## 2 Materials and Methods
### RSEI Construction  
The ecological environment quality of the Shendong mining area was assessed using a Remote Sensing Ecological Index (\(RSEI\)). This \(RSEI\) was formulated based on four key indices: humidity, greenness, dryness, and heat. Principal Component Analysis (PCA) was subsequently employed to analyze the \(RSEI\), which is defined as follows:  
\[RSEI=(FV,WET,LST,NDSI) \tag{1}\]  
where \(FV\), \(WET\), \(LST\), and \(NDSI\) represent the greenness, humidity, heat, and dryness indices, respectively. The Tasseled Cap transformation's Wetness, Greenness, and Brightness components derived from remote sensing data have been widely utilized in ecological and environmental quality assessments. In particular, the Wetness component demonstrates good sensitivity to soil and vegetation moisture content. For example, using Landsat 5 TM data, the Wetness index is calculated from reflectance data using the following formula:  
\[WETTM=0.0315 Rblue+0.2021 Rgreen+0.3102 Rred+0.1594 Rnir-\] \[0.6806 Rswir1-0.6109 Rswir2 \tag{2}\]  
where \(R_{blue}\), \(R_{green}\), \(R_{red}\), \(R_{nir}\), \(R_{swir1}\), and \(R_{swir2}\) represent the reflectance data for the blue, green, red, near-infrared, shortwave infrared 1, and shortwave infrared 2 bands of TM, ETM+, and OLI imagery, respectively. The Normalized Difference Vegetation Index (_NDVI_) is a widely utilized vegetation index capable of reflecting plant biomass, leaf area index, and vegetation cover. Consequently, _NDVI_ was selected for the calculation of vegetation fraction (Fv) and employed as a surrogate for greenness metrics. The specific expression for this is as follows:  
\[NDVI=(\rho_{nir}-\rho_{red})/(\rho_{nir}+\rho_{red}) \tag{3}\]  
where \(\rho_{nir}\) and \(\rho_{red}\) represent the reflectance in the near-infrared and red bands, respectively, as measured by TM and OLI imagery:  
\[F_{v}=(NDVI-NDVI_{min})/(NDVI_{max}-NDVI_{min}) \tag{4}\]  
Specifically, \(NDVI_{max}\) and \(NDVI_{min}\) are usually defined as the maximum and minimum values of \(NDVI\) within a given statistical confidence interval. Figure 1: Research framework. Land surface temperature (_LST_) was used as a proxy for thermal intensity. Brightness temperature (T) was first calculated using the Landsat user manual's model, and then emissivity corrected to obtain _LST_, as follows:  
\[Li=gain\cdot DN+bias \tag{5}\]  
\[T=k_{2}/\ln(k_{1}/Li+1) \tag{6}\]  
In the equation: _Li_ represents the at-sensor radiance of a pixel within the thermal infrared band of the TM/ETM+/OLI sensor; _DN_ denotes the pixel's digital number; _gain_ and _bias_ are the gain and bias values for the thermal infrared band, respectively, which can be obtained from the image header; \(T\) represents the at-sensor brightness temperature; and \(k\)1 and \(k\)2 are calibration parameters. The temperature \(T\) calculated using Equation (6) must be corrected for emissivity to obtain land surface temperature (_LST_), as follows:  
\[LST=T/\left[1+\left(\lambda\cdot T/\rho\right)\ln\varepsilon\right] \tag{7}\]  
where \(\lambda\) is the central wavelength of the TM/ETM+ thermal infrared band (\(\lambda\) = 11.435 \(\upmu\)m) and the OLI thermal infrared band (\(\lambda\) = 10.9 \(\upmu\)m); \(\rho\) = 1.438 \(\times\) 10\({}^{-2}\) mK; and \(\varepsilon\) is the land surface emissivity. The dryness index, denoted as _NDSI_, is synthesized from the bare soil index (_SI_) and the built-up index (_IBI_). Its calculation formula is:  
\[NDSI=\frac{SI+IBI}{2} \tag{8}\]  
\[WETTM=0.0315 Rblue+0.2021 Rgreen+0.3102 Rred+0.1594 Rnir-\]0  
\[WETTM=0.0315 Rblue+0.2021 Rgreen+0.3102 Rred+0.1594 Rnir-\]1  
where \(\rho_{blue}\), \(\rho_{green}\), \(\rho_{red}\), \(\rho_{nir}\), and \(\rho_{swir1}\) represent the reflectance data for the blue, green, red, near-infrared, and shortwave infrared 1 bands of TM, ETM+, and OLI imagery, respectively. Due to differences in units and numerical ranges among the various indices, standardization is necessary.