debris-ice interface. It is assumed that the temperature profile within the debris layer is linear and the heat flux stored in the debris layer is constant from day to day (Kraus 1975; Nakawo and Young 1981). Then, the conductive heat flux across the debris-ice interface is considered as a function of surface (T S ) and ice (T I ) temperatures and debris thickness (h), which is given as Q G = k

(TS − TI ) h

(12.10)

where k is thermal conductivity of debris layer. We can determine the thermal conductivity and thickness of the debris layer through field observations, but field observations are not practical on the glaciers, especially on a large scale. To overcome this difficulty, a parameter of ‘thermal resistance’ of the debris layer is proposed (Nakawo and Young 1981, 1982), expressed as the ratio of debris thickness to the thermal conductivity of the debris cover. Previous study estimated the spatial distribution of the thermal resistance on a debris-covered glacier in the southeastern Tibet Plateau (Zhang et al. 2011), which confirmed that the parameter can be the index of large-scale variations in debris-covered extent and debris thickness. In particular, the spatial variation of the thermal resistance can be estimated from satellite data (e.g., Nakawo and Rana 1999; Suzuki et al. 2007; Fujita and Sakai 2014; Zhang et al. 2011, 2016). In general, the thermal resistance of the debris cover on the large scale is calculated from ASTER TIR and VNIR bands and NCEP/NCAR reanalysis data, in which turbulent fluxes are assumed to be negligible in the energy balance (Fig. 12.5). As shown in Fig. 12.5, the surface temperature is estimated from five TIR bands of ASTER, and the albedo is calculated directly from the spectral reflectance at the top of the atmosphere in ASTER VNIR bands. Then, thermal resistances of debris layers are estimated combined with downward radiation fluxes from NCEP/ NCAR reanalysis data. The resolution of ASTER TIR image is 90 m, therefore, the thermal resistance is obtained using the same resolution.  Based on the thermal resistance (R), the heat flux consumed for melting, Q M , is given by 

Q M

=

Q G

T  − TI = S R

(12.11)

in which TS is the debris surface temperature, and TI is the ice temperature at the debris-ice surface, which is assumed to be the melting point. All terms of the energy balance are in units of W m−2 and defined as positive toward the debris surface. With the exception of the shortwave radiation term, all components of the energy balance at the debris-covered surface are clearly determined by the debris surface temperature. Here the surface temperature at the debris-covered area is estimated by iterative calculation. Then, melt rates underlying the debris cover, M, are calculated from the available energy, which is calculated as

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Fig. 12.5 Schematic diagram of the method for estimating the thermal resistance 

M =

QM ρi L f

(12.12)

For other parts of glacier mass budget, the calculation methods are consistent with Sect. 12.3.2. Accumulation and refreezing amount are estimated using the methods mentioned above. Compared to the net radiation, the turbulent heat fluxes slightly contribute to the total energy balance on debris-covered glaciers, which is normally neglected (Mattson and Gardner 1989; Takeuchi et al. 2000). A study on the Haiuogou Glacier in southeastern Tibetan Plateau observed such feature at the debriscovered surface, especially at the surface with debris thickness of >0.1 m (Zhang et al. 2011). Consequently, the method mentioned above estimates the thermal resistance assuming that the turbulent heat fluxes are neglected. In addition, some previous studies found that the turbulent heat fluxes exert a crucial role in the energy balance at the debris-covered surface with the exception of the net radiation (e.g. Brock et al. 2010; Reid et al. 2012; Lejeune et al. 2013). However, Suzuki et al. (2007) assessed the uncertainty in the approach resulted from the assumption mentioned above for calculating thermal resistance, which calculated thermal resistances of debris layers from observed meteorological data through two methods. First, all components of the energy balance at the debris-covered surface were taken into account. Second, only net radiation was considered (Suzuki et al. 2007). The assessment results revealed

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that the spatial distribution of the thermal resistance is unlikely to be affected by this assumption. Applying the mass-balance model mentioned above, glacier mass balance of Hailuogou catchment during past decades was reconstructed (Zhang et al. 2012). Hailuogou catchment is located in Mount Gongga of the southeastern Tibetan Plateau and contains three debris-covered glaciers. Satellite-derived debris-covered extent represents about 39% of total ablation area of three debris-covered glaciers. Glacier mass-balance modeling results revealed that the presence of debris cover significantly enhances glacier mass loss of the catchment. A comparison between two simulations, in which, one considers the spatial distribution of debris cover (real surface condition), the other assumes no debris cover on three debris-covered glaciers in the catchment (no-debris surface), found that significant excess meltwater is generated from the debris-covered area due to the acceleration effect of debris cover relative to the assumed no-debris surface, which contributes to 70% of the total increased meltwater; especially, much more meltwater is generated from the debris-covered area below 3600 m a.s.l. (Zhang et al. 2019). As a consequence, the total runoff of Hailuogou catchment increases by about 11% caused by significant excess meltwater from the debris-covered surface (Zhang et al. 2019).

12.4 Conclusion This chapter introduce the recent study progress of two glacier mass-balance models, which are surface energy-mass balance model and degree-day model. The two models are important methods to understand and predict the response of glacier to climate change, which compute each component of the glacier mass budget based on different forced datasets. The degree-day model is based on an empirical relationship between ablation and air temperature, while the surface energy-mass balance model involves the assessment of the energy fluxes between glacier surface and atmosphere, which is forced by more input data and parameters compared to the degree-day model. During studying glacier mass balance, the two models can be chosen based on the study purpose and datasets. Furthermore, debris-covered glaciers are widespread in many high-mountainous regions of the world, on which the debris accelerates/inhibits ice melt rate depending on the thickness of debris layer, and then alters the spatial pattern of ice melt rate. Consequently, we need to differently treat debris-covered glaciers and clean-ice glaciers when predicting mass balance and runoff, as well as the assessment of the risk of outburst floods. The chapter explores the role of debris cover and its spatial distribution in the ice melting process, and introduce a model to calculate glacier mass change on debris-covered glaciers based on satellite and meteorological data. With the development of geospatial technology, GI has revolutionized glacier mass-balance modeling through providing satellite data and algorithms.

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Chapter 13

Geo-Intelligence-Based Approach to Investigate Temporal Changes in the Length and Surface Area and Ice Velocity of Sakchum Glacier Rakesh Sahu, Dharmaveer Singh, A. S. Gagnon, and P. K. Singh Abstract The Himalayan-Karakoram (HK) region is often referred as the ‘Water Tower of Asia’, as the melt water from the glaciers and snow cover of this mountainous region provide a continuous supply of water to the major rivers of South Asia, notably the Indus, Ganga, and Brahmaputra. Recent studies reveal that climate change has altered the accumulation of snow and the snowmelt processes of the region, leading to changes in the size and mass of glaciers, with potential impacts on the river flow downstream and the ecosystems and livelihoods depending on it. Consequently, mapping and monitoring glaciers, mainly through estimates of their spatial extent, recession rate, debris cover extent and surface velocity, are integral parts of water resource management. Because of the rugged topography and harsh climatic conditions of the HK region, estimating these parameters based on traditional techniques is tedious and challenging. The exploitation of geospatial data and information through Geo-intelligence (GI) has, thus, emerged as a data collection and processing method in the study of glaciers. It processes images from satellites and airborne vehicles with high-end mathematical and statistical algorithms. This chapter demonstrates the effectiveness of GI techniques to map and monitor the Sakchum Glacier of the HK region with the results suggesting that these techniques could potentially contribute to long-term planning and management of water resources in regions whose water supply depends on the melt water of glaciers.

R. Sahu (B) GIS Cell , Motilal Nehru National Institute of Technology Allahabad , Prayagraj 211004, India D. Singh Symbiosis Institute of Geo-Informatics , Symbiosis International (Deemed University) , Pune 411016, India A. S. Gagnon School of Biological and Environmental Sciences, Liverpool John Moores University, Liverpool L3 3AF, UK P. K. Singh Water Resources Systems Division, National Institute of Hydrology, Roorkee 247667, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 T. P. Singh et al. (eds.), Geo-intelligence for Sustainable Development, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-16-4768-0_13

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Keywords Geo-intelligence (GI) · Remote sensing · Sakchum Glacier · Temporal changes

13.1 Introduction The Himalaya–Karakoram (HK) region is a mountainous region covering a surface area of 40,800 km2 and comprises the largest glacier area outside of the polar region. In the Himalayan region of India alone, there are approximately 9600 glaciers of varying lengths and shapes (Singh and Ramanathan 2017; Raina and Shrivastava 2008). The melt water from the glaciers and snow cover of the HK region provide a continuous supply of water to the major rivers of South Asia, notably the Indus, Ganga, and Brahmaputra (Immerzeel et al. 2010; Bolch et al. 2012), thereby supporting ecosystems and the livelihoods of approximately 1.3 billion people. The HK region is highly susceptible to global climate change. Temperatures in the region are increasing at a faster rate than the global average (Singh et al. 2015a), with researchers predicting that this trend will continue into the future (Kraaijenbrink et al. 2017; Singh et al. 2015b). In addition to this rise in temperature, there is evidence of changes in the accumulation and melting of snow (Tawde et al. 2019), affecting the size and mass of glaciers (Maurer et al. 2019), which could potentially impact river flow downstream (Rai et al. 2019; Singh et al. 2015c). For this reason, the monitoring of glaciers and regular mapping is an integral part of water resource management in regions depending on the melt waters of glaciers such as the HK region. The influence of climate change on glaciers can be assessed by studying longterm changes in their spatial area, retreat rate, debris cover extent and surface velocity (Bhambri and Bolch 2011; Chand and Sharma 2015; Bhambri et al. 2017; Sahu and Gupta 2019a; Shukla and Garg 2020). These parameters have traditionally been estimated using field-based methods, but because of the harsh climatic conditions and the difficulty of access to many areas of the Himalayas, regular monitoring of many glaciers using in situ measurements is difficult. Hence, Geo-intelligence (GI), which combines the collection of remotely sensed images and their analysis in a Geographical Information System (GIS), has emerged as an alternative and complementary approach to field-based techniques. GI processes images from satellites and airborne vehicles with high-end mathematical and statistical algorithms. Satellite images are available at a spatial resolution varying from 10 to 30 m from various sensors, for instance, Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) carried on board the Landsat missions from the National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS), the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on board the Terra satellite, a collaborative mission between the United States of America and Japan, and the MultiSpectral Instrument (MSI) on board of the Sentinel-2 satellite of the European Space Agency (ESA). Higher resolution satellite images at a 2–12 m spatial resolution are also available

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from declassified images from the CORONA satellite operated by the Central Intelligence Agency and the U.S. Air Force during the period 1959–1972, and the Key Hole (KH)-9 Hexagon satellite operated between 1971 and 1986 by the National Reconnaissance Office. Both missions had for purpose to spy on Soviet military capabilities. These satellite images have previously been used to examine temporal changes in a glacier of the Himalayan region (Chand et al. 2017). Several studies have used remote sensing to investigate changes in glaciers in the HK region, notably their recession, retreat as well as changes in their surface velocity (Bhambri et al. 2013; Bhushan et al. 2018; Garg et al. 2017a, b; Kaushik et al. 2018; Sahu and Gupta 2019b; Shukla et al. 2019). Within the Western Himalaya, the section of the HK region is Chandra Basin, an important basin with a total of 395 glaciers covering a surface area of 703.3 ± 20.4 km2 (Sahu and Gupta 2020b). Chandra Basin is the source of the Chenab River, a major river of the Punjab region flowing in both India and Pakistan, and a tributary of the Indus River. However, few studies have examined temporal changes in the characteristics of the glaciers of Chandra Basin. Pandey and Venkataraman (2013) analysed temporal changes in the surface area and the length of 15 glaciers in that basin during the period 1980–2010 using satellite data from Landsat Multispectral Scanner (MSS) and TM, and the Linear Imaging Self-Scanning Sensor 3 (LISS III) and the Advanced Wide-Field Sensor (AWiFS) from the Indian Space Research Organisation (ISRO). Similarly, Garg et al. (2017b) analysed not only changes in the surface area and the length of Chhota Shigri, Bara Shigri, and Sakchum glaciers from 1993 to 2015 using Landsat TM, ETM+, and ASTER images but also changes in their surface velocity over the shorter 1999– 2015 period. In a study covering a larger geographical area, Sahu and Gupta (2020b) investigated changes in the surface area of 169 glaciers in Chandra Basin, including the Chhota Shigri, Bara Shigri, Gepang Gath, Samudra Tapu, and Hamtah glaciers, during the period 1971–2016, using images from Corona KH-4, Landsat ETM+, and Sentinel-2. Singh et al. (2020) estimated the seasonal and annual surface velocity of glaciers for 2009–2010 and 2015, respectively. However, Sakchum Glacier in Chandra Basin has not yet been investigated for long-term changes in its surface area, length, and surface velocity as a result of climate change. Hence, the present study examines changes in the recession rate of the surface area of Sakchum Glacier as well as its changes in its length and debris cover extent during the period 1971– 2019. Moreover, changes in the surface velocity of Satchum Glacier are investigated for the period 1990–2018, although a shorter period is used for this parameter, it is still significantly longer than the previous study focusing on this glacier. This will provide insights into the dynamics of the Sakchum Glacier.

13.2 Study Area Sakchum Glacier is located in Chandra Basin between latitudes 31°10 45 N and 32°14 45 N and longitudes 77°24 0 E and 77°28 0 E in Lahaul and Spiti district of the northern Indian state of Himachal Pradesh in the Western Himalayas (Fig. 13.1). It

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Fig. 13.1 Location of study area

is a valley-type glacier located at 4356–5741 m above sea level; it is north facing and has debris on its surface. Sakchun Glacier is located in the monsoon–arid transition zone and has a tundra climate given its altitude. Although there is no in situ weather observation taken on Sakchum Glacier, there is an Automatic Weather Station (AWS) situated near the study area within Chandra Basin at an altitude of approximately 4860 m, with data available for the 7-year period extending from 2009 to 2016. According to these data, the mean annual temperature is 5.4 °C, with average winter and summer temperatures of −11.3 °C and 0.5 °C, respectively. The weather station also indicated that the region receives precipitation not only from the Indian Summer Monsoon (ISM) but also from Western Disturbances (WD) in winter (~70%) (Bajpai

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1995). Thus, the basin is completely covered by snow in winter when it becomes isolated and unreachable (Sahu and Gupta 2020a).

13.3 Materials and Methods This section describes the sources of the satellite data and the methods used to process them. Satellite images were obtained from the CORONA KH-4B (spatial resolution: 3 m), Landsat-7 ETM + (spatial resolution: 30 m for bands 1–7 and 15 m for panchromatic band 8) and Sentinel-2 MSI (spatial resolution: 10 m) missions (Table 13.1), which were used to calculate the recession rate of Sakchum Glacier from 1971 to 2019. Elevation was estimated from the ASTER Global Digital Elevation Map Version 2 (GDEM V2) database, which provides data at a spatial resolution of 30 m with a vertical accuracy of 20 m. The satellite data were downloaded from the website of the USGS (http://earthexplorer.usgs.gov/). They were obtained at the beginning of the accumulation period (end of the ablation period), as there is a minimum snow and cloud cover during that period. The satellite images were checked for geometric and radiometric errors using the software ERDAS Imagine 2016 and ArcGIS 10.3. This analysis found that the CORONA KH-4B image lacked spatial reference. The image was, thus, coregistered with respect to the georeferenced Landsat-7 imagery using 20 common ground control points, which were visible on both images, and the spline adjustment method. There are three methods available to delineate the boundaries of a glacier on the satellite image: automatic, semiautomatic, and manual. Sahu and Gupta (2018) suggested that when analysing only one glacier, the manual method based on visual interpretation is the most suitable, as it provides high positional accuracy. For this reason, the boundary of Sakchum Glacier was delineated through visual interpretation. Moreover, a centre flow line was drawn within the glacier to estimate changes in the length of the glacier from 1971 to 2019. Table 13.1 Satellite data used to calculate temporal changes in surface area and length of Sakhum Glacier Sensor

Date of acquisition

Spatial Scene ID resolution (m)

RMSEa X, Y (m)

CORONA 27-09-1971 3 KH-4B

DS1115-2282DF064

Landsat 7/ETM+

LE07_L1TP_147037_20020802_20170130_01_T1