Journal
LANDSLIDES
Volume 20, Issue 5, Pages 1083-1097Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s10346-022-02024-0
Keywords
Landslide inventory; MTInSAR; Sentinel-1; P-SBAS; Italy
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The use of synthetic aperture radar imagery for landslide inventory updates is essential for risk management and territorial planning. The application of automatic SAR data processing has been used to update the Italian national landslide database. The study demonstrates that the nationwide use of Sentinel-1 MTInSAR data could provide fundamental support for landslide inventory updates.
The redaction of landslide inventory is a fundamental task for risk management and territorial planning activities. The availability of synthetic aperture radar imagery, especially after the launch of Sentinel-1 mission, enables to systematically update landslide inventories covering wide areas in a reduced time frame and at different scales of analysis. In this work, SAR data processed from the fully automatic P-SBAS pipeline have been adopted to update the Italian national landslide database. Specifically, a matrix has been introduced by comparing past landslide state of activity obtained with Envisat data (2003-2010) and that computed with Sentinel-1 (2014-2018). The state of activity was defined by obtaining the projected velocity along the slope dip direction. The analysis involved about 56,000 landslides which showed at least one Sentinel-1 measurement point, of which 74% were classified as dormant, having annual average velocity < 7 mm/year (considering a value of two times the standard deviation) and 26% as active (mean velocity > 7 mm/year). Furthermore, a landslide reliability matrix was introduced on the landslide inventory updated with S1 data, using the measurement point (MP) density within each landslide and the standard deviation of the mean V-slope value of each landslide. In this case, the analysis revealed that more than 80% of landslides has values of reliability from average to very high. Finally, the 2D horizontal and vertical components were computed to characterize magnitude and direction of every type of landslides included in this work, showing that spreadings, deep-seated gravitation slope deformations, and slow flows showed a main horizontal movement, while complex and translational/rotational slides had more heterogeneity in terms of deformation direction. Hence, the work demonstrated that the application of fast and automatically nationwide Sentinel-1 MTInSAR (multi-temporal interferometry SAR) may provide a fundamental aid for landslide inventory update.
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