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Widespread ground fissures caused by coal mining subsidence are a main cause of ecological destruction in coal mining areas, and the rapid monitoring of ground fissures is essential for ecological restoration. Traditional fissure monitoring technologies are time consuming and laborious. Therefore, we developed a method to automatically extract ground fissures from high-resolution UAV images. First, a multiscale Hessian-based enhancement filter was utilized to enhance the ground fissures in grayscale images. Then, a simple single-thresholding operation was applied to segment the enhanced image to generate a binary ground fissure map. Finally, incomplete path opening was performed to eliminate the noises in the fissure extraction results. We selected the N1212 working face of the Ningtiaota Coal Mine in Shenmu County, China, as the study area. The results indicated that the ranges of correctness, completeness, and the kappa coefficient of the extracted results were 66.23–79.00%, 69.03–73.22%, and 67.91–75.88%, respectively. Image resolution is the key factor for successful fissure detection; the method proposed in this paper can extract ground fissures with a width greater than one pixel (2.64 cm), and the detection ratio for fissures with a width greater than two pixels was over 87%. Our research has solved the problem of the rapid monitoring of ground fissures to a certain extent and can act as a valuable tool for ecological restoration in mining areas.

Carbon sequestration in terrestrial ecosystems plays an essential role in coping with global climate change and achieving regional carbon neutrality. In mining areas with high groundwater levels in eastern China, underground coal mining has caused severe damage to surface ecology. It is of practical significance to evaluate and predict the positive and negative effects of coal mining and land reclamation on carbon pools. This study set up three scenarios for the development of the Yanzhou coalfield (YZC) in 2030, including: (1) no mining activities (NMA); (2) no reclamation after mining (NRM); (3) mining and reclamation (MR). The probability integral model (PIM) was used to predict the subsidence caused by mining in YZC in 2030, and land use and land cover (LULC) of 2010 and 2020 were interpreted by remote sensing images. Based on the classification of land damage, the LULC of different scenarios in the future was simulated by integrating various social and natural factors. Under different scenarios, the InVEST model evaluated carbon storage and its temporal and spatial distribution characteristics. The results indicated that: (1) By 2030, YZC would have 4341.13 ha of land disturbed by coal mining activities. (2) Carbon storage in the NRM scenario would be 37,647.11 Mg lower than that in the NMA scenario, while carbon storage in the MR scenario would be 18,151.03 Mg higher than that in the NRM scenario. Significantly, the Nantun mine would reduce carbon sequestration loss by 72.29% due to reclamation measures. (3) Carbon storage has a significant positive spatial correlation, and coal mining would lead to the fragmentation of the carbon sink. The method of accounting for and predicting carbon storage proposed in this study can provide data support for mining and reclamation planning of coal mine enterprises and carbon-neutral planning of government departments.