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

Department of Earth and Environmental Sciences, University of Pavia, Via Ferrata 1, 27100 Pavia, Italy
British Geological Survey, Natural Environment Research Council, Nicker Hill, Keyworth, Nottinghamshire NG12 5GG, UK
Geohazards InSAR Laboratory and Modeling Group, Instituto Geológico y Minero de España (IGME), C/. Alenza 1, 28003 Madrid, Spain
Unidad Asociada de Investigación IGME-UA de Movimientos del Terreno Mediante Interferometría Radar (UNIRAD), Universidad de Alicante, P.O. Box 99, 03080 Alicante, Spain
Earth Observation and Geohazards Expert Group (EOEG), EuroGeoSurveys, the Geological Surveys of Europe, 36–38, Rue Joseph II, 1000 Brussels, Belgium
CGG, NPA Satellite Mapping, Crockham Park, Edenbridge Kent TN8 6SR, UK
Departamento de Ingeniería Civil, Escuela Politécnica Superior, Universidad de Alicante. P.O. Box 99, 03080 Alicante, Spain
Author to whom correspondence should be addressed.
Academic Editors: Ruiliang Pu and Jesus Martinez-Frias
Geosciences 2017, 7(2), 25;
Received: 28 February 2017 / Revised: 4 April 2017 / Accepted: 6 April 2017 / Published: 11 April 2017
(This article belongs to the Special Issue Observing Geohazards from Space)
PDF [21042 KB, uploaded 11 April 2017]


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. View Full-Text
Keywords: A-DInSAR time series; land subsidence; groundwater level change; principal component analysis (PCA) A-DInSAR time series; land subsidence; groundwater level change; principal component analysis (PCA)

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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, 25.

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