Analysis at medium scale of low-resolution DInSAR data in slow-moving landslide-affected areas |
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| Authors: | Leonardo Cascini Gianfranco Fornaro Dario Peduto |
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| Institution: | 1. Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano (SA), Italy;2. Science and Technology Corporation BV, Olaf Palmestraat 14, 2616 LR Delft, The Netherlands;1. Departamento de Ingeniería de la Construcción, Obras Públicas e Infraestructuras Urbanas, Escuela Politécnica Superior, Universidad de Alicante, P.O. Box 99, E-03080 Alicante, Spain;2. Unidad Asociada de Investigación de Movimientos del Terreno Mediante Interferometría Radar (UNIRAD), UA-IGME, Spain;3. Instituto Universitario de Investigación Informática, Universidad de Alicante, P.O. Box 99, E-03080 Alicante, Spain;4. Geohazards InSAR laboratory (InSARlab), Grupo de Riesgos Geológicos, Departamento de Investigación y Prospectiva Geo-científica, Instituto Geológico y Minero de España (IGME), Ministerio de Economía y Competitividad, c/Alenza 1, E-28003 Madrid, Spain;5. Remote Sensing Lab. (RSLab), Departament de Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya, c/Jordi Girona, 1-3, Ed. D-3, 08034 Barcelona, Spain |
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| Abstract: | Landslide studies over large areas call for multidisciplinary analyses supported by accurate ground displacement measurements. At present, conventional techniques can be valuably complemented by innovative satellite techniques such as Differential SAR Interferometry (DInSAR), furnishing huge amounts of data at competitively affordable costs. This work investigates the remote sensed data potential in landslide studies starting from the awareness of the present constraints of the technique. To this end, with reference to a sample area–within the territory of the National Basin Authority of Liri-Garigliano and Volturno rivers (Central-Southern Italy)–for which detailed base and thematic maps are available, quantitative examples of DInSAR data coverage on both different land-uses and landslide-affected areas are shown. Then, an original tool for “a priori DInSAR landslide visibility zoning” is proposed to address the choice of the most suitable image datasets. Finally, referring to the visible zones, the outcomes of DInSAR data for checking/updating landslide inventory maps at 1:25,000 scale highlight appealing perspectives, also holding the promise of obtaining relevant information in the landslide hazard evaluation. |
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