Exploitation of Satellite A-DInSAR Time Series for Detection, Characterization and Modelling of Land Subsidence

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Title: Exploitation of Satellite A-DInSAR Time Series for Detection, Characterization and Modelling of Land Subsidence
Authors: Bonì, Roberta | Meisina, Claudia | Cigna, Francesca | Herrera García, Gerardo | Notti, Davide | Bricker, Stephanie | McCormack, Harry | Tomás, Roberto | Béjar Pizarro, Marta | Mulas de la Peña, Joaquín | Ezquerro Martín, Pablo
Research Group/s: Ingeniería del Terreno y sus Estructuras (InTerEs)
Center, Department or Service: Universidad de Alicante. Departamento de Ingeniería Civil
Keywords: A-DInSAR time series | Land subsidence | Groundwater level change | Principal component analysis (PCA)
Knowledge Area: Ingeniería del Terreno
Issue Date: 11-Apr-2017
Publisher: MDPI
Citation: Bonì R, Meisina C, Cigna F, Herrera G, Notti D, Bricker S, McCormack H, Tomás R, Béjar-Pizarro M, Mulas J, Ezquerro P. Exploitation of Satellite A-DInSAR Time Series for Detection, Characterization and Modelling of Land Subsidence. Geosciences. 2017; 7(2):25. doi:10.3390/geosciences7020025
Abstract: In the last two decades, advanced differential interferometric synthetic aperture radar (A-DInSAR) techniques have experienced significant developments, which are mainly related to (i) the progress of satellite SAR data acquired by new missions, such as COSMO-SkyMed and ESA’s Sentinel-1 constellations; and (ii) the development of novel processing algorithms. The improvements in A-DInSAR ground deformation time series need appropriate methodologies to analyse extremely large datasets which consist of huge amounts of measuring points and associated deformation histories with high temporal resolution. This work demonstrates A-DInSAR time series exploitation as valuable tool to support different problems in engineering geology such as detection, characterization and modelling of land subsidence mechanisms. The capabilities and suitability of A-DInSAR time series from an end-user point of view are presented and discussed through the analysis carried out for three test sites in Europe: the Oltrepo Pavese (Po Plain in Italy), the Alto Guadalentín (Spain) and the London Basin (United Kingdom). Principal component analysis has been performed for the datasets available for the three case histories, in order to extract the great potential contained in the A-DInSAR time series.
Sponsor: Part of this work was supported by the Spanish Ministry of Economy and Competitiveness and EU FEDER funds under projects TIN2014-55413- C2-2-P and ESP2013-47780-C2-2-R.
URI: http://hdl.handle.net/10045/65449
ISSN: 2076-3263
DOI: 10.3390/geosciences7020025
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Peer Review: si
Publisher version: http://dx.doi.org/10.3390/geosciences7020025
Appears in Collections:INV - INTERES - Artículos de Revistas

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