Bappaditya Koley – författare

Geospatial Techniques in Climate Change Monitoring and Environmental Mapping: An Interdisciplinary Approach to Risk Reduction presents a comprehensive examination of innovative geospatial methods utilized to address the challenges posed by climate change across various sectors. The book encompasses a wide array of topics, including the application of remote sensing in agriculture, urban climate change mitigation, and the evaluation of land use effects on surface temperatures. It highlights the integration of advanced technologies such as machine learning and satellite imagery to enhance drought risk mapping, deforestation detection, and flood prediction, providing valuable insights into environmental sustainability. Moreover, the text emphasizes participatory approaches and the role of local communities in climate resilience. By synthesizing interdisciplinary perspectives, the book underscores the importance of collaborative efforts in developing effective mitigation and adaptation strategies. Ultimately, this work serves as a vital resource for researchers and practitioners committed to leveraging geospatial techniques for effective climate change risk reduction and environmental management.Members of the Royal Meteorological Society are eligible for a 35% discount on all Developments in Weather and Climate Science series titles. See the RMetS member dashboard for the discount code.Emphasizes an interdisciplinary approach to climate change impacts, risk reduction, and mitigation strategiesHighlights the role of geospatial data, methods, and tools in monitoring climate change with advanced techniques in remote sensing and spatial analysisExamines practical applications of geospatial techniques and machine learning techniques in risk management

Geospatial Techniques in Climate Change Monitoring and Environmental Mapping: An Interdisciplinary Approach to Risk Reduction presents a comprehensive examination of innovative geospatial methods utilized to address the challenges posed by climate change across various sectors. The book encompasses a wide array of topics, including the application of remote sensing in agriculture, urban climate change mitigation, and the evaluation of land use effects on surface temperatures. It highlights the integration of advanced technologies such as machine learning and satellite imagery to enhance drought risk mapping, deforestation detection, and flood prediction, providing valuable insights into environmental sustainability. Moreover, the text emphasizes participatory approaches and the role of local communities in climate resilience. By synthesizing interdisciplinary perspectives, the book underscores the importance of collaborative efforts in developing effective mitigation and adaptation strategies. Ultimately, this work serves as a vital resource for researchers and practitioners committed to leveraging geospatial techniques for effective climate change risk reduction and environmental management.Members of the Royal Meteorological Society are eligible for a 35% discount on all Developments in Weather and Climate Science series titles. See the RMetS member dashboard for the discount code. - Emphasizes an interdisciplinary approach to climate change impacts, risk reduction, and mitigation strategies- Highlights the role of geospatial data, methods, and tools in monitoring climate change with advanced techniques in remote sensing and spatial analysis- Examines practical applications of geospatial techniques and machine learning techniques in risk management

Geospatial Techniques in Climate Change Monitoring and Environmental Mapping: Advances and Applications, Volume Ten delves into the novel methods and applications of remote sensing in climate-change studies. The book begins by exploring climate observation and monitoring, consolidating remote sensing data systems for environmental mapping, and tracking air pollution using remote sensing data. The volume covers the use of remote sensing data for monitoring and predicting climate change, characterizing land changes with climate time series data, and tracking climate change and tropical cyclones with geospatial techniques.It also discusses the system dynamics model of economic development and climate change, as well as climate change mitigation, adaptation, and future directions. Additionally, it addresses climate modeling, validation, uncertainty mapping, and the application of remote sensing data in government climate policies and sustainable risk reduction approaches for global climate change issues.Members of the Royal Meteorological Society are eligible for a 35% discount on all Developments in Weather and Climate Science series titles. See the RMetS member dashboard for the discount code.Integrates the latest advancements in sensor technologies and data analysis methods, offering readers insights into the management of climate risks and hazards as well as adaptation strategiesProvides an interdisciplinary understanding of climate change challenges and solutions by gathering high-quality contributions from scientific, social, economic, political, and cultural perspectivesExamines potential responses to climate change hazards and how to implement strategies in various sectors, considering vulnerability and exposure factors and addressing the social, economic, political, and cultural dimensions of climate change

Geospatial Techniques in Climate Change Monitoring and Environmental Mapping: Advances and Applications, Volume Ten delves into the novel methods and applications of remote sensing in climate-change studies. The book begins by exploring climate observation and monitoring, consolidating remote sensing data systems for environmental mapping, and tracking air pollution using remote sensing data. The volume covers the use of remote sensing data for monitoring and predicting climate change, characterizing land changes with climate time series data, and tracking climate change and tropical cyclones with geospatial techniques.It also discusses the system dynamics model of economic development and climate change, as well as climate change mitigation, adaptation, and future directions. Additionally, it addresses climate modeling, validation, uncertainty mapping, and the application of remote sensing data in government climate policies and sustainable risk reduction approaches for global climate change issues.Members of the Royal Meteorological Society are eligible for a 35% discount on all Developments in Weather and Climate Science series titles. See the RMetS member dashboard for the discount code. - Integrates the latest advancements in sensor technologies and data analysis methods, offering readers insights into the management of climate risks and hazards as well as adaptation strategies- Provides an interdisciplinary understanding of climate change challenges and solutions by gathering high-quality contributions from scientific, social, economic, political, and cultural perspectives- Examines potential responses to climate change hazards and how to implement strategies in various sectors, considering vulnerability and exposure factors and addressing the social, economic, political, and cultural dimensions of climate change

This edited collection provides a comprehensive exploration of cutting-edge ideas, approaches, simulations, evaluations of risk, and systems that enhance the practicality of current geospatial technologies for reducing hazard risks. The various sections within this book delve into subjects such as the foundational principles of Earth Observation Systems (EOS) and geospatial methodologies. Additionally, the text serves as an advisory resource on the collaborative use of satellite-derived data and artificial intelligence to track and alleviate geo-environmental threats. The volume imparts extensive understanding regarding geo-environmental dangers and their analysis via EOS along with geospatial strategies. It encompasses key hazard-related themes including coastal degradation, predisposition to landslides, mapping vegetation coverages, tropical storm patterns, soil depletion due to erosion processes, vulnerability to rapid or extended flooding events, variations in oceansurface temperatures alongside chlorophyll-a levels; it also addresses assessments related to groundwater reserves and quality measures as well as sustainable management practices for watersheds that support community livelihoods—all through leveraging AI-integrated geospatial tools in conjunction with earth observation technologies. Furthermore, this work engages in discourse about systems designed for mitigating these ecological challenges sustainably. Scholars engaged in research activities; educational professionals; those involved in landscape design; engineers working at ground level; individuals responsible for policy-making—all who are concerned with geo-environmental hazards or associated domains—will find valuable insights within these pages.

This edited collection provides a comprehensive exploration of cutting-edge ideas, approaches, simulations, evaluations of risk, and systems that enhance the practicality of current geospatial technologies for reducing hazard risks. The various sections within this book delve into subjects such as the foundational principles of Earth Observation Systems (EOS) and geospatial methodologies. Additionally, the text serves as an advisory resource on the collaborative use of satellite-derived data and artificial intelligence to track and alleviate geo-environmental threats. 

The volume imparts extensive understanding regarding geo-environmental dangers and their analysis via EOS along with geospatial strategies. It encompasses key hazard-related themes including coastal degradation, predisposition to landslides, mapping vegetation coverages, tropical storm patterns, soil depletion due to erosion processes, vulnerability to rapid or extended flooding events, variations in oceansurface temperatures alongside chlorophyll-a levels; it also addresses assessments related to groundwater reserves and quality measures as well as sustainable management practices for watersheds that support community livelihoods—all through leveraging AI-integrated geospatial tools in conjunction with earth observation technologies. 

Furthermore, this work engages in discourse about systems designed for mitigating these ecological challenges sustainably. Scholars engaged in research activities; educational professionals; those involved in landscape design; engineers working at ground level; individuals responsible for policy-making—all who are concerned with geo-environmental hazards or associated domains—will find valuable insights within these pages. 

This edited collection provides a comprehensive exploration of cutting-edge ideas, approaches, simulations, evaluations of risk, and systems that enhance the practicality of current geospatial technologies for reducing hazard risks. The various sections within this book delve into subjects such as the foundational principles of Earth Observation Systems (EOS) and geospatial methodologies. Additionally, the text serves as an advisory resource on the collaborative use of satellite-derived data and artificial intelligence to track and alleviate geo-environmental threats. The volume imparts extensive understanding regarding geo-environmental dangers and their analysis via EOS along with geospatial strategies. It encompasses key hazard-related themes including coastal degradation, predisposition to landslides, mapping vegetation coverages, tropical storm patterns, soil depletion due to erosion processes, vulnerability to rapid or extended flooding events, variations in oceansurface temperatures alongside chlorophyll-a levels; it also addresses assessments related to groundwater reserves and quality measures as well as sustainable management practices for watersheds that support community livelihoods—all through leveraging AI-integrated geospatial tools in conjunction with earth observation technologies. Furthermore, this work engages in discourse about systems designed for mitigating these ecological challenges sustainably. Scholars engaged in research activities; educational professionals; those involved in landscape design; engineers working at ground level; individuals responsible for policy-making—all who are concerned with geo-environmental hazards or associated domains—will find valuable insights within these pages.

This contributed volume details how Artificial Intelligence (AI) and Earth observation systems can effectively improve the prediction of groundwater quality and support decision-making in arid and semi-arid regions. Earth observation systems, including remote sensing and geographic information systems (GIS), play a crucial role in assessing and monitoring groundwater quality. Remote sensing data, such as satellite imagery, can provide valuable information on land cover, vegetation indices, and water quality parameters. GIS tools enable the spatial analysis and visualization of groundwater quality data.AI and Earth observation-based methods support effective water resource management by identifying suitable areas for artificial groundwater recharge (AGR) and assessing the impact of pollution on water resources. These techniques help formulate conservation policies and sustainable water management strategies. Various AI techniques, including ANN, SVM, KNN, and decision trees, have been applied to model groundwater quality and predict water quality indices. These models capture complex relationships between hydro chemical parameters and groundwater quality, enabling accurate predictions and informed decision-making.The application of AI and Earth observation systems in groundwater quality prediction contributes to the sustainability of water resources. Identifying pollution sources, assessing water quality, and guiding decision-making processes support preserving and managing water resources in arid and semi-arid regions.

This contributed volume details how Artificial Intelligence (AI) and Earth observation systems can effectively improve the prediction of groundwater quality and support decision-making in arid and semi-arid regions. Earth observation systems, including remote sensing and geographic information systems (GIS), play a crucial role in assessing and monitoring groundwater quality. Remote sensing data, such as satellite imagery, can provide valuable information on land cover, vegetation indices, and water quality parameters. GIS tools enable the spatial analysis and visualization of groundwater quality data.AI and Earth observation-based methods support effective water resource management by identifying suitable areas for artificial groundwater recharge (AGR) and assessing the impact of pollution on water resources. These techniques help formulate conservation policies and sustainable water management strategies. Various AI techniques, including ANN, SVM, KNN, and decision trees, have been applied to model groundwater quality and predict water quality indices. These models capture complex relationships between hydro chemical parameters and groundwater quality, enabling accurate predictions and informed decision-making.The application of AI and Earth observation systems in groundwater quality prediction contributes to the sustainability of water resources. Identifying pollution sources, assessing water quality, and guiding decision-making processes support preserving and managing water resources in arid and semi-arid regions.

This SpringerBrief uses geospatial techniques to assess shoreline change dynamics in the Indian Sundarban, a unique region located in the delta of three major rivers - the Ganges, Brahmaputra and Meghna – in India and Bangladesh. It is one of the largest mangrove forests in the world. The geomorphological characteristics of this region are quite distinct from other parts of India due to its low elevation, high tidal range, and vast expanse of shallow water bodies. The chapters here cover the basics of the use of geoinformatics applications for shoreline analysis and move to a detailed description of both short- and long-term shoreline change using automated Digital Shoreline Change Analysis (DSAS). The book also covers erosion-accretion hazard zone mapping and shoreline vegetation mapping.

This SpringerBrief uses geospatial techniques to assess shoreline change dynamics in the Indian Sundarban, a unique region located in the delta of three major rivers - the Ganges, Brahmaputra and Meghna - in India and Bangladesh. It is one of the largest mangrove forests in the world. The geomorphological characteristics of this region are quite distinct from other parts of India due to its low elevation, high tidal range, and vast expanse of shallow water bodies. The chapters here cover the basics of the use of geoinformatics applications for shoreline analysis and move to a detailed description of both short- and long-term shoreline change using automated Digital Shoreline Change Analysis (DSAS). The book also covers erosion-accretion hazard zone mapping and shoreline vegetation mapping.

This contributed volume, the second in a set of two, details how Artificial Intelligence (AI) and Earth observation systems can effectively improve the prediction of groundwater quality and support decision-making in arid and semi-arid regions. Earth observation systems, including remote sensing and geographic information systems (GIS), play a crucial role in assessing and monitoring groundwater quality. Remote sensing data, such as satellite imagery, can provide valuable information on land cover, vegetation indices, and water quality parameters. GIS tools enable the spatial analysis and visualization of groundwater quality data.AI and Earth observation-based methods support effective water resource management by identifying suitable areas for artificial groundwater recharge (AGR) and assessing the impact of pollution on water resources. These techniques help formulate conservation policies and sustainable water management strategies. Various AI techniques, including ANN, SVM, KNN, and decision trees, have been applied to model groundwater quality and predict water quality indices. These models capture complex relationships between hydro chemical parameters and groundwater quality, enabling accurate predictions and informed decision-making.The application of AI and Earth observation systems in groundwater quality prediction contributes to the sustainability of water resources. Identifying pollution sources, assessing water quality, and guiding decision-making processes support preserving and managing water resources in arid and semi-arid regions.

This contributed volume, the second in a set of two, details how Artificial Intelligence (AI) and Earth observation systems can effectively improve the prediction of groundwater quality and support decision-making in arid and semi-arid regions. Earth observation systems, including remote sensing and geographic information systems (GIS), play a crucial role in assessing and monitoring groundwater quality. Remote sensing data, such as satellite imagery, can provide valuable information on land cover, vegetation indices, and water quality parameters. GIS tools enable the spatial analysis and visualization of groundwater quality data.AI and Earth observation-based methods support effective water resource management by identifying suitable areas for artificial groundwater recharge (AGR) and assessing the impact of pollution on water resources. These techniques help formulate conservation policies and sustainable water management strategies. Various AI techniques, including ANN, SVM, KNN, and decision trees, have been applied to model groundwater quality and predict water quality indices. These models capture complex relationships between hydro chemical parameters and groundwater quality, enabling accurate predictions and informed decision-making.The application of AI and Earth observation systems in groundwater quality prediction contributes to the sustainability of water resources. Identifying pollution sources, assessing water quality, and guiding decision-making processes support preserving and managing water resources in arid and semi-arid regions.

This book pose significant risks to human populations, infrastructure, and ecosystems. The volume Geospatial Application of Coastal Hazards -Monitoring, Mitigation, Adaptation and Prediction’ is an innovator attempt that aims to explore all the phenomenon. Coastal hazard monitoring, mitigation, adaptation, and prediction can benefit greatly from geospatial applications. Geospatial analysis allows for the identification of areas at risk and vulnerable to coastal hazards such as storm surges, sea-level rise, and erosion. By integrating various data sources like topography, hydrology, climate data, and satellite imagery, decision-makers can assess the exposure and vulnerability of coastal zones. Geospatial big data solutions hold the potential to improve risk-based decision making in coastal areas. By analyzing and integrating high-resolution data, decision-makers can generate robust risk profiles and develop effective strategies for coastal regions. Remote sensing techniques, including satellite imagery, can provide valuable information for monitoring and mapping coastal hazards. Geospatial techniques, such as digital elevation models and spatial analysis, help in understanding environmental changes and predicting hazards. Geospatial decision support tools enable the assessment of potential flood risks, changes in flood-prone areas due to sea-level rise, and the identification of thresholds where current policies and resources may no longer be viable. These tools assist in operational and strategic decision-making processes for coastal adaptation planning. Comprehensive coastal mapping programs provide baseline data on seafloor morphology, habitats, geology, and environments. These maps and data sets are essential for scientific research, resource management, and the mitigation of hazards in the coastal ocean. Geospatial data and tools play a crucial role in emergency management and coastal hazard mitigation. Precise water level information, real-time monitoring, and long-term assessments support activities such as coastal development, habitat restoration, storm surge monitoring, and emergency preparedness. Geospatial applications are instrumental in coastal hazard monitoring, mitigation, adaptation, and prediction. This volume can be utilizing geospatial data, remote sensing techniques, and decision support tools, stakeholders can better understand coastal risks, develop effective strategies, and make informed decisions for the sustainable management of coastal areas.