Application of PSI techniques to landslide investigations in the caramanico area (Italy): Lessons learnt

Wa?sowski, Janusz; Bovenga, Fabio; Casarano, Domenico; Nutricato, Raffaele; Refice, Alberto, 2006, Application of PSI techniques to landslide investigations in the caramanico area (Italy): Lessons learnt, FRINGE, Frascati (Roma), Italy, 2005,
URL: http://www.cnr.it/prodotto/i/281189

The standard application of multi-temporal, Persistent Scatterer (PS) synthetic aperture radar differential interferometry (DInSAR) techniques to the Caramanico area posed considerable problems linked to the low density of PS [1]. The application of an optimised PSI approach [2], together with an alternative, classification-based PS candidate detection method [3,4], allowed obtaining sets of PS with more stable targets and higher average inter-image coherence, and thus a lower probability of detecting false displacements. Although the harsh conditions encountered in Caramanico confer some uncertainty to the exact significance of the SAR target displacement results, the reliability of the SAR interferometry results is supported by the presence of PS groups exibiting similar deformation behaviour. Furthermore, the locations of the groups of moving PS appear consistent with the areal distribution of recent and past landslide activity in Caramanico. The moving PS also tend to coincide with places including distressed buildings and structures. In general, however, some variability in the local ground surface displacement patterns is to be expected in manmodified, geologically and geomorphologically complex hillslope settings like that of Caramanico. The variability may be related not only to landslide processes but also to other more or less local ground deformation phenomena such as subsidence (whether natural or man-made), settlement of engineering structures, volumetric changes of geological materials. It follows that in situ data and monitoring controls will usually be needed to discriminate the exact cause of very slow ground surface deformations detected on the basis of PS analysis.

Data from https://intranet.cnr.it/people/