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.
Highlights
Is one of the European countries with the highest percentage of landslide prone landscape (13%, according to Nadim et al 2006)
This work presented the InSAR characterization of the 12th February 2017 Ponzano complex landslide that affected part of this village of the Civitella del Tronto municipality (Abruzzi Region)
An integrated strategy that combines phase-derived PSI (SqueeSAR algorithm) data and amplitude-derived rapid motion tracking (RMT) data has been proposed for evaluating the displacement pattern of the landslide before and immediately after its failure
Summary
Is one of the European countries with the highest percentage of landslide prone landscape (13%, according to Nadim et al 2006). Satellite-based earth observation (EO) techniques guarantee a data coverage with high temporal and spatial density of measurements, ideally suited for slow-moving landslide monitoring (Tralli et al 2005). In this framework, the use of synthetic aperture radar (SAR) images for deriving the phase information related to the ground displacements is nowadays commonly used in the civil protection practices and as a valuable tool for monitoring pre- and post-event movements (Corsini et al 2006; Canuti et al 2007; Farina et al 2007; Pagliara et al 2014; Raspini et al 2017; Solari et al 2017)
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