Identification of landslides at the regional scale has always been a challenging problem. Various automatic landslide identification methods, mainly relying on spectral information from aerial photographs or satellite imagery, have been developed. This paper proposes a semi-automatic approach to identify locations of small-sized shallow debris slides and flows using airborne lidar (light detection and ranging) data. Cells related to landslide components were first extracted by using a new method based on local Moran's I (LMI). Subsequently, cell clusters representing landslide components and other terrain objects were discriminated through geometric and contextual analysis at cluster level. The approach was tested in a study area in Hong Kong and the identification result was verified by a landslide inventory. Locations of 93.5% of recent landslides and 23.8% of old landslides were identified by the proposed approach. The result indicates that the proposed approach is able to identify both recent and old landslides with distinct morphological features. However, the proposed approach also identified a large number of locations (77.6% of all locations) unrelated to landslides. These locations may correspond to terrain objects with similar morphology to debris slides or flows, and indicate rough terrain in the study area. In addition, the effects of DEM (digital elevation model) resolution on landslide identification were analysed by applying the LMI-based method to digital elevation models (DEMs) at different resolutions. The results indicate that the smoothing effect caused by lowering DEM resolution led to extraction of fewer landslide components.
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)