Semi-automated regional classification of the style of activity of slow rock slope deformations using PS InSAR and SqueeSAR velocity data

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.