Study on Factors Affecting Remote Sensing Ecological Quality Combined with Sentinel-2

Reasonable allocation of urban resources can effectively control changes in ecological quality. This study used Sentinel-2 images, taking urban functional areas as the dividing scale, and combined spatial analysis, statistics, and other relevant methods to explore the factors influencing remote sensing ecological quality in Puxi, Shanghai, China. Landsat-8 and high-resolution Sentinel-2 data fusion achieved more refined remote sensing ecological index (RSEI) distribution data, which is of great significance for ecological quality exploration in small areas; the degree of influence of the selected research factors on the RSEI was spectral index > building > social perception > terrain. The R-value of the soil-adjusted vegetation index (SAVI) was 0.970, and it exerted the strongest influence. The R-value of the average building height was 0.103, indicating that it had the weakest influence. The interactions among the selected factors were mainly two-factor and nonlinear enhancements. Most factor combinations exhibited two-factor enhancement. There were six groups of factor combinations for nonlinear enhancement, of which five were related to the average building height. The results of the present study provide a reference for multi-path ecological quality control in small-area regions.