Landslide Detection and Susceptibility Mapping Using Innovative Remote Sensing Data Sources

Publikation aus Digital

Proske H., Granica K., Hirschmugl M., Michael Wurm

Interpraevent 2008, Dornbirn. , 2008


Landslide susceptibility analysis using univariate statistical models is a complex and sensitive task. The resulting quality of the functional models is directly dependant on the quality of the input data with respect to spatial resolution, classification accuracy and completeness. In this paper, the application of innovative Remote Sensing data sources is evaluated. The classification of Very High Resolution (VHR) Satellite data proved to deliver accurate land cover classes. Results show that congruent quality from QuickBird data compared to aerial photographs can be obtained. As QuickBird images have a larger coverage and a better radiometric stability, the development of automatic tools is favoured. Interpretations based on Earth Observation data seem to be the only possibility to obtain landslide inventories that cover large areas and are widely complete. Only VHR imagery allows the detection of small landslides. Digital Terrain Models based on airborne Laserscanner data facilitate a precise derivation of geomorphometric parameters. The analysis of the susceptibility modelling results shows the high significance of geological and land cover parameters.

Keywords: Remote Sensing, Landslides, Susceptibility Modelling