Article

Improved Ground Subsidence Monitoring Using Small Baseline SAR Interferograms and a Weighted Least Squares Inversion Algorithm

Details

Citation

Akbari V & Motagh M (2012) Improved Ground Subsidence Monitoring Using Small Baseline SAR Interferograms and a Weighted Least Squares Inversion Algorithm. IEEE Geoscience and Remote Sensing Letters, 9 (3), pp. 437-441. https://doi.org/10.1109/lgrs.2011.2170952

Abstract
We present the application of a weighted least squares (WLS) method based on image mode interferometric data to monitor the spatiotemporal evolution of land surface subsidence in Mashhad valley, northeast Iran. The technique is based on an appropriate combination of differential interferograms produced by image pairs with small orbital separation to limit the spatial decorrelation phenomena. Our data consist of 17 ASAR single-look-complex images acquired from a descending orbit by the European ENVISAT satellite in image mode (I2), spanning a time interval from June 2004 to November 2007. Fifty-three reliable differential interferograms with relatively little noise and a continuous unwrapped phase are constructed from this data set and are analyzed using a WLS adjustment technique to produce time series of the displacement field. The time-series analysis suggests that the subsidence occurs within a northwest–southeast elongated elliptically shaped bowl along the axis of Mashhad valley. The maximum accumulated subsidence during the 1260-day period reaches approximately 86 cm, located northeast of Mashhad city. The comparison between SAR-interferometry time-series results with continuous Global Positioning System measurements yields an estimated root-mean-square error of ∼ 1.0 cm.

Journal
IEEE Geoscience and Remote Sensing Letters: Volume 9, Issue 3

StatusPublished
FundersThe Research Council of Norway
Publication date31/05/2012
Publication date online11/11/2011
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN1545-598X

People (1)

Dr Vahid Akbari

Dr Vahid Akbari

Lect in Artificial Intelligence/Data Sci, Computing Science and Mathematics - Division