Environmental Monitoring with Integrated Earth Observation Data and Machine Learning
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Beschreibung
This book offers practical insights for researchers and policymakers to demonstrate how EO and ML can strengthen environmental monitoring to support informed decision-making, and advance sustainable development. The Earth faces rising challenges like climate instability, land degradation, water scarcity, rapid urban expansion, etc., making reliable environmental monitoring imperative. Traditional methods lack the precision and scale required for global environmental monitoring. However, advances in Earth Observation (EO) and Machine Learning (ML) enable accurate, large-scale environmental monitoring through accessible open-source datasets. Further ML integration with these datasets supports predictive analysis and detailed environmental assessments. Thus, this book outlines the principles of EO, data management, ML integration, and applies them through a case study of the Narmada River Basin, India to examine land use, pollution, and the overall environmental conditions.
This book offers practical insights for researchers and policymakers to demonstrate how EO and ML can strengthen environmental monitoring to support informed decision-making, and advance sustainable development. The Earth faces rising challenges like climate instability, land degradation, water scarcity, rapid urban expansion, etc., making reliable environmental monitoring imperative. Traditional methods lack the precision and scale required for global environmental monitoring. However, advances in Earth Observation (EO) and Machine Learning (ML) enable accurate, large-scale environmental monitoring through accessible open-source datasets. Further ML integration with these datasets supports predictive analysis and detailed environmental assessments. Thus, this book outlines the principles of EO, data management, ML integration, and applies them through a case study of the Narmada River Basin, India to examine land use, pollution, and the overall environmental conditions.
Discusses integration of Earth observation (EO) datasets and machine learning (ML) for environmental monitoring Highlights transformative approaches for sustainable environmental monitoring and management Offers actionable recommendations for hydrological modeling, land use assessment, and pollution monitoring
Autor*in
Srija Roy
Themen in »Environmental Monitoring with Integrated Earth Observation Data and Machine Learning«
Earth Observation Data Integration Open-Source Datasets Pollution Monitoring Land Use Change Sedimentation Studies Urban Expansion Analysis Big Data for Environmental Science