Recognition of geomorphic features, such as landslide scarps, is the first key step for landslide risk assessment and mitigation. Geomorphic features can be identified from high-resolution digital elevation model (DEM). Light Detection and Ranging (LiDAR) is a useful tool to collect high-density point elevation data from ground surfaces. LiDAR ground points are used to generate high-resolution DEMs. However, LiDAR sample sizes and interpolation methods are critical parameters for DEM estimation under various land cover types. To discuss the effect of the parameters, this study used a series of cases to estimate the DEMs and identify the landslide scarps, especially potential landslide scarps hidden in the forest.Results show that LiDAR sample size affects the visual identification rate of the landslide scarps. The point density of LiDAR data controls the level of detail that can be resolved in the LiDAR-derived DEM. Given low-density LiDAR ground points, the DEM accuracy is the worst, especially in dense forest. Particularly in sparse samples, the identification rate of the landslide scarp is sensitive to the interpolation method. In sparse samples, landslide scarp identification based on Kriging-estimated DEM showed the best results among the three interpolation methods. Hence, this study provides information for the assessment of the effects of sample sizes under land cover for further geomorphic monitoring, assessment and management.
All Science Journal Classification (ASJC) codes
- Earth and Planetary Sciences(all)