Earth Observation Applications to Landslide Mapping, Monitoring and Modelling: Cutting-edge Approaches with Artificial Intelligence, Aerial and Satellite Imagery focuses on the applications of drone and satellite imagery for mapping landslides. Topics covered include the use of ultra-high aerial imagery, obtained with drones, for mapping and predicting landslide activity (like runoff, volume displacement, exposure to risk, etc.). Subjects related to EO data assimilations into landslide hazard prediction models are also included. The primary aim of this book is to advance the scientific understanding and application of technologies to address a variety of fields related to landslide mapping and monitoring.Because of the intensification of the catastrophic events in the last 20 years, landslide monitoring gained considerable interest from the geoscience community. This book will be beneficial for the postgraduates, PhD research scholars, professors and scientists, geospatial experts, modelers, oceanography scientists and conservation communities, environmental consultants, big data compilers, and computing experts.
1. Section 1: Introduction - p. xiv 2. Chapter one. A review of UAV-based data applications for landslide mapping and monitoring - p. 2 3. Chapter two. A review of the state-of-the-art use of satellite Earth observation data for landslide mapping and monitoring - p. 37 4. Section 2: Satellite data in landslide mapping and monitoring - p. 60 5. Chapter three. On the use of the EGMS data for studying landslides in Great Britain - p. 69 6. Chapter four. Deciphering the kinematics of urban landslides through SAR imagery analysis - p. 87 7. Chapter five. Artificial intelligence applications for landslide mapping and monitoring on EO data - p. 119 8. Chapter six. Mapping landslides on Earth, Moon, and Mars using satellite imagery and deep learning techniques - p. 147 9. Section 3: Drone applications for landslide mapping and monitoring - p. 165 10. Chapter seven. Landslide volume and runoff monitoring using UAV photogrammetry - p. 172 11. Chapter eight. Landslide 3D reconstruction and monitoring using oblique and nadiral drone aerial imagery - p. 199 12. Chapter nine. Geomorphic monitoring and assessment of debris flows using drone-based structure from motion - p. 215 13. Chapter ten. Machine learning and object-based image analysis for landside mapping using UAV-derived data - p. 241 14. Chapter eleven. Estimating kinematic uncertainties of landslides using UAV time series - p. 257 15. Chapter twelve. Detailed landslide kinematics mapping using short-term UAV time-series. Case study: Livadea landslide, Romania - p. 279 16. Section 4: EO data assimilations in landslide susceptibility, hazard mapping and risk assessment - p. 293 17. Chapter thirteen. Building landslide inventory with LiDAR data and deep learning - p. 296 18. Chapter fourteen. Landslide susceptibility mapping using machine-learning algorithms and earth observation data - p. 313 19. Chapter Fifteen. Microwave remote sensing for investigating hydrological preconditions triggering landslides: a case study: Ialomita Subcarpathians, Romania - p. 335 20. Chapter sixteen. Use of UAV imagery for the detection and measurement of damages to road networks in landslide areas - p. 353 21. Section 5: Future challenges and future outlook - p. 368 22. Chapter seventeen. Mapping the existing challenges and pathway forward - p. 380 23. Index - p. 391
Earth Observation Applications to Landslide Mapping, Monitoring and Modelling: Cutting-edge Approaches with Artificial Intelligence, Aerial and Satellite Imagery focuses on the applications of drone and satellite imagery for mapping landslides. Topics covered include the use of ultra-high aerial imagery, obtained with drones, for mapping and predicting landslide activity (like runoff, volume displacement, exposure to risk, etc.). Subjects related to EO data assimilations into landslide hazard prediction models are also included. The primary aim of this book is to advance the scientific understanding and application of technologies to address a variety of fields related to landslide mapping and monitoring.Because of the intensification of the catastrophic events in the last 20 years, landslide monitoring gained considerable interest from the geoscience community. This book will be beneficial for the postgraduates, PhD research scholars, professors and scientists, geospatial experts, modelers, oceanography scientists and conservation communities, environmental consultants, big data compilers, and computing experts.
1. Section 1: Introduction - p. xiv 2. Chapter one. A review of UAV-based data applications for landslide mapping and monitoring - p. 2 3. Chapter two. A review of the state-of-the-art use of satellite Earth observation data for landslide mapping and monitoring - p. 37 4. Section 2: Satellite data in landslide mapping and monitoring - p. 60 5. Chapter three. On the use of the EGMS data for studying landslides in Great Britain - p. 69 6. Chapter four. Deciphering the kinematics of urban landslides through SAR imagery analysis - p. 87 7. Chapter five. Artificial intelligence applications for landslide mapping and monitoring on EO data - p. 119 8. Chapter six. Mapping landslides on Earth, Moon, and Mars using satellite imagery and deep learning techniques - p. 147 9. Section 3: Drone applications for landslide mapping and monitoring - p. 165 10. Chapter seven. Landslide volume and runoff monitoring using UAV photogrammetry - p. 172 11. Chapter eight. Landslide 3D reconstruction and monitoring using oblique and nadiral drone aerial imagery - p. 199 12. Chapter nine. Geomorphic monitoring and assessment of debris flows using drone-based structure from motion - p. 215 13. Chapter ten. Machine learning and object-based image analysis for landside mapping using UAV-derived data - p. 241 14. Chapter eleven. Estimating kinematic uncertainties of landslides using UAV time series - p. 257 15. Chapter twelve. Detailed landslide kinematics mapping using short-term UAV time-series. Case study: Livadea landslide, Romania - p. 279 16. Section 4: EO data assimilations in landslide susceptibility, hazard mapping and risk assessment - p. 293 17. Chapter thirteen. Building landslide inventory with LiDAR data and deep learning - p. 296 18. Chapter fourteen. Landslide susceptibility mapping using machine-learning algorithms and earth observation data - p. 313 19. Chapter Fifteen. Microwave remote sensing for investigating hydrological preconditions triggering landslides: a case study: Ialomita Subcarpathians, Romania - p. 335 20. Chapter sixteen. Use of UAV imagery for the detection and measurement of damages to road networks in landslide areas - p. 353 21. Section 5: Future challenges and future outlook - p. 368 22. Chapter seventeen. Mapping the existing challenges and pathway forward - p. 380 23. Index - p. 391
