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Deep-Seated Landslide and Rockfalls Threatening the Village of Pietracamela in Central Italy: Deciphering Phenomena from Interferometric Synthetic Aperture Radar and Point Cloud Analysis
- Source :
- Remote Sensing, Vol 16, Iss 17, p 3151 (2024)
- Publication Year :
- 2024
- Publisher :
- MDPI AG, 2024.
-
Abstract
- Landslides represent a major problem in human activities, especially in the proximity of cities or infrastructure. In this paper, we present the peculiar case of Pietracamela, Italy. Pietracamela is a small village located in the Central Apennines, a few kilometers north of the Gran Sasso Mountain. The peculiarity of the case study is the fact that the village is simultaneously affected by two different types of slope instabilities. The southwest sector, representing the historical part of the village, has been affected by large rockfalls generated from the “Capo le Vene” cliff located in the south of the village. The northeastern sector of the village represents the most recent urbanized area and is involved in a deep-seated landslide that, in the last decades, has damaged buildings and infrastructure. In this context, we have used two different types of remote sensing techniques to study the two phenomena. The rockfall area has been surveyed through the use of an Unmanned Aerial Vehicle (UAV) that allowed the definition of main joint sets and the volume of blocks associated with the most recent (2011) rockfall event. Three main joint sets have been highlighted, which are responsible for the failure of the “Capo le Vene” cliff. The volume of blocks that failed during the last rock failure in 2011 ranged from a few to 1500 m3. The deep-seated landslide has been studied by analyzing borehole data and 20 years of InSAR data from ERS1/2, ENVISAT, COSMO-SkyMed, and SENTINEL-1. It has been highlighted by InSAR analysis that the northeast sector of the village shows a perfectly linear displacement trend that generates movements up to about 230 mm (about 1 cm/year).
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 16
- Issue :
- 17
- Database :
- Directory of Open Access Journals
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.63df4b7486e64788ba3e75a420dbc1b0
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/rs16173151