Matteo Mantovani, Giulia Bossi, Gianluca Marcato, Luca Schenato, Giacomo Tedesco, Giacomo Titti and Alessandro Pasuto, 2019, New Perspectives in Landslide Displacement Detection Using Sentinel-1 Datasets, Remote sensing (Basel) 11 (2019). doi_10.3390/rs11182135,
URL: http://www.cnr.it/prodotto/i/406599

Space-borne radar interferometry is a fundamental tool to detect and measure a variety of ground surface deformations, either human induced or originated by natural processes. Latest development of radar remote sensing imaging techniques and the increasing number of space missions, specifically designed for interferometry analyses, led to the development of new and more effective approaches, commonly referred to as Advanced DInSAR (A-DInSAR) or Time Series Radar Interferometry (TS-InSAR). Nevertheless, even if these methods were proved to be suitable for the study of a large majority of ground surface dynamic phenomena, their application to landslides detection is still problematic. One of the main limiting factors is related to the rate of displacement of the unstable slopes_ landslides evolving too fast decorrelate the radar signal making the interferometric phase useless. This is the reason why A-DInSAR techniques have been successfully applied exclusively to measure very slow landslides (few centimetres per year). This study demonstrates how the C-band data collected since 2014 by the Sentinel-1 (S1) mission and properly designed interferometric approaches can pull down this restriction allowing to measure rate of displacements ten times higher than previously done, thus providing new perspectives in landslides detection. The analysis was carried out on a test site located in the Cortina d'Ampezzo valley (Eastern Italian Alps), which is affected by several earth flows characterized by different size and kinematics

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