Coal plays a key role in the composition of energy production and consumption in China. The mineral is a crucial resource for human economic and social development. The surface subsidence caused by coal mining has attracted disturbance and damage to the surface ecological environments of mining areas. To quantitatively study vegetation carbon sequestration in coal mining areas, this article takes the Shendong mining area as its research object and analyzes the spatiotemporal distribution characteristics and influencing factors of vegetation net primary productivity (NPP). Based on meteorological data, remote sensing images, and land use data in the mining area, this research analyzed the vegetation NPP and its relationship with meteorological factors in the Shendong mining area from 2000 to 2023. The results show that (1) in the past 24 years, the range of NPP variation in the Shendong mining area has been 0–350 gC/m². The overall NPP shows a fluctuating upward trend. The annual average change in NPP in the mining area is bounded by the two central dividing lines, with a lower value in the central part and a gradually increasing trend in the western and eastern parts. (2) The average vegetation NPP of the Shendong mining area over 24 years was 0–250 gC/m²; after 2011, the annual average NPP of vegetation was higher than this average level, which shows that the ecological environment of the mining area is gradually improving. Coal mining in the mining area has not caused a significant impact on the overall environment of the mining area. (3) There is a significant relationship between NPP and annual cumulative rainfall, average annual temperature, and average annual Normalized Difference Vegetation Index (NDVI). Overall, it is not significantly related to mining factors. The mining factors affect local areas and have a certain correlation.

This study uses the Shangwan coal mine in Shendong Mine as its research area and evaluates the vegetation net primary productivity (NPP)’s impact in the mining area based on the multi-scale research unit of working face. The probability integral model (PIM) was employed to analyze the characteristics of spatiotemporal variation and mining impact laws of surface vegetation NPP in the entire Shangwan coal mine and working face impact zone. We proposed vegetation NPP impact assessment scheme based on working face and annual mining impact spatiotemporal scales, as well as impact distance and duration evaluation parameters, and multi-scale evaluation results of NPP in the mining area were calculated. (1) The vegetation NPP of the Shangwan coal mine has shown a fluctuating growth trend from 2000 to 2023. The annual average NPP variation value is 98.5–280.7 gC/m², and the average annual value is 198.8 gC/m². (2) By analyzing the fourth district impact zone, the impact patterns of the underground mining area, subsidence area, and vegetation NPP above the mining area were revealed for each mining year. (3) From the impact of mining on the 12401 working face in 2018, the mining impact distance on surface vegetation NPP is 300–400 m, and the impact duration is 3–4 years. It reveals that the impact of underground mining on surface vegetation NPP in the entire coal mining area is not significant. The NPP in mining area shows a temporal variation pattern of fluctuating growth and stabilizing trends. The research results have comprehensively revealed the degree and characteristics of underground mining’s impact on surface vegetation from different evaluation scales, providing a basis for effective management of the mining area environment.

The surface subsidence duration and the maximum subsidence velocity are critical indicators to evaluate the stability and severity of surface damage. Precisely predicting them is important for guiding engineering design and protecting ground infrastructure. Traditional manual measurement methods are time-consuming and laborious, and the existing empirical formulas have low accuracy and poor applicability. Therefore, a new prediction method was established in this paper. Measured data from 30 mining areas were used for verification. The results show that the predicted surface subsidence duration is basically consistent with the measured value. The standard deviation of the two is 61 d, and the relative standard deviation is 6.6%. The predicted surface maximum subsidence velocity is basically consistent with the measured value. The standard deviation of the two is 10.0 mm/d, and the relative standard deviation is 1.6%. The surface subsidence duration and the maximum subsidence velocity are positively correlated with the coal seam thickness, negatively and positively correlated with the mining speed, and positively and negatively correlated with the mining depth. The mining speed and mining depth have the same sensitivity to the two indicators, and the coal seam thickness is more sensitive to the surface subsidence duration. Furthermore, construction within the subsidence basin may further contribute to surface subsidence. Therefore, land reuse measures should be implemented following the predicted surface subsidence duration in this paper. This study addresses the knowledge gap in this field by deriving theoretical formulas for surface subsidence duration and maximum subsidence velocity. In the absence of sufficient measured data, engineers can calculate predicted values in combination with geological mining conditions and develop appropriate mining plans based on the extent of surface subsidence.

Using long-time series remote sensing images to study the spatio-temporal variation characteristics of surface vegetation in mining areas and evaluate the impact of coal development on vegetation coverage has important practical value for ecological protection and sustainable development of mining areas. However, few existing studies simultaneously meet the requirements of long-term and high-frequency Normalized Difference Vegetation Index (NDVI) remote sensing inversion, and few studies simultaneously obtain the spatiotemporal evolution trends and dominant patterns of NDVI, which is not conducive to further research on the laws and mechanisms of the impact of mineral resource mining activities on the ecological environment. This study utilized the Google Earth Engine (GEE) platform to obtain long-term and high-frequency NDVI data from the Shendong mining area. The spatiotemporal evolution characteristics of NDVI in the Shendong mining area were studied using methods such as time series Univariate Linear Regression Equation (ULRE) combined with Empirical Orthogonal Function (EOF). The results are as follows: (1) From 1986 to 2024, the NDVI of the Shendong mining area exhibited a notable fluctuating growth trend. Over the past 39 years, the mean annual NDVI increased by 0.14, with an increase rate of 0.0036 per year, an increase of 54.46%, and an average annual growth rate of 3.18%. The R of the linear fitting equation reached 0.8975, indicating that the overall surface vegetation in the Shendong mining area has shown a significant positive trend over the past 39 years. (2) The annual average NDVI of all 18 coal mines exhibited a clear fluctuating growth trend, with R ranging from 0.8188 (Wujiata) to 0.9118 (Cuncaota). Furthermore, the overall fluctuation trends of the 18 coal mines were largely similar. (3) The five types of NDVI mean evolution trends in the Shendong mining area generally conform to a normal distribution and exhibit strong spatial heterogeneity. The area proportion of the non-significant change region is the largest, reaching up to 55.38%; the area proportion of the extremely significant reduction region is 5.42%, mainly distributed in areas such as construction land and open-pit mining; the area proportion of the extremely significantly increased region is 6.69%, mainly distributed around water bodies, forest land, and valley land; the overall area proportion of the reduction region is 19.32%, while the overall area proportion of the increased region is 25.30%, indicating that the overall surface vegetation in the study area is showing a positive trend. (4) The spatiotemporal distribution characteristics of NDVI in the Shendong mining area from 1986 to 2024 are extremely obvious and single. EOF₁ can represent all the characteristics of NDVI changes in the entire Shendong mining area, and its spatial distribution presents a pattern of “medium coverage as the main, high coverage as the supplement”, reflecting the comprehensive results of the interaction between ecological restoration and mining disturbance. This study can provide a reference for the ecological management, restoration, and planning of land and space in mining areas.