Analysis of DInSAR Displacement time series for monitoring slope instability

Multi-temporal SAR interferometry (MTInSAR), by providing both mean displacement maps and displacement time series over coherent objects on the Earth's surface, allows analysing wide areas, identifying ground displacements, and studying the phenomenon evolution on long time scales. This technique has also been proven to be very useful for detecting and monitoring instabilities affecting both terrain slopes and man-made objects. In this contest, an automatic and reliable characterization of MTInSAR displacements trends is of particular relevance as pivotal for the detection of warning signals related to pre-failure of natural and artificial structures. Warning signals are typically characterised by high rates and non-linear kinematics. The Sentinel-1 (S1) C-band mission from the European Space Agency (ESA) as well as the high-resolution X-band COSMO-SkyMed (CSK) constellations from Italian Space Agency, both shorten the revisit times up to a few days, thus being very promising for detecting non-linear displacement trends related to warning signals. However, a detailed analysis of MTInSAR displacement products looking for specific trends, is often hindered by the large number of coherent targets (up to millions) to be inspected by expert users to recognize different signal components and also possible artifacts, such as, for instance, those related to phase unwrapping errors. This work concerns the development of methods able to fully exploit the content of MTInSAR products, by automatically identifying relevant changes in displacement time series and to classify the targets on the ground according to their kinematic regime. We introduced a new statistical test based on the Fisher distribution with the aim of evaluating the reliability of a parametric displacement model fit with a determined statistical confidence. We also proposed a new set of rules based on the statistical characterization of displacement time series, which allows different polynomial approximations for MTInSAR time series to be ranked. The method was applied to model warning signals. Moreover, in order to measure the degree of regularity of a given time series, an innovative index was introduced based on the fuzzy entropy, which basically evaluates the gain in information by comparing signal segments of different lengths. This fuzzy entropy index, without postulating any a priori model, allows highlighting time series which show interesting trends, including strong non linearities, jumps related to phase unwrapping errors, and the so-called partially coherent scatterers. These procedures were used for analysing MTInSAR products derived by processing both S1 and CSK datasets acquired over Southern Italian Apennine (Basilicata region), in an area where several landslides occurred in the recent past. 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 which deserved further detailed geotechnical investigations. Moreover, the joint exploitation of MTInSAR datasets acquired at different wavelengths, resolutions, and revisit times provided valuable insights, with CSK more effective over man-made structures, and S1 over outcrops. Specifically, the work presents an example of slope pre-failure monitoring on Pomarico landslide, an example of slope post-failure monitoring on Montescaglioso landslide, and few examples of structures (such as buildings and roads) affected by instability related to different causes. Our analysis performed on CSK MTInSAR products over Pomarico was able to capture the building deformations preceding the landslide and the collapse. This allows 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. Results from the analysis performed on S1 MTInSAR products were instead useful to identify post-failure signals within the Montescaglioso landslide body. The selected trends confirm the stability of the landslide area with some local displacements due to restoration works. In this case, the value of the MTInSAR displacement time series analysis emerges in the assessment phase of post-landslide stability, resulting in a useful support tool in the planning of safety measures in landslide areas.