Introduction

The ecological environment refers to the total quantity and quality of water resources, land resources, biological resources, and climate resources that affect human survival and development. It is a complex ecosystem that relates to human activities, changes in the natural environment, and sustainable economic development1,2. In pursuit of their own survival and development, humans excessively exploit and transform nature, leading to frequent ecological environment problems such as land degradation, vegetation changes, air pollution, and water pollution3. According to statistics from the United Nations Environment Programme (UNEP), mining activities directly affect approximately 0.3% of the global terrestrial area. In some mining regions, the degradation rate reaches 5-10% annually. Furthermore, approximately 60% of abandoned mining sites in developing countries remain unrepaired. In recent years, the negative impacts of human activities such as resource extraction, energy consumption, and excessive agricultural development on global ecosystems have been escalating, severely undermining the sustainable development of regional ecological environments4,5. Changes in the ecological environment have become one of the most prominent and challenging issues in today’s world6,7,8. Therefore, timely and accurate dynamic monitoring and assessment of regional ecological environment quality have significant practical implications, providing scientific evidence for regional ecological environment protection and management9.

Mineral resources are an important fundamental resource for China, holding strategic significance for promoting China’s economic development10. However, while mining promotes economic development, it also occupies and damages original ecological land and vegetation, causing varying degrees of damage to the surrounding ecological environment and severely affecting the quality of life for humans11,12,13,14. With China’s vigorous promotion of ecological civilization construction and the continuous enhancement of people’s environmental awareness, the monitoring and evaluation of ecological quality in mining regions have received increasing attention12,14,15,16,17. With the development of technology, remote sensing technology has advantages in acquiring regional monitoring data, such as fast speed, wide monitoring range, low cost, and high efficiency18. It has been successfully applied in vegetation cover monitoring, land use change detection, urban ecosystem research, and ecological environment quality assessment13,16,17,19,20. In early ecological environment research, a single ecological indicator was often used, Yu Haoxuan, et al.19 analyzed the ecological environment status of the mining area by studying the spatiotemporal changes in vegetation coverage in the Tashan mining area of China. Yaming Fan et al.21 analyzed ecological environment quality through the study of land use changes in the Lijiang River Basin; Zhang Peipei et al.22, Li Ning et al.23 combine multiple vegetation indices to study the ecological environment status of the mining area. However, the ecological quality of mines is influenced by multiple factors, and a single indicator is difficult to comprehensively reveal the systematic changes in the ecological environment. Xu Hanqiu et al.24 integrating multiple ecological factors, the proposed RSEI is entirely based on remote sensing images to obtain information such as vegetation index (greenness), bare soil index (aridity), humidity component (humidity), and surface temperature (thermal intensity), providing a comprehensive evaluation of the regional ecological environment quality. Currently, many scholars have adopted RSEI or improved RSEI to evaluate regional ecological environment quality. For example, Li Haochen et al.25 using the RSEI method to assess the ecological environment quality of the Zhongliang Mountain mining area from 2000 to 2020, and comparing the results with those from the comprehensive index method; Ma Pengfei et al.3 constructed a novel RSEI using PM2.5 to evaluate the ecological environment of the mining area. This study demonstrates that the new RSEI model can more accurately reflect the ecological environment status of the study area; Li Jing et al.26 based on the ecological impact factors of the mining area, an improved RSEI model incorporating soil erosion factors was proposed on the foundation of greenness, humidity, aridity, and thermal intensity factors of RSEI, to analyze the ecological environment quality of the coal mining area in Shanxi Province.

The four indicators of RSEI can respectively reflect the vegetation cover and quality status of the mine region, the degree of soil exposure, the risk of soil and water loss caused by reduced soil moisture, and the increase in surface temperature due to human activities and large-scale mechanized operations. Furthermore, mining development induces surface subsidence and geological hazards, which significantly impact the ecological environment. Surface deformation information constitutes a critical factor affecting ecological quality in mining regions. Additionally, meteorological and socioeconomic factors also exert important influences on ecological environmental quality. Therefore, these four indicators were incorporated into a comprehensive evaluation framework, integrating additional factors including elevation (DEM), slope, aspect, annual average temperature (TEM), annual average precipitation (PRE), population density (Pop), gross domestic product (GDP), and nighttime light intensity (NLI). This integrated approach enables comprehensive and rapid monitoring of the spatial distribution characteristics and influencing factors of the ecological environment in mining areas. In this study, a mining area cluster in Northwest China was selected as the research site. The applicability of the methodology was validated through the integrated utilization of Landsat5 and Landsat8 series imagery. Long-term time-series data and the differential method were incorporated to investigate the changing trends and influencing factors of land ecological environment quality in Northwest China’s mining areas over the past two decades. Furthermore, a geographic detector was introduced to quantitatively analyze the impacts of different geographical environments on the ecological system. This approach elucidated the spatiotemporal patterns of land ecological degradation and restoration in mining areas. The findings aim to provide scientific evidence and theoretical support for formulating ecological management and restoration strategies in mining regions.

Overview of the study area

The study area (110°1′-110°39′E, 38°59′-39°38′) is located at the border between Yulin City and Ordos City, on the edge of the Mu Us Desert. It is a typical transitional region of farmland-grassland-bare land in China (Fig. 1). This region is characterized by a typical continental monsoon climate, with arid conditions and water scarcity, sparse precipitation, a fragile ecological environment, and land use types mainly consisting of grassland, shrubland, and bare land. The average annual temperature in the study area is around 7 ℃, with average annual precipitation ranging from 350 to 450 millimeters, decreasing from southeast to northwest. There are significant interannual and seasonal variations in precipitation. The region boasts abundant coal resources with stable deposits and favorable mining conditions. It is characterized by ultra-low ash, ultra-low phosphorus, ultra-low sulfur, and medium to high calorific value, making it a high-quality power and environmentally clean coal. It is the largest coal production base in China. Additionally, the region contains mineral resources such as silica, petroleum, and iron ore, making it the most significant mining area in Northwest China27.

Fig. 1
figure 1

Location map and remote sensing image of the study area. (Map created using ArcGIS Desktop version 10.8 downloaded from a third-party source. URL: https://pan.baidu.com/s/1s5bL3QsCP5sgcftCPxc88w Extractioncode: kw4j).

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Data and research methods

Data sources and processing

This study focuses on the mining area in the northern part of Shenmu City, Northwest China, as the study area. The Landsat imagery covering the study area has a path/row number of (127, 033) and is sourced from the Geospatial Data Cloud. Affected by cloud cover and partial data loss, to ensure that the image data can fully cover the study area, this study selected five periods of original Landsat 5 or Landsat 8 remote sensing images from 2000-07-23, 2005-07-05, 2009-06-30, 2015-07-01, and 2021-07-17 (Table 1). The selected data focuses on the summer season of the study area, which is the period when vegetation growth is optimal and surface temperature differences are most pronounced, making it suitable for studying the spatiotemporal distribution of ecological environment quality. Before being input into the model, the Landsat series remote sensing data need to undergo preprocessing such as radiometric calibration and atmospheric correction, all of which were completed using ENVI software in this study.

Table 1 Main data source and pre-processing.
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Calculation process of RSEI

Humidity index

The remotely sensed tassel-cap transformed humidity component, Wetness Index (WI), can reflect the humidity of the soil and vegetation in the mining area better, so the tassel-cap transformed humidity component was chosen to represent the humidity information of the study area. The WI formulas are as follows:

$${\text{WI = c1B1 + c2B2 + c3B3 + c4B4 + c5B5 + c6B6 }}$$
(1)

Here, B1-B6 represent blue band, green band, red band, near red band, mid-infrared band 1, and mid-infrared band 2, respectively; c1-c6 are the sensor parameters. The humidity component calculation parameters for TM and OLI sensors differ due to the different satellite sensor types. In the case of the TM sensor, c1-c6 are 0.0315, 0.2021, 0.3012, 0.1594, −0.6806 and − 0.6109, respectively28. And in the case of the OLI sensor, c1-c6 are 0.1511, 0.1973, 0.3283, 0.3407, −0.7117 and − 0.4559, respectively29.

Greenness Index.

Greenness index is expressed by vegetation indices. Normalized vegetation index (NDVI) plays a very important role in detecting the growth status of vegetation, reflecting the growth of crops, and reflecting the vegetation cover. NDVI was selected as the greenness index based on the environmental characteristics of the study area. The NDVI formulas are as follows30:

$$\:\text{N}\text{D}\text{V}\text{I}=({B}_{NIR}-{B}_{R})/({B}_{NIR}+{B}_{R})$$
(2)

Here, \(\:{B}_{NIR}\) and \(\:{B}_{R}\:\)represent near red band and red band, respectively.

Dryness index

The dryness index is used to reflect the degree of dryness of the surface. The non-vegetated areas in the study area are dominated by bare soil, exposed mines and buildings. Therefore, the index-based built-up index (IBI) and the soil index (SI) can be used to construct a Normalized Differential Soil Index (NDSI) to represent the dryness of RSEI. This is calculated as follows31:

$$\:\text{SI=}\text{[}\left({\text{B}}_{\text{5}}\text{+}{\text{B}}_{\text{3}}\right)\text{-}\left({\text{B}}_{\text{4}}\text{+}{\text{B}}_{\text{1}}\right)\text{]}/\text{[}\left({\text{B}}_{\text{5}}\text{+}{\text{B}}_{\text{3}}\right)\text{+}\left({\text{B}}_{\text{4}}\text{+}{\text{B}}_{\text{1}}\right)\text{]}$$
(3)
$$\:\text{I}\text{B}\text{I}=\frac{\left\{{\text{2B}}_{\text{5}}/\left({\text{B}}_{\text{5}}\text{+}{\text{B}}_{\text{3}}\right)-\left[{\text{B}}_{\text{4}}/\left({\text{B}}_{\text{4}}\text{+}{\text{B}}_{\text{3}}\right)+{\text{B}}_{\text{2}}/\left({\text{B}}_{\text{2}}\text{+}{\text{B}}_{\text{5}}\right)\right]\right\}}{\left\{{\text{2B}}_{\text{5}}/\left({\text{B}}_{\text{5}}\text{+}{\text{B}}_{\text{3}}\right)+\left[{\text{B}}_{\text{4}}/\left({\text{B}}_{\text{4}}\text{+}{\text{B}}_{\text{3}}\right)+{\text{B}}_{\text{2}}/\left({\text{B}}_{\text{2}}\text{+}{\text{B}}_{\text{5}}\right)\right]\right\}}$$
(4)
$$\:\text{NDSI=}\frac{\text{SI+IBI}}{\text{2}}$$
(5)
Heat index

Human activities in the study area, such as mining, change the surface cover type and also generate some heat, warming the surface, so the heat index was chosen to be expressed in terms of land surface temperature (LST). This is calculated as follows32:

$${\text{LST = T}}/\left[ {{\text{1 + (}}\lambda {\text{T}}/\rho {\text{)ln}}\varepsilon } \right]$$
(6)
$$\:\text{T=}{\text{K}}_{\text{2}}/\text{ln}\left({\text{K}}_{\text{1}}/{\text{B}}_{\text{TIR}}\text{+1}\right)$$
(7)

Here, \(\:{\text{B}}_{\text{TIR}}\:\)is the thermal infrared band radiation value, 6th band for TM sensors and 10th band for OLI sensors; T is the bright temperature value at the sensor; K1 and K2 are the sensor calibration parameters. For the 6th band of Landsat-5 TM, K1 = 607.76 W/(m2·sr·µm) and K2 = 1260.56 K18. For the 10th band of Landsat-8 OLI/TIR, K1 = 774.89 W/(m2·sr·µm) and K2 = 1321.08 K. λ is the central wavelength of the thermal infrared band;\(\:\text{\:ρ}\)=1.438 × 10− 2 (m·K); ε is the specific emissivity of the feature.

Calculation process

Principal component analysis (PCA) was performed on four standardized indicators. First, the covariance matrix and correlation matrix were calculated, followed by eigenvalue decomposition to obtain eigenvalues. The proportion of each eigenvalue was then computed as the variance explained ratio. For example, the variance explained by the first principal component was calculated using the following formula: the first eigenvalue divided by the sum of all eigenvalues* 100%. The detailed steps for PCA implementation were as follows: (1) PCA Tool Invocation: The operation was conducted via ENVI software using the following path: Transform → Principal Components → Forward PC Rotation → Compute New Statistics and Rotate. (2) Data Input: The image file to be analyzed was loaded in the *Principal Components Input File* interface. (3) Parameter Configuration: Statistical parameters were set in the *Principal Components Parameters* dialog box. (4) Computation Execution: Core calculations, including eigenvalue and eigenvector extraction, were performed. (5) Result Interpretation: Principal component statistics were reviewed via *Transform → Principal Components → View Principal Component Statistics*.

To ensure that a higher value of the first principal component (PC1) represents better ecological conditions, 1 can be subtracted from the calculated PC1 to obtain the initial ecological index RSEI0, which is then normalized24:

$$\:{RSEI}_{0}=1-\left\{PC1*\left[f(NDVI,WET,NDSI,LST)\right]\right\}$$
(8)
$$\:RSEI=({RSEI}_{0}-{RSEI}_{0\_min})/({RSEI}_{0\_max}-{RSEI}_{0\_min})$$
(9)

RSEI is the constructed Remote Sensing Ecological Index, with values ranging between [0,1]. The closer the RSEI value is to 1, the better the ecology. See Fig. 2 for the calculation flowchart.

Fig. 2
figure 2

Full flowchart of RSEI calculation.

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Results analysis

Overall evaluation of ecological environment quality

The results of the principal component analysis are shown in (Table 2). In the five RSEI images from 2000 to 2021, the total contribution rate of the first two principal components (PC1, PC2) exceeds 80%, indicating that PC1 and PC2 have already integrated the main features of the four indices, allowing for a reasonable interpretation of the ecological environment quality in the study area. Moreover, remote sensing data inherently contain noise (e.g., cloud interference, atmospheric correction errors), with PC3 predominantly comprising such interference signals rather than genuine ecological information. Therefore, PC3 was excluded from consideration. From the PC1, it can be observed that among the five sets of data from 2000 to 2021, the NDVI and WET index factors, which provide positive feedback to the ecological environment, are positive values, while the LST and NDSI index factors, which provide negative feedback to the ecological environment, are negative values. The results are consistent with theoretical expectations. Additionally, the contribution of each index factor to the PC1 from 2000 to 2021 shows that the WET index is greater than the NDVI index, indicating that among the positive feedback indicators in the Northwest China mining area, the WET index has a significant impact on the ecological environment quality of the study area.

Table 2 Principal component analysis result.
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Table 3 shows the means of each indicator and the RSEI for the Northwest China mining area in the years (a) 2000, (b) 2005, (c) 2009, (d) 2015, and (e) 2021. According to the trend of each indicator in the table, from 2000 to 2021, LST continuously decreased; NDSI showed a ‘V’ shaped trend of first decreasing and then increasing; NDVI showed an upward trend; WI exhibited an inverted ‘V’ shaped trend. Between 2000 and 2005, the Ecological Environment Quality in the study area was significantly improved, with the mean RSEI increasing from 0.3116 to 0.3946. However, from 2005 to 2021, there was no significant change in the Ecological Environment Quality, which aligns with the trend of each indicator. During 2000–2005, the greenness and humidity indicators in the study area increased significantly, while the thermal intensity and aridity decreased significantly, thereby improving the Ecological Environment Quality. Between 2005 and 2021, greenness and aridity showed an increasing trend, while humidity and thermal intensity exhibited a declining trend. Both positive and negative feedback indicators had upward or downward trends, resulting in an unclear trend in ecological environment quality changes.

Table 3 Average values of the RSEI and indicators on Northwest China mining area, 2000–2021.
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Spatiotemporal distribution characteristics of RSEI

To further quantitatively analyze the spatiotemporal changes in land ecological environment quality in the Northwest China mining area, this study used the equal interval method to divide the RSEI into five levels, ranked from high to low as excellent, good, medium, poor, and very poor. This classification aids in better studying the changing patterns of the mining area’s ecological environment. The results are shown in (Fig. 3; Table 4). In 2000, the overall ecological environment quality was predominantly poor and very poor, accounting for 48.75% and 27.62% of the area, respectively. However, from 2005 to 2021, it mainly shifted to poor and moderate, with overall fluctuations being minimal. In 2021, these categories accounted for 36.67% and 24.71%, respectively. Overall, the ecological environment quality in the study area exhibits a spatial distribution pattern where the eastern region is superior to the western region. From 2000 to 2005, there was a significant improvement in ecological environment quality, with a noticeable increase in areas categorized as moderate, good, and excellent, primarily distributed in the eastern region of the study area. From 2005 to 2021, the overall change in ecological environment quality in the study area was relatively small, but a block of poor ecological environment quality emerged in the northeastern part.

Based on the periodic impact of mine development on the soil ecological environment, a comparative analysis of ecological environment changes was conducted between the study area and the mining area within it. The results indicate that from 2000 to 2009, the ecological environment quality of the mining area was generally consistent with the comprehensive zone, with significant improvements in ecological environment quality. Between 2000 and 2005, the areas classified as poor and inferior in the mining area decreased by 14.78% and 15%, respectively, while the areas classified as medium, good, and excellent increased by 15.04%, 10.16%, and 4.58%, respectively. The reason for this is related to the implementation of ecological projects and climate change in China (Fig. 4). In 1999, the project of returning farmland to forests was firstly piloted in Shaanxi Province, and the implementation of the project of returning farmland to forests has realized the historical transformation from deforestation and reclamation to returning farmland to forests, which effectively improves the ecological situation, and the increase of precipitation and the decrease of temperature in the study area from 2000 to 2009 have effectively increased the growing conditions of the vegetation and reduced the drought risk, thus improving the quality of the local ecological environment. This improvement is related to the implementation of China’s ecological engineering projects. In 1999, the grain for green project was piloted in Shaanxi Province, marking a historic shift from deforestation and reclamation to reforestation, effectively improving the ecological conditions. After 2009, the ecological environment quality of the mining area gradually fell below that of the comprehensive zone. Compared to 2009, by 2021, the area classified as ‘Poor’ in the mining area increased by 28.79%, while the areas classified as ‘Inferior’, ‘Medium’, ‘Good’, and ‘Excellent’ decreased by 0.8%, 16.59%, 7.91%, and 3.49%, respectively. This trend is closely related to China’s economic development, which relies heavily on the support of mineral resources. The extensive development of newly added mineral resources disrupts the original surface ecosystems, leading to land occupation, geological structure damage, groundwater level decline, and reduced vegetation cover, thereby causing a rapid increase in areas with ‘Poor’ ecological environment quality. Meanwhile the quality of the ecosystem in 2021 has been affected by extreme weather, with a steady decrease in precipitation in the area after 2016, which has had a direct impact on the growth of vegetation in the area.

Fig. 3
figure 3

Spatiotemporal distribution of RSEI in Mining areas in northwest, China. (Map created using ArcGIS Desktop version 10.8 downloaded from a third-party source. URL: https://pan.baidu.com/s/1s5bL3QsCP5sgcftCPxc88w Extractioncode: kw4j).

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Table 4 Percentage of area with different grades of RSEI, Northwest China mining areas, 2000–2021.
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Fig. 4
figure 4

Interannual variations of precipitation and annual mean temperature in Mining areas in northwest, China.

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Analysis of spatiotemporal changes in ecological environment quality

To clarify the spatiotemporal characteristics of ecological environment quality changes in the Northwest China mining area from 2000 to 2021, this study employs the difference method to conduct a differential analysis of the remote sensing ecological index in the study area for the years 2000 and 2021. The changes in the region are categorized into severe degradation (≤-2), slight degradation (−1), basically unchanged (0), slight improvement (1), and significant improvement (≥ 2). The results are shown in Fig. 5(a. 2000-2009; b. 2009-2021; c. 2000-2021), and the proportions of each ecological environment quality change category are shown in (Table 5). As can be seen from Table 5, during the period from 2000 to 2009, the ecological environmental quality of the comprehensive zone (CZ) and mining area (MA) mainly showed improvement or remained unchanged, with a relatively low degree of degradation. However, a certain degradation trend had already emerged, with areas of slight degradation accounting for 13.6% and 12.58% in the CZ and MA, respectively. Compared to the CZ, the ecological environment quality of the MA showed a slight improvement, with the area of improvement increasing by 5.08%. During the period from 2009 to 2021, the degree of degradation in both the CZ and MA significantly intensified. The degraded region in the CZ increased by 12.11%, and the degree of degradation in the MA also intensified, with the degraded region accounting for 55.72%. This indicates that the ecological environment quality in the CZ experienced a relatively serious degradation trend during this period, especially with more significant degradation in the MA. Between 2000 and 2021, the ecological environment quality of the CZ primarily showed improvement or remained unchanged, with improvement areas accounting for 43.78% of the CZ and unchanged areas accounting for 35.67%. The area with degraded ecological environment quality in the CZ accounted for 20.55%. However, the MA exhibited a significant trend of degradation in ecological environment quality, with the degraded area accounting for 41.55% of the MA, which is significantly higher than the CZ, indicating that the Mining Area faces considerable pressure on its ecological environment quality. The results indicate that between 2000 and 2021, the ecological environment quality in the CZ initially improved and then degraded, primarily due to the impact of the degradation of ecological environment quality in the MA.

Fig. 5
figure 5

Spatial distribution in changes to RSEI in mining areas in Northwest, China from 2000 to 2021. (Map created using ArcGIS Desktop version 10.8 downloaded from a third-party source. URL: https://pan.baidu.com/s/1s5bL3QsCP5sgcftCPxc88w Extractioncode: kw4j).

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Table 5 Percentage change of RSEI in mining areas in northwest, China from 2000 to 2021.
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Figure 6(a. 2000-2009; b. 2009-2021; c. 2000-2021) shows the spatial distribution of hotspots and cold spots in the ecological environment of the study area. From 2000 to 2009, the ecological environment hotspots were mainly concentrated in the eastern part of the study area, while the cold spots were primarily located in the western region. Between 2009 and 2021, the ecological cold spots were mainly concentrated in the southern and central parts of the comprehensive zone, whereas the hotspots were more dispersed. Comparing the two periods, there is a significant difference in the distribution of ecological hotspots and cold spots in the study area. The number of cold spot patches has increased significantly, with the newly added cold spots mainly concentrated in the mining area, where the proportion of cold spots increased to 70.37%, significantly higher than the comprehensive zone’s 35.04% (Table 6). This indicates that there has been a certain degree of improvement in the ecological environment of the comprehensive zone, but the ecological environment quality of the mining area continues to decline, gradually widening the gap with the ecological environment quality of the comprehensive zone.

Table 6 Distribution proportion of cold areas and hot spots of ecological environment quality change in the study area.
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Fig. 6
figure 6

Distribution cold and hot spots of ecological environment quality changes in the study area. (Map created using ArcGIS Desktop version 10.8 downloaded from a third-party source. URL: https://pan.baidu.com/s/1s5bL3QsCP5sgcftCPxc88w Extractioncode: kw4j).

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Discussion

Analysis of influencing factors

In order to further explore the impact of factors on RSEI, according to the actual situation of the study area and data availability, twelve factors, including NDSI, NDVI, WI, LST, elevation (DEM), slope, aspect, annual average temperature (TEM), annual average precipitation (PRE), Population Density (Pop), Gross Domestic Product (GDP) and Nighttime Light Intensity (NLI), were selected from the natural geographic system to explore the driving mechanism of RSEI change on the Northwest China mining area.

Based on the method of discrete classification, combined with the actual situation of the region, the NDVI, NDSI, WI, LST, DEM, TEM, PRE, Pop, GDP and NLI are divided into six categories using the natural discontinuity method. The slope and aspect are divided into six categories and nine categories using the equal spacing method.

The regular grid division of the Northwest China mining area was carried out by using the ArcGIS software fishing net tool. Considering the density of sampling points and the calculation efficiency of the model, the grid row and column numbers are set to 150 × 120, and each grid center is sampled, with a total of 18,000 sampling points. The RSEI and the corresponding factor values are extracted to the sampling points. The influence intensity of each factor on the RSEI change of Northwest China mining area is obtained by using the geographic detector for calculation.

The factor detection results reflect the explanatory power of each factor on RSEI change in the Northwest China mining area. The detection results of each factor are shown in (Table 7). In 2000, the order of explanatory power (EP) of each factor on RSEI change in the Northwest China mining area from large to small is: NDSI > WI > NDVI > LST > TEM > Pop > NLI > DEM = PRE = aspect > slope = GDP. The factors with explanatory power q value greater than 0.01 are NDSI, WI, NDVI, and LST, respectively. NDSI is highly correlated with the distribution of RSEI. Besides these four indicators, TEM, Pop, and NLI have relatively high explanatory power for RSEI. TEM is highly correlated with LST. Pop and NLI reflects the degree of regional population aggregation and has an important impact on the change of build-up land area. Related to NDSI. The results show that the land use change of the LP in 2000 was mainly affected by NDSI, WI, NDVI, LST, TEM, Pop and NLI.

In 2021, the order of explanatory power of each factor on RSEI change in the Northwest China mining area from large to small is: NDSI > WI > NDVI > LST > PRE > Pop > GDP > TEM > NLI > aspect > DEM > slope. The factors with explanatory power q value greater than 0.01 are NDSI, WI, NDVI and LST. Compared with 2000, the impact of TEM on RSEI has weakened, and artificial conditions such as Pop, GDP and NLI have become the important factors affecting RSEI change on the Northwest China mining area.

In summary, the intensity of factors affecting the ecological environment index change in the Northwest China mining area varies at different stages, but factors such as Pop, GDP, and NLI have strong explanatory power during the study period. Analyze its causes: Pop, GDP, and NLI exert cascading ecological effects through their influence on bare soil indices. The stepwise mechanisms are explained as follows: (1) Population growth increases demand for residential, agricultural, and infrastructural development. Land exploitation destroys natural vegetation and expands bare soil areas. The absence of vegetative cover makes exposed soil vulnerable to wind and water erosion, leading to soil degradation and land deterioration. (2) GDP growth intensifies the urban heat island effect. Bare soil exhibits lower albedo and thermal capacity, thereby exacerbating urban heat islands and altering local microclimates. (3) Nighttime light intensity serves as an indicator of human activity density. Areas with high light intensity typically correspond to intensive land development, which indirectly disrupts the ecological balance of bare soil regions. In the study area, the development of the mining area largely directly determines the ecological environment quality.

Table 7 Results of factor detection.
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Impact of land reclamation on the ecological environment

To further investigate the long-term impact of mining area development on ecological environment quality, this paper analyzes the dynamic changes in ecological environment quality for three typical plots (Fig. 7). The ecological environmental quality of all three plots experienced a pattern of first rising, then declining, and rising again (Fig. 8). From 2000 to 2005, the study area implemented ecological engineering projects such as afforestation and the conversion of farmland back to forests and grasslands, promoted the ecological environment quality of the study area33. After 2005, the development of the mining area destroyed surface vegetation and underground water resources, leading to a decline in ecological environment quality. However, as China experienced rapid economic development, it initiated the construction of green smart mines and began restoration work on mines that had been fully excavated. By 2021, the ecological environment quality of three plots was significantly improved. Therefore, from the perspective of specific point analysis, land reclamation work has played a relatively significant role in ecological restoration in the three typical mining area plots.

Fig. 7
figure 7

Typical land reclamation plots in mining areas.

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Fig. 8
figure 8

Interannual variation of RSEI in typical land reclamation plots in mining areas.

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In summary, the ecological environment quality in Northwest China shows an improvement trend, but mining area development has delayed this process. The initial phase of mining area development caused significant disturbances to the ecological environment quality in the study area, resulting in many low-value patches in the RSEI. This indicates that the initial phase of mining area development has a clear negative impact on ecological environment quality. However, typical mining area plots have shown an improvement in ecological environment quality after ecological restoration, demonstrating that the impact of mining area development on ecological environment quality can be gradually offset through ecological restoration.

The research results show that WET and NDVI have a positive impact on RSEI, while NDSI and LST have a negative impact on RSEI, similar to the conclusions of existing studies34,35,36. This indicates that these two sets of indicators have opposite effects on the ecological environment quality of the study area, with NDSI having a more significant impact than other indicators37. In terms of ecological indicators, the continuous decrease in LST and the upward trend in NDVI are related to the overall improvement in ecological environment quality in Northwest China. Since 2000, China has implemented ecological engineering, which has effectively improved the vegetation coverage in Northwest China. NDSI shows a ‘V’ trend of first decreasing and then increasing, while WI presents an inverted ‘V’ trend, which aligns with the characteristics of mining area development. The development of the mining area, including the construction of new buildings, roads, and other infrastructure, has increased the impervious surface area in the study area, damaging groundwater and leading to a decrease in the humidity index.

Although we have identified the spatiotemporal trends and influencing factors of ecological environment quality in the Northwest China mining area, the limitations of this study need to be further explored in future research. Firstly, the RSEI evaluation method primarily considers surface parameters such as greenness, humidity, thermal intensity, and aridity within the study area, without taking into account above-ground indicators like air quality and wind speed. The factors influencing ecological environment quality are complex and diverse, including not only natural and socio-economic development but also government policies and management capabilities. On the other hand, the relationship between land use changes caused by mining area development and ecological environment quality requires further analysis to enhance the accuracy and reliability of the research results. We believe that extending the research period on the ecological environment quality of mining areas and introducing multi-dimensional parameters to improve the RSEI evaluation system will more comprehensively reveal the periodic impact of mining area development on ecological environment quality, marking the next breakthrough point. The assessment of ecological environmental quality in mining areas is evolving from single environmental indicators towards multidimensional, intelligent, and dynamic approaches. Future efforts should focus on further integrating novel technologies and interdisciplinary theories to establish globally unified evaluation standards, thereby facilitating sustainable management of mining ecosystem.

Conclusion

Focusing on the Northwest China mining area, an evaluation model based on RSEI was constructed to assess changes in ecological environment quality. This model provides a comprehensive and intuitive analysis of the RSEI change process in the Northwest China mining area. The study found: (1) From 2000 to 2021, the ecological environment quality of the Northwest China mining area showed an overall trend of initial improvement followed by fluctuations, which is related to the implementation of ecological engineering in China. (2) The evolution of the ecological environment quality in the study area is primarily influenced by human factors. From 2000 to 2005, it was affected by the implementation of ecological policies, and from 2005 to 2021, it was influenced by mining area development. (3) Mining area development has a negative impact on the ecological environment quality of the study area, but subsequent ecological restoration can mitigate the destructive effects of mining development on the ecology. (4) NDSI has the most significant impact on ecological environment quality, while WET and NDVI play a crucial role in its improvement. It is recommended to implement effective measures, such as afforestation and grass planting for ecological restoration, removal of impervious surface facilities, and controlling the expansion and development speed of built-up areas. These measures can effectively improve the overall ecological environment quality of the study area by enhancing NDVI and WET and reducing NDSI.