Kinematic and Geometric Characterization of the Vogelsberg Rockslide (Tyrol, Austria) by Means of MT-InSAR Data

This paper focuses on the study of the Vogelsberg landslide located in the municipality of Wattens (Tyrol, Austria), which reactivated in 2016, causing damages to nearby buildings and infrastructures. Since the date of reactivation, a modern monitoring system has been implemented with the installation of in-situ geodetic automated tracking total stations (ATTS), an inclinometer and two piezometers. Here, we describe two distinctive methods, the Breaks for Additive Seasonal and Trend (BFAST) and the Vector Inclination Method (VIM) used to characterize the landslide from the kinematic and geometrical point of view. The main input data, used for both methods, derive from processing a stack of several Sentinel-1 differential interferograms with the Multiple Small Baseline Subset (MSBAS) 2D and 3D algorithms. BFAST allowed highlighting the seasonality of the phenomenon from the analysis of the time series as well as the trend and the breakpoints that identify the landslide reactivation phases. These latter were then correlated with the main triggering factors such as rain and snow melting. The application of the VIM through the exploitation of the MSBAS displacement vectors allowed the reconstruction of the depth of the landslide slip surface along both the longitudinal and transversal direction and, in turn, the evaluation of the volumes of material mobilized by the landslide. The results obtained further prove that procedures for the in-depth analysis of Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) data can contribute to slow-moving landslide characterization, which represents a fundamental step for landslide hazard assessment within quantitative risk analyses.

Automated summary

This paper focuses on the study of the Vogelsberg landslide in Austria, using the BFAST and VIM methods to characterize the landslide from the kinematic and geometrical point of view. The analysis of Sentinel-1 differential interferograms and MSBAS algorithms allowed the identification of the seasonality and trend of the landslide, which were correlated with triggering factors such as rain and snow melting. The VIM method reconstructed the depth of the landslide slip surface and evaluated the volumes of material mobilized. The results highlight the importance of in-depth analysis of MT-InSAR data for landslide characterization and hazard assessment.