Landslide Mapping and Monitoring Using Persistent Scatterer Interferometry (PSI) Technique in the French Alps

Abstract

Continuous geodetic measurements in landslide prone regions are necessary to avoid disasters and better understand the spatiotemporal and kinematic evolution of landslides. The detection and characterization of landslides in high alpine environments remains a challenge associated with difficult accessibility, extensive coverage, limitations of available techniques, and the complex nature of landslide process. Recent studies using space-based observations and especially Persistent Scatterer Interferometry (PSI) techniques with the integration of in-situ monitoring instrumentation are providing vital information for an actual landslide monitoring. In the present study, the Stanford Method for Persistent Scatterers InSAR package (StaMPS) is employed to process the series of Sentinel 1-A and 1-B Synthetic Aperture Radar (SAR) images acquired between 2015 and 2019 along ascending and descending orbits for the selected area in the French Alps. We applied the proposed approach, based on extraction of Active Deformation Areas (ADA), to automatically detect and assess the state of activity and the intensity of the suspected slow-moving landslides in the study area. We illustrated the potential of Sentinel-1 data with the aim of detecting regions of relatively low motion rates that be can attributed to activate landslide and updated pre-existing national landslide inventory maps on a regional scale in terms of slow moving landslides. Our results are compared to pre-existing landslide inventories. More than 100 unknown slow-moving landslides, their spatial pattern, deformation rate, state of activity, as well as orientation are successfully identified over an area of 4000 km2 located in the French Alps. We also address the current limitations due the nature of PSI and geometric characteristic of InSAR data for measuring slope movements in mountainous environments like Alps.

A systematic exploration of satellite radar coherence methods for rapid landslide detection

“…This is similar to the resolution of other landslide products generated for emergency response (e.g. Bessette-Kirton et al, 2019;Nowicki Jessee et al, 2018)…”

“…The aggregated resolution of 200 m × 220 m that we used here was not high enough to identify individual landslides. However it was higher than the resolution of most empirical landslide susceptibility models designed for rapid response (Allstadt et al, 2018;Nowicki Jessee et al, 2018) and landslide maps generated from optical satellite imagery for use in aid efforts by Bessette-Kirton et al (2019) following Hurricane Maria, Puerto Rico, in 2017 and by Williams et al (2018) following the 2015 Gorkha earthquake. SAR data seem best suited to producing products at this spatial scale.…”

“…This information is commonly generated from analysis of optical satellite imagery (e.g. Bessette-Kirton et al, 2019;Kargel et al, 2016). However, relying solely on optical satellite imagery in landslide assessment is problematic as the mapping process can be significantly delayed by cloud cover (Robinson et al, 2019).…”

On rapid multidisciplinary response aspects for Samos 2020 M7.0 earthquake

“…Since the initial development of spaceborne synthetic aperture radar (SAR) systems and the demonstration of the SAR interferometric capability, the technique has reached its maturity for the detection of earthquake-induced ground displacements (Cornou et al 2020;Bacques et al 2020), among other geohazards (Delgado Blasco et al 2019;Papageorgiou et al 2019;Aslan et al 2020;Cigna and Tapete 2020;Vassilakis et al 2020). In the last decade, Earth Observation (EO) satellite missions are providing a richness of data at improved spatial resolution and coverage, as well as reduced revisit time, while the latest generation of SAR sensors ensures the millimeter precision of interferometric measurements (Gisinger et al 2019).…”

Investigation of the Ground Motion Near the Leaning Tower of Bad Frankenhausen Using Sentinel-1 Persistent Scatterer Interferometry

“…Approaches for the automatic detection of such irregular patterns in space and time have been developed already (Zhu et al, 2018), few also considering the interaction between geology and observed subsidence (Pratesi et el., 2016;North et al, 2017). While some approaches rely principally on the velocity estimates for clustering (Kalia, 2018;Aslan, 2020), other approaches consider the time series of the displacements for identifying different deformation regimes (Zhu et al, 2018). This information is particularly important for infrastructure analysis, as many factors may impact the deformation (e.g.…”