Exploiting SAR interferometry for assessing rock glacier activity

Rock glaciers are characterised by a mix of ice and rock, which is related to the presence of permafrost in mountainous areas.  The external temperature is considered one of the most important factors controlling rock glacier flow variation at both inter-annual and seasonal time scales, showing mean velocities ranging from centimetres to meters per year. Hence, the temperature rising due to climate change leads to changes in kinematics of rock glaciers that increase hazards for mountainous settlements and infrastructures.Despite differential SAR interferometry (DInSAR) is a very effective tool for measuring ground stability, its application to rock glacier monitoring poses critical issues relate to signal decorrelation due to changeable snow cover conditions, as well as to displacement kinematics characterised by both linear and non-linear components and high displacement rates leading to measurements corrupted by aliasing.This work investigates the rock glacier stability in Val Senales (Italian Alps) by processing a dataset of 345 Sentinel-1 SAR images acquired between 2015 and 2022. Multi-temporal DInSAR processing has been performed by exploiting both persistent and distributed scatterers through SPINUA algorithm. Ad hoc processing strategies have been adopted in order to overcome both signal decorrelation due to changeable snow cover conditions, and aliasing due to very high displacement rates. The algorithm has been run by selecting spring-summer acquisitions, and forced to search for solutions corresponding to phase changes behind the aliasing limit.The resulting mean velocity map shows several areas affected by ground displacements, that have been further analysed for investigating the rock glacier activity in the area of interest. To this aim, the DInSAR results (both mean velocity and displacement time series) have been ingested into a GIS environment together with other informative layers such as rock glacier classes (according to [1]) optical orthoimages, multi-temporal mean SAR amplitude, DInSAR coherence maps, permafrost index map, and  Difference Vegetation Index (NDVI).Then, the rock glacier activity has reclassified by adopting the more recent procedure proposed in [2], which is based on the DInSAR products too. This new classification has been compared to that derived according to [1] showing several differences.An further interesting issue is related to the lacking of DInSAR coherent targes just within the rock glacier borders that could be related to the presence of very high displacement rates. This has been investigated by exploring changes in orthoimages from different years as well as maps of DInSAR phase and coherence.References[1] Bollmann, L. Rieg, L., M. Spross, R. Sailer, k. Bucher, M. Maukisch, M. Monreal, A. Zischg, V. Mair, K. Lang, and J. Stötter, “Blockgletscherkataster in Südtirol-Erstellung und Analyse,” Permafrost in Südtirol, Innsbrucker Geographische Studien. J. Stötter & R. Sailer Eds., pp. 147–171, 2012.[2] IPA Action Group - Rock glacier inventories and kinematics. Towards standard guidelines for inventorying rock glaciers: practical concepts (version 2.0), pp. 1–10, 2022. Acknowledgments This work was carried out in the framework of the project “CRIOSAR: Applicazioni SAR multifrequenza alla criosfera”, funded by ASI under grant agreement n. ASI N. 2021-12-U.0.