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 probability 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 landslides. 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 inventory 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 exceeding 90%. The approach is general and flexible, and can be used with different satellite imagery and topographic 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 positive consequences for geomorphological investigations, landslide hazard and risk modeling, and for post event recovery efforts.