We exploit advanced Differential SAR Interferometry (DInSAR) techniques for enhanced analyses of slope instability phenomena. In particular, we focus on the Ivancich urban area, in the worldwide known historic town of Assisi, Central Italy, affected by a deep-seated, slow moving active landslide for which geological and geotechnical data are also available. Furthermore over the landslide site, large datasets of SAR acquisitions collected in the last two decades by the C-band ERS-1/2 and ENVISAT SAR sensors, and by the X-band radars of the COSMO-SkyMed constellation are available. In our study, we applied the advanced DInSAR technique referred to as Small BAseline Subset (SBAS), benefiting from its ability to generate deformation time series at full spatial resolution from multi-sensor SAR data, that allowed us to characterize the temporal and spatial pattern of ground deformations induced by the landslide. Furthermore, we experimented an innovative approach for landslide modeling, based on the integration of DInSAR measurements with conventional geological and geotechnical data used in landslide studies. © 2013 IEEE.
Landslide analysis through the multi-sensor SBAS-DInSAR approach: The case study of Assisi, Central Italy
Lollino P.;
2013-01-01
Abstract
We exploit advanced Differential SAR Interferometry (DInSAR) techniques for enhanced analyses of slope instability phenomena. In particular, we focus on the Ivancich urban area, in the worldwide known historic town of Assisi, Central Italy, affected by a deep-seated, slow moving active landslide for which geological and geotechnical data are also available. Furthermore over the landslide site, large datasets of SAR acquisitions collected in the last two decades by the C-band ERS-1/2 and ENVISAT SAR sensors, and by the X-band radars of the COSMO-SkyMed constellation are available. In our study, we applied the advanced DInSAR technique referred to as Small BAseline Subset (SBAS), benefiting from its ability to generate deformation time series at full spatial resolution from multi-sensor SAR data, that allowed us to characterize the temporal and spatial pattern of ground deformations induced by the landslide. Furthermore, we experimented an innovative approach for landslide modeling, based on the integration of DInSAR measurements with conventional geological and geotechnical data used in landslide studies. © 2013 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
