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

^{1}

^{2}

^{3}

^{4}

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area Profile

_{4}

^{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

_{1}and C

_{2}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):

_{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 C

_{1}, and C

_{2}, 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, C

_{1}= 6, C

_{2}= 7.5 and G = 2.5.

#### 2.3. Methodology

#### 2.3.1. Maximum Value Composite

_{ij}is the EVI value of the jth month of the ith year.

#### 2.3.2. Time Series Analysis

#### 2.3.3. Trend Analysis

_{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

_{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

_{xyz}is the multiple correlation coefficient between x and y, z, as seen in Equation (5):

_{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

_{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

^{2}= 0.4943, indicating a slow fluctuation and an upward trend, which implies the study area vegetation growth has been gradually improving.

#### 3.1.2. Spatial Distribution Characteristics of Vegetation

#### 3.1.3. Spatial Pattern of EVI Variation

#### 3.2. Sensitivity Analysis of EVI to Precipitation

#### 3.3. Correlation Analysis of EVI to Coal Mining and Precipitation

_{2}y

^{3}). 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

_{1}is an independent variable that indicates the coal production of the Yingpanhao coal mine, unit: 100 million tons; X

_{2}is an independent variable that indicates the August precipitation in Uxin Qi, unit: m; β

_{0}is the intercept; β

_{1}, β

_{2}are regression coefficients.

^{2}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 β

_{1}is −0.208, which means that coal production has a negative impact on the EVI of the mined area, and the regression coefficient β

_{2}is 0.251, which means that precipitation has a positive impact on the EVI of the mined area.

#### 3.3.2. Partial Correlation Analysis

_{1}of coal production and the EVI while controlling the variable, precipitation, is −0.979, and its significant probability is sig

_{1}= 0.021 < 0.05, which means there is a significant negative correlation between coal production and the EVI. The partial correlation coefficient r

_{2}of precipitation and the EVI is 0.985 while controlling the variable, coal production, and its significant probability is sig

_{2}= 0.015 < 0.05, which means there is a significant positive correlation between precipitation and EVI. In addition, |r

_{2}| > |r

_{1}|, 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

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 2.**Overview of surface eco-geo-environment in Yinpanhao mine field: (

**a**) geomorphic; (

**b**) elevation distribution; (

**c**–

**f**) main landscape types; (

**g**) Artemisia desertorum; (

**h**) Astragalus adsurgens; (

**i**) Hipophae rhamnoides; (

**j**) Prundo phragmites; (

**k**) Achnatherum Splendens; (

**l**) Salix psammophila; (

**m**) Kalidium foliatum; (

**n**) Puccinellia distans parl.

**Figure 3.**August EVI distributions of Yingpanhao mine field from 2010 to 2019. (

**a**) August 2010; (

**b**) August 2011; (

**c**) August 2012; (

**d**) August 2013; (

**e**) August 2014; (

**f**) August 2015; (

**g**) August 2016; (

**h**) August 2017; (

**i**) August 2018; (

**j**) August 2019.

**Figure 5.**Analysis of interannual differences in August EVI in the Yingpanhao mine field from 2010 to 2019.

**Figure 6.**The distribution of the average of August EVI from 2010 to 2019 and phreatic water level depth in the Yingpanhao mine field. (

**a**) Average of August EVI, (

**b**) Phreatic water level depth.

**Figure 8.**Vegetation development trends spatial distribution and its area proportions of the Yingpanhao mine field. (

**a**) Vegetation development trends; (

**b**) Area proportions.

**Figure 10.**Statistics for variations in August precipitation variation in Uxin Qi from 2010 to 2019.

**Figure 11.**Correlation between precipitation and EVI in the Yingpanhao mine field from 2010 to 2019.

**Figure 13.**Statistics of precipitation, coal production and the EVI of mined stope in the Yingpanhao minefield.

**Figure 14.**Effect of coal mining on the growth of xerophytic vegetation. (

**a**) Before mining, (

**b**) After mining.

**Figure 15.**Effect of coal mining on the growth of hydrophilous vegetation. (

**a**) Before mining, (

**b**) After mining.

Model Summary ^{a} | AVOVA ^{a} | Coefficients ^{a} | |||||
---|---|---|---|---|---|---|---|

F | Sig. | Unstandardized Coefficients | Collinearity Statistics (VIF) | ||||

R | 0.992 ^{b} | Regression | 59.052 | 0.017 | Constant (β_{0}) | 0.241 | |

R^{2} | 0.983 | Residual | Coal production (β_{1}) | −0.208 | 1.005 | ||

Adjusted R^{2} | 0.967 | Total | Precipitation (β_{2}) | 0.251 | 1.005 |

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 |

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## Share and Cite

**MDPI and ACS Style**

Yang, Z.; Li, W.; Li, L.; Lei, S.; Tian, J.; Wang, G.; Sang, X.
Spatiotemporal Variation and Influencing Factors of Vegetation Growth in Mining Areas: A Case Study in a Colliery in Northern China. *Sustainability* **2022**, *14*, 9585.
https://doi.org/10.3390/su14159585

**AMA Style**

Yang Z, Li W, Li L, Lei S, Tian J, Wang G, Sang X.
Spatiotemporal Variation and Influencing Factors of Vegetation Growth in Mining Areas: A Case Study in a Colliery in Northern China. *Sustainability*. 2022; 14(15):9585.
https://doi.org/10.3390/su14159585

**Chicago/Turabian Style**

Yang, Zhi, Wenping Li, Liangning Li, Shaogang Lei, Jiawei Tian, Gang Wang, and Xuejia Sang.
2022. "Spatiotemporal Variation and Influencing Factors of Vegetation Growth in Mining Areas: A Case Study in a Colliery in Northern China" *Sustainability* 14, no. 15: 9585.
https://doi.org/10.3390/su14159585