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Remote Sens. 2016, 8(8), 686; doi:10.3390/rs8080686

Methodology for Detection and Interpretation of Ground Motion Areas with the A-DInSAR Time Series Analysis

Department of Earth and Environmental Sciences, University of Pavia, Via Ferrata 1, Pavia 27100, Italy
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Academic Editors: Zhenhong Li, Roberto Tomas, Zhong Lu, Richard Gloaguen and Prasad S. Thenkabail
Received: 30 June 2016 / Revised: 5 August 2016 / Accepted: 16 August 2016 / Published: 22 August 2016
(This article belongs to the Special Issue Earth Observations for Geohazards)
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Abstract

Recent improvement to Advanced Differential Interferometric SAR (A-DInSAR) time series quality enhances the knowledge of various geohazards. Ground motion studies need an appropriate methodology to exploit the great potential contained in the A-DInSAR time series. Here, we propose a methodology to analyze multi-sensors and multi-temporal A-DInSAR data for the geological interpretation of areas affected by land subsidence/uplift and seasonal movements. The methodology was applied in the plain area of the Oltrepo Pavese (Po Plain, Italy) using ERS-1/2 and Radarsat data, processed using the SqueeSAR™ algorithm, and covering time spans, respectively, from 1992 to 2000 and from 2003 to 2010. The test area is a representative site of the Po Plain, affected by various geohazards and characterized by moderate rates of motion, ranging from −10 to 4 mm/yr. Different components of motion were recognized: linear, non-linear, and seasonal deformational behaviors. Natural and man-induced processes were identified such as swelling/shrinkage of clayey soils, land subsidence due to load of new buildings, moderate tectonic uplift, and seasonal ground motion due to seasonal groundwater level variations. View Full-Text
Keywords: Advanced Differential Interferometric SAR (A-DInSAR); land subsidence; expansive soils; time-series analysis; principal component analysis; Oltrepo Pavese; Po Plain Advanced Differential Interferometric SAR (A-DInSAR); land subsidence; expansive soils; time-series analysis; principal component analysis; Oltrepo Pavese; Po Plain
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Bonì, R.; Pilla, G.; Meisina, C. Methodology for Detection and Interpretation of Ground Motion Areas with the A-DInSAR Time Series Analysis. Remote Sens. 2016, 8, 686.

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