Buch, Englisch, 380 Seiten, Format (B × H): 152 mm x 229 mm
Geospatial Approaches to Risk, Monitoring, and Mitigation
Buch, Englisch, 380 Seiten, Format (B × H): 152 mm x 229 mm
ISBN: 978-0-443-45468-4
Verlag: Elsevier Science
Remote Sensing and AI for Climate-Driven Hazard Assessment: Geospatial Approaches to Risk, Monitoring, and Mitigation addresses the urgent need for advanced geospatial tools to manage increasing climate-related hazards. This volume combines satellite data, GIS technologies, and artificial intelligence to improve hazard detection, risk assessment, and mitigation strategies. It is designed for researchers, environmental scientists, GIS specialists, and policymakers working on climate vulnerability and disaster risk reduction. The book offers a structured approach across chapters, covering theory, data sources, analytical methods, and practical case studies from regions like the UAE, China, and Southeast Asia, emphasizing real-world applications. Its regional focus and integration of cutting-edge analytics enable professionals to develop innovative solutions for urban resilience and climate adaptation. By bridging foundational knowledge with advanced geospatial analysis, this reference empowers users to make data-driven decisions, enhance disaster preparedness, and support sustainable development in a changing climate.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Part I: Foundations
1. Introduction: Exploring the Role of Remote Sensing and GIS in Environmental Hazard Science
2. Climate Change and Natural Hazards: A Geospatial Analysis Perspective
3. Data Sources, Sensor Technologies, and AI Tools for Hazard Assessment
Part II: Thematic Applications by Hazard Type
4. Mapping and Monitoring Drought Risk with Satellite-Derived Indices
5. Assessing Flood Hazards Using Remote Sensing and Hydrological Modeling
6. Detecting and Predicting Wildfires with Multi-Sensor Data Integration
7. Evaluating Urban Heat Islands and Heatwave Vulnerability
8. Coastal Hazards and Sea-Level Rise through a Remote Sensing Lens
Part III: Analytical Methods and Decision Support
9. Utilizing Machine Learning for Environmental Risk Forecasting
10. Applying Multi-Criteria Decision Analysis (MCDA) in Geospatial Hazard Modeling
Part IV: Regional Case Studies and Forward-Looking Perspectives
11. Case Study: Managing Flood Risks in Arid Urban Settings (e.g., UAE)
12. Case Study: Mapping Climate Vulnerability in Coastal Regions
13. Bridging Policy, Practice, and Future Strategies in Geospatial Climate Hazard Management




