Ecological and Geological Environment Risk Assessment of Wangwa Mining Area Based on DInSAR Technology

Abstract

Mining activities in coal mining areas have exacerbated ecological and geological environmental risks. To explore the impact of mineral resources on the ecological and geological environment risk (EGER) in coal mining areas, we developed a novel ecological and geological risk assessment framework. This framework first quantifies the impact of mining activities on the surface of coal mining areas using remote sensing interpretation and Differential Interferometric Synthetic Aperture Radar (DInSAR) technology. Then, this framework selected six indicators, including subsidence, surface occupation and damage, FVC, RSEI, precipitation, and temperatures. The weights of the evaluation indicators were calculated using a coupled weighting model combining the Analytic Hierarchy Process (AHP) and the Entropy Method (EM). This approach was applied to the Wangwa mining area to assess its ecological and geological risks. The results show that the surface subsidence increase year by year. The EGER in the study area was medium and the change rate of the EGER index in Wangwa mining area from 2017 to 2022 was −0.460 to 0.598. The EGER index increased southwest of the study area but reduced in the pre-investigation area and north of the investigation area. This study can support decision-making to reduce the adverse environmental impact of coal mining activities.

1. Introduction

Coal is the main energy source in China [1]. However, the development, processing, and utilization of mineral resources inevitably bring a string of eco-geological environmental problems, such as land subsidence, ground fissures, soil erosion, soil desertification, and vegetation destruction. Coal mining activities occupy and destroy land resources and landform landscapes, making it difficult to restore fragile eco-geological environments. Geological disasters such as ground subsidence cause significant economic losses and casualties [2]. Therefore, a comprehensive assessment of the EGER caused by mining activities contributes to a better management of these potential risks, protecting the environment and human health [3].

EGER can reflect the quality of eco-geological environments [4]. It assesses the risk situation of eco-environments in study areas by analyzing the geological environment, human activities, and current potential factors [5]. In terms of assessment indicators, ecological environment and climate characteristics are widely used in ecological environmental risk assessment. It is worth mentioning that the latest research results take into account the extent of coal mining activities and the surface disturbances they cause. Li et al. [6] quantified the spatial distribution of coal mining intensity in mining cities by using the production capacity of mining areas in a comprehensive assessment framework. Zhu et al. [7] used SBAS-InSAR technology to quantify the impact of mining activities and groundwater extraction on surface disturbance, and incorporated the subsidence rate into the assessment framework, and the calculated results were more consistent with the eco-geological conditions of the study area. From the perspective of EGER assessment methods, the main methods used are Analytic Hierarchy Process (AHP) [8], Fuzzy Analytic Hierarchy Process (FAHP) [9,10,11], and EM [12,13]. AHP and FAHP are common subjective weighting methods that allow for hierarchical decision-making in complex problems [14,15]. However, they are highly subjective. Compared to AHP, FAHP requires more mathematical computations and its consistency check is more complex. A single subjective weighting method is limited by the decision-maker’s experience and lacks theoretical support for the weights of the original indicators. Entropy is an objective weighting method that can reflect the fluctuation characteristics of the original data and the relationships between the data [16]. However, it is overly dependent on the original data and can easily result in weight distortion. Therefore, this study combines the subjective weight of AHP with the objective weight of the EM to evaluate the EGER, making the assessment results more objective.

At present, DInSAR has become an important tool in the field of earth sciences. Through satellite remote sensing data, it can produce high-resolution images and accurately measure surface deformation, including ground subsidence and the formation of ground fissures [17,18]. Peduto et al. established a common framework and related procedures for the analysis of urban subsidence, which were applied and validated in the Campania region of Italy [19]. Valencia Ortiz et al. used DInSAR technology to analyze ground deformation in the Bucaramanga region and explored its relationship with regional tectonic activity and geological hazards [20]. The non-invasive nature of this technology makes it an ideal tool for studying and monitoring EGER. By combining DInSAR technology with ecological data, we can gain an in-depth understanding of the relationship between geological environments and ecosystems, revealing the impacts of coal mining activities on eco-geological environments.

The eco-geological environmental issues of mining areas are increasingly attracting the attention of local decision-makers. This paper integrates DinSAR technology, visual interpretation methods, and a combined subjective–objective weighting model to construct an ecological and geological environmental risk assessment model for the Wangwa mining area. The research objectives are as follows: (1) Quantify the impact of coal mining activities on the surface of the mining area using DinSAR technology and visual interpretation methods. (2) Couple AHP and the entropy weight method to construct an ecological and geological risk assessment model for the Wangwa mining area. (3) Analyze the spatiotemporal distribution characteristics of EGER to provide a basis for the formulation of effective prevention and control measures in the mining area.

2. Study Area and Data

2.1. Study Area

The Wangwa mining area is located in Wangwa Town, Ningxia Hui Autonomous Region. It falls within the BSk climate zone [21]. The Wangwa No. 2 mining area is situated in the southern part of the Wangwa mining area. The Yindonggou mining area is situated in the northern part of the Wangwa mining area. The administrative division of both mining areas is under the jurisdiction of Luowu Township, Pengyang County, Guyuan City, as shown in the left image of Figure 1.

2.2. Data

2.2.1. SAR Data

The data used for monitoring are Sentinel-1 radar imagery. Sentinel-1 operates in the C-band with a wavelength of 5.6 cm and a repeat cycle of 12 days. The regular and evenly distributed acquisition of data over time enhances its capability to detect rapid deformations. The time range for this paper using Sentinel-1 data is from 25 August 2017, to 11 August 2022 (

Table 1. Sentinel-1A data list.

Date	Path	Frame	Track Direction	Resolution	Model	Width
20170825	157	112	Ascending	5 × 20 m	IW	250 km
20171024	157	112	Ascending	5 × 20 m	IW	250 km
20171223	157	112	Ascending	5 × 20 m	IW	250 km
20180221	157	112	Ascending	5 × 20 m	IW	250 km
20180422	157	112	Ascending	5 × 20 m	IW	250 km
20180621	157	112	Ascending	5 × 20 m	IW	250 km
20180820	157	112	Ascending	5 × 20 m	IW	250 km
20181031	157	112	Ascending	5 × 20 m	IW	250 km
20181230	157	112	Ascending	5 × 20 m	IW	250 km
20190228	157	112	Ascending	5 × 20 m	IW	250 km
20190429	157	112	Ascending	5 × 20 m	IW	250 km
20190628	157	112	Ascending	5 × 20 m	IW	250 km
20190827	157	112	Ascending	5 × 20 m	IW	250 km
20191026	157	112	Ascending	5 × 20 m	IW	250 km
20191225	157	112	Ascending	5 × 20 m	IW	250 km
20200223	157	112	Ascending	5 × 20 m	IW	250 km
20200423	157	112	Ascending	5 × 20 m	IW	250 km
20200622	157	112	Ascending	5 × 20 m	IW	250 km
20200821	157	112	Ascending	5 × 20 m	IW	250 km
20201020	157	112	Ascending	5 × 20 m	IW	250 km
20201231	157	112	Ascending	5 × 20 m	IW	250 km
20210217	157	112	Ascending	5 × 20 m	IW	250 km
20210430	157	112	Ascending	5 × 20 m	IW	250 km
20210617	157	112	Ascending	5 × 20 m	IW	250 km
20210828	157	112	Ascending	5 × 20 m	IW	250 km
20211027	157	112	Ascending	5 × 20 m	IW	250 km
20211226	157	112	Ascending	5 × 20 m	IW	250 km
20220224	157	112	Ascending	5 × 20 m	IW	250 km
20220413	157	112	Ascending	5 × 20 m	IW	250 km
20220612	157	112	Ascending	5 × 20 m	IW	250 km
20220811	157	112	Ascending	5 × 20 m	IW	250 km

). DEM adopts SRTM 30 m resolution.

2.2.2. Assessment Indicators

In order to objectively evaluate the EGER of Wangwa mining area, Fraction Vegetation Coverage (FVC), Remote Sensing Ecological Index (RSEI), precipitation, and temperature are selected to reflect the ecological environment conditions of Wangwa mining area. FVC is an indicator of vegetation coverage, used to describe the distribution of surface vegetation of the study area. RSEI combines greenness, wetness, dryness, and heat through principal component analysis, which can provide a more comprehensive reflection of the ecological quality of mining areas. Precipitation and temperature have a significant impact on the health of the ecological environment in mining areas. Slope is selected to reflect the geological environment conditions of the Wangwa mining area and is closely related to the occurrence of geological hazards. In addition, the time series InSAR processing and remote sensing image interpretation of the compression damage data will be used as the indicators for evaluating the mining-induced ground compression and damage and subsidence, which can reflect the impact of coal mining activities on the surface. The geographic coordinate system of the layer of indicators was unified as WGS84, and the projected coordinate system was WGS_1984_UTM_Zone_48N, and the evaluation indicator grid was divided into a 50 m × 50 m cell grid.

3. Methods

3.1. DInSAR Technology

The phase of InSAR interferograms is divided into several main components, including surface elevation phase, surface deformation phase, atmospheric effect phase, flat earth phase and noise phase [22]. The DInSAR technique is a further development of the InSAR technique, which obtains the surface deformation information by retaining only the phase caused by surface deformation through differential processing [23]. The phase of InSAR interferograms can be described as

$$\mathrm{\varnothing }={\mathrm{\varnothing }}_{\mathrm{topography}}+{\mathrm{\varnothing }}_{\mathrm{displacement}}+{\mathrm{\varnothing }}_{\mathrm{atmosphere}}+{\mathrm{\varnothing }}_{\mathrm{flat}}+{\mathrm{\varnothing }}_{\mathrm{noise}}$$

(1)

where ${\mathrm{\varnothing }}_{\mathrm{topography}}$ is the topographic phase; ${\mathrm{\varnothing }}_{\mathrm{displacement}}$ is the deformation phase; ${\mathrm{\varnothing }}_{\mathrm{atmosphere}}$ is the atmospheric delay effect; ${\mathrm{\varnothing }}_{\mathrm{flat}}$ is the flat phase; and ${\mathrm{\varnothing }}_{\mathrm{noise}}$ is the noise phase.

${\mathrm{\varnothing }}_{\mathrm{topography}}$ is the phase difference due to variations in the height of the terrain. It can be obtained by the existing digital elevation model (DEM) or stereo survey. ${\mathrm{\varnothing }}_{\mathrm{displacement}}$ is the phase difference due to ground deformation and is the part relevant to our study. ${\mathrm{\varnothing }}_{\mathrm{atmosphere}}$ is the phase difference due to changes in atmospheric conditions (e.g., humidity, temperature, etc.), which can be simulated and removed by atmospheric models or other methods. ${\mathrm{\varnothing }}_{\mathrm{flat}}$ is the phase difference due to the curvature of the Earth and the observed geometry. It can be calculated and removed by flooding the reference plane phase model. ${\mathrm{\varnothing }}_{\mathrm{noise}}$ includes both the system noise and the noise caused by the scatterer variation. It can be partially removed by the filtering method.

The equation is organized as follows:

$$\mathrm{\varnothing }={\mathrm{\varnothing }}_{\mathrm{topography}}+{\mathrm{\varnothing }}_{\mathrm{displacement}}+{\mathrm{\varnothing }}_{\mathrm{atmosphere}}+{\mathrm{\varnothing }}_{\mathrm{flat}}+{\mathrm{\varnothing }}_{\mathrm{noise}}={\displaystyle \frac{4\pi }{\lambda }}\mathsf{\Delta }R$$

(2)

where $\lambda$ is the wavelength of the SAR system and $\mathsf{\Delta }R$ represents the slant range between the satellite and the target point during satellite imaging.

Therefore, the phase information of the interference is removed, and the tiny deformation information of the surface can be obtained by using the two-scene SAR data through the imaging principle. The resolution of the obtained deformation effect map is 30 m.

3.2. Comprehensive Assessment Model Construction

This paper first analyzes the developmental characteristics, distributional features and hazardous degree of ecological problems in the study area. It constructs the EGER evaluation level through the AHP, and the expert scoring method is adopted to give each assessment indicator a suitable score so as to construct the discriminant matrix and calculate the subjective weight. Then, the objective weight of each assessment indicator is calculated by using the EM, and finally, the two weights are linearly combined through the distance function. In addition, the risk index of each assessment unit is calculated according to the mining EGER assessment standard, and the comprehensive evaluation of mining eco-geological environment impact is graded according to the risk index. The entire process is shown in Figure 2.

3.3. Weight Calculations

3.3.1. AHP

AHP method is a kind of decision-making analysis method combining qualitative and quantitative analyses, which obtains the weight value of each assessment indicator by constructing the hierarchical structure model of EGER assessment indicator system and its judgment matrix [24]. The calculation formula is shown in Equation (3) and the classification quantification table is shown in

Table 2. The classification quantification of the assessment indicators.

Score	2	4	6	8
Slope/°	<5	5~20	20~30	>30
Subsidence/m	<0.1	0.1~0.15	0.15~0.2	≥0.2
Ground occupation and damage	other	occupy by force	slag heap	sinkhole
FVC	0.8~1.0	0.5~0.8	0.2~0.5	0.0~0.2
RSEI	0.8~1.0	0.5~0.8	0.2~0.5	0.0~0.2
Precipitation/mm	270~300	300~320	250~270	<250 or >320
Temperature/°C	16~16.5	15.5~16	16.5~17.5	>17.5 or <15.5

. The indicators must be quantified to avoid the impact of excessively large or small values on the weights, as this step is crucial for EGER.

$${\alpha }_j={\displaystyle \frac{w_i}{{\sum }_{i=1}^n{w}_i}}$$

(3)

where $w_i$ is the weight of the i-th indicator.

The test formula is shown in Formulas (4) and (5):

$$\mathrm{C}\mathrm{R}={\displaystyle \frac{CI}{RI}}$$

(4)

$$CI={\displaystyle \frac{{\mathsf{\lambda }}_{max}}{n-1}}$$

(5)

where ${\mathsf{\lambda }}_{max}$ is the largest characteristic root of the consistency matrix; $RI$ is the random consistency test index; $n$is the number of the indicators; $CI$ is the consistency index; and $\mathrm{C}\mathrm{R}$ is the consistency ratio.

3.3.2. EM

The EM determines the weights of indicators according to the amount of information contained in the vulnerability evaluation indicators [25]. The smaller the entropy, the higher the weight. The process of entropy weight is as follows:

Step1: Construct the original sequence.

$$X={\left[x_{ij}\right]}_{m\times n}\left(i=\mathrm{1,2}\cdots ,m;j=\mathrm{1,2}\cdots ,n\right)$$

(6)

where $m$ and $n$ refer to the number of assessment grids and indicators, respectively.

Step2: Matrix standardization.

$$y_{ij}={\displaystyle \frac{x_{ij}-{\left(x_{ij}\right)}_{min}}{{\left(x_{ij}\right){\left(x_{ij}\right)}_{min}}_{max}}}$$

(7)

where $x_{ij}$ is the original matrix sequence, ${\left(x_{ij}\right)}_{min}$ and ${\left(x_{ij}\right)}_{max}$ are the minimum and maximum values of the sequence of vulnerability assessment indicators, respectively.

Step3: Calculate the information entropy and weights.

$$H_j=-{\displaystyle \frac{1}{\mathit{ln}m}}\left(\sum _{i=1}^m{f}_{ij}\mathit{ln}(f_{ij})\right)$$

(8)

$$f_{ij}={\displaystyle \frac{y_{ij+1}}{\sum _{i=1}^m\left(y_{ij}+1\right)}}$$

(9)

$$w_j={\displaystyle \frac{1-H_j}{n-\sum _{j=1}^n{H}_j}}$$

(10)

where $H_j$ refers to information entropy; $w_j$ refers to weights of assessment indicators; and $f_{ij}$ refers to the probability of the occurrence of the assessment indicator.

3.3.3. Comprehensive Weight

In this paper, the distance function is introduced to couple AHP and EM in a linear combination to determine the comprehensive weight of the indicators, and its expression is as follows:

$$w=\eta w_j^1+\varphi w_j^2$$

(11)

where $w_j^1$ is the weight obtained by the AHP, $w_j^2$is the weight determined by the EM, and $\eta \mathrm{a}\mathrm{n}\mathrm{d}\varphi$ are the coefficients.

The distance function is

$$d\left(w_j^1,w_j^2\right)={\left[{\displaystyle \frac{1}{2}}\sum _{j=1}^n{\left(w_j^1-w_j^2\right)}^2\right]}^{{\displaystyle \frac{1}{2}}}$$

(12)

The difference between the distribution coefficients is

$$D=\left|\eta -\varphi \right|$$

(13)

$$d{\left(w_j^1,w_j^2\right)}^2={\left|\eta -\varphi \right|}^2$$

(14)

The results of the weighting of the indicators are shown in

Table 3. Results of comprehensive weighting calculation.

	Indicators	AHP
		Weight	CR	Weights	CR	Standard Weight	EM	Comprehensive Weight
geological condition	slope	0.6667	0	0.1095	0.0032	0.0547	0.1225	0.0739
	subsidence			0.5816		0.2907	0.1366	0.2471
	surface occupation and damage			0.3089		0.1545	0.2394	0.1785
environmental conditions	FVC	0.3333		0.3129	0.0126	0.1565	0.1782	0.1626
	RSEI			0.499		0.2496	0.1041	0.2085
	precipitation			0.1202		0.0601	0.1259	0.0787
	temperatures			0.0679		0.0339	0.0933	0.0507

.

From the table, it can be seen that the final weights for slope, subsidence, surface occupation and damage, FVC, RSEI, precipitation, and temperature are 0.0739, 0.2471, 0.1785, 0.1626, 0.2085, 0.0787, and 0.0507, respectively.

3.4. Calculation of EGER Index

Calculate the EGER index of each grid through the integrated weight of evaluation indexes obtained by the linear weighting of the AHP and the EM and the standardized value of grid indexes:

$$V_i=\sum _{j=1}^n{a}_{ij}\times w_j$$

(15)

where $w_j$ is the comprehensive weight and $a_{ij}$ is the standardized value of the j-th assessment indicator of the i-th grid.

3.5. Trend Analysis

In order to further analyze the trend change of the comprehensive index of EGER, the univariate linear regression was used for trend analysis, and the trend test is carried out by the merger T-test [26]. The univariate linear regression is as follows:

$$slope={\displaystyle \frac{n\times {\sum }_{i=1}^n\left(i\times V_i\right)-{\sum }_{i=1}^{n}i{\sum }_{i=1}^n{V}_i}{n\times {\sum }_{i=1}^n{i}^2-{\left({\sum }_{i=1}^{n}i\right)}^2}}$$

(16)

where $n$ represents time. When slope > 0, it indicates that the comprehensive index is increasing and the ecological geological environmental quality is deteriorating. Conversely, when slope < 0, it suggests that the ecological geological environmental quality is improving.

The calculation method of T-test for one-way linear regression is as follows:

$$t={\displaystyle \frac{\widehat{b}}{\widehat{\sigma }}}\sqrt{\sum _{i=1}^n{\left(x_i-\overline{x}\right)}^2}\sim t_{1-\alpha }\left(n-2\right)$$

(17)

$$\widehat{\sigma }=\sqrt{{\displaystyle \frac{1}{n-2}}\sum _{i=1}^n{\left(y_i-{\widehat{y}}_i\right)}^2}$$

(18)

where $\widehat{b}$ represents the slope value obtained from the one-way linear regression method; $i$ is the year serial number; and $n$ = 6$;$ $y_i$ represents the actual value of the comprehensive ecological geological environmental index in year $i$; ${\widehat{y}}_i$ represents the regression value in year $i$; and $\alpha$ is the significance level. When 0.01 ≤ $\alpha$ ≤ 0.05, it indicates a significant decrease (increase); when $\alpha$ < 0.01, it indicates an extremely significant decrease (increase); and when $\alpha$ > 0.05, it indicates no significant change.

4. Results

4.1. Surface Subsidence Result

DInSAR technology was used to calculate the cumulative subsidence of superimposed every two months from 25 August 2017 to 11 August 2022. The result can be seen in Figure A1 of Appendix A. There are obvious subsidence areas in the three mines, and the subsidence amount increases year by year, which is consistent with the mining collapse area. The subsidence of every two months is superimposed to obtain the cumulative subsidence of each year (Figure 3), and the maximum subsidence is about 0.708 m, among which the cumulative subsidence of Yantonggou Mining area is the largest, followed by Wangwa Mining area, and Wangwa No. 2 mining area is the smallest. Most census and pre-census areas are relatively stable. The annual surface collapse range of each coal mine is shown in

Table 4. Annual surface collapse range of coal mine based on DInSAR (km²).

Name	2017	2018	2019	2020	2021	2022	Total
Yintonggou mining area	0.17	2.42	2.76	2.79	2.80	2.80	2.80
Wangwa mining area	11.70	17.04	21.26	23.82	23.93	23.94	23.94
Wangwa No. 2 mining area	8.73	8.731	8.733	8.734	8.74	8.74	8.74
Total	20.60	28.19	32.75	35.34	35.47	35.48	35.48

. With continuous mining, the surface subsidence values have been increasing, and the affected surface area has expanded year by year. The cumulative surface subsidence area from 2017 to 2022 is 35.48 km².

4.2. EGER Assessment Zoning of Mining Areas

According to the degree of damage to topography and geomorphology, the EGER of the investigation area, census area and mining area is divided into low risk, medium risk and high risk. The assessment results from 2017 to 2022 are categorized into the above three grades using the natural breakpoint method. As the EGER index is different every year, the average value of the six years’ grading values is taken as the final grading standard, as shown in

Table 5. Grading standard for EGER.

Risk Level	Low Risk	Medium Risk	High Risk
Risk index	<3.2531	[3.2531~4.5137]	>4.5137

below.

4.3. Assessment Results and Distribution Characteristics

The low risk is mainly located in the middle and south of the pre-survey area and census area, and in the south of Wangwa No. 2 mining area. The precipitation in this area is higher than other areas, the temperature is moderate, and the superior water and heat conditions ensure the growth of vegetation, so the vegetation cover and RSEI index are high, and the vegetation cover is concentrated around 0.7, reflecting the good ecological environment conditions in this area. The degree of disturbance by mining activities is small, there is no obvious subsidence area in the whole, and there are only a few areas of compression, occupation, and damage, such as slag heaps and subsidence pits.

The medium-risk areas are the widest, accounting for 55.8~61.7%. They are mainly distributed in the northern part of the pre-survey area and census area, and are interspersed with low-risk areas. Around the mining area, the medium-risk area is higher due to the influence of multiple factors such as vegetation coverage and pressure–occupation damage.

The high-risk areas account for the lowest percentage, accounting for 2.5~5.9%. They are concentrated in the inner part of the mining area, while the vegetation cover and RSEI index in the middle part of the mining area gradually increase from the inner part to the surrounding environment. The mean values of the area of compaction damage and collapse pit are 6.89 km² and 6.07 km², respectively, and the time-series InSAR subsidence results further indicate that the intensity of subsidence in this area is higher than that in other areas, resulting in poorer geological conditions. The assessment results are shown in Figure 4 and the area of each risk level is shown in Figure 5.

4.4. Dynamic Analysis of EGER Impact Zoning

Figure 5 shows that the overall eco-geological environment quality of Wangwa mining area and that the surrounding environment gradually turned better from 2017 to 2022. The high-risk zones were spatially more widely distributed in 2017, 2019, and 2020, but significantly reduced in 2021~2022, where the disturbance of the EGER to the surrounding area gradually reduced and was mainly concentrated in the interior of the mining area. In 2017~2019, the high-risk areas were more distributed in the north of the census area, but significantly reduced in 2020~2022. From Figure 6, the high-risk area shows a decreasing trend, with a decrease of 19.96 km² in 2022 compared with 2017; the low-risk area shows an increasing trend, with a total increase of 47.05 km² compared with 2017. On the one hand, the overall ecological quality was good in 2018 due to the fact that the area of the compaction area and the coverage of the collapse pit were 5.80 km² and 5.58 km², which are lower than the percentage of other years. On the other hand, the precipitation from July to October in 2018 was 312 mm to 326 mm, which was significantly higher than that of other years, resulting in superior vegetation growth conditions and good ecological environment.

4.5. Trend Change of EGER

The change rate and significant changes of the comprehensive EGER index in the study area are calculated using Equations (16)–(18), as shown in Figure 6. The change rate of the EGER index in Wangwa mining area from 2017 to 2022 was from −0.460 to 0.598. The EGER index increased southwest of the study area while it reduced in the pre-investigation area and north of the investigation area. The EGER index of the study area had no significant change from 2017 to 2022, and the proportion of no significant change was 80.5%. The area of risk index reduction was larger than the area of increase, and the area of risk index reduction was 70.38 km², accounting for 11.1%. The extremely significant and significant decreases were 21.28 km² and 49.10 km², respectively. The area of risk index increase was 52.81 km², and the area of extremely significant increase and significant increase were 13.54 km² and 39.27 km², respectively, accounting for 2.13% and 6.20%, respectively.

5. Discussion

In the study of regional ecological and geological risk assessment, scholars have developed evaluation models based on various regional risk factors [27,28,29]. However, these models primarily utilize intrinsic regional indicators such as elevation, slope, lithology, and fault zones, lacking indicators that account for surface changes. Consequently, these evaluation models are not particularly suitable for mining areas affected by extraction activities. This study employs DInSAR technology to quantify the impact of coal mining on the surface of mining areas, presenting a notable advantage over other frameworks. Additionally, the determination of evaluation indicator weights is crucial for EGER research. Many scholars have coupled subjective and objective methods using techniques such as multiplicative comprehensive normalization and the minimum discrimination information principle [4,30] without considering the intrinsic differences in the weights obtained from these methods. This paper addresses these shortcomings by coupling the Analytic Hierarchy Process (AHP) with the entropy weight method based on a distance function, thus rendering the weight calculation more objective.

6. Conclusions

In order to master the dynamic changes in the surface ecological environment of the subsidence area after mining in the Wangwa mine area, the study conducted long-term remote sensing interpretation of surface damage, a calculation of surface subsidence using DInSAR technology, and a scientific evaluation of the ecological geological environment. The main conclusions are as follows:

(1) Using Sentinel-2 remote sensing imagery, the main types of surface damage in the Wangwa mining area from 2016 to 2022 were interpreted and obtained, including four categories: ground pressure damage, waste piles, water bodies, and subsidence pits. The scope of surface damage in each year of the Wangwa mining area was calculated. In 2022, the ground pressure damage area was 8.171 km², the waste pile area was 0.738 km², the water body area was 0.709 km², and the subsidence area was 9.155 km². The subsidence area of the Wangwa mining area increases within the year, and the ground pressure damage also shows a growth trend, but at a slower rate. The areas of water bodies and waste piles are relatively small, with a relatively flat overall normal trend.

(2) DInSAR technology was used to obtain the ground subsidence range affected by mining in Yingtonggou mining area, Wangwa mining area, and Wangwa No. 2 mining area from 2017 to 2022. The mining-affected area in Yingtonggou was 2.80 km², in the Wangwa mining area it was 23.94 km², and in the Wangwa No. 2 mining area it was 8.74 km².

(3) An EGER model based on distance function optimization was constructed. Slope, subsidence, ground occupation and damage, FVC, RSEI, precipitation, and temperature were selected as indicators for the EGER assessment frame. The weight calculation of the model uses a coupled method of AHP and EM, which reduces the influence of subjective weight model over-reliance on experience and improves the reliability of the model.

(4) The EGER of the study area was categorized into three levels. The high-risk areas are primarily located within the Wangwa mining area, while the pre-investigation area and general investigation area maintain a good eco-geological environment quality. By 2022, the low-risk area increased by 47.05 km² compared to 2017, and the high-risk area had shrunk by 19.96 km². From 2017 to 2022, the areas with no significant change accounted for 80.5% and the areas with very significant decrease accounted for 11.1%, respectively, at 21.28 km² and 49.10 km².