In this manuscript, an integrated strategy that exploits both phase and amplitude features of satellite SAR (synthetic aperture radar) images and ground data is proposed for deriving the deformation field induced by a complex landslide that affected part of the village of Ponzano (Abruzzi Region, Central Italy). The February 12, 2017, landslide was triggered by the combined effects of intense rainfalls and snowmelt that saturated the slope. The SqueeSAR algorithm was applied to two C-band SAR datasets, composed by Radarsat-2 and Sentinel-1 images, spanning a nine-year time interval before the landslide occurrence. Moreover, the amplitude information carried by two TerraSAR-X images, acquired immediately before and after the event, was exploited to derive the total displacement generated by the landslide movement by means of the RMT (rapid motion tracking) algorithm. The obtained results allow describing the landslide behavior before and after its failure. In particular, the back-monitoring analysis shows that the landslide was already slowly moving, with deformation rates increasing from the Radarsat-2 to the Sentinel-1 monitored periods, 10 years before its complete mobilization. The landslide failure of February 2017 produced maximum displacements of about 10 m in some sectors of the affected area. The registered deformation rates and the localization of the maximum displacements areas were confirmed by field data, collected during a field campaign and a helicopter recognizance of the damaged areas, both performed after the event.

Satellite radar data for back-analyzing a landslide event: the Ponzano (Central Italy) case study

Solari, Lorenzo;Ciampalini, Andrea;
2018

Abstract

In this manuscript, an integrated strategy that exploits both phase and amplitude features of satellite SAR (synthetic aperture radar) images and ground data is proposed for deriving the deformation field induced by a complex landslide that affected part of the village of Ponzano (Abruzzi Region, Central Italy). The February 12, 2017, landslide was triggered by the combined effects of intense rainfalls and snowmelt that saturated the slope. The SqueeSAR algorithm was applied to two C-band SAR datasets, composed by Radarsat-2 and Sentinel-1 images, spanning a nine-year time interval before the landslide occurrence. Moreover, the amplitude information carried by two TerraSAR-X images, acquired immediately before and after the event, was exploited to derive the total displacement generated by the landslide movement by means of the RMT (rapid motion tracking) algorithm. The obtained results allow describing the landslide behavior before and after its failure. In particular, the back-monitoring analysis shows that the landslide was already slowly moving, with deformation rates increasing from the Radarsat-2 to the Sentinel-1 monitored periods, 10 years before its complete mobilization. The landslide failure of February 2017 produced maximum displacements of about 10 m in some sectors of the affected area. The registered deformation rates and the localization of the maximum displacements areas were confirmed by field data, collected during a field campaign and a helicopter recognizance of the damaged areas, both performed after the event.
Solari, Lorenzo; Raspini, Federico; Del Soldato, Matteo; Bianchini, Silvia; Ciampalini, Andrea; Ferrigno, Federica; Tucci, Stefano; Casagli, Nicola
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11568/935715
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