Spatiotemporal Variation and Influencing Factors of Vegetation Growth in Mining Areas: A Case Study in a Colliery in Northern China

Abstract

Based on MODIS EVI data of August collected from 2010 to 2021, and taking the Yingpanhao coal mine as an example, the spatiotemporal variation features of vegetation are analyzed using time series analysis, trend analysis and correlation analysis methods in the eco-geo-environment of the phreatic water desert shallows oasis. A significant increase trend is found for vegetation variation, and its development has improved generally in most areas. There is an obvious positive correlation between precipitation and vegetation growth, and a negative correlation between coal mining intensity and vegetation growth, but the influence of atmospheric precipitation on vegetation growth is stronger than that of coal mining intensity in the eco-geo-environment. The research results effectively reflect that atmospheric precipitation is the primary factor advancing the vegetation growth status in the coal mining regions. Vegetation development response to coal mining would be degraded first, then improved, and finally restored in areas with a deeply buried phreatic water level; that would promote the transformation of vegetation species from hydrophilous plants to xerophyte plants in areas with a shallowly buried phreatic water level. Therefore, it is necessary to carry out reasonable mine field planning according to the phreatic water level and the vegetation type distribution and to adopt different coal mining methods or corresponding engineering and technical measures to realize water conservation to avoid damaging the original hydrogeological conditions as far as possible. This information is helpful for promoting the eco-geo-environmental protection and further establishing the need for the dynamic monitoring of the eco-environment in the coal mining regions in the arid and semi-arid ecologically vulnerable areas of Northern China, which play a significant role in the long-term protection and rehabilitation of the eco-geo-environment and in the promotion of sustainable development.

1. Introduction

Northern China is located inland in an arid and semi-arid area, with features such as surface water shortages, scarce rainfall, and uneven seasonal distribution. These features lead to sparse vegetation, intensified desertification trends and an extremely fragile eco-geo-environment [1]. Vegetation is an important bridge and carrier that connects the atmosphere, water and soil, and it is instrumental in water conservation, the prevention of soil erosion, and in maintaining ecosystem stability in the region [2,3]. However, Northern China has the highest regional proportion of raw coal output, accounting for 55.0% of China’s total raw coal production. The raw coal output of the Inner Mongolia Autonomous Region ranked second in the country after Shanxi Province, with a cumulative output of 1.000913 billion tons in 2020 [4]. Coal mining has made incalculable contributions to the rapid national socio-economic development, but it has caused damage to the eco-geo-environment of coal mine districts, thus threatening local ecological security and social sustainable development [5,6]. Therefore, it is necessary to monitor and analyze the eco-geological environmental impact caused by coal mining systematically, and to take targeted preservation and treatment measures to ensure the sustainable development of coal resource exploitation and environmental protection [7].

Traditional environmental monitoring generally adopts field investigation methods, which have a number of shortcomings such as poor timeliness, long cycle, low efficiency, and incomplete monitoring results among others [8]. With the rapid development of satellite remote sensing technology, its application provides reliable technical support for the investigation and research of the eco-geo-environment and has the characteristics of good timeliness, a wide space range, objectivity, accuracy, high-precision positioning etc. [9]. Satellite remote sensing technology is extensively used in the field of large-scale eco-geo-environment monitoring and assessment [10,11]. Some scholars have studied the impact of urban construction on local ecology and have investigated regions that have experienced ecological damage using multi-source remote sensing monitoring [12,13,14]. A number of researchers have also studied large-scale quality changes and hydrological patterns as well as seasonal variation trends in water environments using the remote sensing inversion method [15,16,17]. Some Other researchers have proposed a new vegetation index, namely the hyperspectral image-based vegetation index (HSVI) and have constructed three new models to reflect vegetation growth using large-area hyperspectral remote sensing monitoring [18,19].

Coal mining destroys the integrity of the overlying strata structure of the coal seam, resulting in its roof overlaying the rock bending, fracturing, and collapsing, which further lead to surface subsidence and ground fissures [20]. Surface subsidence and ground fissures lead to the direct strain and fracture of plant roots, groundwater level decline, and soil erosion. They further lead to reduced soil water content, soil physical structure destruction, nutrient loss, and reduced vegetation growth. In recent years, some scholars have studied the vegetation changes in the coal mining area by using field investigation and remote sensing technology. Based on the measured results and field survey, it has been found that vegetation root damage main types include pulling apart, skin cracking, twisting and pulling out in coal mining areas experiencing subsidence [21,22]. Vegetation coverage and aboveground biomass are negatively correlated with groundwater depth, and continuous decline in the groundwater level caused by coal mining will lead to the inevitability of plant replacement in arid and semi-arid areas [23,24]. Coal mining also causes surface cracks and collapses, leading to serious water and soil loss around the cracks, further affecting the growth and distribution of vegetation [25,26]. Groundwater level changes induced by coal mining subsidence accelerate soil water content loss, physical structure destruction, nutrient deficiency, and cause further vegetation degradation. However, it is different in the corresponding influence on plant growth and soil features in the different coal mining areas [27,28,29]. Some scholars have also conducted a lot of research on land degradation, eco-geo-environmental monitoring, reclamation, and greening evaluations in coal mining districts [30,31]. Some of the spatiotemporal data extracted from remote sensing images have been used to establish a multi-factor integrated assessment and prediction model [32,33,34]. Some scholars have monitored the effects of open-pit mining on the eco-environment based on hyperspectral data and have provided integrated systems to monitor overburdened rock movement and variation in the eco-environment in high-intensity mining regions [35,36]. While these studies mainly focus on the impact of groundwater level and ground subsidence caused by coal mining on vegetation growth, ignoring the impact of weather, especially precipitation, on vegetation growth in the same period, and the relative size analysis of the impact of coal mining and atmospheric precipitation on vegetation growth is insufficient in arid and semi-arid coal mining areas.

Taking the Yingpanhao colliery in Ordos (Northern China) as an example, the regularity of temporal and spatial evolution and the distribution features of vegetation of phreatic water desert shallows oasis are analyzed by studying dynamic variations in the enhanced vegetation index (EVI) using maximum value composite (MVC), time series analysis, trend analysis, and correlation analysis methods to clarify driving factors such as coal production and the degree of precipitation influence on the vegetation dynamic changes based on MODIS and meteorological data. It provides reliable decision support for coal resource planning and reasonable protection measures for the eco-geo-environment in coal mining regions that will play a significant role in the long-term promotion of high quality sustainable development.

2. Materials and Methods

2.1. Study Area Profile

Yingpanhao colliery is located in the center of Mu Us Desert geographically, which belongs to Uxin Qi, southwest of Ordos City, Inner Mongolia Autonomous Region, China. The traffic conditions of Uxin Qi are very convenient, as shown in Figure 1.

The surface eco-geo-environment of the Yingpanhao mine field is classified as the following type, phreatic water desert shallows oasis, which is the most prominent eco-geo-environment type in the arid and semi-arid region in Northern China [37,38], as shown in Figure 2a. The following are features of this specific eco-geo-environment: Aeolian sand(Q₄^eol) is distributed across the whole area; Quaternary loess is exposed in sporadic sections [39,40]. In general, the terrain is relatively flat and has an elevation that ranges from +1173 m to +1317.40 m with a downward trend, as seen in Figure 2b. The general configuration of the surface contains some low-lying wetlands that are irregular in shape and of different scales with relatively flat and undulating fixed and semi-fixed sand dunes, as seen in Figure 2c–f. There are a huge number of surface water resources and shallow groundwater resources. Sources of surface water mainly include desert lake water, stream water and shoal wetland water. Rich groundwater resources are stored in phreatic aquifers comprising Aeolian; they have a shallow buried depth and are easily used, which is important for the development of the vegetation in the local eco-environment. Surface vegetation is mainly composed of xerophytic shrubs and grasses located around sand dunes, including Artemisia desertorum (Figure 2g), Astragalus adsurgens (Figure 2h), and Hippophae rhamnoides Figure 2i) among others, and the vegetation has a relatively sparse distribution. However, some hygrophilous phytobiocoenose and a small number of tall macrophanerophytes are well-distributed and have good vegetation coverage, and there is an abundance of vegetation types, such as Prundo phragmites (Figure 2j), Achnatherum Splendens (Figure 2k), Salix psammophila (Figure 2l), etc., in low-lying wetland areas. Saline–alkaline tolerance vegetation, such as Kalidium foliatum (Figure 2m), Puccinellia distans parl (Figure 2n), etc., are distributed in soil salinization regions. Surface vegetation is important to prevent wind erosion.

2.2. Data Source and Processing

The vegetation index of a long time series extracted from satellite remote sensing data is widely used to monitor vegetation developments [41]. The enhanced vegetation index (EVI) is more susceptible to monitor changes when the vegetation biomass is high, reduces the influence of atmospheric conditions on values of the vegetation index, and corrects for canopy background signals. It has incomparable advantages over other vegetation indexes through further improvements to the vegetation index and synthesis algorithm [42]. The EVI has greatly reduced the limitations of aerosols and the soil background. Therefore, the EVI uses the background regulation parameter L and atmospheric revision parameters C₁ and C₂ to reduce the impact of the atmosphere and soil background at the same time by integrating the above two vegetation indexes mentioned above [43]. The EVI is calculated as per Equation (1):

$$EVI=G\frac{{\rho }_{NIR}-{\rho }_{red}}{{\rho }_{NIR}+C_1\times {\rho }_{red}-C_2\times {\rho }_{blue}+L}$$

(1)

where ρ_NIR, ρ_red, and ρ_blue are atmospherically corrected (or partially atmospherically corrected) surface reflectance, G is is the gain coefficient, L is the soil adjustment coefficient, and C₁, and C₂, are the coefficients correcting the influence of the atmosphere on the red light band through the blue light band. For the standard MODIS EVI production, L = 1, C₁ = 6, C₂ = 7.5 and G = 2.5.

EVI data come from the MODIS vegetation index product data MOD13Q1 provided by NASA and have a temporal resolution of 16D and a spatial resolution of 250 m × 250 m. Due to the cold winters in the study area, the soil freezes and vegetation cannot grow. Vegetation growth is the most prosperous in August every year, and the vegetation coverage is also the best. The maximum EVI value is generally obtained in the middle of August, and its maximum value represents the maximum coverage obtained by surface vegetation coverage. In this period, the EVI can represent vegetation development characteristics. The MODIS EVI grid data sets used in the paper is obtained from satellite remote sensing image by the method of Maximum Value Composite (MVC), which are downloaded from NASA (http://ladsweb.nascom.nasa.gov, accessed on 21 May 2022).

Meteorological data (statistical data set of monthly precipitation in Uxin Qi from 2010 to 2021 and surrounding meteorological stations) are from the China Meteorological Data Center (http://data.cma.cn, accessed on 21 May 2022).

2.3. Methodology

2.3.1. Maximum Value Composite

The maximum value composite (MVC) method is the most commonly used maximization combination method around the world, and is used to eliminate interference from clouds, the atmosphere, and the solar altitude angle from monthly data. Based on the MVC method, the EVI maximum value is obtained to reflect the surface vegetation growth and coverage more accurately, as Equation (2):

$$MEV{I}_{ij}=Max(EV{I}_{ij})$$

(2)

where i is the year serial number of the year, j is the serial number of the month, and EVI_ij is the EVI value of the jth month of the ith year.

2.3.2. Time Series Analysis

Time series analysis studies the statistical laws followed by random data sequences to solve practical problems based on random process theory and mathematical statistics. By analyzing time series data, useful time-related information that is hidden in the data can be obtained, and knowledge can be extracted [44].

2.3.3. Trend Analysis

The univariate linear regression model trend analysis method is used to calculate the spatial differentiation characteristics within the selected time range pixel by pixel to reflect the overall spatial change law of vegetation. The linear regression coefficient of the EVI is calculated using the least squares method to obtain the vegetation’s greenness rate of change (GRC), which is used to analyze the annual change trend of the EVI and to reflect vegetation changes [45,46], as per Equation (3):

$$GRC=\frac{n\cdot {\displaystyle \sum _{i=1}^n\left(i\cdot YEV{I}_i\right)-{\displaystyle \sum _{i=1}^{n}i}\cdot {\displaystyle \sum _{i=1}^{n}YEV{I}_i}}}{n\cdot {\displaystyle \sum _{i=1}^n{i}^2}-{\left({\displaystyle \sum _{i=1}^{n}i}\right)}^2}$$

(3)

where GRC is the vegetation greenness rate of change; variable i is the year’s serial number, the value of which ranges from 1 to 10; n is the length of the time series studied, n = 10; and YEVI_i is the maximum EVI value of August in the ith year. GRC = 0 means that there is no obvious change in vegetation growth and coverage, GRC > 0 indicates that the change trend of the EVI is increasing, that is, vegetation development is improved, on the contrary, vegetation development is reduced.

2.3.4. Simple Correlation Analysis

Simple correlation analysis is used to analyze the correlation between two variables, as seen in Equation (4):

$$r_x{}_y=\frac{{\displaystyle \sum xy-\frac{1}{n}{\displaystyle \sum x{\displaystyle \sum y}}}}{\sqrt{{\displaystyle \sum x^2-\frac{1}{n}\left({\displaystyle \sum x^2}\right)}}\sqrt{{\displaystyle \sum y^2-\frac{1}{n}\left({\displaystyle \sum y^2}\right)}}}$$

(4)

where r_xy is the correlation coefficient of variables x and y, x is the independent variable, and y is the dependent variable.

2.3.5. Multiple Correlation Analysis

Multiple correlation analysis aims to study the correlation between multiple independent variables and a dependent variable at the same time. Assuming that x is the dependent variable, y and z are independent variables, and r_xyz is the multiple correlation coefficient between x and y, z, as seen in Equation (5):

$$r_x{}_{yz}=\sqrt{1-\left(1-r_{xy}^2\right)\left(1-r_{y\cdot xz}^2\right)}$$

(5)

where r_xy is the correlation coefficient of variables x and y, and r_y·xz is the partial correlation coefficient of variables x and z under the control of variable y.

2.3.6. Partial Correlation Analysis

The partial correlation analysis method refers to a process in which two variables are related to a third variable at the same time. The influence of the third variable is eliminated, and only the correlation degree between the other two variables is analyzed by calculating the partial correlation coefficient, as seen in Equation (6):

$$r_{x\cdot }{}_{yz}=\frac{r_{xy}-r_{xz}{r}_{yz}}{\sqrt{\left(1-r_{xz}^2\right)\left(1-r_{yz}^2\right)}}$$

(6)

where r_x·yz is the partial correlation coefficient between variables z and y under the control of variable x. r_xy, r_xz, and r_yz represent the correlation coefficients of variables x and y, x and z, y and z, respectively.

3. Results

3.1. Analysis of Vegetation Spatiotemporal Variation Characteristics

3.1.1. Interannual Variation Trend of Vegetation

The EVI distribution for August of each year from 2010 to 2019 is shown in Figure 3. In order to carry out better comparative analysis of each August’s EVI, the same grade divisions were adopted. EVI values ranging from 0 to 0.1 are generally expressed as water surfaces, areas with sparse vegetation, or even as bare ground and sand dunes. Areas with EVI value greater than 0.5 are mostly irrigated cultivated land or dense forest land. Not all areas with high EVI values are cultivated or forest land. In areas with a shallow groundwater depth, there is luxurious shrub grass vegetation growth with high EVI values that are generally better than the values achieved in other areas [47]. Vegetation growth in the Yingpanhao mine field varies greatly over time. From 2010 to 2012, as seen in Figure 3a–c, the EVI value is only greater than 0.3 in a small area, which accounts for about 10% of the whole area, the EVI value is smaller than 0.2 areas percentage account for about 40.79% on average, and the EVI value between 0.2 and 0.3 areas percentage is averagely 49.13%. In August 2013, as shown in Figure 3d, the area percentage with an EVI value higher than 0.3 increased significantly, accounting for about 32.53% of the whole area, indicating that the vegetation growth condition improved and that its surface vegetation coverage increased significantly. However, during the three years from 2014 to 2016, as seen in Figure 3e–g, the area percentage with an EVI value lower than 0.2 remains stable, accounting for about 36.15% in total averagely. The area percentage with an EVI value higher than 0.3 increased slightly, from 12.35% to 14.81%. From 2017 to 2019, as seen in Figure 3h–j, the area percentage with an EVI value is higher than 0.3 showed an obvious increase from 26.87% to 35.03%, and the area percentage with an EVI value lower than 0.2 remained largely unchanged, accounting for about 16.04% of the total area. In general, the area percentage with an EVI value higher than 0.3 increased from 9.74% to 35.03%; however, the area percentage with an EVI value lower than 0.2 decreased from 40.75% to 16.31%, indicating that the vegetation growth status improved significantly, but the vegetation development is generally poor.

The EVI values of the pixels were analyzed statistically. As shown in Figure 4, it can be seen from the interannual variation in August EVI from 2010 to 2019 that the average EVI varied from 0.2135 to 0.2837. The changes can be roughly divided into three stages: ① from 2010 to 2013, the EVI shows an increasing trend, with an increase of about 26.7%; ② from 2013 to 2014, the EVI shows a significant downward trend, with a decrease of about 20.1%; ③ from 2014 to 2019, the EVI has an upward trend, with an increase of about 28.14%. Its growth slope has a linear fit of 0.00648 and R² = 0.4943, indicating a slow fluctuation and an upward trend, which implies the study area vegetation growth has been gradually improving.

The interannual difference in the EVI was calculated, as shown in Figure 5. The differences in the EVI between 2011 and 2012, 2012 and 2013, 2014 and 2015, 2016 and 2017, 2017 and 2018, and 2018 and 2019 are greater than zero, which illustrates that vegetation growth shows an upward trend, and there is an overall trend of improvement for surface vegetation development; only the EVI differences observed between 2010 and 2011, 2013 and 2014, and 2015 and 2016 are negative, which means the vegetation growth and development were poor, and there was a decrease in the surface vegetation is declined compared to the previous year. However, the vegetation growth was significantly better in 2013, resulting in obvious differences in EVI changes between 2012 and 2013, and 2013 and 2014.

3.1.2. Spatial Distribution Characteristics of Vegetation

As shown in Figure 6a, the average EVI was found to vary from 0.087 to 0.559. There are large areas with EVI values less than 0.2, accounting for about 21.55% of the total area, and these areas are distributed in the northern portion of the mine field. Vegetation growth and development is relatively good in the southern portion, which has large contiguous areas with EVI values greater than 0.3, accounting for about 17.7% of the total area. The area with EVI values less than 0.2 is significantly smaller than that in the northern portion. As per Figure 6b, there are large regions where the depth of the phreatic water level is less than 3 m, accounting for about 27.62% of the whole mine field and mainly distributed in the south. The depth of the phreatic water level is greater than 6 m in 49.33% of the mine field area and mainly distributed in the north. Compared to Figure 6b, the EVI distribution has a relatively high correlation with the phreatic water level depth. Therefore, the uneven distribution of phreatic water level depth leads to a continuous strip distribution in the area greater than 0.3 m that is mainly in the south, and a sporadic patchy distribution found in the north.

The vegetation growth and coverage were analyzed by drawing a distribution frequency histogram of the EVI with an interval of 0.1, as shown in Figure 7. The average EVI does not conform to the standard normal distribution in the statistics and is more in line with the characteristics of a partial normal distribution. They all have an obvious tailing phenomenon that is mainly lower than 0.3, accounting for 82.79% of the whole area.

3.1.3. Spatial Pattern of EVI Variation

The change trends in the spatial distribution of vegetation development from 2010 to 2019 are shown as Figure 8. The GRC can be classified into four grades, namely: basically unchanged, slightly improved, moderately improved, and obviously improved. Vegetation development in the south improved significantly compared to the northern area. The obviously improved regions are largely distributed in the southern area. The areas with an improved EVI reached 77.09% (the obviously improved areas are 6.32%). The basically unchanged areas reached 22.91%, and these areas were mainly concentrated in the north. The GRC values are greater than 0 for the whole study area, which means that the dynamic vegetation growth is generally rising and vegetation development has improved in the period.

3.2. Sensitivity Analysis of EVI to Precipitation

According to the annual precipitation statistics of Uxin Qi, where the Yingpanhao minefield is located, its precipitation is mainly concentrated from May to September, which accounts for 83.1% of the annual precipitation, as shown in Figure 9. The average precipitation of each month from May to September is greater than 30.0 mm, with the precipitation in August reaching the maximum value of the whole year. The cumulative precipitation in December, January, and February is only 6.5 mm, accounting for 1.9% of the annual precipitation, which is very rare.

According to analysis above, the August rainfall is the most important for the vegetation growth and development in Uxin Qi. As shown in Figure 10, it can be seen that the August atmospheric precipitation shows an increasing fluctuation. In August 2015, there was 30.7 mm of precipitation, the minimum amount, but in August 2016, there was 151.41 mm of precipitation, the maximum amount for the decade. Comparing the precipitation with the EVI changes in the Yingpanhao mine field, the precipitation in August increased from 61.18 mm to 129.93 mm, an increase of 68.75 mm, which indicates a growth rate of 112.37%. The vegetation EVI increased from 0.2187 to 0.2837, an increase of 0.065, which indicates a growth rate of 29.72%. Both show an increasing trend, that is, with the gradual increase of precipitation, the surface vegetation EVI increases in the mine field accordingly, indicating that increased precipitation promotes surface vegetation growth and significantly improved coverage.

The multi-year average EVI and precipitation represent the overall situation of regional vegetation growth and coverage and environmental dry and wet conditions, respectively. The linear regression equation between the annual EVI and the annual precipitation from 2010 to 2019 was constructed, and the correlation coefficient was calculated to reflect the spatial variation characteristics of the EVI and precipitation sensitivity in the region. Where the correlation coefficient is the sensitivity index of vegetation to rainfall variation, and when the precipitation sensitivity index is greater than 0, it indicates that there is a positive correlation between precipitation and the EVI and the greater its value, the stronger sensitivity of the vegetation’s EVI is to precipitation, and vice versa [48].

As shown in Figure 11, when the atmospheric precipitation is 151.41 mm, the EVI value of surface vegetation is only 0.2265, which is an obvious deviation from the center of the data set’s distribution. Because the surface vegetation is dominated by some non-groundwater-dependent drought-tolerant plants, and when the precipitation is excessive, and the phreatic water level will be higher, the high groundwater level inhibits the root respiration of this drought tolerant vegetation; on the contrary, it is not conducive to the normal growth and development of vegetation. Therefore, this data point should not be considered in the process of linear fitting. The correlation coefficient is 0.000621, that is, the sensitivity index is 0.000621, which is greater than 0. There is a positive correlation between precipitation and the EVI.

The remaining data are analyzed by SPSS, and the Shapiro–Wilk test demonstrated significant probability with Sig = 0.667 > 0.05, which means that it obeys the laws of normal distribution. It can be seen from the results that the correlation coefficient between precipitation and the vegetation EVI is 0.769, that is, r = 0.769, and p = 0.015 < 0.05, indicating that the correlation is significant, and that there is a significant positive linear correlation between precipitation and the vegetation EVI. When the correlation coefficient |r| ranges from 0.8 to 1.0, it indicates that there is a strong correlation between the two variables; when |r| is in the range from 0.6 to 0.8, it indicates a significant correlation; when |r| ranges from 0.4 to 0.6, it indicates a moderate correlation. There is a weak correlation when |r| ranges from 0.2 to 0.4; when |r| ranges from 0 to 0.2, it means a weak correlation or no correlation.

3.3. Correlation Analysis of EVI to Coal Mining and Precipitation

Currently, the Yingpanhao coal mine currently mainly mines the no.2–2 coal seam, which is located at the top of the third section of the Jurassic Yan’an Formation(J₂y³). The thickness of the coal seam ranges from 3.16 m to 10.24 m, with an average thickness of 6.29 m, and the burial depth ranges from 660.38 m to 783.68 m, with an average depth of 722.88 m. There are six mined stopes that have been worked since 2017, as shown in Figure 12. Coal production increased from 2.18 million tons in 2017 to 10.3 million tons in 2021. As shown in Figure 13, the EVI of the mined area has shown a declining trend, falling from 0.259 to 0.234, from 2017 to 2021. Meanwhile, the August precipitation in Uxin Qi has varied from 87.82 mm to 55.17 mm, showing a downward trend.

3.3.1. Multiple Correlation Analysis

Multiple linear regression analysis is usually used in multiple correlation analysis. The correlation between independent variable coal production and precipitation and the dependent variable EVI is described as a binary linear regression model. The results of the multiple correlation analysis between coal production, precipitation, and the EVI are as shown in

Table 1. Multiple correlations.

Model Summary a		AVOVA a			Coefficients a
			F	Sig.		Unstandardized Coefficients	Collinearity Statistics (VIF)
R	0.992 b	Regression	59.052	0.017	Constant (β0)	0.241
R2	0.983	Residual			Coal production (β1)	−0.208	1.005
Adjusted R2	0.967	Total			Precipitation (β2)	0.251	1.005

a. Dependent variable: EVI, b. Predictors: (constant), precipitation, coal production.

.

Where, X₁ is an independent variable that indicates the coal production of the Yingpanhao coal mine, unit: 100 million tons; X₂ is an independent variable that indicates the August precipitation in Uxin Qi, unit: m; β₀ is the intercept; β₁, β₂ are regression coefficients.

R² is 0.983, which means it has quite a good fitting effect. Meanwhile, R is regarded as a multiple correlation coefficient, and it indicates the degree of linear correlation between the dependent variable and all of the independent variables. The closer R is to 1, the higher the correlation degree is. The multi-correlation coefficient R is 0.992, which means there is a high degree of correlation between the independent variables coal production and precipitation, and the dependent variable, the EVI. The variance inflation factor (VIF) reflects the severity of multicollinearity between independent variables. The closer the VIF is to 1, the weaker the multicollinearity between the independent variables is. The VIF is 1.005, which means that the collinearity between the independent variables, coal production and precipitation, is weak, and that the independence between the two independent variables is good. The significance F-test of the equation shows that p = 0.017 < 0.05, indicating that there is a significant linear correlation between coal production, precipitation, and the EVI of the mined area and that the bivariate linear regression model has been established correctly. The regression coefficient β₁ is −0.208, which means that coal production has a negative impact on the EVI of the mined area, and the regression coefficient β₂ is 0.251, which means that precipitation has a positive impact on the EVI of the mined area.

3.3.2. Partial Correlation Analysis

The results of the partial correlation analysis between to coal production, precipitation and the EVI are shown in

Table 2. Partial correlations.

Control Variable			Coal Production
Precipitation	EVI	Correlation	−0.979
		Significance (2-tailed)	0.021
		df	2
Control Variable			Precipitation
Coal production	EVI	Correlation	0.985
		Significance (2-tailed)	0.015
		df	2

. The partial correlation coefficient r₁ of coal production and the EVI while controlling the variable, precipitation, is −0.979, and its significant probability is sig₁ = 0.021 < 0.05, which means there is a significant negative correlation between coal production and the EVI. The partial correlation coefficient r₂ of precipitation and the EVI is 0.985 while controlling the variable, coal production, and its significant probability is sig₂ = 0.015 < 0.05, which means there is a significant positive correlation between precipitation and EVI. In addition, |r₂| > |r₁|, the partial correlation between precipitation and EVI is closer than that between coal production and EVI. The degree of impact of coal production on the EVI of the mined area is lower than that of precipitation, in other words, atmospheric precipitation is the most critical element influencing the development of surface vegetation.

4. Discussion

Groundwater resources play a vital role in desert vegetation ecology development in phreatic water desert shallows oasis, which is very important to prevent land desertification effectively. With the large-scale and high-intensity exploitation of coal resources, underground coal mining causes groundwater seepage along rock fissures to underground goafs; this leads to the inevitable decline in the phreatic water level.

Vegetation distribution is relatively sparse in areas with a deeply buried phreatic water level, and the vegetation type is mainly xerophytes in these areas. Because the xerophyte roots cannot reach the phreatic water level, the xerophytes can maintain normal growth only by relying on atmospheric precipitation or soil aeration zone water, indicating that atmospheric precipitation is the key factor affecting the xerophyte growth. A phreatic water level that is too high will inhibit the respiration of xerophytic roots. On the contrary, the destruction of vegetation roots, the soil physical structure, aeration zone water, and soil nutrients caused by coal mining collapse greatly inhibit the healthy growth of vegetation, and surface vegetation development will enter a recession period. As the ground fissures are filled under by wind erosion, rainwater wash, and surface gravity settlement, the soil structure and physical and chemical properties will tend to stabilize again, the soil nutrients will be gradually restored, and vegetation growth and development will be improved to a certain extent. Then, vegetation growth and coverage will show a slight upward trend and will gradually stabilize. The plant community structure will also improve, and the initial vegetation conditions will be restored. Therefore, the reduced phreatic water level decline caused by coal mining is not the direct cause of vegetation decline, and surface vegetation development would be degraded first, then improved, and finally restored, as shown in Figure 14.

Areas with a shallowly buried phreatic water level area often have ponding depressions in which hydrophilous vegetation can grow. When the phreatic water level is very shallow or overflows to the surface, it can not only promote vegetation growth, but it also makes it more difficult for the wind to blow sand around than in the anhydrous areas or in the deep buried areas with a deeply buried phreatic water level, because the soil moisture content is large, and its environmental quality is good. Long-term coal mining has caused a decline in the regional phreatic water level. When the phreatic water level drops to a depth that can still be absorbed directly or indirectly (through capillary phenomena) by herbaceous plants, a large number of herbaceous plant and some shrubs and arbors appear one after another. It is not easy for soil erosion and desertification to occur, and the environmental quality is still relatively good. When the phreatic water level decreases continuously, soil water content storage cannot be effectively supplemented, and most herbaceous plants gradually die due to lack of water. Only some extreme drought-tolerant herbaceous plants and shrubs with developed root systems are able to survive under these conditions. Continuous declines in the phreatic water level also make it difficult for deep-rooted shrubs to survive, and the vegetation is comprehensively degraded, making it difficult for the vegetation to defend the area against strong wind and sand invasion, which eventually leads to the complete deterioration of the eco-geo-environment quality, as shown in Figure 15. Declines in the phreatic water level caused by coal mining would promote the transformation of vegetation species from hydrophilous plants to xerophyte plants.

Actually, underground coal mining initially does do harm to the surface eco-geo-environment of the landscapes containing groundwater and a strata structure above mined stopes, leading to some indirect effects on surface vegetation. However, groundwater level decline caused by coal mining will promote non-groundwater-dependent xerophytes to replace hydrophilous plants to occupy the main type of surface vegetation. Atmospheric precipitation will become the key factor in reducing the impact of coal mining on vegetation growth and promote eco-environment restoration.

Therefore, it is necessary to carry out mine field planning reasonably according to the buried depth of the phreatic water and the distribution of vegetation types and adopt different coal mining methods or corresponding engineering and technical measures to realize water conservation in coal mining to avoid damaging to the original hydrogeological conditions. Different species can also be planted in areas with different buried phreatic water level depths before coal mining to help promote the persistence and self-repairing ability of the eco-geo-environment. Thus, the sustainable development of coal resource utilization and promoting the eco-geo-environment quality could be guaranteed effectively.

5. Conclusions

(1) This research shows that using MODIS EVI data can monitor the spatiotemporal changes and distribution characteristics of vegetation growth in the Yingpanhao minefield generally, which is helpful in understanding the mining impact on vegetation in the phreatic water desert shallows oasis;

(2) The average EVI for August in the Yingpanhao mine field vegetation fluctuated between 0.2187 and 0.2837 from 2010 to 2019, which means that the vegetation growth was getting better. The areas with an improved EVI reached 77.09%, vegetation growth and coverage in the southern portion is significantly better than that in the northern area, basically unchanged EVI areas reach 22.91%, and these areas are mainly concentrated in the north, because the phreatic water level is more deeply buried relatively in the north than in the south. Therefore, the surface vegetation type and growth distribution are evidently affected by the groundwater level depth distribution;

(3) There is an obvious positive correlation between precipitation and the EVI. There is a clearly negative correlation between coal mining intensity and EVI, but the influence of atmospheric precipitation on vegetation growth is stronger than that of coal mining intensity. Atmospheric precipitation is the key element affecting vegetation growth than coal mining in ecosystems with phreatic water desert shallows oasis;

(4) The impact of coal mining on vegetation growth in areas with a deeply buried phreatic water level is focused on the destruction of vegetation roots, the soil physical structure, aeration zone water and soil nutrients. Phreatic water level decline caused by coal mining is not the main cause of vegetation decline, and surface vegetation development would be degraded first, then improved, and finally restored. Groundwater level declines in areas with a shallowly buried phreatic water level: this is the direct reason for vegetation decline, and promotes the transformation of vegetation species from hydrophilous plants to xerophyte plants. It is necessary to carry out reasonable mine field planning and adopt different coal mining methods or corresponding engineering and technical measures to guarantee the sustainable development of coal resource utilization and the eco-geo-environment protection.