Impact of Input Data on the Quality of the Landslide Susceptibility Large-Scale Maps: A Case Study from NW Croatia

Abstract

AbstractThe study presents analyses of input data impact on the quality of the landslide susceptibility large-scale maps. For comparison, two input data sets were used to produce two landslide susceptibility maps. The first input data set included free-available, small-scale data with low spatial accuracy, while the second set included high-resolution remote sensing data. The same nine types of landslide causal factors were derived and used for susceptibility analyses. Furthermore, LiDAR-based landslide inventory and bivariate statistical method, i.e. Information Value method, were used for susceptibility modelling. The resulting landslide susceptibility maps were compared with ROC curves. Success and prediction rates showed that the landslide susceptibility model based on causal factors derived from high-resolution remote sensing data is approximately 10% more accurate than the model based on causal factors derived from small-scale input data. Furthermore, based on the conducted research, it can be concluded that susceptibility modelling based on small-scale data and LiDAR-based inventories enables reliable landslide susceptibility assessments at the regional level.