Evaluation and Prediction of Ecological Restoration Effect of Beijing Wangping Coal Mine Based on Modified Remote Sensing Ecological Index

Abstract

As the construction of ecological civilization has become more and more important in recent years, ecological restoration and its effect assessment have also received increasing attention. Taking the Wangping coal mine in Beijing as an example, based on Landsat TM/OLI series remote sensing data, we chose five metrics, i.e., fraction vegetation coverage, humidity, heat, dryness, and black particulates, to construct the model for the modified remote sensing ecological index (MRSEI). It was also combined with the Hurst index to conduct dynamic monitoring, spatiotemporal analysis, and prediction studies of the ecological environment quality in the study area. The results showed that: (1) Compared with the RSEI, the first principal component of the MRSEI better integrates the information of each component indicator, has a better average correlation with each indicator, and better reflects the habitat condition in the study area. (2) The mean value of the MRSEI in the study area increased from 0.433 in 1990 to 0.722 in 2021, an increase of 40.03%. (3) From 1990 to 2001, the poor and fair MRSEI-grade areas in the study area were concentrated in the northeastern and southwestern parts of the area. After the ecological restoration project was carried out, the environmental quality of the Wangping coal mine improved year by year, and a small number of poor areas were concentrated in the northeastern border area. (4) The MRSEI of the Wangping coal mine predicted that the future ecological environment of the study area would show a general trend of continuous improvement, but a certain percentage of the area in the northeast had a weak antisustainability trend. The results of the study could provide a reference for ecological restoration planning, sustainable development, and the management of mining areas.

1. Introduction

While the development and utilization of mineral resources provide strong support for national economic development, they inevitably cause serious damage to the ecological environment [1,2], bringing severe ecological and environmental problems to the mines and the surrounding areas, such as water pollution, atmospheric pollution, geologic hazards, surface subsidence, and fragile ecological carrying capacity [3,4,5]. Ecological quality has far-reaching implications for regional sustainable development [6]. As the capital city of China, Beijing has a long history of mineral resource development and utilization, which is mainly distributed in the remote suburban areas, characterized by many points and wide areas. Long-term exploitation of mineral resources has caused some damage to the ecological environment in Beijing, which has attracted the attention of the local government and the concern of the general public [7,8]. Since 2005, Beijing has successively adopted policy closure measures for solid mines and carried out ecological restoration work. In recent years, with the concept of “ecological civilization construction” put forward, mine ecological environment restoration effect assessment has become an important part of it.

Remote sensing and GIS technologies have been widely used in the field of regional ecological environment evaluation in recent years because of their wide spatial coverage, long-time and all-day real-time monitoring, etc., and have gradually become one of the main means of evaluating the ecological environment [9,10]. Most of the early scholars evaluated the habitat condition of cities, mountains, mines, watersheds, etc., with the help of a single indicator of remote sensing information [11,12,13,14]. However, ecological environment evaluation is a complex, systematic project, and the quality of the ecological environment is often jointly influenced by several ecological factors [15]. Therefore, coupled multi-factor synthesis analysis is more scientific and comprehensive. One after another, scholars have proposed a variety of evaluation index systems, among which the remote sensing ecological index (RSEI) proposed by Xu was representative, which was completely based on remote sensing technology and can quickly and objectively visualize the regional habitat conditions [16]. As it does not rely on field survey data, it ensures the easy accessibility of the indicators, and by objectively assigning weights to the indicators through the PCA method, it ensures the scientific rationality of the evaluation results to a certain extent, and the applicability is better.

The RSEI index is generally used for environmental monitoring in large-scale areas with universal applicability, but mines are mostly small-scale areas and the specificity of mining activities and the ecological and environmental information cannot be adequately expressed [17]. Therefore, in the geographical study of mining areas, scholars mostly adopt the improved RSEI index to make up for the limitation of the insufficient expression of ecological information. Yang et al. [18] incorporated the net primary productivity (NPP) index into RSEI and obtained the ecological index K-RSEI_NPP based on kernel principal component analysis (KPCA), which was verified to better reflect the habitat quality of the small-area features and land types in the mining area. Feng et al. [19] took the rare-earth mining area as the research object and found that there were environmental problems such as soil sanding and soil erosion in the area investigation. Therefore, the soil sanding and soil erosion indices were incorporated into the RSEI model to construct the rare-earth remote sensing ecological index (RE-RSEI). The results showed good applicability in identifying the ecological damage and restoration in the study area under different mining modes. Zhu et al. [20] proposed the moving-window-based remote sensing ecological index (MW-RSEI) based on the theory of landscape ecology, which can effectively exclude the influence of spatially distant areas on the environmental evaluation of this region.

In summary, the current construction of the RSEI index does not adequately consider the characteristics of the mining area [21]. In this paper, the RSEI framework is optimized based on field investigations by adding environmental characteristics of the mining area and thus the RSEI framework. The Beijing Mine Rehabilitation Demonstration Project–Wangping Coal Mine Rehabilitation Area is taken as the research object because the quality of Beijing’s atmospheric environment has always been worrisome, and haze weather often breaks out [22], and secondly, the fly ash and hazardous gases generated from coal mining are particularly serious to the local atmospheric environmental pollution [23]. In this instance, the black particulate indicator was included in the RSEI system after comprehensive consideration, fully taking into account the impact of atmospheric pollution on the ecological environment quality of the mining area to reveal the evolution characteristics of ecological quality before and after mine rehabilitation and to explore the effect of ecological environment rehabilitation in the study area in order to provide a basis for the rational planning of mine land and the maintenance and management of ecological restoration projects in Beijing.

2. Materials and Methods

The modified remote sensing ecological index was constructed by incorporating the black particulates with due consideration of the characteristics of the local mining area, and then compared with the traditional RSEI index to illustrate the applicability of the MRSEI. In addition, based on the inverse MRSEI of remote sensing images from 1990 to 2021, combined with the Hurst index, the study area was analyzed in three aspects: the monitoring of ecosystem dynamics, spatial and temporal changes, and persistence. A detailed flowchart is given in Figure 1.

2.1. Study Area

The Wangping coal mine area is located in Wangping Town, Mentougou District, Beijing, near National Highway 243, surrounded by many administrative villages and natural villages in Wangping Town, belonging to the abandoned mine area of Wangping Village Coal Mine, and the mining method is mainly open-pit mining, with a total area of 123 hm² (Figure 2). There are two most-typical types of ecological damage of coal mine wasteland in this area, namely, gangue mountain compression occupation as well as collapse pits, which are the demonstration project of mine ecological restoration in Mentougou District of Beijing.

Wangping Township has a total of more than 20 closed large and small coal kilns and mines, and many years of disorderly mining of the ecological environment of the Beijing Mentougou District has caused great damage and serious pollution, including the formation of a large area of nearly 6 km² of the mining area. There are two relatively large ones, the largest of which is located near Lujiapo and Wangping Village on the west side of the Wangping Village Coal Mine, and the other is located near Nanjian Village on the east side of the Wangping Village Coal Mine. This study area is located in the area of Lujiapo and West Wangping villages in the lower part of Wangping Dagou, which is the site of the largest coal gangue mountain. In 1958, the construction of the Wangping coal mine began to be put into production, and the mine was closed in 1994. It was one of the eight major coal mines belonging to the Beijing Coal Group. The coal mine extends east to the Yongding River, west over Wangpingkou up to the Muchengjian Mine, south to the village of Beiling Crossroads, and north over Ping’angou. The ecological restoration project has been implemented successively since 2007, and the treatment was completed by 2010. The period of 2011~2020 further improved the ecological environment of the Wangping coal mine area through the projects of the ecological planting area and the Beijing–Tianjin wind- and sand-source treatment project.

2.2. Data Resources and Preprocessing

The remote sensing data used in this paper were obtained from the Geospatial Data Cloud Platform (http://www.gscloud.cn, accessed on 1 May 2022) of the Computer Network Information Center of the Chinese Academy of Sciences, using eight Landsat series of remotely sensed imagery as well as Google historical imagery data, with the data time range of 1990 to 2021.

The Landsat series images selected in the paper involve the years 1990, 2001, 2011, 2013, 2015, 2017, 2019, and 2021, and the remote sensing data used were imaged between May and August, and the cloudiness of the data images was less than 2%. The data have been pre-processed with radiometric calibrations, atmospheric corrections, etc., and the root-mean-square error of the alignment is controlled to within 0.5 image elements. Of these, Landsat TM has a total of 7 bands with a spatial resolution of 30 m; Landsat OLI has a total of 11 bands, with 1 panchromatic band with a spatial resolution of 15 m; and the remaining bands have a resolution of 30 m (

Table 1. Landsat series satellite remote sensing image parameters.

Satellite	Path	Row	Acquisition Date
Landsat TM	129	43	5 June 1990
Landsat TM	129	43	11 May 2001
Landsat TM	129	43	23 May 2011
Landsat OLI	129	43	12 May 2013
Landsat OLI	129	43	18 May 2015
Landsat OLI	129	43	14 May 2017
Landsat OLI	129	43	13 May 2019
Landsat OLI	129	43	6 August 2021

).

2.3. Methodology

2.3.1. Component Indicator Calculation

In this study, the RSEI index was improved, and the constructed MRSEI coupled greenness (FVC), humidity (WET), dryness (NDBSI), heat (LST), and black particulates (BPs), which were obtained by transforming the principal component analysis, and it more scientifically and objectively reflected the changes of ecological environment quality in the study area.

Fractional vegetation cover was selected as the greenness indicator, which is an important indicator for describing the regional ecological environment changes and vegetation growth status, and also has an important impact on the study of vegetation primary productivity and surface carbon cycle [24]. Moisture metrics are characterized by the third component of the tassel-cap transform, which indicates the moisture content of the feature corresponding to the image element, reflecting the vegetation condition and soil moisture status, and are widely used in ecological environment monitoring [25]. Urban building sites and bare rock and bare soil can lead to “drying out” of the ground surface, resulting in ecological degradation [26]. The normalized difference between the bare soil and bare soil index (NDBSI) is constructed by combining both the bare soil index (SI) and the index of built-up land (IBI) to represent the dryness index. Surface temperature is widely used in the evaluation of surface thermal environments, and therefore the land surface temperature (LST) is used as a thermal indicator. The black particulate formula is a formula proposed by Wald in 1999 and obtained by inversion of strong correlation between thermal infrared satellite measurements and air pollution quality parameters, which has a high correlation with atmospheric pollutants such as sulfur dioxide, nitrogen oxides, and nitrogen dioxide, while the atmospheric quality concentration has a strong correlation with black particulate matter, and thus it can better characterize the atmospheric pollution status of the coal-mining area [27].

Table 2. Calculation formula of each component index of the MRSEI model.

Index	Equation
FVC	$FVC=\left(NDVI-NDVI\mathrm{soil}\right)/\left(NDVIveg-NDVI\mathrm{soil}\right)$	(1)
WET	$WE{T}_{\mathrm{TM}}=0.1446a_1+0.1761a_2+0.3322a_3+0.3396a_4-0.621a_5-0.4186a_7$	(2)
	$WE{T}_{\mathrm{OLI}}=0.1511a_2+0.1973a_3+0.3283a_4+0.3407a_5-0.7177a_7-0.4559a_8$	(3)
NDBSI	$IBI=\frac{2\frac{{\mathit{\rho }}_{\mathrm{SWIR}}}{{\mathit{\rho }}_{\mathrm{SWIR}}+{\mathit{\rho }}_{\mathrm{NIR}}}-\left(\frac{{\mathit{\rho }}_{\mathrm{NIR}}}{{\mathit{\rho }}_{\mathrm{NIR}}+{\mathit{\rho }}_{\mathrm{R}}}+\frac{{\mathit{\rho }}_{\mathrm{G}}}{{\mathit{\rho }}_{\mathrm{G}}+{\mathit{\rho }}_{\mathrm{SWIR}}}\right)}{2\frac{{\mathit{\rho }}_{\mathrm{SWIR}}}{{\mathit{\rho }}_{\mathrm{SWIR}}+{\mathit{\rho }}_{\mathrm{NIR}}}+\left(\frac{{\mathit{\rho }}_{\mathrm{NIR}}}{{\mathit{\rho }}_{\mathrm{NIR}}+{\mathit{\rho }}_{\mathrm{R}}}+\frac{{\mathit{\rho }}_{\mathrm{G}}}{{\mathit{\rho }}_{\mathrm{G}}+{\mathit{\rho }}_{\mathrm{SWIR}}}\right)}$	(4)
	$SI=\frac{\left({\mathit{\rho }}_{\mathrm{SWIR}}+{\mathit{\rho }}_{\mathrm{R}}\right)-\left({\mathit{\rho }}_{\mathrm{NIR}}+{\mathit{\rho }}_{\mathrm{B}}\right)}{\left({\mathit{\rho }}_{\mathrm{SWIR}}+{\mathit{\rho }}_R\right)+\left({\mathit{\rho }}_{\mathrm{NIR}}+{\mathit{\rho }}_{\mathrm{B}}\right)}$	(5)
	$NDBSI=\frac{(SI+IBI)}{2}$	(6)
LST	$T=K_2/\ln \left(K_1/L+1\right)$	(7)
	$L=gain\times DN+bias$	(8)
	$LST=T/[1+(\mathit{\lambda }\times T/\mathit{\rho })\times \ln \epsilon ]$	(9)
BP	$BP=1.38\times TIRS-200.25$	(10)

lists the equations for the calculation of the component indicators. Among them, NDVI, NDVIsoil, and NDVIveg in the greenness index denote the normalized vegetation index, pure bare soil NDVI value, and pure vegetation image element, respectively, and a1~a5 and a7 in the humidity index indicate the spectral reflectance of the bands corresponding to TM and OLI. ρ_B, ρ_G, ρ_R, ρ_NIR, and ρ_SWIR denote the blue, green, red, near-infrared, and short-wave infrared bands for each sensor. T in the thermal index denotes the temperature value of the thermal infrared band of each sensor, K₁ and K₂ denote the radiometric calibration parameters, gain and bias denote the incremental and bias values of the bands, and DN, λ, and ε denote the gray value of the pixel, the center wavelength of the outer band, and the specific emissivity. TIRS denotes the spectral reflectance of the data thermal infrared sensor.

2.3.2. MRSEI Model Calculation

In this study, principal component analysis was used to construct the MRSEI index. principal component analysis (PCA) was proposed by Karl, a British scholar, as a more established method of data analysis in statistics [28]. This is a technical method that allows for the compression of multiple variables into a small number of variables by mathematical transformations. For all the variables originally proposed, the redundant variables are removed while constructing as few new variables as possible, and the new variables are both irrelevant so that the new variables can maximize the retention of the original information in terms of reflecting the information. This method is a statistical method that takes a small number of composite variables and maps the information of the original variables to the maximum extent possible, based on actual needs.

Due to the inconsistency of the scale of each component indicator, the above five indicators are first normalized here to remove the difference in scale, and each indicator is classified into the range of 0 to 1 to facilitate the subsequent principal component analysis. The formula is as follows:

$$N{I}_i=\left(I_i-I_{\min }\right)/\left(I_{\max }-I_{\min }\right)$$

(11)

where NI_i is the normalized value; I_i is the indicator value; and I_min and I_max denote the minimum and maximum values of the indicator after 1% confidence interval processing, respectively.

Principal component analysis was performed after quantifying the five component indicators, and PC1 was calculated based on ENVI 5.3 software. For a larger value of this value to represent a better ecological environment, the initial ecological index MRESI₀ can be obtained by subtracting PC1 from 1 [29]. The formula is:

$$MRSE{I}_0=1-\{PC1[f(FVC,NDBSI,LST,WET,BP)]\}$$

(12)

Finally, the MRSEI₀ was normalized to better reflect the differences in ecological quality in the study area [30]. Its calculation formula is:

$$MRSEI=\left(MRSE{I}_0-MRSE{I}_{0\_MIN}\right)/\left(MRSE{I}_{0\_MAX}-MRSE{I}_{0\_MIN}\right)$$

(13)

Based on previous studies, this paper adopts the equally spaced grading method to classify the MRSEI [31,32]. Using 0.2 as a boundary, it is divided into levels 1 (0, 0.2], 2 (0.2, 0.4], 3 (0.4, 0.6], 4 (0.6, 0.8], and 5 (0.8, 1], with the levels corresponding to poor, poorer, fair, good, and excellent, respectively.

2.3.3. Model Accuracy Validation

Whether the MRSEI constructed in this paper is more comprehensively appropriate and representative compared to the RSEI can be compared in the following two ways: one is to compare the contribution of the first principal component after principal component analysis; the other is to measure the correlation between the ecological index and its component indicators [33].

Correlation is an important indicator to weigh the interconnection between things. First of all, the four sub-indicators will be analyzed for correlation, and generally, the greater the degree of correlation, the closer to 1 the coefficient tends to be, which means that the higher the correlation between the indicator and the ecological index is, the more suitable and representative it will be. Here, the average correlation model is used for testing with the following formula:

$$\overline{C_m}=\frac{\left|C_a\right|+\left|C_b\right|+\cdots \left|C_s\right|}{n-1}$$

(14)

where $\overline{C_m}$ denotes average correlation; m, a, b, s denote correlation analysis indicators; and n denotes the number of indicators in the same period.

2.3.4. Sustainability of Ecological Environment Quality

In this paper, the Hurst index is used for ecological sustainability analysis. The index, which quantifies the relative trend of a time series, was initially invented for the study of practical problems in water resources. Because the index can represent time series sustainability, it has been gradually utilized in the prediction studies of future ecological quality in recent years [34]. We calculated the Hurst index for the study area using a rescaled polarity (R/S) analysis. The formulas are as follows:

For the time series, the MRSEI (t), t = 1,2,3..., n, where any positive integer u ≥ 1, the mean sequence is defined as:

$$MRSEI(u)=\frac{1}{u}\sum _{t=1}^{u}MRSEI(t)$$

(15)

Define the cumulative deviation sequence MRSEI (t, u):

$$MRSEI(t,u)=\sum _{t=1}^u\left(MRSE{I}_{(t)}-{\overline{MRSEI}}_{(u)}\right)\hspace{1em}1⩽t⩽u$$

(16)

Define the polar sequence R(u):

$$R(u)=\underset{1\le t\le u}{\max }X(t,u)-\underset{1\le t\le u}{\min }X(t,u)$$

(17)

Define the standard deviation sequence S(u):

$$S(u)={\left\{\frac{1}{u}\sum _{t=1}^u{[NDVI(t)-NDVI(u)]}^2\right\}}^{\frac{1}{2}}$$

(18)

$$\frac{R(u)}{S(u)}={(au)}^H$$

(19)

where H is the Hurst index, a denotes a constant, and u denotes the time series.

According to the principle of R/S, the Hurst exponential law is as follows: (1) 0 < H < 0.5, representing that the sequence is an antipersistence sequence, implying that the trend of change in a certain period of time in the future will be opposite to that in the past, and the smaller H is, the stronger the antipersistence is; (2) H = 0.5, indicating that the trend of the sequence for a certain period of time in the future is independent of the past; (3) 0.5 < H < 1, which represents that the series is a positive persistence series, implying that the trend of change in a certain period of time in the future will be consistent with the past, and the larger H is, the stronger the positive persistence is [35,36].

3. Results

3.1. MRSEI Model Applicability Verification Analysis

1. Comparative test of the contribution of the first principal component

PC1 can better aggregate the information of each indicator feature and objectively derive the contribution rate of each indicator. Through statistical analysis (Figure 3), the contribution rate of the first principal component eigenvalue of the MRSEI and RSEI was more than 64%, and the mean value was close to 80%. It showed that these two ecological index models’ PC1s have integrated most of the characteristics of the component indicators and are well represented.

Upon comparison, it was found that the contribution of MRSEI eigenvalues was higher than that of the RSEI across all years, with values fluctuating from 0.08% to 2.65%. Therefore, the first principal component of MRSEI is relatively more capable of integrating the information of each component indicator, which is more reflective of the ecological environment quality status in the study area compared to the other components.

2. Comparative tests of correlation

Correlation is an indicator of how closely the factors are related. By comparing the average correlation between the image RSEI model and the indicators and between the indicators for the comparison of eight periods from 1990 to 2021 (

Table 3. Mean correlation statistics of each indicator with RSEI.

Years	Average Correlation
	FVC	WET	NDBSI	LST	RSEI
1990	0.528	0.176	0.631	0.448	0.784
2001	0.558	0.159	0.607	0.480	0.787
2011	0.622	0.307	0.707	0.572	0.617
2013	0.865	0.809	0.881	0.711	0.867
2015	0.804	0.713	0.832	0.610	0.880
2017	0.779	0.671	0.829	0.621	0.871
2019	0.808	0.758	0.845	0.645	0.868
2021	0.812	0.698	0.853	0.660	0.857
Mean	0.722	0.536	0.773	0.593	0.816

) with the average correlation between the MRSEI model and the indicators and between the indicators for each year (

Table 4. Mean correlation statistics of each indicator with MRSEI.

Years	Average Correlation
	FVC	WET	NDBSI	LST	BP	MRSEI
1990	0.566	0.146	0.615	0.585	0.573	0.807
2001	0.597	0.160	0.624	0.609	0.620	0.794
2011	0.643	0.296	0.708	0.677	0.667	0.708
2013	0.851	0.767	0.885	0.783	0.777	0.880
2015	0.859	0.727	0.799	0.716	0.699	0.894
2017	0.781	0.692	0.829	0.621	0.711	0.884
2019	0.767	0.764	0.807	0.734	0.728	0.885
2021	0.824	0.647	0.865	0.745	0.744	0.863
Mean	0.736	0.525	0.767	0.684	0.690	0.835

). It could be seen that the average correlation between the MRSEI model and the indicators in all years (range: 0.708 to 0.894) is higher than the RSEI (range: 0.617 to 0.880). It showed that the MRSEI better integrates the information of each indicator compared to the RSEI.

3.2. Analysis of Habitat Status of Wangping Coal Mine

Based on ENVI 5.3 software, the results of the principal component analysis of each component indicator of the MRSEI of the eight-period images from 1990 to 2021 were calculated for the Wangping coal mine area (Figure 4). PC1 gathered most of the characteristic information of the five ecological indicators, with a contribution rate of more than 66%, and the ecological indicators tended to stabilize compared to the PC1 values. Therefore, it can better represent the habitat condition of the study area.

Through the study of each ecological indicator on the PC1 loading value, it was found that FVC and WET indicators are positive, and the higher the value of FVC is, the better the vegetation growth condition is, and FVC had the largest PC1 loading value among the five indicators from 1990 to 2021, which can indicate that greenness has the highest impact on the MRSEI. The larger the WET value was, the better it reflected the water content in the soil vegetation. It showed that these two indicators positively contribute to the ecological environment of the study area. The NDBSI, LST, and BP indicators were all negative for PC1 loading, where the higher the NDBSI value was, the higher the degree of drying of the bare ground soil was, the higher the LST value was, and the stronger the greenhouse effect at the surface was, and the higher the BP was, the worse the air quality condition was. These three showed a negative impact on the ecology of the study area.

The MRSEI mean value in the Wangping coal mine area generally showed a fluctuating upward trend from 1990 to 2021, with an anomalous inflection point in 2011 (Figure 5). This is due to the fact that since the closure of Wangping coal mine in 1994, the mine was abandoned until Beijing enacted relevant environmental protection policies to pay more attention to the ecological environment. The restoration project of the Wangping coal mine abandoned land started in 2007 and was completed by 2010. The extensive anthropogenic tree planting during this period may have had some impact on the 2011 MRSEI inversion. The mean MRSEI value increased from 0.433 in 1990 to 0.722 in 2021, an increase of 40.03%.

Analyzing the component indicators (

Table 5. MRSEI and means of components from 1990 to 2021.

Years	FVC	WET	NDBSI	LST	BP	MRSEI
1990	0.575	0.596	0.523	0.381	0.400	0.433
2001	0.545	0.492	0.563	0.463	0.482	0.475
2011	0.625	0.540	0.491	0.455	0.488	0.557
2013	0.633	0.481	0.549	0.495	0.492	0.548
2015	0.729	0.442	0.605	0.502	0.502	0.586
2017	0.805	0.460	0.709	0.473	0.470	0.625
2019	0.819	0.459	0.654	0.399	0.394	0.666
2021	0.884	0.416	0.738	0.314	0.310	0.722

), the FVC values showed a decreasing trend from 1990 to 2001, decreasing from 0.575 to 0.545, a decrease of 5.50%, and then increasing from 0.545 in 2001 to 0.884 in 2021, an increase of 38.35%. The WET value also showed a decreasing trend from 1990 to 2001, from 0.596 to 0.492, a decrease of 21.14%; thereafter, the overall fluctuating trend was downward. The reason for this was that the WET is influenced by the FVC and NDBSI indicators, where the WET is positively correlated with FVC and negatively correlated with NDBSI and in the present study was somewhat more influenced by NDBSI. From 1990 to 2021, the NDBSI showed a fluctuating upward trend, especially after the completion of the ecological restoration project, and increased significantly from 2011 onwards, with an overall increase from 0.523 in 1990 to 0.738 in 2021, an increase of 29.13%. This is because after the implementation of the restoration project, the local district government began to carry out a series of planning and renovations of the Wangping coal mine area, such as the construction of the G108 and G109 national highways, power substations and plants, etc., so that the degree of “drying” of the study area has increased. LST has been on an upward trend since 1990 and peaked in 2015, numerically increasing from 0.381 to 0.502, a 24.10% increase; since then, it has begun to decline, dropping from 0.502 in 2015 to 0.314 in 2021, a 59.87% decrease. The reason may be that after the closure of the Wangping coal mine area, the accumulation of coal gangue and fly ash has a certain impact on the surface temperature in summer. BP has a high correlation with LST, increasing from 0.4 in 1990 to 0.502 in 2015, an increase of 20.32%, and then decreasing from 0.502 in 2015 to 0.31 in 2021, a decrease of 61.94%, and here there were many factors affecting the BP indicator, in addition to the effect of the gangue accumulation in the Wangping coal mine area, which is close to the residential area in the study area. Prior to 2014, households used coal as a source of energy, but since the local government implemented the “coal-to-electricity” program, air quality in the study area has improved somewhat.

In terms of spatial distribution, see Figure 6. In terms of the time of completion of the ecological restoration works, the proportion of areas with “poor” and “fair” MRSEI ratings before 2011 is the highest. Only a few areas in the northern and southwestern parts of the study area were “excellent” in 1990, and the northeastern part of the Wangping coal area improved in 2001. The ratings of “poor” and “fair” from 1990 to 2001 were mainly concentrated in the central range of the Wangping coal mine area, which was then an area of intensive mineral development with a large degree of anthropogenic disturbance, so the ecological environment was seriously damaged. After the completion of the ecological restoration, the ecological environment of the study area was clearly shown to have improved by 2011, when the MRSEI rating of “excellent” gained a dominant position. As of 2021, the MRSEI has the highest percentage of “excellent” ratings, with only a small number of “poor” and “fair” ratings, located in the northeastern region, indicating that the ecological restoration project is more effective, but due to the long history of mining in its mining area, the environmental damage is serious and a certain ecological environment restoration cycle is required. In addition, a small number of repairs are still required in parts of the northeastern border.

In terms of temporal changes (Figure 7), the proportion of each rating in the Wangping coal mining area in 1990 was the largest, 39.11%, followed by 29.62% for “fair” and 5.43% for “excellent” ratings. The “excellent” rating was the lowest, accounting for only 5.43%. Compared with 1990, the area of the areas rated “good” and “poor” changed considerably in 2001, with the proportion of “poor” areas decreasing by 12.1 percentage points, and the proportion of areas rated “good” increasing by 7.04 percentage points. The proportion of areas rated “good” increased by 7.04 percentage points, a period in which the quality of the environment improved somewhat through ecological self-restoration. After the implementation of the ecological restoration project, compared with 2001, the percentage of the area with the “excellent” grade in 2011 increased significantly to 18.47%, an increase of 52.84 percentage points compared with 2001, and the percentage of the area with the “poor” grade decreased by 7.11% and 4.71%, which shows that the project restoration was effective in the initial stage. The “poor” and “fair” grades were reduced by 7.11% and 4.71%, which shows that the project restoration has begun to bear fruit. In 2013, the proportion of areas of various grades was abnormal; compared with 2011, the proportion of areas of grade “excellent” decreased by 3.67%, and the proportion of grade “good” was dominant at 30.90%, which may be due to the government’s planning and construction of the mining area during this period, resulting in an increase in the bare soil area. This may be due to the government’s planning and construction of the mining area during this period, which led to an increase in the bare soil area. There is a more pronounced pattern of change in the area share of each MRSEI rating in 2015, 2017, 2019, and 2021. Compared to 2013, the proportion of areas rated “excellent” has been increasing year by year, with increases of 30.16%, 44.84%, 57.70%, and 69.91%, respectively. In addition, the ratings of “fair” and “poor” areas of the proportion of the area show a downward trend year by year, with ratings of “fair” in relative decline, with 7.66%, 40.76%, 119.33%, and 238.96%. The ratings for the “poor” relative decline were 53.13%, 65.34%, 88.17%, and 160.66%.

3.3. Dynamic Changes in Ecological Environment Quality

Remote-sensing-based techniques are an effective means of comparative monitoring of regional ecological changes between years [37,38]. Taking the completion node of the ecological restoration project in 2011 as the boundary, the two periods of data from 1990 to 2011 and 2011 to 2021 were analyzed by the red–green method of difference, and the area of change in the Wangping coal mine area was plotted between each year (Figure 8).

Through the statistics for monitoring changes in the area of Wangping coal mine (

Table 6. Grading table of MRSEI index changes in Wangping coal mine area.

Ecological Change Type	1990–2011		2011–2021
	Area (hm2)	Proportion (%)	Area (km2)	Proportion (%)
Improved	36.11	29.36	76.96	62.58
Unchanged	64.83	52.71	9.34	7.59
Degraded	22.06	17.94	36.70	29.84

), from 1990 to 2011, the area of the study area where the quality of the ecological environment became worse was 64.83 hm² and the area where it became better was only 36.11 hm², indicating that the overall quality of the regional ecological environment was poor. From 2011 to 2021, the ecological environment was significantly improved, and the area of the Wangping coal mine with a better environment will be 76.96 hm², and the area with a worse environment will be 9.34 hm². Compared with the period from 1990 to 2011, the percentage of the environmentally improved area in the study area increased by 33.22% year on year, and the area of deterioration decreased by 45.12% year on year. The ecological changes in the Wangping coal mining area have a high correlation with the historical activities of mining exploitation and are categorized into the following two phases based on the magnitude of changes in the MRSEI index:

1. From 1990 to 2011, the ecological environment of the Wangping coal mine area showed a general trend of deterioration, and the percentage of the area of environmental degradation reached 52.71%. Since 1990, the Wangping coal mining area has shut down and will shut down large and small coal kilns and mines, a total of more than 20. After many years of disorderly mining, great damage and pollution has been caused to the ecological environment of the region. According to the “Eleventh Five-Year Plan” report on Mentougou District, Beijing, during the period from 2001 to 2005, the ecosystem of the Wangping coal mine area was very fragile, forming a large mining area of nearly 6 km², which resulted in a number of surface collapses; the underground water resources were seriously damaged, with all of the original 30 or so springs drying up; the surface vegetation was degraded due to the drought and the reduction of water resources, with the varieties and number of plants diminishing day by day; and all the agricultural land was abandoned due to the poor conditions of production. Until 2007, Beijing began to implement comprehensive ecological restoration work for abandoned mines, typically represented by the Wangping coal mine area. According to the current situation of ecological environment damage in the area, the regional ecological restoration and management implemented mainly includes the greening of coal gangue mountains, the management of collapsed land, corresponding water conservancy facilities in the garden area, as well as the restoration of vegetation in the forested land, and other aspects. Specific treatments can be divided into the following categories: gangue pile treatment works, ground subsidence treatment works, mudflow prevention and control works, vegetation restoration works, water conservancy works, and so on. By 2010, the ecological restoration of the Wangping coal mine area had achieved initial results, and the ecological environment began to improve.

2. From 2011 to 2021, the ecological environment of the Wangping coal mine area was significantly improved, with the ratio of the area of ecological environment changed for the better reaching 62.58%, and the area of environment changed for the worse being only 7.59%. During this period, the district government began to tap the socio-economic value of the restoration area of Wangping coal mine area by actively promoting the construction of farming areas, cultural creativity, scientific and technological research and development, and tourism and leisure industries; at the same time, relying on the national ecological restoration of scientific and technological integrated demonstration bases; and successfully promoting a number of abandoned mines, such as Longshang Science and Technology Park, and other projects to utilize the mine. As can be seen from the figure below, there are a small number of areas in the eastern and northeastern part of the study area with a deteriorating trend in the environment due to the construction of roads, railroads, and other buildings, which fall into the category of bare soil in the remote sensing interpretation, in addition to the unrestored areas; at the same time, this area is close to the residential areas, and therefore, it has a certain impact on the final results of remote sensing inversion.

In summary, the evaluation results inverted by using the MRSEI index are basically consistent with the field research. Through the implementation of the ecological restoration project in the Wangping coal mine area, the quality of the local ecological environment has greatly improved.

3.4. Trend Analysis of Ecological Environment Quality

This paper focused on the trend of ecological environment change in the Wangping coal mine after the implementation of an ecological restoration project. Based on the MRSEI data from 2011 to 2021, the image-by-image Hurst index was calculated using R/S analysis, and the spatial distribution of the Hurst index was obtained (Figure 9). Overall, the trend of future changes in the ecological quality of the Wangping coal mine is strongly persistent.

The Hurst index of the Wangping coal mine was statistically analyzed (

Table 7. Persistence trend of ecological quality changes in the study area.

Spatial Trends	Pixels	Area/(hm2)	Percentage
Strong Antipersistence	218	4.06	3.30%
Weak Antipersistence	705	13.11	10.66%
Negative Continuity	1865	34.67	28.19%
Positive Sustainability	3828	71.16	57.85%

), which ranged from 0.06 to 0.99 with an average value of 0.67. The Hurst index was divided into four levels: strong antipersistence (H ≤ 0.35) accounted for 4.06 hm² of the study area, with a proportion of 3.30%; weak antipersistence (0.35 < H ≤ 0.50) accounted for 13.11 hm² of the study area, with a proportion of 10.66%; weak persistence (0.50 < H ≤ 0.65) accounted for 34.67 hm² of the study area, with a proportion of 28.19%; strong persistence (H > 0.65) accounted for 71.16 hm² of the study area, with a proportion of 57.85%.

In summary, the future ecological environment of the study area showed a general trend of continuous improvement, but it should be noted that a certain percentage of the northeastern part of the study area has a weak antisustainability trend.

4. Discussion

In traditional studies, the RSEI has been widely used in large-scale regional and urban ecosystem evaluations because of the easy accessibility of its metrics as well as its ease of computation [39,40]. However, for small-scale areas, such as mines and watersheds, due to the complexity of their topography and geomorphology as well as their regional specificity, the traditional RSEI indexes cannot fully satisfy the ecological environment evaluation capability [41,42]. In addition, since the closure of the Wangping coal mine in 1990, the mine has been in a state of abandonment, and pollutants such as coal gangue and fly ash have not been dealt with in a timely manner, which has caused serious harm to the surrounding environment. The size of the underground mining area has increased, and the slag and dust have had a great impact on the atmospheric environment in the study area. Based on this historical background, this paper improves the traditional RSEI from the perspective of atmospheric pollution and constructs the MRSEI index with easy access to each component index, simpler computation, higher accuracy after validation, and ease of promotion. Thus, it enriches the application of the RSEI index in the environmental evaluation of mining areas.

By comparing and analyzing the RSEI, the constructed MRSEI is better able to integrate most of the characteristic information of each component indicator compared with PC1, which can better reflect the ecological environment quality status in the study area. Taking 2010 as the node of completion of the ecological restoration project, the MRSEI before ecological restoration (1990–2011) shows a continuous decreasing trend, and the MRSEI after ecological restoration (2011–2021) shows a continuous increasing trend, which is basically consistent with the actual situation. At the same time, due to the lack of detailed information on the implementation process of ecological restoration projects, no in-depth discussion has been conducted on the impact of anthropogenic activities in the implementation of the projects on the mechanisms driving the quality of the ecological environment. In addition, climate has a complex response relationship to the ecological environment of the mining area, and MRSEI is affected by precipitation, evapotranspiration, and other climatic factors, so in the future, we need to comprehensively consider the impact of multi-climatic factors on the ecological environment of the mining area.

Wangping coal mine is a typical coal mine ecological restoration project in the Mentougou District of Beijing, which can provide a reference for the development of ecological restoration work in other abandoned mining areas in Beijing. The year 2020 is the final year of the Wangping coal mine restoration project. At the same time, according to Mentougou Mine ecological restoration planning long-term planning objectives (2021–2050), Wangping coal mine will adjust the industrial structure of abandoned mining villages and towns according to the local conditions to transform into ecological tourism villages and towns. The study will not only assess the ecological restoration effect of the Wangping coal mine but also provide some reference for the next village and town transformation planning. It is conducive to the integration of the industrial structure of the Wangping coal mine area and the surrounding villages and towns, thus realizing the virtuous cycle of social, economic, and ecological development in the area.

5. Conclusions

In this study, the MRSEI is constructed based on a total of eight views from Landsat series remote sensing images from 1990 to 2021, and the indicators of greenness, humidity, dryness, heat, and black particulates are extracted. The historical trends of ecological environment changes in the Wangping coal mine area were sorted out, and the effects of the ecological restoration projects in the study area were emphasized to assess the future trends of the study area combined with the HURST index. The conclusions are as follows:

(1)

The contribution rate of MRSEI eigenvalues was higher than that of the RSEI between years, with values fluctuating from 0.08% to 2.65%, and the first principal component of the MRSEI was relatively more capable of integrating the information of each component index. The contribution rate of MRSEI eigenvalues was higher than that of RSEI, with values fluctuating from 0.08% to 2.65%. In addition, the average correlation between the MRSEI and each of the component indicators increased by about 0.9% to 2.9% compared to the RSEI. Therefore, the MRSEI is more reflective of the ecological quality status of the study area.

(2)

The MRSEI mean value of the Wangping coal mine area generally showed a fluctuating upward trend from 1990 to 2021. The MRSEI mean value increased from 0.433 in 1990 to 0.722 in 2021, which was an increase of 40.03%.

(3)

The difference analysis of the three periods of data, from 1990 to 2011 and 2011 to 2021 and from 1990 to 2011, indicated that the ecological environment quality of the study area deteriorated by 64.83 hm², and the area that became better was only 36.11 hm², and the area deterioration percentage was increased by 23.35% in relation to the percentage of the area that became better. From 2011 to 2021, the ecological environment improved significantly, and compared with the period from 1990 to 2011, the percentage of environmentally improved areas in the study area increased by 33.22% year on year, and the area of deterioration decreased by 45.12% year on year.

(4)

According to the results of the Hurst index, the future ecological environment of the study area is generally showing continuous improvement, but it should be noted that a certain percentage of the northeastern part of the study area has a weak antisustainability trend. Overall, it shows that the implementation of the ecological restoration project in the mine area has a more significant effect. It can provide a certain basis for the next industrial structure adjustment and village transformation planning in the restoration area of the Wangping coal mine.

Generally speaking, the MRSEI index that we created has some practical and reference value for managing the environment and assessing mining pollution in mining areas. However, this paper mainly focuses on the study of the primary environment of the Wangping coal mine area without considering the influence of social, economic, human, and other factors, which needs to be further explored in depth.