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Landslide-Induced Damage Probability Estimation Coupling InSAR and Field Survey Data by Fragility Curves

1
Earth Sciences Department, University of Firenze, Via La Pira 4, 50121 Firenze, Italy
2
Departamento de Ingenieria Civil, Universidad de Alicante, 03690 Alicante, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(12), 1486; https://doi.org/10.3390/rs11121486
Received: 16 May 2019 / Revised: 17 June 2019 / Accepted: 17 June 2019 / Published: 22 June 2019
(This article belongs to the Special Issue Remote Sensing of Landslides II)
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Abstract

Landslides are considered to be one of the main natural geohazards causing relevant economic damages and social effects worldwide. Italy is one of the countries worldwide most affected by landslides; in the Region of Tuscany alone, more than 100,000 phenomena are known and mapped. The possibility to recognize, investigate, and monitor these phenomena play a key role to avoid further occurrences and consequences. The number of applications of Advanced Differential Interferometric Synthetic Aperture Radar (A-DInSAR) analysis for landslides monitoring and mapping greatly increased in the last decades thanks to the technological advances and the development of advanced processing algorithms. In this work, landslide-induced damage on structures recognized and classified by field survey and velocity of displacement re-projected along the steepest slope were combined in order to extract fragility curves for the hamlets of Patigno and Coloretta, in the Zeri municipality (Tuscany, northern Italy). Images using ERS1/2, ENVISAT, COSMO-SkyMed (CSK) and Sentinel-1 SAR (Synthetic Aperture Radar) were employed to investigate an approximate 25 years of deformation affecting both hamlets. Three field surveys were conducted for recognizing, identifying, and classifying the landslide-induced damage on structures and infrastructures. At the end, the damage probability maps were designed by means of the use of the fragility curves between Sentinel-1 velocities and recorded levels of damage. The results were conceived to be useful for the local authorities and civil protection authorities to improve the land managing and, more generally, for planning mitigation strategies. View Full-Text
Keywords: A-DInSAR; landslide; fragility curves; SAR data; Sentinel-1; damage; Tuscany A-DInSAR; landslide; fragility curves; SAR data; Sentinel-1; damage; Tuscany
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Del Soldato, M.; Solari, L.; Poggi, F.; Raspini, F.; Tomás, R.; Fanti, R.; Casagli, N. Landslide-Induced Damage Probability Estimation Coupling InSAR and Field Survey Data by Fragility Curves. Remote Sens. 2019, 11, 1486.

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