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 (2 m 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.

VAN DEN EECKHAUT Miet;  KERLE Norman;  HERVAS Javier;  SUPPER Robert; 

2014-01-31

Springer

JRC66232

978-3-642-31324-0,    978-3-642-31325-7,   

http://link.springer.com/chapter/10.1007/978-3-642-31325-7_13,    https://publications.jrc.ec.europa.eu/repository/handle/JRC66232,   

10.1007/978-3-642-31325-7_13,