Bayesian framework for mapping and classifying shallow landslides exploiting remote sensing and topographic data

We propose a semi-automatic approach to detect, map and classify rainfall-induced shallow landslides. The approach combines the classification of a post-event multispectral satellite image with information on the morphometric signature of landslides in a Bayesian framework. We apply the approach in two steps. First, we detect and map the rainfall-induced landslides separating the stable ground from the failed areas. Next, we classify internally the landslides separating the source from the run out areas. We obtain the prior prob- ability from the Mahalanobis discriminant function used to classify the satellite image, and the likelihood from the frequency distribution of terrain slope and cross section convexity in the pre-existing shallow land- slides. We tested the approach in southern Taiwan, in a catchment where Typhoon Morakot caused abundant landslides in August 2009. Using the semi-automatic approach, we obtained a detailed event landslide inven- tory map that we compared to an inventory obtained through the visual interpretation of post-event ortho-photographs taken a few days after the landslide triggering rainfall event. Quantitative comparison in a Geographical Information System revealed a degree of matching between the two event inventories ex- ceeding 90%. The approach is general and flexible, and can be used with different satellite imagery and topo- graphic data. Best suited in landscapes where shallow landslides leave distinct radiometric and topographic signatures, the approach is expected to facilitate the production of event landslide inventory maps with pos- itive consequences for geomorphological investigations, landslide hazard and risk modeling, and for post event recovery efforts.