Deriving Dynamic Subsidence of Coal Mining Areas Using InSAR and Logistic Model
AbstractThe seasonal variation of land cover and the large deformation gradients in coal mining areas often give rise to severe temporal and geometrical decorrelation in interferometric synthetic aperture radar (InSAR) interferograms. Consequently, it is common that the available InSAR pairs do not cover the entire time period of SAR acquisitions, i.e., temporal gaps exist in the multi-temporal InSAR observations. In this case, it is very difficult to accurately estimate mining-induced dynamic subsidence using the traditional time-series InSAR techniques. In this investigation, we employ a logistic model which has been widely applied to describe mining-related dynamic subsidence, to bridge the temporal gaps in multi-temporal InSAR observations. More specifically, we first construct a functional relationship between the InSAR observations and the logistic model, and we then develop a method to estimate the model parameters of the logistic model from the InSAR observations with temporal gaps. Having obtained these model parameters, the dynamic subsidence can be estimated with the logistic model. Simulated and real data experiments in the Datong coal mining area, China, were carried out in this study, in order to test the proposed method. The results show that the maximum subsidence in the Datong coal mining area reached about 1.26 m between 1 July 2007 and 28 February 2009, and the accuracy of the estimated dynamic subsidence is about 0.017 m. Compared with the linear and cubic polynomial models of the traditional time-series InSAR techniques, the accuracy of dynamic subsidence derived by the logistic model is increased by about 50.0% and 45.2%, respectively. View Full-Text
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Yang, Z.; Li, Z.; Zhu, J.; Yi, H.; Hu, J.; Feng, G. Deriving Dynamic Subsidence of Coal Mining Areas Using InSAR and Logistic Model. Remote Sens. 2017, 9, 125.
Yang Z, Li Z, Zhu J, Yi H, Hu J, Feng G. Deriving Dynamic Subsidence of Coal Mining Areas Using InSAR and Logistic Model. Remote Sensing. 2017; 9(2):125.Chicago/Turabian Style
Yang, Zefa; Li, Zhiwei; Zhu, Jianjun; Yi, Huiwei; Hu, Jun; Feng, Guangcai. 2017. "Deriving Dynamic Subsidence of Coal Mining Areas Using InSAR and Logistic Model." Remote Sens. 9, no. 2: 125.
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