# Change and Driving Factor Analysis of Eco-Environment of Typical Lakes in Arid Areas
## 2 Materials and Methods
### Calculation Method
#### 2.4.3 Remote Sensing Ecological Index Model  
Using remote sensing image data, the _normalized difference vegetation index_ (NDVI), the _dryness index_ (NDBSI), the _humidity index_ (WET), and _Land Surface Temperature_ (LST) were extracted to represent four indicators of greenness, dryness, humidity, and heat, respectively, and the remote sensing ecological index (RSEI) was constructed to analyze and evaluate the changes in the ecological environment quality of Taitema Lake [21]. (1) Greenness index  
The normalized difference vegetation index (_NDVI_) was chosen to express the greenness index [40]. This index is the best indicator of plant growth status and vegetation density distribution. It is constructed by using the special red-light absorption valley of vegetation and the characteristics of high reflectance in the near-infrared band. The formula is as follows:  
\[NDVI=(\rho_{NIR}-\rho_{Rd})/(\rho_{NIR}+\rho_{Rcd}), \tag{9}\]  
(2) Dryness index  
The dryness index (_NDBSI_) was chosen to represent the dryness index, that is, the degree of \"drying\" of the surface. This indicator is not the opposite of the humidity indicator. It also includes the hardening degree of the ground. Therefore, in addition to bare soil, it is also necessary to consider building land and analyze the status of land desertification and land degradation using soil brightness. The formula is as follows:  
\[NDBSI=(SI+IBI)/2, \tag{10}\]  
where \(SI\) is the bare soil index and \(IBI\) is the construction index, which is calculated using the following formula:  
\[SI=[(\rho_{SWIR1}+\rho_{Red})-(\rho_{NIR}+\rho_{Blue})]/[(\rho_{SWIR1}+\rho_{ Red})+(\rho_{NIR}+\rho_{Blue})], \tag{11}\]  
\[IBI=\frac{\left\{2\rho_{SWIR1}+(\rho_{SWIR1}+\rho_{NIR})-\left[\frac{\rho_{NIR }}{\rho_{NIR}+\rho_{Red}}+\frac{\rho_{Green}}{(\rho_{Green}+\rho_{SWIR1})} \right]\right\}}{\left\{2\rho_{SWIR1}+(\rho_{SWIR1}+\rho_{NIR})+\left[\frac{ \rho_{NIR}}{\rho_{NIR}+\rho_{Red}}+\frac{\rho_{Green}}{(\rho_{Green}+\rho_{ SWIR1})}\right]\right\}}, \tag{12}\]  
(3) Humidity index  
The humidity component (\(WET\)) was selected to represent the humidity index. The humidity component is obtained using remote sensing tassel cap transformation, which is closely related to vegetation and soil moisture. The formula is as follows:  
\[WET_{TM}=0.0315\rho_{Blue}+0.2021\rho_{Green}+0.3102\rho_{Red}+0.1594\rho_{NIR }-0.6806\rho_{SWIR1}-0.6109\rho_{SWIR2}, \tag{13}\]  
\[WET_{OLI}=0.1511\rho_{Blue}+0.1973\rho_{Green}+0.3283\rho_{Red}+0.3407\rho_{NIR }-0.7117\rho_{SWIR1}-0.4559\rho_{SWIR2}, \tag{14}\]  
(4) Heat index  
In this paper, surface temperature (\(LST\)) was selected to represent the heat index, and surface temperature is derived from thermal infrared radiation. To sum up, in all formulas, \(\rho_{NIR}\) is the reflectance of the near-infrared band; \(\rho_{SWIR1}\) and \(\rho_{SWIR2}\) are the reflectance of shortwave infrared band 1 and the reflectance of shortwave infrared band 2, respectively. \(\rho_{Red}\), \(\rho_{Blue}\), and \(\rho_{Green}\) are the reflectance of the red band, blue band, and green band, respectively. After calculating all the indicators, we needed a standard to measure the quality of the ecological environment, so we needed to use the principal component analysis method. Principal component analysis (\(PCA\)) is an analytical and simplified data processing technique in multivariate statistical methods. Due to the different measurement standards of each indicator, it was necessary to normalize the indicator and unify the value of the indicator between 0 and 1. The normalization formula is as follows:  
\[NI_{i}=(I_{i}-I_{min})/(I_{max}-I_{min}), \tag{15}\]  
where \(NI_{i}\) is the value of an index after normalization, \(I_{i}\) is the value of this index at pixel \(i\), \(I_{min}\) is the minimum value, and \(I_{max}\) is the maximum value of this indicator. The four normalized indices were analyzed using ENVI software.