Large slow rock-slope deformations, including deepseated 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, semiautomated approach to characterize and classify 208 slow rockslope 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.

Crippa, C., Valbuzzi, E., Frattini, P., Crosta, G., Spreafico, M., Agliardi, F. (2021). Semi-automated regional classification of the style of activity of slow rock-slope deformations using PS InSAR and SqueeSAR velocity data. LANDSLIDES, 18(7), 2445-2463 [10.1007/s10346-021-01654-0].

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

Crippa, Chiara
Primo
;
Valbuzzi, Elena;Frattini, Paolo;Crosta, Giovanni;Spreafico, Margherita C.;Agliardi, Federico
Ultimo
2021

Abstract

Large slow rock-slope deformations, including deepseated 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, semiautomated approach to characterize and classify 208 slow rockslope 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.
Articolo in rivista - Articolo scientifico
Deep-seated gravitational slope deformations; InSAR; Kinematics; Landslide activity; Multivariate statistical analysis; PS-InSAR; Slow rock-slope deformation;
English
6-apr-2021
2021
18
7
2445
2463
open
Crippa, C., Valbuzzi, E., Frattini, P., Crosta, G., Spreafico, M., Agliardi, F. (2021). Semi-automated regional classification of the style of activity of slow rock-slope deformations using PS InSAR and SqueeSAR velocity data. LANDSLIDES, 18(7), 2445-2463 [10.1007/s10346-021-01654-0].
File in questo prodotto:
File Dimensione Formato  
Crippa2021_Article_Semi-automatedRegionalClassifi.pdf

accesso aperto

Tipologia di allegato: Publisher’s Version (Version of Record, VoR)
Dimensione 4.2 MB
Formato Adobe PDF
4.2 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/310287
Citazioni
  • Scopus 37
  • ???jsp.display-item.citation.isi??? 30
Social impact