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Characterization of Landslide Deformations in Three Gorges Area Using Multiple InSAR Data Stacks
AbstractIn the areas with steep topography and vulnerable geological condition, landslide deformation monitoring is an important task for risk assessment and management. Differential Synthetic-Aperture Radar interferometry (D-InSAR) and Persistent Scatterer Interferometry (PS-InSAR) are two advanced SAR Interferometry techniques for detection, analysis and monitoring of slow moving landslides. The techniques can be used to identify wide displacement areas and measure displacement rates over long time series with millimeter-level accuracy. In this paper, multiple SAR datasets of Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) and Environmental Satellite (ENVISAT) C-band Advanced Synthetic Aperture Radar (ASAR) are used for landslide monitoring with both D-InSAR and PS-InSAR techniques in Badong at the Three Gorges area in China. Two areas of significant deformation along the southern riverbank of Yangtze River in Badong are identified by joint analyses of PS-InSAR results from different data stacks. Furthermore, both qualitative and quantitative evaluations of the PS-InSAR results are carried out together with preliminary correlation analysis between the time series deformation of a PS point in high risk location and the temporal variation of water level in the Three Gorges Reservoir.
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Tantianuparp, P.; Shi, X.; Zhang, L.; Balz, T.; Liao, M. Characterization of Landslide Deformations in Three Gorges Area Using Multiple InSAR Data Stacks. Remote Sens. 2013, 5, 2704-2719.View more citation formats
Tantianuparp P, Shi X, Zhang L, Balz T, Liao M. Characterization of Landslide Deformations in Three Gorges Area Using Multiple InSAR Data Stacks. Remote Sensing. 2013; 5(6):2704-2719.Chicago/Turabian Style
Tantianuparp, Peraya; Shi, Xuguo; Zhang, Lu; Balz, Timo; Liao, Mingsheng. 2013. "Characterization of Landslide Deformations in Three Gorges Area Using Multiple InSAR Data Stacks." Remote Sens. 5, no. 6: 2704-2719.
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