In contrast to the many studies that use expert-based analysis of LiDAR derivatives for landslide mapping in
forested terrain, only fewstudies have attempted to develop (semi-)automatic methods for extracting landslides from LiDAR derivatives. While all these studies are pixel-based, it has not yet been tested whether objectoriented analysis (OOA) could be an alternative. This study investigates the potential of OOA using only singlepulse LiDAR derivatives, such as slope gradient, roughness and curvature to map landslides. More specifically, the focus is on both LiDAR data segmentation and classification of slow-moving landslides in densely vegetated areas, where spectral data do not allow accurate landslide identification. A multistage procedure has been developed and tested in the Flemish Ardennes (Belgium). The procedure consists of (1) image binarization andmultiresolution segmentation, (2) classification of landslide parts (main scarps and landslide body segments) and non-landslide features (i.e. earth banks and cropland fields) with supervised support vector machines at the appropriate scale, (3) delineation of landslide flanks, (4) growing of a landslide body starting from its main scarp, and (5) final cleaning of the inventory map. The results obtained show that OOA using LiDAR derivatives
allows recognition and characterization of profound morphologic properties of forested deep-seated landslides
on soil-covered hillslopes, because more than 90% of the main scarps and 70% of the landslide bodies of an expert-based inventory were accurately identified with OOA. For mountainous areas with bedrock, on the other hand, creation of a transferable model is expected to be more difficult.