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Abstract
Land subsidence is an environmental, geological phenomenon that often refers to gradual settling or rapid sinking of the ground surface as a result of subsurface movement of earth materials. Satellite-based interferometric synthetic aperture radar (InSAR) has been proved to be an excellent technique for monitoring the subsidence at various temporal and spatial scales.
Differential Interferometric Synthetic Aperture Radar (DInSAR) method has been used to observe such events over the past three decades. However, its result can be affected by spatial/temporal decorrelation and atmospheric disturbance. In recent decade, Time Series InSAR (TS-InSAR) was proposed to minimise these biases by taking advantage of the principle of temporal and spatial statistical analysis. Nevertheless, TS-InSAR has issues due to the tropospheric stratification in high elevation regions and insufficient measurement pixels over rapid subsiding zones.
This dissertation mainly focused on optimisation of the TSInSAR-based technique for land subsidence measuring induced by the extraction of natural resources, such as coal, coalbed methane (CBM) and groundwater. Firstly, TS-InSAR has the problem dealing with the rapid surface subsidence and consequently gaps would appear in such areas. A new method has been proposed to fill these gaps by integrating DInSAR and TS-InSAR. Secondly, ALOS-1 PALSAR and ENVISAT ASAR based TS-InSAR has been conducted to monitor the subsidence over underground mining regions. Nevertheless, the result of the counterpart ENVISAT failed to produce reasonable outcome due to the underground mining effect. An approach has been developed and implemented to address this issue through an IDW (Inverse Distance Weighted)-based integration method. Thirdly, TS-InSAR was being exploited to monitor groundwater and CBM extraction induced subsidence in Beijing Municipality and Liulin County, respectively, by taking both tropospheric stratification and turbulence into consideration. Good correlations were observed between InSAR and levelling derived measurements. Indeed, by applying several established TS-InSAR techniques to different areas, and these significant findings from the TS-InSAR analysis have led to new insights into the processes causing the deformation.