## 3 Results
### Correlations between Meizhou POI and Various Indices  
Through the ArcGIS software, the collected POIs were first analyzed for point density, then the POI density distribution map was obtained (Figure 5). Then, we normalized the GPP, LST, WET, and NDSI raster data of the study area in 2018 (Figure 6). It can be seen from Figure 5 that human activities were mainly distributed in the low-elevation areas of the city, concentrated in the central and western regions, and scattered in the rest of the region.  
Then, through correlation analysis, the correlation coefficient matrix between the POI and each index and the RSEI index was obtained (Table 4). In addition, the average correlation model was used to test the applicability of RSEI. The closer the correlation coefficient was to 1, the stronger the suitability of the model results [7]. The calculation method is shown in Formula (13):  
\[\overline{C_{a}}=\frac{\left|C_{b}\right|+\left|C_{c}\right|+\cdots+\left|C_{j }\right|}{n-1}. \tag{13}\]  
Figure 5: Distribution map of POI density in Meizhou in 2018.  
In the formula, \(\overline{C_{a}}\) represents the average correlation degree of a certain index, \(a\), for correlation analysis, \(n\) represents the number of indicators for correlation analysis, and \(C_{b}\), \(C_{c}\), \(C_{j}\), etc. represent the correlation coefficients between the indicators. The calculation results are shown in Table 4.  
After calculation, there was a significant correlation between the indicators at the 99% confidence level, and the null hypothesis was excluded. Among them, the correlation between GPP and RSEI was the highest, which represents a positive correlation; the correlation between LST and RSEI was the second, which is a negative correlation. The data sources of these two indicators were MODIS images, both of which had a high correlation with RSEI, and their average correlations were close to or higher than the latter, indicating strong applicability. Combined with the advantages of the MODIS data mentioned above, this shows that it is more appropriate to use remote sensing data from different sources to evaluate the quality of the ecological environment. POI, NDSI, WET, and RSEI had close correlations, the former two were negatively correlated, and the latter was positively correlated. This shows that climatic factors have an important impact on the ecological environment, but it is still not as high as the impact of human activities on the ecological environment.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Index** & **POI** & **GPP** & **LST** & **NDSI** & **WET** & **RSEI** \\ \hline POI & \(1\) & \(-0.455\) & \(0.524\) & \(0.214\) & \(-0.227\) & \(-0.361\) \\ GPP & \(-0.455\) & \(1\) & \(-0.644\) & \(-0.329\) & \(0.298\) & \(0.611\) \\ LST & \(0.524\) & \(-0.644\) & \(1\) & \(0.418\) & \(-0.377\) & \(-0.474\) \\ NDSI & \(0.214\) & \(-0.329\) & \(0.418\) & \(1\) & \(-0.269\) & \(-0.309\) \\ WET & \(-0.227\) & \(0.298\) & \(-0.377\) & \(-0.269\) & \(1\) & \(0.331\) \\ \hline \hline Average correlation & \(-\) & \(0.4237\) & \(0.4797\) & \(0.3387\) & \(0.3147\) & \(0.4313\) \\ (\(\overline{C}\)) & \(-\) & \(0.4237\) & \(0.4797\) & \(0.3387\) & \(0.3147\) & \(0.4313\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correlation coefficient matrix of POI with each index and RSEI.  
Figure 6: Normalization results of GPP, LST, WET, and NDSI raster data in Meizhou in 2018.  
environment. According to the calculation results, the average correlation degree of REEI was somewhat high, second only to LST, indicating that an REEI combining multiple indicators was more suitable for evaluating the quality of the ecological environment than a single indicator.