The slope instability in alpine areas is a phenomenon related to superficial landslides, deep-seated gravitational slope deformations and permafrost creep. Rock glaciers are a widespread geomorphological evidence of permafrost in alpine regions and are characterized by creeping processes that generate a downstream displacement which varies from year to year and seasonally. A methodology to classify the activity status of rock glacier was developed using SAR images . A dataset of 13 Sentinel-1 images acquired during the snow free of 2016 were used. The images are SLC type, VV polarization and Interferometric Wide swath sensor mode. In order to minimize the temporal decorrelation, we combined the images by DIn-SAR techniques to obtain 10 interferograms with a maximum temporal baseline of 12 days (tab.1).
To detect the status of the rock glaciers (i.e. active or non-active) we used the spatial variation of the interferometric phase of Sentinel-1 data. The higher is the variation of the interferometric phase inside the rock glacier area, the higher is the probability that the rock glacier is active (i.e. in motion). However, in some cases, high variation of interferometric phase can be caused by the atmospheric noise. In order to take into account this noise, we compute the variation of the interferometric phase around the rock glacier outline, which is assumed to be stable i.e. without displacement. The interferogram with the lowest interferometric phase variation around the rock glacier area is then used for the rock glacier status classification. In detail, the classification is done using as main feature the standard deviation σ of the interferometric phase inside the rock glacier and defining a threshold value. This value is selected on the σ distribution between the active and non-active forms that have been identified through visual interpretation of all the available interferograms.
The results obtained from the automatic classifications (fig.1) have been used to update an existing rock glacier inventory of the entire South Tyrol region. 88 % of rock glaciers are in agreement between the automatic classification and the inventory, 10% were updated to inactive by our method and the remaining 2% were updated as active. Aldo Bertone, Mattia Callegari, Giovanni Cuozzo, Carlo Marin, Claudia Notarnicola, Roberto Seppi, Francesco Zucca; “Exploiting InSAR and multi-source data to study periglacial environments in the Alps at different space and time scales”, ESA Fringe 2017