State of activity classification of deep-seated gravitational slope deformation at regional scale based on Sentinel-1 data

Abstract Deep-seated gravitational slope deformations (DsGSDs) are very slow instabilities that can have a long-term impact on anthropic structures and infrastructures. The characterization of their state activity is, therefore, essential to evaluate it. By employing Differential Interferometry Synthetic Aperture Radar (DInSAR) techniques, dedicated procedure, explore the behavior define 279 DsGSDs, inventoried in regional landslide inventory Aosta Valley Region (Western Italian Alps), has been implemented. proposed methodology consists several steps. Firstly, Sentinel-1 data processed through two-step, advanced, DInSAR processing scheme detect identify Persistent Scatterers (PSs). velocity values measured along radar Line Sight (LOS) projected steepest slope. Subsequently, an analysis PSs within DsGSD polygons, devoted assessment coverage, carried out; particular, considering PS abundance, computing voids point distributions assessing clustering cases with adequate number distribution for suitable definition activity. Finally, spatial based cluster outlier identification out characterize moving phenomena degree variability deformation rates. Overall, implemented provides valid instrument remotely these huge phenomena, often wrongly underestimated or neglected risk management, useful better impacts elements proper land use planning.