Landslide susceptibility maps (LSM) are commonly used by local authorities for land use management and planning activities, representing a valuable tool used to support decision makers in urban and infrastructural planning. The accuracy of a landslide susceptibility map is affected by false negative and false positive errors which can decrease the reliability of this useful product. In particular, false negative errors, are generally worse in terms of social and economic losses with respect to the losses associated with false positives. In this paper, we present a new technique to improve the accuracy of landslide susceptibility maps using Permanent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) data. The proposed approach uses two different data sets acquired in ascending and descending geometry. The PS velocity measured along the line of sight is re-projected into a new velocity along the steepest slope direction (V Slope ). Integration between the LSM and the ground deformation velocity map along the slope was performed using an empirical contingency matrix, which takes into account the average V Slope and the susceptibility degree obtained using the Random Forests algorithm. The Results show that the susceptibility degree increased in 56.41 km 2 of the study area. The combination of PSInSAR data and the landslide susceptibility map (LSM) improved the prediction reliability of slow moving landslides, which particularly affect urbanized areas. The use of this procedure can be easily applied in different areas where PSI data sets are available. This approach will help planning and decision-making authorities produce reliable landslide susceptibility maps, correcting some of the LSM errors.
Ciampalini, A., Raspini, F., Lagomarsino, D., Catani, F., & Casagli, N. (2016). Landslide susceptibility map refinement using PSInSAR data. Remote Sensing of Environment, 184, 302–315. https://doi.org/10.1016/j.rse.2016.07.018