Product details:
| ISBN13: | 9780128238684 |
| ISBN10: | 0128238682 |
| Binding: | Paperback |
| No. of pages: | 400 pages |
| Size: | 234x190 mm |
| Weight: | 1000 g |
| Language: | English |
| Illustrations: | 200 illustrations (120 in full color) |
| 694 |
Category:
Earth Observation Applications to Landslide Mapping, Monitoring and Modeling
Cutting-edge Approaches with Artificial Intelligence, Aerial and Satellite Imagery
Series:
Earth Observation;
Publisher: Elsevier
Date of Publication: 8 November 2024
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Long description:
Earth Observation Applications to Landslide Mapping, Monitoring and Modeling: Cutting-edge Approacheswith Artificial Intelligence, Aerial and Satellite Imagery focuses on the application of drone and satellite imagery for landslide mapping, monitoring, and modeling. The topics covered include the use of ultrahigh spatial resolution imagery acquired by UAVs (Unmanned Aerial Vehicles) for mapping and predicting landslide activity, the use of satellite imagery for monitoring landslide activity, the assimilation of EO (EarthObservation) data into landslide susceptibility and hazard prediction models, and the building of landslide inventories. The primary objective of this book is the advancement of the scientific understanding and application of technologies to address a variety of areas related to landslide mapping and monitoring for robust and sustainable development. Earth Observation Applications to Landslide Mapping, Monitoring and Modeling be useful for PhD students, postdoctoral researchers, professors, and scientists in geoscience.
Table of Contents:
Section 1: Introduction
1. A review of UAV-based data applications for landslide mapping and monitoring
2. A review of the state-of-the-art use of satellite Earth observation data for landslide mapping and monitoring
Section 2: Satellite data in landslide mapping and
monitoring
3. On the use of the EGMS data for studying landslides in Great Britain
4. Deciphering the kinematics of urban landslides through SAR imagery analysis
5. Artificial intelligence applications for landslide mapping/monitoring on satellite EO data
6. Mapping landslides on Earth, Moon, and Mars using satellite imagery and deep learning techniques
Section 3: Drone applications for landslide mapping
and monitoring
7. Landslide volume and runoff monitoring using UAV photogrammetry
8. Landslide 3D reconstruction and monitoring using oblique and nadiral drone aerial imagery
9. Geomorphic monitoring and assessment of debris flows using drone-based structure from motion
10. Machine learning and object-based image analysis for landside mapping using UAV-derived data
11. Estimating kinematic uncertainties of landslides using UAV time series
12. Detailed landslide kinematics mapping using short-term UAV time-series. Case study: Livadea landslide, Romania
Section 4: EO data assimilations in landslide susceptibility, hazard mapping and risk assessment
13. Building landslide inventory with LiDAR data and deep learning
14 Landslide susceptibility mapping using machine-learning algorithms and earth observation data
15. Microwave remote sensing for investigating hydrological preconditions triggering landslides: a case study: Ialomita Subcarpathians, Romania
16. Use of UAV imagery for the detection and measurement of damages to road networks in landslide areas
Section 5: Future challenges and future outlook
17. Mapping the existing challenges and pathway forward
1. A review of UAV-based data applications for landslide mapping and monitoring
2. A review of the state-of-the-art use of satellite Earth observation data for landslide mapping and monitoring
Section 2: Satellite data in landslide mapping and
monitoring
3. On the use of the EGMS data for studying landslides in Great Britain
4. Deciphering the kinematics of urban landslides through SAR imagery analysis
5. Artificial intelligence applications for landslide mapping/monitoring on satellite EO data
6. Mapping landslides on Earth, Moon, and Mars using satellite imagery and deep learning techniques
Section 3: Drone applications for landslide mapping
and monitoring
7. Landslide volume and runoff monitoring using UAV photogrammetry
8. Landslide 3D reconstruction and monitoring using oblique and nadiral drone aerial imagery
9. Geomorphic monitoring and assessment of debris flows using drone-based structure from motion
10. Machine learning and object-based image analysis for landside mapping using UAV-derived data
11. Estimating kinematic uncertainties of landslides using UAV time series
12. Detailed landslide kinematics mapping using short-term UAV time-series. Case study: Livadea landslide, Romania
Section 4: EO data assimilations in landslide susceptibility, hazard mapping and risk assessment
13. Building landslide inventory with LiDAR data and deep learning
14 Landslide susceptibility mapping using machine-learning algorithms and earth observation data
15. Microwave remote sensing for investigating hydrological preconditions triggering landslides: a case study: Ialomita Subcarpathians, Romania
16. Use of UAV imagery for the detection and measurement of damages to road networks in landslide areas
Section 5: Future challenges and future outlook
17. Mapping the existing challenges and pathway forward
