Multi Scale Evaluation of the Impact of High-Intensity Mining on Vegetation Carbon Sequestration Capacity

Abstract

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.

1. Introduction

The sustainable development’s definition was promulgated after the publication of the Brundtland Commission Report by the World Commission on Environment and Development [1]. The development of sustainability means that the present development should not alter future generations’ ability to meet their own needs. More specifically, the concept of a sustainable economy focuses on the welfare value of market opportunities that each generation inherits [2]. Thus, if the government ignores great parts of nature, they may undervalue nature and weaken the comprehensive perspective of importance of nature to society [3]. By reviewing modern progress in nature protection’s, Mace found each period choses “nature” as the synonym for the biosphere [4]. In the 2020s, however, ‘natural’, ‘nature’s services’ or environmental’ should be introduced as holistic [5]. Considering abiotic nature progress, researchers recognize the latency effect of geosciences in completing Sustainable Development Aims [6,7,8]. For many years, developing economies in Asia have been the main consumers of coal worldwide, while at the same time developed economies (especially EU countries) have moved away from the commodity; this is the main factor leading to the change in global thermal coal trade trends [9]. The main reason for the excessive carbon dioxide emissions in China at present is the extensive burning of fossil fuels. Due to China’s resource structure characterized by a shortage of oil and gas, as well as relative abundance in coal, coal has become the main energy source in China [10]. A total of 70–80% of China’s total carbon emissions are attributed to the production and consumption of coal [11], which determines that in the further development of China’s coal will become one of the main energy sources. This characteristic has led to an increasing intensity and scale of coal resource extraction, and a decreasing ecological environment in mining areas [12]. This greatly affects the environmental restoration and ecological governance decisions in coal mines, so studying the trends in the environmental system of mining areas and conducting long-term monitoring is an important and necessary task. There are also many ways of conducting research on the environment in coal mines, monitoring and evaluating ecological environment, for example, assessing the ecological risks, promoting the ecosystems health, and assessing the value of ecological services. The evaluation method mainly involves establishing a corresponding evaluation index system for monitoring and evaluation, including methods such as the carrying capacity and ecological footprint [13,14,15,16,17]. However, these methods have significant limitations, so this article uses the NPP to measure the carbon sequestration capacity of vegetation.

The model of remote sensing application, climate–productivity relationship, light utilization efficiency, and ecophysiological process are the most popular methods of calculate NPP. During the early period, the climate–productivity relationship models were used to study China’s vegetation NPP, which were set up using the meteorological and NPP’s relationship. The models’ parameters, however, are convenient to download, the calculated results focus on point and surface [18]. Thus, the researchers combined the soil factors and vegetation growth characteristics to build the ecophysiological model [19,20]. To compare with climate–productivity relationship models, this method supports a relatively accurate result, but the base bata are harder to obtain, and the study area is harder to convert, which means it is unable to analysis mining area NPP. The model of PEM (production efficiency model) and FOREST-BGC are common methods of combining remote sensing application [21,22]. Based on remote sensing, this method can use LAI (leaf area index) to calculate NPP with daily and annual values [23]. The LAI is an important parameter in the whole calculation process, which can decide the accuracy of NPP value. Therefore, there is a high requirement for the accuracy of LAI. Although the CASA (Carnegie–Ames–Stanford approach) model cannot simulate future NPP value, it is convenient for calculating parameters and transforming the study area’s boundary. In conclusion, choosing the proper model for calculating the NPP has a great significance on the resource’s exploration and ecological environment measurement [24,25].

Li Xiaoting et al. studied the comparison in the non-subsidence area and mining area of the 113,101 first mining face in Bojianghaizi Mine, Ordos City. The results showed that during the growth season, mining work caused a significant effect on the vegetation index above the working face [26]. Quanzhi et al. studied the comparison between the different mining methods of the 110 working face in Lvliang City. The results showed that an innovative method can reduce surface ecological damage [27]. Li Quansheng et al. extracted the openpit mining effect on vegetation trends and quantitatively analyzed the cumulative ecological impact [28]. Further research discovered that microbial inoculation can enhance vegetation biomass and actively enhance soil environment in the Shendong mining area subsidence zone and the Maowusu sandy land coal mining subsidence zone [29,30,31,32]. Dulias R. studied the mining subsidence effect on Rybnik Plateau’s relief; the results showed several areas’ lowering rates are generally higher than estimated rates. The direct and indirect human activities will determine the terrain changes in dozen of years’ time [33].

The Shendong mine is China’s first coal production base with a capacity of 200 million tons. High-intensity mining causing surface subsidence in coal mines will inevitably affect surface ecology and carbon sequestration capacity. There are many factors and complex ways that affect the ecological impact of mining areas. Previous research on the ecological impact of coal mines has mostly analyzed it at a larger scale and over a 10–20-year time period, and has drawn macro-evaluation conclusions on the ecological impact.

In order to conduct a more detailed discussion of the effect of mining activities on carbon sequestration in mining areas during different periods, this article takes Shangwan coal mine in Shendong mine as the study area and evaluates the effect of coal mines on vegetation NPP based on a multi-scale research unit of coal mine mining area working face. In response to the current high-intensity mining conditions of wide working faces and high mining heights in the Shendong mine, as well as the arid and semi-arid meteorological conditions, aeolian sand and valley landforms, and sparse shrub and grassland ecological conditions in the coal mine area, the CASA model was chosen to evaluate used to calculate the NPP values of the study area from 2000 to 2023. Using the Probability Integral Model (PIM), we calculate the different mining periods’ surface subsidence in the coal mines. By analyzing the NPP’s mining impact laws and spatiotemporal variation trends in the entire area of the Shangwan coal mine, the impact area of the fourth district, and the working face’s effect area, we aim to more comprehensively reveal the degree and characteristics of the underground mining effect on surface vegetation.

2. Materials and Methods

2.1. Overview of Shendong Mining Area

The Shendong Mining area is situated in Ulanmulun Town, Ejin Horo Banner, Ordos City, Inner Mongolia. The geographical location of the Shendong mining area is in the transitional zone between the hilly and gully areas of the Loess Plateau and the Mu Us Desert in the upper and middle reaches of the Yellow River, located at 39°23′ N–39°25′ N, 110°04′ E–110°06′ E. Windblown sand covers most of the surface, which has a complex terrain, crisscrossing valleys, and source erosion (Figure 1). Since 2003, the mine has been operating in four mining areas (Figure 2 and Figure 3). The current productivity is 13 million tons each year. It is a large-scale coal mine that adopts comprehensive mechanized mining methods and belongs to an efficient and high-intensity mining mode. There is high subsidence value in coal mines, and the cracks are severely damaged, which may inevitably cause the effect on the ecological environment.

The overall terrain of the coal mine field is high in the north and east, low in the south and west, with an altitude of 1137.7–1295.5 m. The landform is divided into two types: loess landform and valley landform. Figure 1 displays the vegetation situation in the Shendong mining area and the Shangwan coal mine. The climate in the study area shows a northern warm temperate arid and semi-arid plateau continental characteristics, characterized by cold and long winters, hot and short summers, dry and windy spells in spring and autumn, and significant changes in meteorological elements. In the past 20 years, the average annual rainfall has been 300–400 mm, and the average annual evaporation has been 2160 mm.

The main vegetation characteristic in coal mines is dry grassland vegetation, with an average forest and grass coverage of about 35%. Due to drought and sandstorm invasion, especially the continuous drought impact in recent years, the zonal dry grassland vegetation community has gradually retreated and been replaced by drought tolerant and cold resistant sand and drought tolerant plants, presenting a landscape of sparse shrub and dry grassland. In addition, due to the influence of sand matrix and long-term human destruction, the vegetation in the area is sparse. Due to the influence of terrain, landforms, parent materials, climate, vegetation, and other factors, the mining area is distributed with zonal soils—chestnut soil, sandy soil, and tidal soil.

2.2. Datasets

Meteorological data includes precipitation, sunshine hours, temperature, etc., all of which were downloaded at the National Earth System Science Data Center. We used ArcGIS 10.8 to set up the Shendong mining area, and process the resolution of meteorological data (30 m).

Digital Elevation Model data are downloaded at the Geospatial Data Cloud.

The Normalized Difference Vegetation Index (NDVI) is obtained with Landsat 5–8 series. The data are processed from the GEE (Google Earth Engine) platform for the years 2000–2023, including cloud removal, cropping, and maximum value synthesis (30 m).

2.3. Improved CASA Model

The improved CASA model and software were selected to evaluate the vegetation NPP [18,34,35], where the photosynthetically active radiation (APAR) absorbed by plants and the actual light energy utilization efficiency (ε) are two important elements representing NPP:

$$\mathrm{NPP}(\mathrm{x},\mathrm{t})=\mathrm{APAR}(\mathrm{x},\mathrm{t})\times \epsilon (\mathrm{x},\mathrm{t})$$

(1)

$$\mathrm{APAR}(\mathrm{x},\mathrm{t})=SOL(\mathrm{x},\mathrm{t})\times FPAR(\mathrm{x},\mathrm{t})\times 0.5$$

(2)

$$\epsilon (\mathrm{x},\mathrm{t})=T_{\epsilon 1}(\mathrm{x},\mathrm{t})\times T_{\epsilon 2}(\mathrm{x},\mathrm{t})\times W_{\epsilon }(\mathrm{x},\mathrm{t})\times {\epsilon }_{\max }$$

(3)

In this formula, NPP (x, t) means the value of pixel x during time period t; APAR (x, t) means photosynthetically effect radiation obtained by pixel x during time period t; ε (x, t) means the real light energy utilization rate of pixel x during time period t; SOL (x, t) means the whole solar radiation during the time period t at pixel x; FPAR (x, t) means the incident photosynthetically active radiation absorption ratio by the vegetation layer, with 0.5 representing the proportion of photosynthetically active radiation utilized by vegetation to total solar radiation; T_ε1 (x, t), T_ε2 (x, t) means the stress effects of high and low temperatures on light energy utilization efficiency; W_ε (x, t) means the coefficient of water stress; ε_max means the maximum light energy utilization efficiency under ideal surface vegetation conditions.

2.4. NPP Mutation Test

M-K, as a non-parametric statistical testing model, is often used to determine the time of panel data mutation. The formula is [36]:

$$d_k={\displaystyle \sum }_i^k{j}_i$$

(4)

$$U{F}_k={\displaystyle \frac{d_k-E\left(d_k\right)}{\sqrt{\mathit{var}(d_k)}}}$$

(5)

$$\begin{gathered} U{B}_{k^{\prime }}=-U{F}_k \\ {k}^{\prime }=n+1-k \end{gathered}$$

(6)

In the formula, dk is a statistical measure; Ji is the cumulative value of the ith sample. If UF > 0, the time series displays a growth trend; if UF < 0, the time series shows a reduce trend. The intersection points of the UB and UF curves at the confidence level is the time when the time series undergoes a mutation. The units var (d_k) and E (d_k) mean the variance and mean of the cumulative values of the samples.

2.5. PIM

The probability integral model is used to evaluate deformation prediction in mining areas. It determines the correlation and sensitivity of model parameters by conducting stepwise regression analysis on the relevant parameters of deformation in mining areas. It helps to determine key parameters such as the subsidence coefficient and the main influence tangent, which are crucial for accurately predicting deformation in mining areas [37].

Subsidence:

$$W\left(x,y\right)=W_{cm}\cdot {{\displaystyle \iint }}_D{\displaystyle \frac{1}{r^2}}\cdot \mathit{exp}(-\pi {\displaystyle \frac{{(\eta -x)}^2+{(\xi -y)}^2}{r^2}}d\eta d\xi$$

(7)

In the formula W_cm means the maximum surface subsidence value during full mining; r is main impact radius; D means study area; x,y mean the point relative coordinate.

3. Result and Analysis

3.1. Analysis of Temporal and Spatial Distribution Characteristics of NPP in Shangwan Coal Mining

Based on statistical data, the spatial distribution characteristics of the annual average changes in vegetation NPP in the Shangwan coal mine over the past 24 years were analyzed. Figure 4 shows the NPP’s distribution maps in the entire coal mine area in 2000, 2005, 2015, and 2020. Based on the average vegetation NPP pixel values in the Shangwan coal mine over 24 years, the proportion of low value areas (110–170 gC/m²) is 18%; the median area (170–190 gC/m²) accounts for 24%; the high-value part (190–230 gC/m²) accounts for 58% of the total area.

The vegetation NPP in the entire Shangwan coal mine shows a significant increase and an overall growth trend. After 2008, the Shangwan coal mine vegetation NPP was greater than the NPP average level in 24 years, which means the Shangwan coal mine environment continuously improved. The mining activity probably has a slight effect on the mining area’s overall environment. The NPP’s low values in the study area are concentrated in the eastern area of the Shangwan coal mine, namely the surface above the first and second mining areas.

Figure 5 summarizes the analysis of the Shangwan coal mine’s NPP value from 2000 to 2023. From the figure, the NPP value maximum annual of vegetation in Shangwan coal mine over the past 24 years was 280.7 gC/m² in 2019, and the minimum was 98.5 gC/m² in 2011. The average value was 198.8 gC/m², and the annual average growth rate was 4.0%. The overall trend of vegetation NPP shows a “step like” growth pattern with three growth steps and two minimum values. The first growth step occurred from 2001 to 2009, with a relatively low growth rate. The second growth step occurred from 2011 to 2016, with a relatively fast growth rate. The third growth step occurred from 2017 to 2019, with the highest growth rate. In 2019, vegetation NPP reached its highest value in 24 years. Two minimum values appeared in 2001 and 2011, and after 2012, the vegetation NPP in the Shangwan coal mine gradually stabilized.

3.2. Analysis of the Spatiotemporal Evolution of Surface Vegetation NPP Under the Influence of Mining Activities

3.2.1. Development of Vegetation NPP Sampling Block Layout Plan for Working Face Impact Scale

To analyze the effect of annual mining activities at the 12401 working face on surface vegetation NPP, it is necessary to develop a layout plan for vegetation NPP sampling blocks at the working face impact scale. Considering the yearly periodicity of plants in the research area and the fact that the basic unit of coal mining is the working face, this study is designed based on the annual mining of the working face and its surface impact area as the evaluation object. The layout plan for vegetation NPP inspection sampling blocks is designed and formulated as follows:

Based on the annual mining section of the working face, conduct surface evaluation unit zoning. Divide the research area into East, Central, and West regions. The Central region is located directly above the mining area, while the West and East regions are located on the east and west sides of the Central region. The width of the eastern and western areas should extend to the boundary of surface movement, and the length of the eastern, central, and western areas should be the same as the working face’s advancing length in the current year. Choosing the working face mining section should confirm the mining activities area is not impacted by adjacent mining. The annual mining section of the working face should be selected as the time scale, considering the annual variation cycle of surface vegetation. The East, West, and Central districts are further subdivided into blocks with a width of 50 m, and each block is numbered in an orderly manner, such as West 1, West 2, etc. Sampling the yearly vegetation NPP values in each block, calculating the annual change rate of vegetation NPP in the subsidence range, analyzing the relationship between the change rate and the location of the mining section, and the duration of impact. Figure 6 displays the mining area and subsidence map of the 12401 working face in the fourth mining area in 2018 (a), as well as the layout of the surface vegetation NPP sampling area (b). The length of the block is taken as 2.70 km for the annual promotion section of the working face, and the width of the block is taken as 50 m. The central area of the sampling zone is divided into Central 1, Central 2 …, Central 6, the western area is divided into West 1, West 2 …, West 10, and the eastern area is divided into East 1, East 2 …, East 4.

Figure 7 shows the calculation curve of surface subsidence W and horizontal deformation ε of the subsidence basin profile caused by mining at the 12401 working face. Above the horizontal axis is the calculation point number, below is the horizontal distance scale, and the first measuring point is the distance from the starting point. The vertical axis W represents subsidence, i represents inclination along the calculation direction. As shown in the figure, on the surface moving basin, the maximum subsidence is 5989 mm, and the horizontal distance between the subsidence boundary and the mining boundary of the working face is 150 m. The maximum horizontal deformation is 44.4 mm/m, located 45 m outside the working face boundary. The maximum inclination is 86.7 mm/m, located in the vicinity directly above the boundary of the working face.

3.2.2. Time Effectiveness Analysis and Duration Analysis of Vegetation NPP Affected by Mining in Working Face

Figure 8 shows the trend curve of NPP values in each block of the western affected area (west side directly above the working face) of the 12401 working face in 2018. From the figure, the NPP values of different blocks in the sampling area(s) are different, and the NPP value of the same block changes over time.

The NPP change rate curve of in each block of the western region from 2018 to 2023 relative to 2017 before mining on the working face. From the graph, the NPP changes rate of different sections in subsequent years exhibit two change patterns: “first increase and then decrease, then increase again, and tend to stabilize” and “first increase and then decrease, then increase and then decrease, and tend to stabilize”. The maximum annual growth rate of vegetation is 12.2%, and the maximum decrease rate is 4.4%. The ground at a horizontal distance of 225–275 m from the working face boundary after 1–2 years of mining is most significantly affected by mining. The fluctuation of vegetation NPP from increase to decrease, and then from increase to decrease to stability, indicates that the mining effect on vegetation NPP is not infinitely extended, but has a temporal effect.

Therefore, this article defines the duration of the mining effect on vegetation NPP as the interval between the mining year and the year when the rate of change in vegetation NPP, relative to the year before the mining year, tends to stabilize. From the impact process and two change patterns of the 12401-working face, the mining effect duration of vegetation NPP in the western area is 3–4 years. The impact duration of blocks 3, 4, 5, 6, and 7 in the West is 3 years, and the impact duration of blocks 1, 2, 8, 9, and 10 in the West is 4 years. Within the impact period and 10 sampling blocks, the maximum growth rate of vegetation NPP was 15.1% of the West 6 block from 2017 to 2020, with an average yearly increase rate of 5.0%. The center of the maximum growth rate block is 275 m away from the working face return airway. The minimum growth rate is 5.1% for the West 10 block from 2017 to 2021, with an average yearly increase rate of 1.3%. The center of the minimum growth rate block is 475 m away from the working face’s return airway. During the period of “increasing first and then decreasing” within the duration of influence, the change rate of NPP in different blocks has a concentrated distribution. During the “further increase and decrease” period, the distribution of change rate changes from concentrated to discrete. During the period of “stabilization”, there is a significant dispersion in the NPP change rate of different blocks. This change is related to the different vegetation composition and topography within the block. From the 7-year period before to after the working face’s mining activities, the maximum growth rate of vegetation NPP compared to 2017 is 17.5% in the West 6 block (275 m away from the return airway of the working face) in 2023. The minimum growth rate was 2.4% in 2019 for the West 1 block (located 25 m from the corresponding surface position of the working face return airway).

Figure 9 shows the vegetation NPP’s change rate in each block of the central area from 2018 to 2023 compared to 2017 before mining.

The vegetation NPP change rate in each section of the central area under the influence of 12401 working face mining showed a pattern of “first growing and then declining, then growing and then declining, and tending to stabilize” in subsequent years. The vegetation NPP influence duration in the central area was 4 years. From the 7-year period before to after mining on the working face, the maximum vegetation NPP increase rate in 5 sampled blocks was 16.1% of that in the 4 blocks from 2017 to 2020, with an average yearly increase rate of 5.4%. The block with the highest growth rate is located in the middle above the working face, and the center of the block is 175 m away from the working face’s return airway. The minimum growth rate is 1.1% for Block 5 from 2017 to 2021, with an average yearly increase rate of 0.6%. The center of the minimum growth rate block is 225 m away from the working face’s boundary. During the periods of “first increase and then decrease” and “then growth and then decline” within the duration of influence, the NPP change rate in different blocks exhibits concentration in distribution. During the period of “stabilization”, there is a significant dispersion in the NPP change rate of different blocks.

The temporal analysis of the central and western areas shows that (1) the vegetation NPP in the subsidence region of 12401 working face is impacted by the mining activities, showing a temporal trend pattern of “first increasing, then decreasing, then increasing, and tending to stabilize”. The mining effect on vegetation NPP has a time impact, with a duration of 3–4 years. (2) The ground at a horizontal distance of 225–275 m from the working face boundary after 1–2 years of mining is most significantly affected by mining, with the highest annual vegetation growth rate of 12.2% and the highest reduction rate of 4.4%.

3.2.3. Distribution Pattern and Impact Distance Analysis of Vegetation NPP Affected by Mining in Working Face

This section analyses the distribution curve of the vegetation NPP change rate of each block above the mining section of the 12401 working face from 2018 to 2023, relative to 2017 (Figure 10). The figure includes cross-sectional views of the mining face and surface, and the horizontal axis is the horizontal distance from the NPP sampling block center to the boundary of the working face. From left to right, it is distributed in 14 blocks in the western, central, and eastern areas.

The figure displays the surface vegetation NPP change rate distribution along the western, central, and eastern regions presents two patterns; one is the overall uniform growth pattern in 2018 and 2020; The second is the wave shaped growth pattern in 2019, 2021, 2022, and 2023. Through the M-K mutation test, there has been an obvious difference in the distribution of vegetation NPP vary rate in the subsidence affected area of the working face. The spatiotemporal impact analysis of the mining activities on NPP is based on the differences in NPP distribution points in different sections and locations. In the years 2018 and 2019, the change rate of vegetation NPP directly above the working face shows a downward trend, while the change rate of coal pillars shows an upward trend; Afterwards, the vegetation NPP rate change in the two regions gradually tended to be consistent. Since the underground mining effect on vegetation NPP is achieved through surface subsidence that changes soil moisture and fertility, and the working face’s subsidence range is limited, the impact of mining on NPP is also limited in scope and cannot be infinitely expanded. As shown in the above figure, there are significant differences in NPP located above the coal pillar on the west side of the working face (west area), including: ① a horizontal distance of 325 m from the working face’s boundary of the in 2018; ② in 2019, the horizontal distance from the working face boundary was 275 m; ③ in 2020, the distance from the working face boundary was 275 m; ④ the distance from the working face boundary in 2021, 2022, and 2023 is 275 m. Assuming other conditions affecting NPP remain unchanged, it can be considered that the point of difference is the boundary point where surface vegetation NPP is affected by mining.

From this, it can be concluded that the average impact distance of the 2018 mining section of the 12401 working face on the surface vegetation NPP of the working face is 300 m, which is greater than the distance of 140 m from the surface subsidence boundary point. The impact range of NPP is greater than the surface subsidence range.

Within the affected area, the block with the minimum value on the NPP change rate distribution curve can be considered as the area with the greatest mining impact. In the above figure, in 2019, 2021, 2022, and 2023 after the working face’s mining year, the vegetation NPP trend rate showed a minimum value directly above the mining boundary of the working face (block West 1 and middle 1), which is also the location of the maximum surface tilt deformation and the area with a large horizontal tensile deformation, reflecting the severe impact of surface large deformation (tensile deformation of 44.4 mm/m, inclined deformation of 86.7 mm/m) on vegetation NPP.

4. Discussion

One researcher also calculated the NPP value of the Shendong mine by using the BLOME-BGC model from 2000 to 2010 [38]. The results of NPP trend and value are similar to this study, which can prove the data accuracy. This article showed that the Shangwan coal mine NPP concentrated at 98.5–280 gC/m², which is similar to the Niu Hongbo’s result. The author displayed the values of average NPP in the Shendong mine were principal concentrated at 150–200 gC/m², by using the MOD17A3 database in year 2000 to 2016 [39]. Meanwhile, as the Shangwan coal mine is part of the Shendong mine and located in Inner Mongolia, some analysis of NPP in Inner Mongolia may also support the results of this article. The results showed that Inner Mongolia’ NPP has maintained a fluctuating ascending trend from 2000 to 2023 [40,41,42].

Li Quansheng concluded that the mining activities have a distance attenuation characteristic to (FVC) Fractional Vegetation Cover. The high disturbances are mainly concentrated around the mine pit. The farther away from the pit, the lower the disturbance degree [28]. The FVC is an important index to related NPP, which can prove the result “the average distance of the mining effect on the surface vegetation NPP of the working face is 300 m” is reliable. Zhang Wanqiu et al. calculated the effect of NDVI on mining activities, which showed a distance of 2.0 km from the mine as the turning point and 3.0 km as the boundary [43]. This result is significant for the conclusion of this article.

There are also studies indicating the promoting effect of ecological restoration on the environment. Zhong Anya announced that based on natural restoration, the growth rate of NDVI changes is slow. After the complication of environment restoration projects, NDVI changes showed a significant upward trend, and vegetation conditions improved significantly. And the proportion of NDVI reduction area in the mining area is the highest [44]. Song haibin conducted an ecological restoration benefit evaluation of the mining subsidence area in Jiguan District, Jixi City. The results showed that after the environmental projects of the subsidence area, the ecological system structure of the mining area has been well restored, and the mining area ecological system been enhanced, which has a positive effect on the ambient environment [45].

The damage of coal resource development to the mining areas’ ecological system is a very serious problem and a very complex issue that requires further research. There are many facets that should be further researched, mainly including: (1) in-depth research on the essential characteristics and laws of different resource mining methods, and changes in the ecological system of mining areas under different geological conditions. (2) It is important to research the driving forces and different impact factors effects on the ecological system of mining areas, to formulate coal resource development plans, ecological restoration technology measures, and ensure the sustainable and healthy development of the mining areas’ ecological environment.

5. Conclusions

A multi-scale analysis and vegetation NPP evaluation in the coal mining area from 2000 to 2023 were conducted in response to the high-intensity mining and arid semi-arid meteorology, aeolian sandy soil gully topography, and sparse shrub dry grassland ecology in the Shangwan coal mine. The main conclusions can be drawn:

(1)

The evolution characteristics and impact modes of vegetation NPP over the past 24 years were summarized from the overall scale of the Shangwan coal mine. The annual NPP value of vegetation in the Shangwan coal mine is 98.5–280.7 gC/m², with an average of 198.8 gC/m². The vegetation NPP shows a “step like” temporal growth characteristic, with an average yearly increase rate of 4.0%, which is consistent with the trend of 2.2% annual increase in regional rainfall, 0.6% annual increase in temperature, and 1.8% annual increase in NDVI of vegetation coverage in coal mining areas. Although there is a consistent trend of 4.0% increase in coal mining capacity, it cannot be considered that mining has a promoting effect on the overall vegetation NPP. However, it also reflects that underground mining has not had an obvious negative effect on the overall NPP.

(2)

The impact scale of each mining area reveals that there is no significant temporal correlation between the low growth period of surface vegetation NPP and the impact of mining in the mining area; The growth rate of surface vegetation NPP is not significantly correlated with the mining intensity of this mining area.

(3)

The spatial and temporal impact patterns of vegetation NPP were revealed from the impact scale of the mining section in the 12401 working face in 2018. The vegetation NPP in the subsidence area of the 12401 working face showed a temporal variation pattern of “first growing, then declining, then growing, and tending to stabilize” due to the mining impact of the working face. The impact of mining on vegetation NPP has a time effect, with a duration of 3–4 years. The average impact distance on the surface vegetation NPP of the working face is 300 m, and the affected area of NPP is greater than the range of surface subsidence.

(4)

Through multi-scale evaluation of the effect of coal mining on the entire area, mining area, and working face, the inevitability and spatiotemporal impact limits of the influence of surface vegetation NPP on the working face scale caused by high-intensity mining in coal mines were revealed. Based on the specific conditions of the Shangwan coal mine, the mining factors effect on vegetation NPP is not significant across the entire region and long-term time series, and the effects on different evaluation scales are inconsistent. The research results can support a basis for precise ecological evaluation and rational governance in coal mining areas.