Mapping of landslides under dense vegetation cover using object - oriented analysis and LiDAR derivatives

Light Detection and Ranging (LiDAR) and its wide range of derivative products have become a powerful tool in landslide research, particularly for landslide identification and landslide inventory mapping. In contrast to the many studies that use expert-based analysis of LiDAR derivatives to identify landslides, only few studies, all pixel-based, have attempted to develop computer-aided methods for extracting landslides from LiDAR. So far, it has not been tested whether object-oriented analysis (OOA) could be an alternative. Therefore, this study focuses on the application of OOA using LiDAR derivatives such as slope gradient, curvature, and difference in elevation (2m resolution). More specifically, the focus is on the possible use for segmentation and classification of slow-moving landslides in densely vegetated areas, where spectral data do not allow accurate landslide inventory mapping. The test areas are the Flemish Ardennes (Belgium) and Vorarlberg (Austria). In a first phase, a relatively qualitative procedure based on expert-knowledge and basic statistical analysis was developed for a test area in the Flemish Ardennes. The procedure was then applied without further modification to a validation area in the same region. The results obtained show that OOA using LiDAR derivatives allows recognition and characterization of profound morphologic properties of deep-seated landslides, because approximately 70% of the landslides of an expert-based inventory were also included in the object-oriented inventory. For mountain areas with bed rock outcrops like Vorarlberg, on the other hand, it is more difficult to create a transferable model

Mapping of landslides under dense vegetation cover using object - oriented analysis and LiDAR derivatives

Light Detection and Ranging (LiDAR) and its wide range of derivative products have become a powerful tool in landslide research, particularly for landslide identification and landslide inventory mapping. In contrast to the many studies that use expert-based analysis of LiDAR derivatives to identify landslides, only few studies, all pixel-based, have attempted to develop computer-aided methods for extracting landslides from LiDAR. So far, it has not been tested whether object-oriented analysis (OOA) could be an alternative. Therefore, this study focuses on the application of OOA using LiDAR derivatives such as slope gradient, curvature, and difference in elevation (2m resolution). More specifically, the focus is on the possible use for segmentation and classification of slow-moving landslides in densely vegetated areas, where spectral data do not allow accurate landslide inventory mapping. The test areas are the Flemish Ardennes (Belgium) and Vorarlberg (Austria). In a first phase, a relatively qualitative procedure based on expert-knowledge and basic statistical analysis was developed for a test area in the Flemish Ardennes. The procedure was then applied without further modification to a validation area in the same region. The results obtained show that OOA using LiDAR derivatives allows recognition and characterization of profound morphologic properties of deep-seated landslides, because approximately 70% of the landslides of an expert-based inventory were also included in the object-oriented inventory. For mountain areas with bed rock outcrops like Vorarlberg, on the other hand, it is more difficult to create a transferable model