Deformation Detection in Cyclic Landslides Prior to Their Reactivation Using Two-Pass Satellite Interferometry

Abstract

Landslides are widespread geological features in Italy’s Northern Apennines, with slow-moving earthflows among the most common types. They develop in fine-grained rocks and are subject to periodic rainfall-induced reactivations alternating to phases of dormancy. In this paper, we use radar interferometry (InSAR) and information about landslide activity to investigate deformation signals on an areal basis and to assess the dynamics of recently reactivated earthflows. We use traditional two-pass interferometry by taking advantage of the short revisit time of the Sentinel 1 satellite to characterize 4 years of slope deformations over the 60 km2 study area, where 186 landslides are mapped. Our results show that most intense and sustained deformation signals are associated with phenomena on the verge of reactivation, indicating that radar interferometry may have a potential for early warning purposes. By focusing on three specific earthflow reactivations, we analyze their dynamics through the years that preceded their failure. Despite inherent uncertainties, it was possible to retrieve the deformation signal’s temporal evolution, which displayed seasonally recurring accelerations, peaking during the major precipitation episodes in the area.

Monitoring surface deformation with spaceborne radar interferometry in landslide complexes: insights from the Brienz/Brinzauls slope instability, Swiss Alps

“…Stacking methodologies are frequently used in geophysical data processing and remote sensing analyses in order to improve the signal-to-noise ratio (Stumpf et al 2017;Gorelick et al 2017). Regarding InSAR, stacking is generally performed by combining multiple unwrapped differential interferograms covering a pre-defined time period (e.g., one or multiple years), in order to highlight spatial domains retaining surface velocities within this time frame (Lundgren et al 2001;Ciuffi et al 2021). Any artefacts affecting single acquisitions are expected to have random variability over time and thus be mitigated with integration and/or averaging of the signal, provided that enough input images are available.…”

Integrating InSAR Observables and Multiple Geological Factors for Landslide Susceptibility Assessment

“…In recent years, remote sensing technology has effectively detected large-scale landslidesensitive areas and generated landslide inventories, which are crucial for predicting landslides before they occur or recur, especially in far or barely accessible areas [5]. In daytime satellite images without shadows and clouds, landslide positions can be identified through noticeable radiometric contrasts between land cover types [6].…”