Large slow rock-slope deformations, including deep-seated gravitational slope deformations and large landslides, are widespread in alpine environments. They develop over thousands of years by progressive failure, resulting in slow movements that impact infrastructures and can eventually evolve into catastrophic rockslides. A robust characterization of their style of activity is thus required in a risk management perspective. We combine an original inventory of slow rock-slope deformations with different PS-InSAR and SqueeSAR datasets to develop a novel, semi-automated approach to characterize and classify 208 slow rock-slope deformations in Lombardia (Italian Central Alps) based on their displacement rate, kinematics, heterogeneity and morphometric expression. Through a peak analysis of displacement rate distributions, we characterize the segmentation of mapped landslides and highlight the occurrence of nested sectors with differential activity and displacement rates. Combining 2D decomposition of InSAR velocity vectors and machine learning classification, we develop an automatic approach to characterize the kinematics of each landslide. Then, we sequentially combine principal component and K-medoids cluster analyses to identify groups of slow rock-slope deformations with consistent styles of activity. Our methodology is readily applicable to different landslide datasets and provides an objective and cost-effective support to land planning and the prioritization of local-scale studies aimed at granting safety and infrastructure integrity.

Spaceborne radar interferometry is a powerful tool to characterize landslides at local and regional scales. However, its application to very slow rock slope deformations in alpine environments (displacement rates < 5 cm/year) remains challenging, mainly due to low signal to noise ratio, atmospheric disturbances, snow cover effects, and complexities resulting from heterogeneous displacement in space and time. Here we combine SqueeSAR^TM data, targeted multi-temporal baseline DInSAR, GPS data, and detailed field morpho-structural mapping, to unravel the kinematics, internal segmentation, and style of activity of the Mt. Mater deep-seated gravitational slope deformation (DSGSD) in Valle Spluga (Italy). We retrieve slope kinematics by performing 2D decomposition (2D InSAR) of SqueeSAR^TM products derived from Sentinel-1 data acquired in ascending and descending orbits. To achieve a spatially-distributed characterization of DSGSD displacement patterns and activity, we process Sentinel-1 A/B images (2016-2019) with increasing temporal baselines (ranging from 24-days to 1-year) and generate several multi-temporal interferograms. Unwrapped displacement maps are validated using ground-based GPS data. Interferograms derived with different temporal baselines reveal a strong kinematic and morpho-structural heterogeneity and outline nested rockslides and active sectors, that arise from the background displacement signal of the main DSGSD. Seasonal interferograms, supported by GPS displacement measurements, reveal non-linear displacement trends suggesting a complex response of different slope sectors to rainfall and snowmelt. Our analyses clearly outline a composite slope instability with different nested sectors possibly undergoing different evolutionary trends towards failure. The results herein outline the potential of a targeted use of DInSAR for the detailed investigation of very slow rock slope deformations in different geological and geomorphological settings.

Alpine periglacial landforms like rock glaciers and protalus ramparts are key indicators of the state of permafrost. They are characterized by complex deformation mechanisms and temporal trends, possibly evolving towards destabilization. A quantitative assessment of their activity is thus fundamental in climatological and geohazard perspectives. Spaceborne interferometric synthetic-aperture radar (InSAR) techniques provide powerful tools to document the surface deformations of periglacial features, yet their application to the rapid screening of rock glacier activity over wide areas is still limited. We propose a semi-automated methodology that combines wrapped-phase deformation signals obtained from differential interferometric synthetic-aperture radar (DInSAR), available information on permafrost extent, geomorphological data, and multivariate statistics to characterize the activity of 514 periglacial landforms over 1000 km² in Upper Valtellina (Central Alps, Italy). We process Sentinel-1 A and B SAR images with increasing temporal baselines (12 to 120 d) to generate 124 interferograms in ascending and descending geometries. We analyse the statistical distribution of the wrapped interferometric phase to assess the state of activity of each periglacial landform through an objective activity index. This is combined with regional-scale information on the likelihood of permafrost occurrence to classify periglacial landforms based on their activity on different temporal scales. We obtain four activity classes, validated by comparison to geomorphological evidence and related to environmental variables through multivariate statistics. Our results demonstrate the potential of wrapped DInSAR products, routinely generated over large areas and unaffected by unwrapping errors, to support a regional-scale screening of periglacial landform activity and the identification of candidates for site-specific studies.

As a consequence of atmospheric warming, high-altitude periglacial and glacial environments exhibit clear signs of cryosphere degradation, and the Alps serve as a natural laboratory for studying the primary effects on permafrost-related features. Our research in South Tyrol, northeastern Italy, aimed to develop an updated classification system, based on remote sensing data and statistical models, for rock glacier activity, categorizing it as active, transitional, or relict according to the new Rock Glacier Inventories and Kinematic (RGIK) guidelines. While the current regional inventory includes activity attributes based on morphological observations and differential interferometric synthetic aperture radar (DInSAR) coherence, it lacks a comprehensive classification that also considers climatic drivers, displacement rates, and morphometric parameters. To fill this gap, we utilized the Alaska Satellite Facility's interferometric synthetic aperture radar (InSAR) cloud computing, employing the Small Baseline Subset (SBAS) and Miami InSAR time-series software in Python (MintPy) algorithms to extract velocity data for each rock glacier investigated in this study. Additionally, we analysed geomorphological and climatic maps derived from in situ and remote sensing data to obtain descriptive parameters influencing rock glacier development and activity. From a wide range of potential variables, we selected eight key predictors, representing physical (e.g. temperature), morphological (e.g. roughness), and dynamic attributes (e.g. velocity and coherence indicators). These predictors were integrated in a multiclass generalized additive model (GAM) classifier to categorize the mapped landforms. Applying this model to the entire dataset (achieving an area under the curve (AUC) over 0.9) allowed us to address gaps in previous classification methods and provided activity attributes for previously unclassified rock glaciers, along with associated uncertainty values. Our approach enhanced the previous classification, leaving only 3.5 % of features unclassified compared to 13 % in morphological classification and 18.5 % in the DInSAR-based method. The results revealed a predominance of relict features (∼75 %) and a smaller number of active ones (∼10 %). The result of the distribution of active, transitional, and relict classes suggests that the transition from active to relict states is not direct. Instead, an intermediate transitional phase is commonly observed. This comprehensive approach refines the categorization of mapped features and improves our understanding of the factors influencing rock glacier activity in the alpine environment in South Tyrol.

Large slow rock-slope deformations are widespread in alpine environments and mountainous regions worldwide. They evolve over long time by progressive failure processes, resulting in slow movements that impact infrastructures and can eventually evolve into catastrophic rockslides. A robust characterization of the activity of these phenomena is thus required to cope with their long-term threats.Displacement rates measured by remote sensing and ground-based techniques only provide a snapshot of long-term, variable trends of activity and are insufficient to capture the behavior of slow rock slope deformations in a long-term risk management perspective. We thus propose to adopt a more complete approach based on a re-definition of “style of activity”, including displacement rate, segmentation/heterogeneity, kinematics, internal damage and accumulated strain. To this aim, we developed a novel approach combining persistent-scatterer interferometry (PSI) and systematic geomorphological mapping, to obtain an objective semi-automated characterization and classification of 208 slow rock slope deformations in Lombardia (Italian Central Alps). Through a peak analysis of displacement rate distributions we characterized the degree of internal segmentation of mapped slow rock slope deformations and highlighted the presence of nested sectors with differential activity. Then, we used an original approach to automatically characterize the kinematics of each landslide (translational, compound, or rotational) by combining a 2DInSAR velocity vector decomposition and a supervised machine learning classification. Finally, we combined Principal Component and K-medoid Cluster multivariate statistical analyses to classify slow rock slope deformations into groups with consistent styles of activity. We classified DSGSDs and large landslides respectively in five and two representative groups described by different degree of internal segmentation and kinematics that significant influence the evolutionary behavior and affect the definition of representative displacement rates. Our results provide a statistical evidence that phenomena classified as “Deep-Seated Gravitational Slope deformations” (DSGSD) and “large landslides” actually have different mechanisms and/or evolutionary stages, mirrored by different morphological features that testify higher accumulated internal deformation for large landslides with respect to DSGSDs. Our statistical classification of rock-slope deformation style of activity further highlighted the different risk potentials associated to each one of the seven descriptive groups in a practical perspective, taking into account the most significant parameters (rate, volume and heterogeneity) to assess risks related to the interaction between slow movements and sensitive elements.Our analysis benefits from both deterministic and statistical components to perform a complete regional screening of slow rock slope deformations and to prioritize site-specific, engineering geological analyses of critical slopes depending on the most important factors conditioning their long-term style of activity. Our methodology is readily applicable to different datasets and provides an objective and cost-effective support to land planning and the prioritization of local-scale studies aimed at granting safety and infrastructure integrity.