Data-Driven Earth Observation for Disaster Management: From Theory to Practical Applications delves into the critical role of Earth observation data and technologies in predicting, managing, and mitigating a spectrum of disasters. With a multidisciplinary approach encompassing geography and geospatial science, the book addresses the challenges of comprehending and managing disasters in our rapidly changing world. Offering solutions through insights into early detection, prediction, management, and prevention, it provides strategies for understanding Earth surface changes and the application of Earth Observation technologies. Readers will gain understanding on how to use Earth observation data and techniques in studying different domains of disaster management.
Chapters follow a consistent format, featuring introductory summaries, key takeaways, and a focus on the role of Earth observation, enhancing both comprehensibility and searchability. The book also includes case studies and practical applications of Earth observation technologies, providing context and demonstrating the tangible impact of the techniques covered.
1. Chapter 1 Introduction to data-driven earth observation for disaster management - p. 1 2. Section I: Geological disasters - p. S1 3. Chapter 2 Seismic exposure and risk assessment - p. 15 4. Chapter 3 Volcanic eruptions - p. 25 5. Chapter 4 Landslides - p. 45 6. Chapter 5 Snow avalanches - p. 69 7. Chapter 6 Surface subsidence - p. 89 8. Section II: Weather-related disasters - p. S2 9. Chapter 7 Extreme precipitation - p. 119 10. Chapter 8 Tropical cyclone - p. 133 11. Chapter 9 Tornadoes - p. 157 12. Chapter 10 Heatwaves - p. 167 13. Section III: Water-related disasters - p. S3 14. Chapter 11 Algal blooms - p. 183 15. Chapter 12 Beach-dune erosion - p. 207 16. Chapter 13 Floods - p. 223 17. Chapter 14 Drought - p. 239 18. Chapter 15 Riverbank erosion - p. 251 19. Chapter 16 Glacial lake outburst floods - p. 271 20. Section IV: Environmental degradation - p. S4 21. Chapter 17 Soil erosion - p. 289 22. Chapter 18 Ecosystem degradation - p. 303 23. Chapter 19 Coastal vegetation degradation - p. 327 24. Chapter 20 Deforestation assessment - p. 347 25. Chapter 21 Invasive species - p. 369 26. Chapter 22 Coral reef degradation - p. 385 27. Section V: Pollution and environmental crises - p. S5 28. Chapter 23 Shrub encroachment - p. 397 29. Chapter 24 Forest disturbance evaluation - p. 423 30. Chapter 25 Atmospheric composition and sensing - p. 431 31. Chapter 26 Climate-air quality dynamics - p. 443 32. Chapter 27 Inland water quality crisis - p. 469 33. Chapter 28 Oil spill detection - p. 481 34. Chapter 29 Dust storm monitoring - p. 493 35. Chapter 30 Wildfires - p. 503 36. Section VI: Socio-political disasters - p. S6 37. Chapter 31 Informal settlement mapping - p. 517 38. Chapter 32 Conflicts - p. 529 39. Section VII: Health-related disasters - p. S7 40. Chapter 33 Pandemics and epidemics - p. 547 41. Chapter 34 Vector-borne diseases - p. 559 42. Chapter 35 Radon hazard - p. 573 43. Section VIII: Advanced topics - p. S8 44. Chapter 36 An overview of Google Earth Engine for disaster risk management - p. 591 45. Chapter 37 Cloud-based flood mapping with Sentinel-1 SAR on Google Earth Engine: Insights from the global flood mapper - p. 609 46. Chapter 38 A Google Earth Engine framework for real-time river ice hazard monitoring in northern US watersheds - p. 623 47. Chapter 39 Leveraging participatory monitoring for enhanced disaster monitoring and management - p. 643 48. Chapter 40 GIS-based hazard vulnerability assessment: A case study in Zakynthos, Greece - p. 659 49. Chapter 41 Mapping Arctic impervious surface changes with Earth observation and machine learning - p. 679 50. Chapter 42 Advanced Earth observation techniques for damage estimation - p. 697 51. Chapter 43 Enhanced disaster monitoring through Earth observation and social sensing integration - p. 709 52. Chapter 44 Synergizing volunteered geographical information and Earth observation for enhanced disaster management - p. 729 53. Chapter 45 Future challenges and opportunities in data-driven Earth observation - p. 743 54. Index - p. 755
Data-Driven Earth Observation for Disaster Management: From Theory to Practical Applications delves into the critical role of Earth observation data and technologies in predicting, managing, and mitigating a spectrum of disasters. With a multidisciplinary approach encompassing geography and geospatial science, the book addresses the challenges of comprehending and managing disasters in our rapidly changing world. Offering solutions through insights into early detection, prediction, management, and prevention, it provides strategies for understanding Earth surface changes and the application of Earth Observation technologies. Readers will gain understanding on how to use Earth observation data and techniques in studying different domains of disaster management.
Chapters follow a consistent format, featuring introductory summaries, key takeaways, and a focus on the role of Earth observation, enhancing both comprehensibility and searchability. The book also includes case studies and practical applications of Earth observation technologies, providing context and demonstrating the tangible impact of the techniques covered.
1. Chapter 1 Introduction to data-driven earth observation for disaster management - p. 1 2. Section I: Geological disasters - p. S1 3. Chapter 2 Seismic exposure and risk assessment - p. 15 4. Chapter 3 Volcanic eruptions - p. 25 5. Chapter 4 Landslides - p. 45 6. Chapter 5 Snow avalanches - p. 69 7. Chapter 6 Surface subsidence - p. 89 8. Section II: Weather-related disasters - p. S2 9. Chapter 7 Extreme precipitation - p. 119 10. Chapter 8 Tropical cyclone - p. 133 11. Chapter 9 Tornadoes - p. 157 12. Chapter 10 Heatwaves - p. 167 13. Section III: Water-related disasters - p. S3 14. Chapter 11 Algal blooms - p. 183 15. Chapter 12 Beach-dune erosion - p. 207 16. Chapter 13 Floods - p. 223 17. Chapter 14 Drought - p. 239 18. Chapter 15 Riverbank erosion - p. 251 19. Chapter 16 Glacial lake outburst floods - p. 271 20. Section IV: Environmental degradation - p. S4 21. Chapter 17 Soil erosion - p. 289 22. Chapter 18 Ecosystem degradation - p. 303 23. Chapter 19 Coastal vegetation degradation - p. 327 24. Chapter 20 Deforestation assessment - p. 347 25. Chapter 21 Invasive species - p. 369 26. Chapter 22 Coral reef degradation - p. 385 27. Section V: Pollution and environmental crises - p. S5 28. Chapter 23 Shrub encroachment - p. 397 29. Chapter 24 Forest disturbance evaluation - p. 423 30. Chapter 25 Atmospheric composition and sensing - p. 431 31. Chapter 26 Climate-air quality dynamics - p. 443 32. Chapter 27 Inland water quality crisis - p. 469 33. Chapter 28 Oil spill detection - p. 481 34. Chapter 29 Dust storm monitoring - p. 493 35. Chapter 30 Wildfires - p. 503 36. Section VI: Socio-political disasters - p. S6 37. Chapter 31 Informal settlement mapping - p. 517 38. Chapter 32 Conflicts - p. 529 39. Section VII: Health-related disasters - p. S7 40. Chapter 33 Pandemics and epidemics - p. 547 41. Chapter 34 Vector-borne diseases - p. 559 42. Chapter 35 Radon hazard - p. 573 43. Section VIII: Advanced topics - p. S8 44. Chapter 36 An overview of Google Earth Engine for disaster risk management - p. 591 45. Chapter 37 Cloud-based flood mapping with Sentinel-1 SAR on Google Earth Engine: Insights from the global flood mapper - p. 609 46. Chapter 38 A Google Earth Engine framework for real-time river ice hazard monitoring in northern US watersheds - p. 623 47. Chapter 39 Leveraging participatory monitoring for enhanced disaster monitoring and management - p. 643 48. Chapter 40 GIS-based hazard vulnerability assessment: A case study in Zakynthos, Greece - p. 659 49. Chapter 41 Mapping Arctic impervious surface changes with Earth observation and machine learning - p. 679 50. Chapter 42 Advanced Earth observation techniques for damage estimation - p. 697 51. Chapter 43 Enhanced disaster monitoring through Earth observation and social sensing integration - p. 709 52. Chapter 44 Synergizing volunteered geographical information and Earth observation for enhanced disaster management - p. 729 53. Chapter 45 Future challenges and opportunities in data-driven Earth observation - p. 743 54. Index - p. 755
