BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.staging.osgeo.org//foss4g-it-2023//speaker//M9QSLC 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:20260512T203411Z 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 BEGIN:VEVENT UID:pretalx-foss4g-it-2023-EHR9BQ@talks.staging.osgeo.org DTSTART;TZID=GMT:20230614T143000 DTEND;TZID=GMT:20230614T144500 DESCRIPTION:Multi-temporal SAR Interferometry (MTInSAR) techniques allow de tecting and monitoring millimetric displacements occurring on selected poi nt targets that exhibit coherent radar backscattering properties over time . Successful applications to different geophysical phenomena have been alr eady demonstrated in literature. New application opportunities have emerge d in the last years thanks to the greater data availability offered by rec ent launches of radar satellites\, and the improved capabilities of the ne w space radar sensors in terms of both resolution and revisit time. Curren tly\, different space-borne Synthetic Aperture Radar (SAR) missions are op erational\, e.g. the Italian COSMO-SkyMed (CSK) constellation and the Cope rnicus Sentinel-1 (S1) mission. \n\nEach CSK satellite is equipped with an X-band SAR sensor that acquires data with high spatial resolution (3x3 m2 )\, thus leading to a very high spatial density of the measurable targets and allowing the monitoring of very local scale events. Thanks to the nati onwide acquisition plan “MapItaly”\, CSK constellation covers the Ital ian territory with a best effort revisit time of 16 days since 2010. \n\n\ nS1 mission is instead operational since 2014 and acquires in C-band at me dium resolution (5x20 m2) with a minimum revisit time of 12 days (only 6 d ays between 2016 and 2021\, when the full S1 constellation was operational )\, thus allowing to monitor ground instabilities back in time almost all over the Earth. Moreover\, all data acquired by the S1 mission are provide d on an open and free basis by the European Space Agency (ESA) and the Eur opean Commission (EC)\, for promoting full utilization of S1 data\, with t he aim of increasing the scientific research\, growing the EO markets and fostering the development of continuous monitoring services\, such as the European Ground Motion Service (EGMS) and the Rheticus® Displacement Geo- information Service. \n\nThe EGMS is based on the MTInSAR analysis of S1 r adar images at full resolution\, updated annually\, and provides consisten t and reliable information regarding natural and anthropogenic ground moti on over the Copernicus Participating States and across national borders.\n \nRheticus® offers monthly updates of the millimetric displacements of th e ground surface\, through the MTInSAR processing chain based on the SPINU A© algorithm (“Stable Point Interferometry even in Un-urbanized Areas ”). Rheticus® is capable to process SAR images acquired by different SA R missions\, including CSK and S1. Thanks to the technological maturity as well as to the wide availability of SAR data\, these ground motion servic es can be used to support systems devoted to environmental monitoring and risk management. This work shows the results obtained in the framework of the SeVaRA project (“Environmental Risk Assessment Service”)\, coordin ated by Omnitech srl. The goal of SeVaRA is to implement an innovative sys tem for calculating an aggregate environmental risk index\, derived from s everal parameters related to hydrogeological instability phenomena and/or Weather-related extreme events. In particular\, the present work is focuse d on the analysis of the “Deformation Sub-System”\, that has been desi gned for the computation of risk indices related to structural and ground instabilities (landslides). The first step consists in the Hazard Map comp utation\, which requires the following input data:\n\n- Susceptibility Map (i.e.\, the European Landslide Susceptibility Map\, provided by the Joint Research Centre European Soil Data Centre)\n- National mosaic of landslid e hazard zones\, provided by ISPRA (River Basin Plans PAI)\n- Cumulated pr ecipitations (derived by cumulating ground measurement data collected by w eather stations\, if available\, or by interpolating hourly rainfall data provided by the Global Satellite Mapping of Precipitation service\, GSMaP\ , offered by the JAXA Global Rainfall Watch)\n- Land Cover Change (i.e.\, the CORINE Land Cover inventory)\n- Seismic events inventory\, provided by INGV\, to account for earthquake-induced landslides\n- MTInSAR ground dis placement time series.\n\nThe last input is essential for detecting instab le areas\, whose MTInSAR displacement trend exhibits a significant velocit y in the whole observation period and/or an acceleration in the acquisitio n dates of the last year. The SeVaRA “Deformation Sub-System” has been primarily designed to be interfaced with the Rheticus® Displacement Serv ice\, but it supports also products offered by the EGMS service as well as by other MTInSAR services available on the EO market. The final step cons ists in the computation of the landslide risk index\, obtained by combinin g the previous hazard index with the vulnerability and the exposure of the area of interest. The results of this study over specific areas of intere st will be presented and commented.\n\nAcknowledgments\n\nStudy carried ou t in the framework of the SeVaRA project\, funded by Apulia Region (PO FES R 2014/2020). DTSTAMP:20260512T203411Z LOCATION:Sala Biblioteca @ PoliBa SUMMARY:Exploitation of Multi-Temporal InSAR data for Environmental Risk As sessment Services - Davide Oscar Nitti\, Alberto Morea\, Khalid Tijani\, N icolò Ricciardi\, Fabio Bovenga\, Raffaele Nutricato URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/EHR9BQ/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-it-2023-WKL8U7@talks.staging.osgeo.org DTSTART;TZID=GMT:20230614T153000 DTEND;TZID=GMT:20230614T154500 DESCRIPTION:Giacomo Caporusso(1)\, Alberto Refice(1)\, Domenico Capolongo(2 )\, Rosa Colacicco(2)\, Raffaele Nutricato(3)\, Davide Oscar Nitti(3)\, Fr ancesco P. Lovergine(1)\, Fabio Bovenga(1)\, Annarita D’Addabbo(1)\n1 IR EA-CNR – Bari\, Italy\n2 Earth and Geoenvironmental Sciences Dept.\, Uni versity of Bari\, Italy\n3 GAP srl\, Bari\, Italy\n\nAs part of the analys is of flood events\, ongoing studies aim to identify methods of using opti cal and SAR data in order to be able to map in an ever more precise way th e flooded areas that are defined following a flood. At the same time\, ins titutions responsible for territorial security have concrete needs of both monitoring tools capable of describing the susceptibility to flooding and of forecast tools for events with a fixed return time\, consistent with t he hazard and risk approaches defined\, for example\, at European or Natio nal regulatory level.\nAs far as flood hazards are concerned\, hydraulic m odeling is currently the most widely used reference for responding to fore casting needs\, while the concrete value of remote sensing support emerges in the monitoring context\, given the possibility of examining historical series of images referring to any portion of the territory.\nA statistica l approach to the analysis of historical series of satellite images can ta ke into consideration the study of the probability connected to the presen ce/absence of water in the area\, through the analysis of specific indices derived from multi- and hyperspectral optical images (NDVI\, NDWI\, LSWI) and/or intensity\, coherence and radar indices derived from SAR images. I n particular\, for the study of time series of the variables considered\, algorithmic approaches of a probabilistic nature are suitable\, such as th e Bayesian model and the Theory of Extreme Values.\nThe objective of this work is the assessment of a methodology to return the historical series of the probability of flooding\, as well as the corresponding maps\, relatin g to a test area. \nIn this context we present some results related to the study of an agricultural area near the city of Vercelli (Northern Italy)\ , characterized by the presence of widespread rice fields and affected by a major flood of the Sesia river in October 2020.\nSentinel-1 SAR images w ere considered\, from which the intensity and interferometric coherence va riables can be deduced. The hydrogeomorphological support consist of slope \, Height Above the Nearest Drainage (HAND)\, and Land Cover maps. Through the Copernicus Emergency Management\, the flood maps relating to the 2020 event were acquired\, to validate the results.\nRegarding the methodology \, the probabilistic modeling of the InSAR intensity and coherence time st acks is cast in a Bayesian framework. It is assumed that floods are tempor ally impulsive events lasting a single\, or a few consecutive acquisitions . The Bayesian framework also allows to consider ancillary information suc h as the above-mentioned hydrogeomorphology and satellite acquisition geom etry\, which allow to characterize the a priori probabilities in a more re alistic way\, especially for areas with low probability of flooding. Accor ding to this approach it is possible to express the posterior probability p(F|v) for the presence of flood waters (F) given the variable v (intensit y or coherence) at a certain pixel and at a certain time t as a function o f the a priori and conditioned probabilities\, through the Bayes equation: \np(F|v) = p(v|F)p(F) / (p(v|F)p(F) + p(v|NF)p(NF))\,\nwith p(F) and p(NF) = 1 − p(F) indicating respectively the a priori probability of flood or no flood\, while p(v|F) and p(v|NF) are the likelihoods of v\, given the two events.\nThe flood likelihood can be estimated on permanent water bodi es\, while\, to estimate the likelihood of areas potentially affected by f lood events\, the residuals of the historical series are considered with r espect to a regular temporal modeling of the variable v.\nGaussian process es (GP) are used to fit the time series of the variable v. GPs are valid a lternatives to parametric models\, in which data trends are modeled by "le arning" their stochastic behavior by optimizing some "hyperparameters" of a given autocorrelation function (kernel). The residuals with respect to t his model can be used to derive conditional probabilities and then plugged into the Bayes equation.\nThe availability of the flood maps will allow t o tackle the forecasting aspect in the next future\, taking the time serie s of satellite images as a reference. DTSTAMP:20260512T203411Z LOCATION:Sala Biblioteca @ PoliBa SUMMARY:Probabilistic approach to the mapping of flooded areas through the analysis of historical time series of SAR intensity and coherence. - Giaco mo Caporusso\, Davide Oscar Nitti\, Fabio Bovenga\, Raffaele Nutricato\, A lberto Refice\, Domenico Capolongo\, Rosa Colacicco\, Francesco P. Lovergi ne\, Annarita D’Addabbo( URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/WKL8U7/ END:VEVENT END:VCALENDAR