BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.staging.osgeo.org//foss4g-it-2023//speaker//YLXMNN BEGIN:VTIMEZONE TZID:GMT BEGIN:STANDARD DTSTART:20001029T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:GMT TZOFFSETFROM:+0100 TZOFFSETTO:+0000 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T020000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:BST TZOFFSETFROM:+0000 TZOFFSETTO:+0100 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-it-2023-3JY9P3@talks.staging.osgeo.org DTSTART;TZID=GMT:20230613T143000 DTEND;TZID=GMT:20230613T144500 DESCRIPTION: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 grou nd displacements\, and studying the phenomenon evolution on long time scal es. This technique has also been proven to be very useful for detecting an d monitoring instabilities affecting both terrain slopes and man-made obje cts. In this contest\, an automatic and reliable characterization of MTInS AR displacements trends is of particular relevance as pivotal for the dete ction 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 Europea n Space Agency (ESA) as well as the high-resolution X-band COSMO-SkyMed (C SK) constellations from Italian Space Agency\, both shorten the revisit ti mes up to a few days\, thus being very promising for detecting non-linear displacement trends related to warning signals. However\, a detailed analy sis of MTInSAR displacement products looking for specific trends\, is ofte n hindered by the large number of coherent targets (up to millions) to be inspected by expert users to recognize different signal components and als o possible artifacts\, such as\, for instance\, those related to phase unw rapping errors. \n\nThis work concerns the development of methods able to fully exploit the content of MTInSAR products\, by automatically identifyi ng relevant changes in displacement time series and to classify the target s on the ground according to their kinematic regime. We introduced a new s tatistical test based on the Fisher distribution with the aim of evaluatin g 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 di fferent polynomial approximations for MTInSAR time series to be ranked. Th e method was applied to model warning signals. Moreover\, in order to meas ure 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. Thi s fuzzy entropy index\, without postulating any a priori model\, allows hi ghlighting time series which show interesting trends\, including strong no n linearities\, jumps related to phase unwrapping errors\, and the so-call ed 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 seve ral landslides occurred in the recent past. Both approaches were very effe ctive in supporting the analysis of ground displacements provided by MTInS AR\, since they helped focusing on a smaller set of coherent targets ident ifying areas or structures on the ground which deserved further detailed g eotechnical investigations. Moreover\, the joint exploitation of MTInSAR d atasets acquired at different wavelengths\, resolutions\, and revisit time s provided valuable insights\, with CSK more effective over man-made struc tures\, and S1 over outcrops.\n\nSpecifically\, the work presents an examp le of slope pre-failure monitoring on Pomarico landslide\, an example of s lope post-failure monitoring on Montescaglioso landslide\, and few example s of structures (such as buildings and roads) affected by instability rela ted to different causes. Our analysis performed on CSK MTInSAR products o ver Pomarico was able to capture the building deformations preceding the l andslide and the collapse. This allows the understanding of the phenomenon evolution\, highlighting a change in velocities that occurred two years b efore 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-fail ure 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 displace ment time series analysis emerges in the assessment phase of post-landslid e stability\, resulting in a useful support tool in the planning of safety measures in landslide areas. \n\n**Acknowledgments** - This work was supp orted in part by the Italian Ministry of Education\, University and Resear ch\, D.D. 2261 del 6.9.2018\, Programma Operativo Nazionale Ricerca e Inno vazione (PON R&I) 2014–2020 under Project OT4CLIMA\; and in part by ASI under the Project “CRIOSAR: Applicazioni SAR multifrequenza alla criosfe ra”\, grant agreement N. 2021-12-U.0. DTSTAMP:20260427T180215Z LOCATION:Sala Videoconferenza @ PoliBa SUMMARY:Analysis of DInSAR Displacement time series for monitoring slope in stability - Davide Oscar Nitti\, Fabio Bovenga\, Raffaele Nutricato\, Albe rto Refice\, Ilenia Argentiero\, Guido Pasquariello\, Giuseppe Spilotro URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/3JY9P3/ END:VEVENT END:VCALENDAR