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Semi-automated regional classification of slow rock slope deformations integrating kinematics, activity and spatial complexity

Semi-automated regional classification of slow rock slope deformations integrating kinematics, activity and spatial complexity

Authors :
Crippa, C
Agliardi, F
Frattini, P
Spreafico, M
Crosta, G
Valbuzzi, E
Spreafico, MC
Crosta, GB
Valbuzzi,E
Crippa, C
Agliardi, F
Frattini, P
Spreafico, M
Crosta, G
Valbuzzi, E
Spreafico, MC
Crosta, GB
Valbuzzi,E
Publication Year :
2020

Abstract

Slow rock slope deformations are widespread in alpine environments. They affect giant volumes and evolve over thousands of years by progressive failure, resulting in long-term slow movements threatening infrastructures and potential evolution into massive collapses. In the alpine sector of Lombardia (Italian Central Alps), 208 mapped slow rock slope deformations affect a total area exceeding 580 km2 and interact with a variety of elements at risk including settlements, hydroelectric facilities and lifelines characterized by different vulnerability to both slow and progressive deformations. In this context, a systematic, reliable and cost-effective approach is required to classify slow rock slope deformations on the regional scale for landplanning, prioritization and analysis of interactions with elements at risk, depending on their style of activity, including not only mean deformation rate, but also their kinematics and spatial complexity. In this work, we implemented a toolbox that integrates different approaches to classify a large dataset of slow rock slope deformations in discrete groups, according to the deformation style and morpho-structural expression of individuals, mapped on regional scale and characterized through remote sensing techniques. The landslide dataset used in this study was obtained by a “semi-detail” geomorphological and morpho-structural mapping on aerial imagery and DEM, performed on regional scale yet including local-scale information (e.g. tectonic lineaments, morpho-structures, landforms, nested deep-seated landslides) and a full set of geological and morphometric attributes. To characterize landslide activity, we use Persistent-Scatterer Interferometry (PSI) data, including PS-InSARTM and SqueeSARTM acquired by different sensors (ERS, Radarsat, Sentinel 1A/B) over different time periods from 1992 to 2017. Since Line-of-Sight velocity of point like data can hamper a correct evaluation of both landslide kinematics and deformation rates, f

Details

Database :
OAIster
Notes :
ELETTRONICO, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1311398051
Document Type :
Electronic Resource