How to improve the accuracy of landslide susceptibility maps using PSInSAR data

Landslide susceptibility maps (LSM) are frequently used by local authorities for land-use management and planning activities. They are valuable tools used by decision makers for urban and infrastructural plans and for civil protection purposes. False negative and false positive errors can affect the accuracy of a LSM, decreasing the reliability of this useful product. False negative errors are usually the worst in terms of social and economic losses because they are related to a misclassification of areas at risk. In this paper we present a new methodology aimed at improve the accuracy of the LSMs using measurement points (PS, Permanent Scatterers and DS, Distributed Scatterers) retrieved through the multi-interferometric SqueeSAR technique. The proposed approach uses two different TerraSAR-X datasets acquired in ascending and descending geometry. PS/DS velocity are re-projected along the steepest slope direction. The integration between the LSM and the ground deformation velocity maps was performed by using an empirical contingency matrix, which takes into account the average Vslope module and the susceptibility degree obtained by using the Random Forests algorithm for an area located within Messina Province (Sicily, Italy). Results highlight that 33.37 km2 have been updated. The combination among SqueeSAR data and the LSM improves the reliability in predicting slow moving landslide which, especially, affect urbanized areas. The use of this procedure can be easily applied in different areas where multi-interferometric datasets are available. The proposed approach will help civil protection and decision making authorities to use reliable landslide susceptibility maps, correcting part of the errors of the original LSM.