A Sentinel-1 based hot-spot analysis: landslide mapping in north-western Italy

• Published in: International journal of remote sensing Vol. 40; no. 20; pp. 7898 - 7921
• Main Authors: Solari, Lorenzo, Del Soldato, Matteo, Montalti, Roberto, Bianchini, Silvia, Raspini, Federico, Thuegaz, Patrick, Bertolo, Davide, Tofani, Veronica, Casagli, Nicola
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 18-10-2019; Taylor & Francis Ltd
• Subjects: Alpine regions; Analysis; Climatic analysis; Constellations; Data analysis; Environmental risk; Ground motion; Hot spots; Information retrieval; Interferometric synthetic aperture radar; Interferometric techniques; Interferometry; Land cover; Landslides; Landslides & mudslides; Mapping; Mass wasting; Mountain regions; Radar data; Radar imaging; Risk management; SAR (radar); Spatial analysis; Synthetic aperture radar; Italy
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431161.2019.1607612

The 6-days repeatability of Sentinel-1 constellation allows building up an interferometric stack with unprecedented velocity. Easily updatable hot-spot analyses, frequently repeated following the update of Sentinel-1 images, represent very useful tools for MTInSAR (Multi-Temporal Interferometric Synthetic Aperture Radar) data analysis. Mountain regions are a challenging environment for interferometric analyses because of their climatic, morphological and land cover characteristics. In this context, MTInSAR data can retrieve reliable information over wide areas, with high cost-benefits ratio and where the installation of ground-based devices is not feasible. Considering the well-known limitations of interferometric techniques (such as fast motions or temporal and spatial decorrelation), hot-spot analyses are a viable solution for semi-automatic ground movements extraction over mountain regions. In this work, we present an example of a hot-spot analysis applied to a large stack of MTInSAR products generated by means of SqueeSAR technique over an alpine region (Valle d'Aosta, north-western Italy). The obtained outputs allow detecting 277 moving areas connected to landslides and mass wasting processes in general. These products are intended to be implemented in the landslide risk management chain of the region, focusing on landslide state of activity definition and landslide mapping.