Biswajeet Pradhan – författare

Water, Land, and Forest Susceptibility and Sustainability, Volume 1: Geospatial Approaches & Modeling brings an interdisciplinary perspective to solving complex problems in sustainability, utilizing the latest research and technologies, and includes case studies that emphasize the applications of remote sensing, GIS, and image processing for addressing the current state and future needs to achieve sustainability. As forests, land, and water are among the most precious resources on earth, emphasizing the need to conserve them for future generations and, of course, a safe and sustainable planet. The assessment of the susceptibility of all these three precious resources must therefore be addressed to inform their sustainable management.

This 1st volume encourages adaptive activities among experts employed in interdisciplinary fields, from data mining and machine learning to environmental science by linking geospatial computational intelligence technology to forest, land and water issues.

Presents theoretical context and practical solutions for understanding the current knowledge and where future efforts should be directed Includes case studies in each chapter demonstrating the use of geospatial technologies Offers an interdisciplinary approach to addressing susceptibility and achieving sustainability

Big Data Analytics in Agriculture focuses on the quantitative and qualitative assessment of agricultural systems using state-of-the-art technologies to deliver practical improvements in agricultural production.Addressing the challenge of translating data into real-world applications, the book provides a comprehensive mapping of the entire data lifecycle-from data generation, storage, and curation to processing and implementation. It guides readers through the steps required to produce high-quality, reliable information that supports effective decision-making. Following a logical progression, the volume demonstrates how diverse data streams converge into decision-support systems and how they can be transformed into actionable outcomes, aligned with intelligent, efficient, technologically advanced, economically viable, and politically and culturally sustainable practices.The book further explores the integration of information and communication technologies (ICT) and the Internet of Things (IoT) for managing rural assets and enhancing economic and environmental performance in spatially and temporally variable agricultural environments. Topics covered include big data analytics, data management and processing, and a range of algorithms and applications relevant to agriculture. Subtopics encompass artificial intelligence- and machine-learning-enabled smart and precision irrigation, disease and pest management, microclimatic forecasting, preventive fertigation and chemigation, data-driven smart farming through the Internet of Everything (IoE), and supply-chain analytics for improved farm-level operations.

Introduces readers to the evolution from conventional analytical approaches to large-scale analytical paradigms through a stepwise, sectioned framework that supports progressive skill developmentExamines practical considerations surrounding storage architectures, multi-source datasets, scalability constraints, and cost trade-offs in agricultural computing environmentsDetails algorithm-centric workflows, including feature construction, model calibration, and comparative performance evaluation across diverse agricultural contextsShowcases a wide range of sector-specific implementations, covering crop monitoring, irrigation planning, weed control, nutrient stress identification, climate assessment, pest detection, and agri-food supply-chain operations in both advanced and resource-limited settings

Water, Land, and Forest Susceptibility and Sustainability, Volume 2: Insight Towards Management, Conservation and Ecosystem Services brings an interdisciplinary perspective to solving complex problems in sustainability, utilizing the latest research and technologies, and includes case studies that emphasize the applications of remote sensing, GIS, and image processing for addressing the current state and future needs to achieve sustainability. As forests, land, and water are among the most precious resources on earth, emphasizing the need to conserve them for future generations and, of course, a safe and sustainable planet. The assessment of the susceptibility of all these three precious resources must therefore be addressed to inform their sustainable management.

This second volume focuses on environmental management, conservation, and ecosystem services and provides information on forest, land, and water resources, presenting in integrated manner various aspects of their characterization, susceptibility, and sustainability.

Presents theoretical context and practical solutions for understanding the current knowledge and where future efforts should be directed Includes case studies in each chapter demonstrating the use of geospatial technologies Offers an interdisciplinary approach to addressing susceptibility and achieving sustainability

Machine Learning in Geohazard Risk Prediction and Assessment: From Microscale Analysis to Regional Mapping presents an overview of the most recent developments in machine learning techniques that have reshaped our understanding of geo-materials and management protocols of geo-risk. The book covers a broad category of research on machine-learning techniques that can be applied, from microscopic modeling to constitutive modeling, to physics-based numerical modeling, to regional susceptibility mapping. This is a good reference for researchers, academicians, graduate and undergraduate students, professionals, and practitioners in the field of geotechnical engineering and applied geology.Introduces machine-learning techniques in the risk management of geo-hazards, particularly recent developmentsCovers a broader category of research and machine-learning techniques that can be applied, from microscopic modeling to constitutive modeling, to physics-based numerical modeling, to regional susceptibility mappingContains contributions from top researchers around the world, including authors from the UK, USA, Australia, Austria, China, and India

Google Earth Engine and Artificial Intelligence for Earth Observation: Algorithms and Sustainable Applications explores a wide range of transformative data fusion techniques of Artificial Intelligence (AI) technologies applied to Google Earth Engine (GEE) techniques. It includes a wide range of scientific domains that can utilize remote sensing and geographic information systems (GIS) through detailed case studies. This book delves into the challenges of AI-driven tools and technologies for Earth observation data analysis, offering possible solutions and directly addressing current and upcoming needs within Earth observation. Google Earth Engine and Artificial Intelligence for Earth Observation: Algorithms and Sustainable Applications is a useful reference for geospatial scientists, remote sensing experts, and environmental scientists utilizing remote sensing to apply the latest AI techniques to data obtained from GEE for their research and teaching.Includes utilization of AI with GEE tools for a spectrum of scientific domains in remote sensing and geographic information systems (GIS) including natural hazard assessment, aquatic and hydrological applications, and forest coverHighlights the challenges and possible solutions for AI-driven tools and technologies for Earth observation data analysisIncludes detailed case studies showing specific considerations and exceptions for applications of AI in GEE for Earth observation

Spatial Intelligence for Hydrometeorological Hazards in a Changing Climate explores the intersection of advanced machine learning techniques and the intricate challenges posed by shifting hydrometeorological patterns. This comprehensive guide navigates readers through a diverse landscape of topics, ranging from data-driven models for early prediction of floods, landslides, soil erosion, heat waves, droughts, and novel approaches for assessing the impacts of extreme weather events on ecosystems and societies. Through real-world case studies and practical examples, the book demystifies complex algorithms and equips readers with the skills to harness the power of machine learning.Whether you are a researcher, practitioner, or enthusiast, this book empowers you to leverage cutting-edge technology to address the urgent and evolving threats of a changing climate.Contains detailed case studies for mastering machine learning techniques in order to address specific climate hazard challengesProvides an interdisciplinary approach by blending spatial-based machine learning with hydrometeorological science to enhance hazard prediction accuracyIncludes step-by-step instructions and code samples that facilitate the hands-on implementation of advanced machine learning algorithms

Technology-Based Solutions for Sustainable Groundwater Management: Challenges, Models, and Strategies provides an in-depth exploration of the environmental and technological challenges impacting global groundwater resources. It examines the complexities of water scarcity driven by climate change, agricultural expansion, and increasing human demand. Through detailed case studies and analyses, the book illustrates the pivotal role of cutting-edge spatial technologies in mapping, monitoring, and managing groundwater. The book integrates diverse scientific disciplines, including geography, hydrology, and environmental science to offer readers a multidimensional understanding of groundwater dynamics.It showcases a range of innovative tools, from remote sensing and GIS to artificial intelligence and machine learning, highlighting their application in developing predictive models and real-time management strategies. Framed within the context of sustainable practices, these solutions aim to secure groundwater for future generations while supporting current economic and social needs. It is an indispensable resource for environmental scientists, students, and researchers, providing foundational knowledge and a guide to applying the latest technological advancements in the field.Provides information on the latest technologies and modeling used for measuring sustainability of groundwater resourcesOffers knowledge on diversified topics including groundwater potentiality, stress zonation, trends of groundwater level, declining quality, and risk zonationIncludes unique case studies to illustrate the mitigation of groundwater-crisis, groundwater-supply, groundwater quality, and the overexploitation of groundwater resources

Machine Learning and AI Technology in Agricultural Applications offers a comprehensive overview of how artificial intelligence and machine learning are transforming the agricultural industry. By delving into image processing and advanced data analysis, the book demonstrates how technology addresses modern agricultural challenges, including climate change, urbanization, and increasing global populations. It emphasizes the importance of integrating sensors and data collection methods to generate vast pools of information, which can be efficiently analyzed through AI-driven solutions. The text lays a strong foundation for understanding the role of technological innovation in supporting sustainable and secure food production.Beyond introducing core machine learning models such as random forest, support vector machines, logistic regression, and decision trees, the book highlights the centralization of critical agricultural data in the cloud. This resource benefits both students and seasoned agricultural scientists, providing practical insights for optimizing crop yields, monitoring soil and weather conditions, and managing resources like fertilizers and pesticides. The book also explores the rapid analysis of complex datasets, empowering users to make informed, timely decisions in real-world agricultural scenarios.Connects core concepts of AI and ML to real-world application practices in agricultureExplores applications in crop scouting and optimizing variable rate crop inputDemonstrates the value in real or near real-time crop monitoring, yield estimation, and predictionHighlights SAR/optical data applications and analysis in agriculture

Remote Sensing and Climate Dynamics: Monitoring Impacts, Understanding Change, and Guiding Solutions delivers a thorough exploration of how remote sensing technologies are revolutionizing climate science by providing critical data to observe and analyze Earth's changing climate system. The book begins by establishing the urgency of climate change monitoring and the unique advantages of remote sensing, followed by an in-depth review of the climate system’s key processes, feedbacks, and drivers. It details the wide array of remote sensing tools, sensors, and platforms that enable precise measurement of atmospheric, oceanic, and terrestrial variables. The text examines how remote sensing tracks essential climate indicators such as surface and atmospheric temperatures, precipitation, soil moisture, cryosphere dynamics, sea level changes, and ocean health. It further explores the impacts of climate change on terrestrial ecosystems, coastal zones, and human settlements, highlighting methodologies for assessing vulnerability and resilience. The book also addresses how remote sensing supports climate mitigation efforts through carbon cycle monitoring and emission verification, while aiding adaptation planning via risk assessments and early warning systems. Supplementary appendices provide valuable resources on terminology, satellite missions, and data workflows. This comprehensive volume equips scientists and professionals with the knowledge and tools necessary to harness remote sensing for effective climate monitoring, impact assessment, and informed decision-making.Connects technical remote sensing methods directly to climate processes and actionable outcomesAccelerates real-world application with case studies that include replicable workflows, data links, and policy impact metricsTurns abstract concepts into accessible insights that empower students and cross-disciplinary users to quickly grasp technical content

This book facilitates a deeper understanding of the diverse applications of geographic information systems (GIS) and remote sensing (RS) in land and ocean mapping and surveying across different fields and regions, bringing forward the best practices and innovations happening worldwide. It includes a variety of analytical methodologies used for managing different landscapes and ocean sites and explores how emerging technologies, like artificial intelligence, will shape the future of digital geospatial surveying and analysis. With real-world projects and step-by-step applications, this book helps professionals and students in various fields navigate these technologies effectively and stay current with the latest trends.Key Features:Provides a detailed and thorough overview of GIS and RS applications, covering principles and techniques.Explores unique insights into practices across North America, Europe, Asia, South America, Africa, and Oceania, offering a broad range of perspectives and practical solutions.Includes a range of practical applications and case studies from real-world projects that provide valuable lessons and insights from experienced professionals.Addresses emerging technologies like artificial intelligence, machine learning, unmanned aerial vehicles (UAVs), and augmented reality as applied to geospatial science.Breaks down complex concepts into easily understandable language for those new to the field while offering depth for experienced practitioners seeking to broaden their expertise.This is an ideal text for professionals working with geospatial information, mapping, and surveying who seek practical examples, innovative approaches, and awareness of emerging trends. It’s suitable for geospatial data analysts, planners, environmental scientists, surveyors, disaster management professionals, logistics managers, and those who work in related fields.

This edited volume is based on the best papers accepted for presentation during the 1st Springer Conference of the Arabian Journal of Geosciences (CAJG-1), Tunisia 2018. The book compiles a wide range of topics addressing various issues by experienced researchers mainly from research institutes in the Mediterranean, MENA region, North America and Asia.Remote sensing observations can close gaps in information scarcity by complementing ground-based sparse data. Spatial, spectral, temporal and radiometric characteristics of satellites sensors are most suitable for features identification. The local to global nature and broad spatial scale of remote sensing with the wide range of spectral coverage are essential characteristics, which make satellites an ideal platform for mapping, observation, monitoring, assessing and providing necessary mitigation measures and control for different related Earth's systems processes.Main topics in this book include: Geo-informatics Applications, Land Use / Land Cover Mapping and Change Detection, Emerging Remote Sensing Applications, Rock Formations / Soil Lithology Mapping, Vegetation Mapping Impact and Assessment, Natural Hazards Mapping and Assessment, Ground Water Mapping and Assessment, Coastal Management of Marine Environment and Atmospheric Sensing.

This book aims to promote the core understanding of a proper modelling of road traffic accidents by deep learning methods using traffic information and road geometry delineated from laser scanning data.

We believe that a diverse group of academics, scientists, geographers, hydrologist, remote sensing and GIS expertise, environmentalists, meteorologists and computing experts with a common interest in geospatial sciences within the earth environmental sciences and humanistic and social sciences will find this book to be of great value.

We believe that a diverse group of academics, scientists, geographers, hydrologist, remote sensing and GIS expertise, environmentalists, meteorologists and computing experts with a common interest in geospatial sciences within the earth environmental sciences and humanistic and social sciences will find this book to be of great value.

This volume gathers the latest advances, innovations, and applications in the field of geographic information systems and unmanned aerial vehicle (UAV) technologies, as presented by leading researchers and engineers at the 2nd International Conference on Unmanned Aerial System in Geomatics (UASG), held in Roorkee, India on April 2-4, 2021. It covers highly diverse topics, including photogrammetry and remote sensing, surveying, UAV manufacturing, geospatial data sensing, UAV processing, visualization, and management, UAV applications and regulations, geo-informatics and geomatics. The contributions, which were selected by means of a rigorous international peer-review process, highlight numerous exciting ideas that will spur novel research directions and foster multidisciplinary collaboration among different specialists.

This volume gathers the latest advances, innovations, and applications in the field of geographic information systems and unmanned aerial vehicle (UAV) technologies, as presented by leading researchers and engineers at the 2nd International Conference on Unmanned Aerial System in Geomatics (UASG), held in Roorkee, India on April 2-4, 2021. It covers highly diverse topics, including photogrammetry and remote sensing, surveying, UAV manufacturing, geospatial data sensing, UAV processing, visualization, and management, UAV applications and regulations, geo-informatics and geomatics. The contributions, which were selected by means of a rigorous international peer-review process, highlight numerous exciting ideas that will spur novel research directions and foster multidisciplinary collaboration among different specialists.

This book uncovers and presents various real-life applications in the areas of transportation, smart cities, manufacturing, agriculture, disaster management, finance, health care and in other areas by using cutting-edge advanced Machine Learning (ML) techniques such as Deep Learning and Explainable AI (XAI) models using IoT sensor data.

This book uncovers and presents various real-life applications in the areas of transportation, smart cities, manufacturing, agriculture, disaster management, finance, health care and in other areas by using cutting-edge advanced Machine Learning (ML) techniques such as Deep Learning and Explainable AI (XAI) models using IoT sensor data. The book provides various examples of analyzing large amounts of data, detecting patterns, and making predictions in real-time applications and detailed case studies with practical solutions using various state-of-the-art machine learning and IoT sensor data and all these aspects will benefit the stakeholders. The book is useful for academics, researchers, upper-undergraduate, master and Ph.D. students, engineers and practitioners in sensor/IoT and AI/ML technologies, methods, applications, and related areas, and it also offers valuable insights by suggesting future research directions and providing recommendations within the fields of AI and IoT.

Multi-stage hydrothermal processes at gold deposits, placer gold deposits, and gold deposits in Nasser High Dam Reservoir are given in Chapters 13, 14, and 15.

This book uncovers and presents insights into the intersecting realms of IoT sensors and AI/ML technologies, providing a unique opportunity for researchers and developers to contribute to progress in these fields. By exploring methods, applications, and similar aspects, it sheds light on how these technologies will continue to shape and impact our lives.

This volume gathers the latest advances, innovations, and applications in the field of geographic information systems and unmanned aerial vehicle (UAV) technologies, as presented by leading researchers and engineers at the 3rd International Conference on Unmanned Aerial System in Geomatics (UASG), held in Kuala Lumpur, Malaysia on November 20-21, 2023. It covers highly diverse topics, including photogrammetry and remote sensing, surveying, UAV manufacturing, geospatial data sensing, UAV processing, visualization, and management, UAV applications and regulations, geo-informatics and geomatics. The contributions, which were selected by means of a rigorous international peer-review process, highlight numerous exciting ideas that will spur novel research directions and foster multidisciplinary collaboration among different specialists.

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.

This book discusses the application of Geospatial data, Geographic Information System (GIS) and Remote Sensing (RS) technologies in analysis and modeling of urban growth process, and its pattern, with special focus on sprawl and compact form of urban development. The book explains these two kinds of urban forms (sprawl and compact urban development) in detail regarding their advantages, disadvantages, indicators, assessment, modeling, implementation and their relationship with urban sustainability. It confirms that the proposed modeling approaches, geospatial data and GIS are very practical for identifying urban growth, land use change patterns and their general trends in future. The analyses and modeling approaches presented in this book can be employed to guide the identification and measurements of the changes and growth likely to happen in urban areas. In addition, this book can be helpful for town planning and development in order to design urban areas in a compact formand eventually sustainable manner.

This book is related to various applications of laser scanning in landslide assessment. Landslide detection approaches, susceptibility, hazard, vulnerability assessment and various modeling techniques are presented. Optimization of landslide conditioning parameters and use of heuristic, statistical, data mining approaches, their advantages and their relationship with landslide risk assessment are discussed in detail. The book contains scanning data in tropical forests; its indicators, assessment, modeling and implementation. Additionally, debris flow modeling and analysis including source of debris flow identification and rockfall hazard assessment are also presented.

This book discusses the application of Geospatial data, Geographic Information System (GIS) and Remote Sensing (RS) technologies in analysis and modeling of urban growth process, and its pattern, with special focus on sprawl and compact form of urban development.

This book is related to various applications of laser scanning in landslide assessment. Optimization of landslide conditioning parameters and use of heuristic, statistical, data mining approaches, their advantages and their relationship with landslide risk assessment are discussed in detail.

Terrestrial mass movements (i.e. cliff collapses, soil creeps, mudflows, landslides etc.) are severe forms of natural disasters mostly occurring in mountainous terrain, which is subjected to specific geological, geomorphological and climatological conditions, as well as to human activities. It is a challenging task to accurately define the position, type and activity of mass movements for the purpose of creating inventory records and potential vulnerability maps. Remote sensing techniques, in combination with Geographic Information System tools, allow state-of-the-art investigation of the degree of potential mass movements and modeling surface processes for hazard and risk mapping. Similarly, through statistical prediction models, future mass-movement-prone areas can be identified and damages can to a certain extent be minimized. Issues of scale and selection of morphological attributes for the scientific analysis of mass movements call for new developments in data modeling and spatio-temporal GIS analysis. The book is a product of a cooperation between the editors and several contributing authors, addressing current issues and recent developments in GI technology and mass movements research. Its fundamental treatment of this technology includes data modeling, topography, geology, geomorphology, remote sensing, artificial neural networks, binomial regression, fuzzy logic, spatial statistics and analysis, and scientific visualization. Both theoretical and practical issues are addressed.

Terrestrial mass movements (i.e. cliff collapses, soil creeps, mudflows, landslides etc.) are severe forms of natural disasters mostly occurring in mountainous terrain, which is subjected to specific geological, geomorphological and climatological conditions, as well as to human activities. It is a challenging task to accurately define the position, type and activity of mass movements for the purpose of creating inventory records and potential vulnerability maps. Remote sensing techniques, in combination with Geographic Information System tools, allow state-of-the-art investigation of the degree of potential mass movements and modeling surface processes for hazard and risk mapping. Similarly, through statistical prediction models, future mass-movement-prone areas can be identified and damages can to a certain extent be minimized. Issues of scale and selection of morphological attributes for the scientific analysis of mass movements call for new developments in data modeling and spatio-temporal GIS analysis. The book is a product of a cooperation between the editors and several contributing authors, addressing current issues and recent developments in GI technology and mass movements research. Its fundamental treatment of this technology includes data modeling, topography, geology, geomorphology, remote sensing, artificial neural networks, binomial regression, fuzzy logic, spatial statistics and analysis, and scientific visualization. Both theoretical and practical issues are addressed.