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).

High-strength mining of super-large coalfaces in the Shendong coalfield causes significant damage to the ground surface. To study the key parameters of undermined coalfaces that affect ground damage, 25 numerical simulation models were designed using an orthogonal experimental method based on the geological and mining conditions of the Bulianta Mine. In the orthogonal design, four factors (the lengths in both the dip and strike directions, the thickness and the mining speed of the coalface) were considered, with five levels designed for each factor. The subsidence displacements and deformations caused by the excavation were then simulated and verified using field surveying data. A damage extent index (DEI) was introduced and used to assess the extent of global ground damage caused by each simulative excavation. Analysis of variance (ANOVA) method was then employed to determine the key parameters of the coalface that significantly influence the ground damage. It was found that the coalface dip length and thickness and the coalface thickness can be regarded as the key parameters for ground objects of building and timberland, respectively. This research provides theoretical and technical support for the coordinated exploitation of resources and environments in Shendong and other similar, ecologically fragile coalfields.

In urban regions, mining subsidence induced by underground exploitation may cause damages to buildings. This paper attempts to improve a complete method and develop a fast computational tool for large-scale building damage assessment in a mining subsidence area. Two plane frame structural models are defined to analyse the mechanical behaviours of every building exposed to the subsidence phenomenon. The matrix displacement method is chosen to compute the internal forces within the structural models. In the calculation, the integrated structural models, including specific model shapes, initial loads and element properties, should be prepared for all the buildings; the subsidence is placed into the models as imposed support displacements. The influence grades of the buildings can finally be obtained by comparing the internal forces with their corresponding grading criteria. A developed code based on this research shows that approximately 1/3 of the buildings in the city of Joeuf (France) are at a high influence grade due to the subsidence expected in this city as a result of the collapse of two mined iron layers.

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.