Persistent Scatterer Interferometry (PSI) Technique for Landslide Characterization and Monitoring

Abstract

Abstract:The measurement of landslide superficial displacement often represents the most effective method for defining its behavior, allowing one to observe the relationship with triggering factors and to assess the effectiveness of the mitigation measures. Persistent Scatterer Interferometry (PSI) represents a powerful tool to measure landslide displacement, as it offers a synoptic view that can be repeated at different time intervals and at various scales. In many cases, PSI data are integrated with in situ monitoring instrumentation, since the joint use of satellite and ground-based data facilitates the geological interpretation of a landslide and allows a better understanding of landslide geometry and kinematics. In this work, PSI interferometry and conventional ground-based monitoring techniques have been used to characterize and to monitor the Santo Stefano d'Aveto landslide located in the Northern Apennines, Italy. This landslide can be defined as an earth rotational slide. PSI analysis has contributed to a more in-depth investigation of the phenomenon. In particular, PSI measurements have allowed better redefining of the boundaries of the landslide and the state of activity, while the time series analysis has permitted better understanding of the deformation pattern and its relation with the causes of the landslide itself. The integration of ground-based monitoring data and PSI data have provided sound results for landslide characterization. The punctual information deriving from inclinometers can help in defining the actual location of the sliding surface and the involved volumes, while the measuring of pore water pressure conditions or water table level can suggest a correlation between the deformation patterns and the triggering factors.

A simple method to help determine landslide susceptibility from spaceborne InSAR data: the Montescaglioso case study

“…Indeed, the use of spaceborne radar measures has proven over time to be a reliable source to improve landslide and subsidence identification and characterization (Metternicht et al 2005;Strozzi et al 2005;Farina et al 2007;Righini et al 2008;Cigna et al 2011;Raspini et al 2013;Tofani et al 2013). The possibility to monitor the ground deformation even in remote areas, the short revisiting time of the Cosmo satellite constellation (16 days in standard orbit cycle mode; ASI 2007 andCovello et al 2010) and the release of the new SqueeSAR algorithm, which guarantees a remarkable increase in detectable measuring points, are key features in helping to overcome some of the aforementioned issues.…”

The Maoxian landslide as seen from space: detecting precursors of failure with Sentinel-1 data

“…InSAR data are now commonly used for a variety of purposes, such as landslide detection and mapping (Herrera et al 2011;Lu et al 2012;Cigna et al 2013;Raspini et al 2013;Bardi et al, 2016), monitoring and modelling (Berardino et al 2003;Hilley et al 2004;Tofani et al 2013;Komac et al 2015;Carlà et al 2016), hazard and risk assessment (Lu et al 2014) and, through time series analysis, for the identification of changes in deformation rates (Frodella et al 2016) and seasonal trends (Tomás et al 2016).…”

Preliminary results from active landslide monitoring using multidisciplinary surveys

“…Furthermore, in the southeast of China, the Small Baseline Subsets DInSAR technique was applied using Cosmo-SkyMed data and it led to reliable results (Rao & Tang, 2014). Persistent Scatterer Interferometry (PSI) is another approach of interferometry which may contribute in subsidence mapping with effective results with millimeter accuracy (Crosetto, Monserrat, Cuevas-González, Devanthéry, & Crippa, 2016;Rosi, Agostini, Tofani, & Casagli, 2014;Strozzi et al, 2001;Righini, Raspini, Moretti, & Cigna, 2011;Tofani, Raspini, Catani, & Casagli, 2013). PSI using ERS-1/2 and Envisat data has been applied for landslide monitoring in the Italian Alps (Del Ventisette, Righini, Moretti, & Casagli, 2014).…”