Sentinel-1 imagery for wide-scale quantitative landslide vulnerability assessment of buildings

The occurrence of landslides can potentially cause considerable economic impact on a global scale, resulting in damage to exposed structures and infrastructures, including buildings. In order to determine the most efficacious risk mitigation strategies, the scientific community is engaged in the analyses aimed at assessing the expected consequences of landslide activation/reactivation. This approach involves the implementation of quantitative risk assessment procedures, based on the definition of the landslide susceptibility and intensity, the identification of exposed elements, and the vulnerability assessment, which represents the most challenging parameter. The present work proposes an approach to the quantitative vulnerability assessment for buildings exposed to slow-kinematic landslides via empirical fragility and vulnerability curves, which express the probabilistic relationship between the damage severity to exposed buildings and the intensity of the landslide. A comprehensive database was compiled, collating information on landslide-induced damage to over four thousand buildings in the Northern Apennines of Central Italy. The landslide intensity is evaluated by exploiting the worldwide freely accessible Sentinel-1 SAR images, which have been processed by using the Parallel-Small BAseline (P-SBAS) – Differential SAR Interferometry (DInSAR) processing chain. The Institute for Electromagnetic Sensing of the Environment (IREA) of the National Research Council (CNR) of Italy is responsible for the processing of the Sentinel-1 SAR images, within the framework of an agreement with the Italian Ministry of Environment and Energy Security (MASE) for the detection and analysis of ground displacement over the entire Italian territory. The ultimate objective of the present study is the exploitation of the derived vulnerability curve into the quantitative risk assessment procedure for a comprehensive evaluation of buildings in the Northern Apennines. • A-DInSAR data derived from Sentinel-1 SAR images to define the landslide intensity. • Generation of a large database of severity damage landslides induced to buildings. • Construction of empirical fragility and vulnerability curves. • Application of vulnerability curve in quantitative risk assessment procedure.