Casagli, Nicola

Cigna, Francesca

Del Ventisette, Chiara

English

DEL VENTISETTE, Chiara

Florence Research

Geophysical Research AbstractsVol. 13, EGU2011-8389, 2011EGU General Assembly 2011© Author(s) 2011Monitoring of ground displacements and identification of trend deviationsduring post-processing of satellite InSAR time seriesFrancesca Cigna, Chiara Del Ventisette, and Nicola CasagliDepartment of Earth Sciences, University of Firenze, Firenze, Italy (francesca.cigna@unifi.it, chiara.delventisette@unifi.it,nicola.casagli@unifi.it)Satellite-based InSAR (Synthetic Aperture Radar Interferometry) and PSI (Persistent Scatterer Interferometry)techniques have recently demonstrated their potentials for mapping and monitoring ground displacements associ-ated with geological processes, with millimetre accuracy. Most of the multi-pass approaches exploit simple linearfunctions of phase variation through time to model the deformation phase components and to extract time series ofground displacements for the Persistent Scatterers (PS) identified in the investigated area. Although linear modelsare usually chosen during the PSI processing, frequently some non-linear signals can be detected inside the timeseries, as long as they do not compromise the phase coherence of the radar targets (with respect to the deformationfunction) and cause their exclusion from the subsequent processing steps or, in some cases, the ascription of thesenon-linear components to other phase terms (e.g. atmospheric phase screen).We are developing innovative post-processing approaches for the analysis and interpretation of PS time series andfor the identification of potential trend deviations from the deformation models employed during the processingphase. These approaches are based on the temporal undersampling of the PSI monitoring interval into two or moresubsamples and the subsequent comparison of their respective registered trends, with the aim of highlighting accel-erations, changes and/or anomalies occurred during the observed period. We are testing our methodology on threeItalian case studies, Agrigento, Naro and Caltanissetta, all located in south-western Sicily in the Caltanissetta basin,but affected by different typologies of ground instability, i.e. landslides, tectonics and mud volcanoes respectively.We used ERS 1992-2001 and RADARSAT 2003-2008 data, processed with the PSInSAR (Permanent ScatterersInSAR) technique, which belongs to the PSI approaches. The analysis made possible a better understanding oftemporal and spatial deformation patterns; in particular, despite the linear model employed during the processing,the implementation of our methodology allowed: i) detection of rapid structural displacements and accelerations inthe historic buildings of the Seminary and Cathedral of Agrigento, located at the edge of the steep slope of Girgentihill, historically affected by widespread landslide processes; ii) identification of urban sectors of Naro affected andunaffected by the tectonically induced land instability of February 2005; iii) recognition of critical zones in theeastern sector of Caltanissetta urban area, where mud volcanoes caused significant damages in August 2008. Asrevealed by these first applications, the reliability and suitability of our methodology are influenced – and mayalso be limited – by the phase model for deformation components chosen during the processing to unwrap theinterferometric phases and, secondarily, by the potential aliasing of PSI measures (i.e. phase-unwrapping errors)and consequent underestimation or misunderstanding of ground displacements.The improved temporal sampling of PSI measures achievable using new high-resolution SAR data (e.g. 3-4 acqui-sitions per month with COSMO-SkyMed constellation), encourages a further development of these semi-automaticapproaches for the advanced exploitation of PSI time series and the consequent support for monitoring and earlywarning activities in the framework of risk management and mitigation.

Monitoring of ground displacements and identification of trend deviations during post-processing of satellite InSAR time series

https://flore.unifi.it/bitstream/2158/406287/1/Cigna%20et%20al%20GRA%20vol%2013%20EGU%202011.pdf

Monitoring of ground displacements and identification of trend deviations during post-processing of satellite InSAR time series

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