Large deep-seated landslides can be reactivated during intense events, and they can evolve into destructive failures. They are generally difficult to recognize in the field, especially when they develop in densely forested areas. A detailed and constantly updated inventory map of such phenomena, and the recognition of their topographic signatures is absolutely a key tool for landslide risk mitigation.The aim of this work is to test in forested areas, the performance of the new automatic and objective methodology developed by Tarolli et al. (2012) for geomorphic features extraction (landslide crowns) from high resolution topography (LiDAR derived Digital Terrain Models - DTMs). The methodology is based on the detection of landslides through the use of thresholds obtained by the statistical analysis of variability of landform curvature. The study was conducted in a high-risk area located in the central-south Taiwan, where an accurate field survey on landsliding processes and a high-quality set of airborne laser scanner elevation data are available. The area has been chosen because some of the deep-seated landslides are located near human infrastructures and their reactivation is highly dangerous. Thanks to LiDAR's capability to detect the bare ground elevation data in forested areas, it was possible to recognize in detail landslide features also in remote regions difficult to access. The results, if compared with the previous work of Tarolli et al. (2012), mainly focused on shallow landslides, and in a not forested area, indicate that for deep-seated landslides, where the crowns are more evident, and they are present at large scale, the tested methodology performs better (higher quality index). The method can be used to interactively assist the interpreter/user on the task of deep-seated landslide hazard mapping, and risk assessment planning of such regions.
All Science Journal Classification (ASJC) codes
- Earth-Surface Processes