Landslide Classification Using Lidar-derived Data and Spot Imagery

Using spectral-only information for landslides classification is usually confusing with houses, roads, and other bare lands because these ground features have similar spectral patterns on images. In this study, 3D airborne LiDAR data are integrated with SPOT images for landslide classification for improving classification accuracy. A study area is selected in a subbasin of Shimen Reservoir. SPOT images and LiDAR data are taken after Typhoon Longwang in November of 2005. The LiDAR-derived data include DEM slope and roughness indices including Fractal dimension, diversity, dominance and relative richness. These derivatives are then combined with spectral bands for classification algorithms including Maximum Likelihood and Mahalanobis Distance. It is concluded that with the inclusion of LiDAR-derived diversity, an improvement of more than 11% of user’s accuracy and 27% of producer’s accuracy by Maximum Likelihood Classification algorithm can be achieved.