Next Article in Journal
The Influence of Cryogenic Mass Exchange on the Composition and Stabilization Rate of Soil Organic Matter in Cryosols of the Kolyma Lowland (North Yakutia, Russia)
Next Article in Special Issue
Analysis of Costantino Landslide Dam Evolution (Southern Italy) by Means of Satellite Images, Aerial Photos, and Climate Data
Previous Article in Journal
Mineralogy of Paleocene Petrified Wood from Cherokee Ranch Fossil Forest, Central Colorado, USA
Previous Article in Special Issue
Combined Use of C- and X-Band SAR Data for Subsidence Monitoring in an Urban Area
Article Menu

Export Article

Open AccessArticle
Geosciences 2017, 7(2), 25; doi:10.3390/geosciences7020025

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

1
Department of Earth and Environmental Sciences, University of Pavia, Via Ferrata 1, 27100 Pavia, Italy
2
British Geological Survey, Natural Environment Research Council, Nicker Hill, Keyworth, Nottinghamshire NG12 5GG, UK
3
Geohazards InSAR Laboratory and Modeling Group, Instituto Geológico y Minero de España (IGME), C/. Alenza 1, 28003 Madrid, Spain
4
Unidad Asociada de Investigación IGME-UA de Movimientos del Terreno Mediante Interferometría Radar (UNIRAD), Universidad de Alicante, P.O. Box 99, 03080 Alicante, Spain
5
Earth Observation and Geohazards Expert Group (EOEG), EuroGeoSurveys, the Geological Surveys of Europe, 36–38, Rue Joseph II, 1000 Brussels, Belgium
6
CGG, NPA Satellite Mapping, Crockham Park, Edenbridge Kent TN8 6SR, UK
7
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
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)
View Full-Text   |   Download PDF [21042 KB, uploaded 11 April 2017]   |  

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. 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)
Figures

Figure 1

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Geosciences EISSN 2076-3263 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top