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Application of DInSAR-GPS optimization for derivation of fine-scale surface motion maps of Southern California

Lookup NU author(s): Professor Zhenhong Li


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A method based on random field theory and Gibbs-Markov random fields equivalency within Bayesian statistical framework is used to derive 3-D surface motion maps from sparse global positioning system (GPS) measurements and differential interferometric synthetic aperture radar (DInSAR) interferogram in the southern California region. The minimization of the Gibbs energy function is performed analytically, which is possible in the case when neighboring pixels are considered independent. The problem is well posed and the solution is unique and stable and not biased by the continuity condition. The technique produces a 3-D field containing estimates of surface motion on the spatial scale of the DInSAR image, over a given time period, complete with error estimates. Significant improvement in the accuracy of the vertical component and moderate improvement in the accuracy of the horizontal components of velocity are achieved in comparison with the GPS data alone. The method can be expanded to account for other available data sets, such as additional interferograms, lidar, or leveling data, in order to achieve even higher accuracy

Publication metadata

Author(s): Samsonov S, Tiampo K, Rundle J, Li Z

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Geoscience and Remote Sensing

Year: 2007

Volume: 45

Issue: 2

Pages: 512-521

Print publication date: 01/02/2007

Online publication date: 22/01/2007

ISSN (print): 0196-2892

ISSN (electronic): 1558-0644

Publisher: IEEE


DOI: 10.1109/TGRS.2006.887166


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