Coordinated and sustainable development of production-living-ecological space (PLES) is directly related to the global energy security and quality of life in coal areas. However, the current surface subsidence control methods have some problems, such as low resource recovery rate, high cost, insufficient materials, which are difficult to meet the requirements for the PLES sustainable development in coal areas. Under this background, based on characteristics of surface subsidence and deformation due to sub-critical extraction, the large protection area, and high deformation tolerance in PLES of mining areas, the new method of regional mining subsidence control was proposed, with the combination of source control and ground rehabilitation. The effectiveness of the method was verified by numerical simulation results and practical applications, and the application principles and implementation methods were proposed. The research results could provide technical support for the sustainable development of coal areas in major coal producing countries of the world.

Underground coal fire is a global geological disaster that causes the loss of resources as well as environmental pollution. Xinjiang, China, is one of the regions suffering from serious underground coal fires. The accurate monitoring of underground coal fires is critical for management and extinguishment, and many remote sensing-based approaches have been developed for monitoring over large areas. Among them, the multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques have been recently employed for underground coal fires-related ground deformation monitoring. However, MT-InSAR involves a relatively high computational cost, especially when the monitoring area is large. We propose to use a more cost-efficient Stacking-InSAR technique to monitor ground deformation over underground coal fire areas in this study. Considering the effects of atmosphere on Stacking-InSAR, an ERA5 data-based estimation model is employed to mitigate the atmospheric phase of interferograms before stacking. Thus, an adaptive ERA5-Corrected Stacking-InSAR method is proposed in this study, and it is tested over the Fukang coal fire area in Xinjiang, China. Based on original and corrected interferograms, four groups of ground deformation results were obtained, and the possible coal fire areas were identified. In this paper, the ERA5 atmospheric delay products based on the estimation model along the LOS direction (D-LOS) effectively mitigate the atmospheric phase. The accuracy of ground deformation monitoring over a coal fire area has been improved by the proposed method choosing interferograms adaptively for stacking. The proposed Adaptive ERA5-Corrected Stacking-InSAR method can be used for efficient ground deformation monitoring over large coal fire areas.

No abstract

Urban land use classification is significant for urban development planning. Considering complex environments of urban surface features, traditional semantic segmentation methods are difficult to solve the problems of mixed pixels and limited spatial resolution of images. The subpixel mapping technology is an effective method to solve the above problems in urban land use classification. However, traditional subpixel mapping methods are sensitive to mountain shadow, high-rise building shadow and impermeable surface heterogeneity, resulting in false classification. Therefore, we propose a subpixel mapping method that can reduce the shadow effect. This method uses a multi-index feature fusion strategy to optimize the abundance of the shadow errors in the abundance image, and uses a super-resolution reconstruction neural network model to reconstruct the optimized abundance image for the subpixel mapping of urban land use. Experiments were conducted on sentinel-2 images obtained over Yuelu District of Changsha City, Hunan Province, China. The experimental results show that the method proposed in this article can effectively overcome the influence of building shadows and mountain shadows in urban land cover classification and is superior to traditional subpixel/pixel spatial attraction model, radial basis function, super-resolution subpixel mapping, and other methods in the effect and accuracy of urban land use subpixel mapping.