| Session Details | |
| Section | SE - Solid Earth Sciences |
| Session Title | Advances in Mapping and Modeling Rainfall-Triggered Landslides |
| Main Convener | Dr. Alessandro Mondini (National Research Council, Italy) |
| Co-convener(s) | Prof. Karl Chang (University of Idaho, United States) |
| Session Description | Landslide is a widespread hazard in mountainous regions around the world. Landslides cause not only considerable financial losses but also major ecological and environmental problems. Many landslides, especially shallow ones, are triggered by heavy rainfall during storm events. The purpose of this session is to highlight current developments in mapping and modeling rainfall-triggered landslides and unravel their interconnections. Landslide maps document the extent of landslide phenomena in a region, and they are important for investigating the distribution, types, pattern, recurrence and statistics of slope failures. New and emerging techniques based on analyses of HR and VHR optical and SAR satellite images, and LiDAR DEMs promise to facilitate the production of landslide maps by reducing the time and resources required for their compilation and systematic update. Landslide maps are necessary input for the calibration and validation of landslide models, generally grouped into physically-based and statistical. A physically-based model delineates areas prone to landsliding by analyzing the influence of surface topography on near-surface hydrologic response, whereas a statistical model predicts the likelihood of landslide occurrence by analyzing the relationship between past landslides and geoenvironmental factors. This session encourages submission of abstracts that cover current developments in mapping and modeling rainfall-triggered landslides from different regions of the world. Papers and posters on the following topics are welcome: 1) Qualitative and quantitative analysis of surface morphology through the exploitation of VHR DEMs 2) Visual interpretation of satellite images for landslide recognition and mapping 3) Analysis of multispectral images 4) Object oriented image analysis 5) Use of SAR data 6) Physically-based model 7) Statistical model 8) Dynamic landslide model 9) Landslide early warning system 10) Integrated landslide model |