Sentinel-1 SAR amplitude imagery for rapid landslide detection
Por:
Mondini, AC, Santangelo, M, Rocchetti, M, Rossetto, E, Manconi, A, Monserrat, O
Publicada:
1 ene 2019
Categoría:
Earth and planetary sciences (miscellaneous)
Resumen:
Despite landslides impact the society worldwide every day, landslide information isinhomogeneous and lacking. When landslides occur in remote areas or where the availability ofoptical images is rare due to cloud persistence, they might remain unknown, or unnoticed for longtime, preventing studies and hampering civil protection operations. The unprecedented availabilityof SAR C-band images provided by the Sentinel-1 constellation offers the opportunity to proposenew solutions to detect landslides events. In this work, we perform a systematic assessment ofSentinel-1 SAR C-band images acquired before and after known events. We present the resultsof a pilot study on 32 worldwide cases of rapid landslides entailing different types, sizes, slopeexpositions, as well as pre-existing land cover, triggering factors and climatic regimes. Results showthat in about eighty-four percent of the cases, changes caused by landslides on SAR amplitudesare unambiguous, whereas only in about thirteen percent of the cases there is no evidence. On theother hand, the signal does not allow for a systematic use to produce inventories because only in8 cases, a delineation of the landslide borders (i.e., mapping) can be manually attempted. In a fewcases, cascade multi-hazard (e.g., floods caused by landslides) and evidences of extreme triggeringfactors (e.g., strong earthquakes or very rapid snow melting) were detected. The method promises toincrease the availability of information on landslides at different spatial and temporal scales withbenefits for event magnitude assessment during weather-related emergencies, model tuning, andlandslide forecast model validation, in particular when accurate mapping is not required. © 2019 by the authors.
Filiaciones:
Mondini, AC:
CNR, IRPI, Via Madonna Alta 126, I-06128 Perugia, Italy
Santangelo, M:
CNR, IRPI, Via Madonna Alta 126, I-06128 Perugia, Italy
Rocchetti, M:
CNR, IRPI, Via Madonna Alta 126, I-06128 Perugia, Italy
Manconi, A:
Swiss Fed Inst Technol, Dept Earth Sci, Sonneggstr 5, CH-8092 Zurich, Switzerland
Monserrat, O:
CTTC, Div Geomat, Av Gauss 7, E-08860 Barcelona, Spain
Green Submitted, Green Published, gold, All Open Access; Gold Open Access; Green Open Access
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