Landslide Susceptibility Mapping at National Scale: A First Attempt for Austria

Abstract

Numerous publications that addressing landslide susceptibility were published over the past decades, also due to an increasing demand of spatial information regarding potentially endangered areas. However, studies that provide an overview on landslide susceptibility at national scale are still scarce. This research presents a first attempt to generate a national scale landslide susceptibility map for Austria based on statistical techniques. Binary logistic regression has been applied to delineate susceptible areas using three different predictor sets. The initial predictor set relates to topographic variables only (model A), and was gradually expanded with the factors geology (model B) and land cover (model C). The Area Under the Receiver Operating Characteristic Curve (AUROC) was used to validate the predictions by means of a k-fold cross-validation. The obtained acceptable prediction performances (mean AUROC of model A: 0.76, B: 0.81 and C: 0.82) suggest a relatively high predictive performance of all models. However, during this study, several limitations of the conducted analysis (e.g. limited landslide data, bias propagation, overoptimistic performance estimates) became evident. The main drawbacks and further steps towards a more reliable representation of landslide susceptibility at national scale are discussed.