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Damage caused by underground coal mining is a serious problem in mining areas in China; therefore, studying and obtaining the rules of ground movement and deformation under different geological conditions is of great importance. The numerical software ANSYS was used in this study to simulate mining processes under two special geological conditions： （1） thick unconsolidated soil layer and thin bedrock; （2） thin soil layer and thick bedrock. The rules for ground movement and deformation for different soil layer to bedrock ratios were obtained. On the basis of these rules, a prediction parameter modified model of the influence function was proposed, which is suitable for different values of unconsolidated soil layer thickness. The prediction results were verified using two sets of typical field data.

As one of the largest coal-rich provinces in China, Shanxi has extensive underground coal-mining operations. These operations have caused numerous ground cracks and substantial environmental damage. To study the main geological and mining factors influencing mining-related ground cracks in Shanxi, a detailed investigation was conducted on 13 mining-induced surface cracks in Shanxi. Based on the results, the degrees of damage at the study sites were empirically classified into serious, moderate, and minor, and the influential geological and mining factors (e.g., proportions of loess and sandstone in the mining depth, ratio of rock thickness to mining thickness, and ground slope) were discussed. According to the analysis results, three factors (proportion of loess, ratio of rock thickness to mining thickness, and ground slope) play a decisive role in ground cracks and can be respectively considered as the critical material, mechanical, and geometric conditions for the occurrence of mining surface disasters. Together, these three factors have a strong influence on the occurrence of serious discontinuous ground deformation. The results can be applied to help prevent and control ground damage caused by coal mining. The findings also provide a direct reference for predicting and eliminating hidden ground hazards in mining areas.

Monitoring ground disasters caused by underground mining has an important role in safe mining operations. In this study, terrestrial laser scanning (TLS) was performed to capture the point-cloud data of the slope landslide, ground steps and cracks, and tilt of a high-voltage tower during underground mining. The captured point-cloud data were processed by denoising, modeling, sectioning, and spatial analysis. TLS was also conducted to collect the point cloud of high-voltage towers in different mining periods, analyze the tilt degree of the towers, and estimate the stability of the tower body. The entire station was also used to collect the coordinates of single points around the study area. Two methods are compared, namely TLS monitoring and analysis, which is advantageous in describing the detailed local situation of a ground disaster, and total station monitoring, which focuses on absolute displacement monitoring of a single point. The monitoring process can combine both methods, including the absolute displacement and description of detailed status, thereby providing technical assistance to ensure mining safety. The monitoring solution has been applied in Gaoyang coal mine, which is located in Shanxi Province. Results show that the TLS method is more effective than the total station in capturing detailed spatial data on high-voltage towers and ground disasters such as landslides.

Subsidence data acquisition methods are crucial to mining subsidence research and an essential component of achieving the goal of environmentally friendly coal mining. The origin and history of the existing methods of field monitoring, calculation, and simulation were introduced. It summarized and analyzed the main applications, flaws and solutions, and improvements of these methods. Based on this analysis, the future developing directions of subsidence data acquisition methods were prospected and suggested. The subsidence monitoring methods have evolved from conventional ground monitoring to combined methods involving ground-based, space-based, and air-based measurements. While the conventional methods are mature in technology and reliable in accuracy, emerging remote sensing technologies have obvious advantages in terms of reducing field workload and increasing data coverage. However, these remote sensing methods require further technological development to be more suitable for monitoring mining subsidence. The existing subsidence calculation methods have been applied to various geological and mining conditions, and many improvements have already been made. In the future, more attention should be paid to unifying the studies of calculation methods and mechanical principles. The simulation methods are quite dependent on the similarity of the model to the site conditions and are generally used as an auxiliary data source for subsidence studies. The cross-disciplinary studies between subsidence data acquisition methods and other technologies should be given serious consideration, as they can be expected to lead to breakthroughs in areas such as theories, devices, software, and other aspects.

Surface subsidence caused by coal mining has become an important factor that affects and restricts the sustainable development of mining districts. It is necessary to use appropriate methods for effective subsidence monitoring. It is hard to monitor large gradient ground deformations with a high accuracy by using differential interferometric synthetic aperture radar (DInSAR) technology. Unmanned aerial vehicle (UAV) photogrammetry is limited in that it monitors the basin edge by subtracting two DEMs (digital elevation models). Therefore, in this paper we propose a combination of DInSAR and UAV photogrammetry to complement the two data advantages and to achieve a high-precision monitoring of mining subsidence areas. The subsidence of coal panel 81,403 in the Yangquan coal mine was obtained using DInSAR and UAV photogrammetry technologies. The appropriate fusion points were selected for the two datasets and the agreement between the fusion data and the leveling data was verified. The results indicated that the combination of DInSAR and UAV technology could monitor the settlement more accurately than the single use of DInSAR or UAV technology.

Surface movement and deformation induced by underground coal mining causes slopes to collapse. Global Navigation Satellite System (GNSS) real-time monitoring can provide early warnings and prevent disasters. A stability analysis of high-steep slopes was conducted in a long wall mine in China, and a GNSS real-time monitoring system was established. The moving velocity and displacement at the monitoring points were an integrated response to the influencing factors of mining, topography, and rainfall. Underground mining provided a continuous external driving force for slope movement, the steep terrain provided sufficient slip conditions in the slope direction, and rainfall had an acceleration effect on slope movement. The non-uniform deformation, displacement field, and time series images of the slope body revealed that ground failure was concentrated in the area of non-uniform deformation. The non-uniform deformation was concentrated ahead of the working face, the speed of deformation behind the working face was reduced, the instability of the slope body was increased, and the movement of the top of the slope was larger than at the foot. The high-steep slope stability in the mine was influenced by the starting deformation (low stability), iso-accelerated deformation (increased stability), deformation deceleration (reduced stability), and deformation remaining unchanged (improved stability).

To support carbon stock assessment and ecological restoration under the “Carbon Neutrality” objective, this paper developed a high-precision vegetation biomass model for expressway corridors in Shanxi Province, China, by integrating Unmanned Aerial Vehicle (UAV) technology and the random forest algorithm. Based on climatic zoning and DEM data, 70 sample plots representing diverse vegetation and topography were selected. LiDAR point clouds and multispectral data were spatially connected using the BallTree algorithm, achieving an average matching rate of 73.98–82.01%. A joint biomass model incorporating tree height and crown width was constructed with spatial cross-validation. The results indicate that the model substantially outperformed single-factor models, with R2 values ranging from 0.839 to 0.934 (highest in the Hengshan–Wutaishan forest area). Accuracy was higher in forest-dominated zones but lower in areas with significant human disturbance. A representative sample library was established for model optimization. This paper provides a robust technical framework for biomass monitoring across comparable Northern Hemisphere latitudes, thereby supporting sustainable green transport development.

Accurate assessment of expressway roadside vegetation carbon storage is essential for achieving carbon balance and ecological sustainability in the transportation sector. However, this is challenging due to the extensive spatial coverage, marked climatic variation, and pronounced spatial dependence of these vegetation corridors. Focusing on the expressway network of Shanxi Province, this study integrates UAV sample data with Sentinel-2 imagery. A graph convolutional network (GCN) is used to extract road network spatial topology, and estimation models for aboveground vegetation carbon storage are constructed for Shanxi's northern, central, and southern climatic zones. The performance of XGBoost, Random Forest, and Support Vector Machine models is then compared across these climatic zones, emphasizing the importance of model adaptability and spatial feature integration. The results showed that the incorporation of GCN-derived spatial features significantly enhanced the model's ability to characterize spatial autocorrelation along linear corridors. Among all models, the XGBoost-GCN model performed best in southern Shanxi, with a validation R² of 0.723. Compared with the overall model, the climate-zoned models improved estimation accuracy by an average of 18.3%, indicating that zoned modeling is more suitable for estimating carbon storage of roadside vegetation under strong climatic gradients. The carbon storage of expressway roadside vegetation in Shanxi Province exhibited an overall spatial pattern of decreasing from south to north. Hydrothermal conditions were identified as the main factors driving its spatial differentiation, while finely managed areas such as interchanges showed relatively high carbon sink potential. These findings indicate that the multi-scale remote sensing approach integrating GCN-derived spatial features with climate-zoned modeling can improve both the accuracy and regional adaptability of carbon storage estimation for roadside vegetation along long-distance expressways, and can provide methodological support for carbon monitoring, carbon sink identification, and differentiated ecological management of transportation infrastructure ecosystems.

Damage caused by underground coal mining is a serious problem in mining areas in China; therefore, studying and obtaining the rules of ground movement and deformation under different geological conditions is of great importance. The numerical software ANSYS was used in this study to simulate mining processes under two special geological conditions: (1) thick unconsolidated soil layer and thin bedrock; (2) thin soil layer and thick bedrock. The rules for ground movement and deformation for different soil layer to bedrock ratios were obtained. On the basis of these rules, a prediction parameter modified model of the influence function was proposed, which is suitable for different values of unconsolidated soil layer thickness. The prediction results were verified using two sets of typical field data.