Spaceborne Synthetic Aperture Radar (SAR) sensors obtain regular and frequent radar images from which ground motion can be precisely detected using a variety of different techniques. The ability to remotely measure slope displacements over large regions has many uses and advantages, although the limitations of an increasingly common technique, Differential SAR Interferometry (D-InSAR), must be considered to avoid the misinterpretation of results. Areas of low coherence and the geometrical effects of mountainous terrain in SAR imagery are known to hinder the exploitation of D-InSAR results. A further major limitation for landslide studies is the assumption that variable rates of movement over a given distance cannot exceed a threshold value, dependent upon the SAR image pixel spacing, the radar sensor wavelength and satellite revisit frequency. This study evaluates the use of three SAR image modes from TerraSAR-X and ENVISAT satellites for monitoring slow-moving landslides in the densely vegetated Three Gorges region, China. Low coherence and episodically fast movements are shown to exceed the measureable limit for regular D-InSAR analysis even for the highest resolution, 11-day interferograms. Subsequently, sub-pixel offset time-series techniques applied to corner reflectors and natural targets are developed as a robust method of resolving time-variable displacements. Verifiable offsets are generated with the TerraSAR-X imagery and the precise movement history of landslides is obtained over a period of up to four years. The capability to derive two-dimensional movements from sub-pixel offsets is used to infer a rotational failure mechanism for the most active landslide detected, and a greater understanding of the landslide behaviour is achieved through comparisons with likely triggering factors and 2D limit equilibrium slope stability analysis.
Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:631020 |
Date | January 2014 |
Creators | Singleton, Andrew G. |
Publisher | University of Glasgow |
Source Sets | Ethos UK |
Detected Language | English |
Type | Electronic Thesis or Dissertation |
Source | http://theses.gla.ac.uk/5676/ |
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