Earth Observation Applications to Landslide Mapping, Monitoring and Modeling

Auteur

Ionu andric, currently an Associate Professor at the Faculty of Geography, University of Bucharest, brings extensive experience in research and commercial projects. He specializes in combining geospatial knowledge with software engineering to develop geospatial environmental models. His research focuses on the spatial and temporal modeling of landslides, including tools for assessing the propagation of uncertainty in landslide hazards. He has led projects on multi-hazard and multi-risk assessment, drone-based landslide feature mapping, agricultural pathogen detection using drone imagery, urban climate applications using satellite imagery, and soil moisture satellite product analysis for Romania.

Viorel Ilinca, PhD in Geography, has a rich background in physical geography and geology. Since joining the Geological Institute of Romania in 2010, he has been an integral part of the Geological Mapping Group, where he focuses on landslide research, geomorphological and geological mapping, GIS, cartography and geoheritage. With extensive experience in both national and international research projects, he has worked on various applications of geomorphology and geological mapping for natural hazard assessment. In the field of landslides, he uses field surveys and earth observation to study different types of landslides.

Zenaida Chiu, PhD in Geography, has expertise in the integration of physical modeling, GIS, earth observation and ground measurement networks in the monitoring of landslide activity. Her work covers hydrology, climatology and geomorphology, with a special focus on landslides. She has led national research projects aimed at improving our understanding of landslides by combining methods from different disciplines including geomorphology, engineering geology, hydrology and meteorology. Her recent research includes estimating soil moisture using a mix of hydrological modelling, remote sensing and field measurements, and investigating the impact of climate change on different sectors.

Texte du rabat

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.

Contenu

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
14. Microwave remote sensing for investigating hydrological preconditions triggering landslides: a case study: Ialomita Subcarpathians, Romania
15. 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