## 2 Materials and Methods
### Methods  
#### 2.3.1 Remote Sensing Ecological Index  
The RSEI is an integrated index that includes four factors: greenness, humidity, heat, and dryness, which reflect the vegetation index, the humidity of the vegetation and soil, the surface temperature, and the index of buildings and bare ground, respectively [31; 32]. The remote sensing index is defined as  
\[\text{RSEI}\;=\;\text{f}(\text{NDVI},\text{Wet},\text{NDBSI},\text{LST}) \tag{1}\]  
where NDVI is the greenness index, Wet is the humidity index, NDBSI is the dryness index, and LST is the heat index. The humidity component of the tassel cap transformation was selected to represent Wet, which can reflect the humidity content of the vegetation, soil, and artificial ground surfaces in the study area. Due to the presence of large water bodies in the study area, the normalized difference water index was used to mask the water body information constructed by [PERSON] [33] to keep the humidity indicator from being affected by the water bodies. In this study, Landsat operational land imager (OLI) imagery was used as an example, and the following equation was used to calculate the humidity index. Using Landsat@ OLI imagery as an example, the following equation was used to calculate the humidity indicator:  
\[\text{Wet}\;=0.1511\text{B1}+0.1973\text{B2}+0.3283\text{B3}+0.3407\text{B4}- 0.7117\text{B5}-0.4559\text{B6} \tag{2}\]  
where B1 to B6 represent the blue, green, red, near-infrared, short-wavelength infrared (SWIR1), and SWIR2 bands of the Landsat@ OLI, respectively. The normalized difference vegetation index (NDVI) is closely related to the plant biomass, leaf area index, and vegetation cover. It is the preferred indicator for analyzing vegetation changes and has been widely used in remote sensing monitoring of vegetation. Therefore, the NDVI was chosen to represent the greenness indicator in this study. \[\text{NDVI}\;=\;\frac{\text{B4}\;-\;\text{B3}}{\text{B4}\;+\;\text{B3}} \tag{3}\]  
The normalized difference building and soil index (NDBSI) represents the dryness index in the RSEI model, and the bare earth and building surfaces in urban areas are always in a dry state. Therefore, the NDBSI can be calculated in combination with the index-based built-up index (IBI) and the bare earth index (SI):  
\[\text{NDBSI}\;=\;\frac{\text{SI}\;+\;\text{IBI}}{2} \tag{4}\]  
\[\text{IBI}\;=[\frac{2\text{B5}}{\text{B4}+\text{B5}}-(\frac{\text{B4}}{\text {B3}+\text{B4}}+\frac{\text{B2}}{\text{B2}+\text{B5}})]/[\frac{2\text{B5}}{ \text{B4}+\text{B5}}+(\frac{\text{B4}}{\text{B3}+\text{B4}}+\frac{\text{B2} }{\text{B2}+\text{B5}})] \tag{5}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Name**} & \multicolumn{1}{c}{**Description**} & \multicolumn{1}{c}{**Source**} \\ \hline Landsat data & Land use cover/change (30 m) & Geospatial Data Cloud ([[https://www.gscloud.cn/](https://www.gscloud.cn/)]([https://www.gscloud.cn/](https://www.gscloud.cn/)), (accessed on 24 October 2020)) \\ \hline \multirow{6}{*}{Driving factors} & Natural & DEM (30 m) & National Geomatics Center of China \\  & Slope (30 m) & ([[https://www.ngcc.cn/ngcc/](https://www.ngcc.cn/ngcc/)]([https://www.ngcc.cn/ngcc/](https://www.ngcc.cn/ngcc/)), (accessed on 21 June 2021)) \\ \cline{2-4}  & \multirow{4}{*}{Transportation and location} & Distance to river & Resource and Environment Science and Data Center \\  & & Distance to rails & ([[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)), (accessed on 25 June 2021)) \\ \cline{1-1}  & & Distance to refinet in the city & Euclidean Distance \\ \cline{1-1} \cline{2-4}  & Socio-economic & Population density & Statistical Yearbook of Sanya City \\ \cline{1-1}  & & GDP & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data sources. \[\text{SI }=\frac{(\text{B3}+\text{B5})-(\text{B4}+\text{B1})}{(\text{B3}+\text{B5})+( \text{B4}+\text{B1})} \tag{6}\]  
where SI is the bare earth index, and IBI is the building index.