In this work, we present a methodology for improving persistent scatterer interferometry (PSI) data analysis for landslide studies. This methodology is a revision of previously described procedures with several improved and newly proposed aspects. To both evaluate and validate the results from this methodology, we used various persistent scatterer (PS) datasets from different satellites (ERS – ENVISAT, Radarsat, TerraSAR-X, and ALOS PALSAR) that were processed using three PSI techniques (stable point network – SPN, permanent scatterer interferometry – PSInSAR™, and SqueeSAR™) to map and monitor landslides in various mountainous environments in Spain and Italy. This methodology consists of a preprocessing model that predicts the presence of a PS over a certain area and a post-processing method used to determine the stability threshold, project the line of sight (LOS) velocity along the slope, estimate the E–W and vertical components of the velocity, and identify anomalous areas.

The aim of this work is to analyse the advantages and disadvantages of using the new X-band SAR data acquired by TerraSAR-X sensors for landslides mapping. This dataset has been processed using a Persistent Scatterer Interferometry technique over the Upper Tena Valley (Central Pyrenees, Spain). In the first section, the geological and geomorphological setting of the study area is introduced, focusing on the description of the landslide inventory. Then the Stable Point Network technique is briefly described, followed by the assessment of the performance of the X-band SAR dataset. In this context, we present first a model to predict the distribution of Persistent Scatterers based on the slope geometry and the land use information, which has then been validated with X-band data results. On a second stage, we have assessed the performance of X-band dataset to detect and monitor mapped landslides. Finally some illustrative case studies are shown demonstrating the potential of using X-band SAR data not only for landslide mapping but also to detect and monitor deformations affecting human infrastructures.