The direct removal of surface vegetation during surface coal mining has a negative impact on the surrounding ecological environment. Effective vegetation restoration is essential to mitigate these impacts. Therefore, accurate monitoring and assessment of vegetation restoration following mining disturbance is critical for ecological protection in mining areas. This study employs the Detecting Breakpoints and Estimating Segments in Trend (DBEST) to map the historical patterns of vegetation disturbance and subsequent recovery at the Shendong coal base. This is the first large-scale application of DBEST for such purposes. To examine the spatio-temporal trends in post-mining vegetation restoration, the Years to Recovery (Y2R) and amount of NDVI recovery were calculated based on the Normalized Difference Vegetation Index (NDVI) time-series. The results show that the DBEST has an accuracy of 0.90 in detecting vegetation destruction and 0.78 in detecting restoration. These findings highlight the substantial potential of this algorithm for monitoring vegetation disturbance in mining areas. The total area of vegetation destruction within the Shendong coal base is 449.65 km², and the restoration area is 156.62 km². Between 1992 and 2017, 46.90% of the disturbed areas achieved 80% of the pre-mining vegetation level, exceeding the average restoration level in China. The average Y2R was 4.68 years. Furthermore, NDVI restoration showed an initial increase followed by a decline with longer Y2R values, suggesting that while early restoration efforts were more effective, long-term restoration efficiency decreased. This finding emphasizes the necessity of concentrating on the restoration process at each stage of the planning and implementation of revegetation projects, particularly regarding the difficulties associated with long-term restoration. This is crucial for the development of more comprehensive and sustainable strategies.

Analyzing the current trends and causes of carbon storage changes and accurately predicting future land use and carbon storage changes under different climate scenarios is crucial for regional land use decision-making and carbon management. This study focuses on Beijing as its study area and introduces a framework that combines the Markov model, the Patch-based Land Use Simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to assess carbon storage at the sub-district level. This framework allows for a systematic analysis of land use and carbon storage spatiotemporal evolution in Beijing from 2000 to 2020, including the influence of driving factors on carbon storage. Moreover, it enables the simulation and prediction of land use and carbon storage changes in Beijing from 2025 to 2040 under various scenarios. The results show the following: (1) From 2000 to 2020, the overall land use change in Beijing showed a trend of “Significant decrease in cropland area; Forest increase gradually; Shrub and grassland area increase first and then decrease; Decrease and then increase in water; Impervious expands in a large scale”. (2) From 2000 to 2020, the carbon storage in Beijing showed a “decrease-increase” fluctuation, with an overall decrease of 1.3 Tg. In future carbon storage prediction, the ecological protection scenario will contribute to achieving the goals of carbon peak and carbon neutrality. (3) Among the various driving factors, slope has the strongest impact on the overall carbon storage in Beijing, followed by Human Activity Intensity (HAI) and Nighttime Light Data (NTL). In the analysis of carbon storage in the built-up areas, it was found that HAI and DEM (Digital Elevation Model) have the strongest effect, followed by NTL and Fractional Vegetation Cover (FVC). The findings from this study offer valuable insights for the sustainable advancement of ecological conservation and urban development in Beijing.

Rapid coal mining results in a series of mining subsidence damages. Predicting surface movement and deformation accurately is essential to reducing mining damage. The accurate determination of parameters for a mining subsidence prediction model is crucial for accurately predicting mining subsidence. In this research, with the incorporation of the Sobol sequence and Lévy flight strategy, we propose an improved whale optimization algorithm (IWOA), thereby enhancing its global optimization capability and mitigating local optimization issues. Our simulation experiment results demonstrate that the IWOA achieved a root mean square error and relative error of less than 0.42 and 0.27%, respectively, indicating its superior accuracy compared to a basic algorithm. The IWOA inversion model also exhibits superior performance compared to a basic algorithm in mitigating gross error interference, Gaussian noise interference, and missing observation point interference. Additionally, it demonstrates enhanced global search capabilities. The IWOA was employed to perform parameter inversion for the working face 1414(1) in Guqiao Coal Mine. The root mean square error of the inversion results did not exceed 6.03, while the subsidence coefficient q, tangent of the main influence angle tanβ, horizontal movement coefficient b, and mining influence propagation angle θ were all below 0.32. The average value of the fitted root mean square error for the subsidence value’s fitted root mean square error and horizontal movement value’s fitted root mean square error of the IWOA was 91.51 mm, which satisfies the accuracy requirements for general engineering applications.

To gain insights into the impact of lithological and geometric configurations of Shendong strata on the propagation of damage within overlying rock layers, a model simulating overlying rock movement and damage propagation under varied lithological combinations was developed. The study involved four physical and seventeen numerical simulations to assess geological factors influencing strata be?havior post-mining. Key findings include: Decreasing the thickness of the key stra?tum reduces the first weighting interval, accelerates fracture initiation, enlarges the subsidence area, and increases maximum subsidence; Raising the key stratum elevation diminishes both subsidence and its range, while delaying fracture initi?ation; Reducing mining thickness results in smaller pressure fluctuations, slightly extends the first weighting interval, and decreases both subsidence and its range; Enhancing key stratum hardness delays fracture occurrence, lowers the fracture zone height, and decreases the plastic zone volume.

Amidst the intensifying challenges of global climate change and the increasing demand for regional sustainable development, accurately assessing the contributions and dynamic characteristics of different land-use types to regional carbon sink patterns is essential for understanding ecosystem carbon cycling mechanisms and optimizing carbon management strategies. Based on land-use and Net Primary Productivity (NPP) remote sensing data from 2018 to 2022, this study employs a land-use change coding method and a hydrothermal (temperature and precipitation) sensitivity coefficient approach to analyze the spatiotemporal variation in NPP in Guangxi Zhuang Autonomous Region and its differential responses to hydrothermal conditions. On this basis, sensitivity coefficients were calculated to assess the spatial patterns of NPP sensitivity to temperature and precipitation, revealing spatial sensitivity characteristics and potential ecological risks. The results indicate significant differences in NPP variations among different land-use types, with broadleaf forests, mixed forests, savannas, and croplands identified as the primary contributors to NPP flows. Additionally, the response of NPP to hydrothermal factors exhibits clear spatial heterogeneity: precipitation sensitivity hotspots are mainly concentrated in the northern and southern ecosystems, while temperature sensitivity hotspots are predominantly located in the northern region. Further analysis reveals that the ecosystems in the central and northern regions are more sensitive to temperature changes, whereas coastal areas exhibit higher stability.