## 4 Discussion
### Final Comments  
At present, MTInSAR products may consist of millions of targets covering a large area, each associated with time series covering long time periods (potentially more than 20 years). In order to fully exploit such products, methods are needed for the automatic identification of targets with nonlinear displacement time series that may be relevant for ground phenomena such as reactivation/deactivation phases of known landslides. We adopted two approaches recently introduced in the literature, one based on the fuzzy entropy indicator, the other performing nonlinear trend analysis based on the Fisher statistics. The \(FE\) index was able to recognize coherent targets affected by phase unwrapping errors, which should be corrected to provide reliable displacement time series to be further analyzed. The NLTA was used for classifying targets according to the optimal degree of a polynomial function describing the displacement trend. This allowed selecting targets showing nonlinear displacement trends related to the several ground and structure instabilities as pre-failure, post-failure, and evolution signals. Thus, both approaches were very effective in supporting the analysis of ground displacements provided by MTInSAR, since they helped focusing on a smaller set of coherent targets identifying areas or structures on the ground that deserved further detailed geotechnical investigations. Moreover, the joint exploitation of MTInSAR  
Figure 12: Pictures of the road section in Montescalioso village corresponding to the area where target P1 in Figure 6 is located. datasets acquired at different wavelengths, resolutions, and revisit times provided valuable insights, with CSK more effective over man-made structures, and S1 over outcrops. The analysis of pre-failure signals related to the Pomarico landslide showed that CSK has been able to capture better the building deformations preceding the landslide and the collapse. This allowed the understanding of the phenomenon evolution, highlighting a change in velocities that occurred two years before the collapse. This variation probably influenced the dynamics of the landslide leading to the collapse of an area considered to be at a medium-risk level by the regional landslide risk map. MTInSAR datasets also provided valuable results in the monitoring of the displacement evolution. CSK has been able to capture the building and structure deformations both in the Montescaglioso cemetery area and in the NE area of the Pomarico village. Displacement trends of selected targets have been confirmed in both test sites by on-site surveys. The analysis of the signals in the Montescaglioso cemetery area showed the displacement evolution of a site not included in the regional landslide risk map but of interest for local authorities and already affected by restoration works in recent years. The analysis of the NE side of the Pomarico village confirmed the very-high-risk R4 level of the area and showed the retrogressive evolution of the phenomenon, expanding beyond the borders currently established by the regional landslide risk map. These results clearly confirm the valuable use of MTInSAR products as a tool that is additional to the established techniques for studying the dynamics of instability phenomena and their evolution. The analysis of MTInSAR-based displacement time series, possibly performed though ad hoc automated procedures, can provide very useful information for long-term monitoring, management, and risk assessment at the regional level, when combined with planning tools such as the PAI. The better sensitivity of CSK to capture building and structure deformations was confirmed by the analysis of structures with instability signals, i.e., the building and the road connection in the Montescaglioso village. In the first case, the signal analysis described a very complex phenomenon, due to clay swelling, which led to the evacuation of the building. In the second case, the analysis detected continuous surface displacements that require continuous maintenance interventions.