SAR monitoring of buildings damaged by slow-moving landlsides in the Italian southern Apennines

Reale D., Nicodemo G., Peduto D., Ferlisi S., Gullà G., Fornaro G., 2018, SAR monitoring of buildings damaged by slow-moving landlsides in the Italian southern Apennines, EGU General Assembly 2018, Vienna, 12/04/2018,
URL: http://www.cnr.it/prodotto/i/388896

Manyurbanareasallovertheworldareaffectedbyawidespectrumofdangersrelatedtoeithernaturalphenomena or human activities. Among natural phenomena, slow-moving landslides are widespread and their interaction with the urban environment often originate detrimental effects to existing facilities (e.g., buildings and infrastructures) withrelatedsocialconsequencesandeconomiclosses.Forthesereasons,landslidehazardandvulnerabilityanalysesrepresentkeystepsforareliablepredictionoftheexpecteddamagetoexposedfacilitiesaswellasforproperly designing and implementing the most suitable risk mitigation strategies. Synthetic Aperture Radar (SAR) data processed via advanced interferometric techniques (DInSAR), such as the onebasedontheuseofSARTomography,canbeextremelyusefulinprovidinglong-termground/facilitydisplacementarchives.Inthisregard,theavailabilityofrecenthigh-resolutionX-BandSARdatapromotedthemonitoring capabilities in urban environments, leading to a major step toward the risk analysis at single facility level. In this study, with reference to an urban area located in Calabria region (southern Italian Apennines) - were the existence of several slow-moving landslides of different types interacting with masonry and reinforced concrete buildings can be recognized - the DInSAR-derived differential settlements experienced by a given building are combined with the corresponding damage severity level (recorded via in-situ surveys) to retrieve the relationship between cause (differential settlements) and effect (damage) for both masonry and reinforced concrete buildings. The obtained results represent the knowledge basis to generate more sophisticated tools (e.g. fragility and vulnerability curves) useful for risk analysis purposes.

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