Statistical Modelling of Europe-wide Landslide Susceptibility Using Limited Landslide Inventory Data

In many regions, the absence of a landslide inventory hampers the production of susceptibility or hazard maps. Therefore, a method combining a procedure for sampling of landslide-affected and landslide-free grid cells from a limited landslide inventory and logistic regression modelling was tested for susceptibility mapping of slide- and flow-type landslides on a European scale. Landslide inventories were available for Norway, Campania (Italy) and the Barcelonnette Basin (France) and from each inventory a random subsample was extracted. In addition, a landslide dataset was produced from the analysis of Google Earth images in combination with extraction of landslide locations reported in scientific publications. Attention was paid to have a representative distribution of landslides over Europe. In total, the landslide-affected sample contained 1340 landslides. Then, a procedure to select landslide-free grid cells was designed taking account of the incompleteness of the landslide inventory and the high proportion of flat areas in Europe. Using stepwise logistic regression, a model including slope gradient, standard deviation of slope gradient, lithology, soil and land cover types was calibrated. The classified susceptibility map produced from the model was then validated by visual comparison with national landslide inventory or susceptibility maps available from literature. The first results are promising and suggest that in case of landslide disasters the method can be used for urgently required landslide susceptibility mapping in regions where currently only sparse landslide inventory data are available.

VAN DEN EECKHAUT Miet;  HERVAS DE DIEGO Francisco;  JAEDICKE Christian;  MALET Jean-Philippe;  MONTANARELLA Luca;  NADIM Farrokh; 

2012-12-06

SPRINGER HEIDELBERG

JRC63969

1612-510X,   

http://link.springer.com/article/10.1007%2Fs10346-011-0299-z,    https://publications.jrc.ec.europa.eu/repository/handle/JRC63969,   

10.1007/s10346-011-0299-z,