Evaluation and Prediction of the Ecological Environment Quality in the City Proper in Chongqing Based on the Improved Remote Sensing Ecological Index

Abstract In this study, the improved remote sensing ecological index (DRSEI) was utilized in the Google Earth Engine (GEE) platform to assess and predict the ecological environment quality (EEQ) in the city proper of Chongqing from 2002 to 2022. The DRSEI was obtained by replacing the land surface temperature (LST) index in the remote sensing ecological index (RSEI) with the difference index (DI), which represents the PM2.5 concentration. It was found that the DRSEI outperformed the RSEI in terms of the contribution of the first principal component, average correlation, and proximity to the ecosystem status index (EI). Using the DRSEI for EEQ assessment, it was found that the overall trend of the EEQ in the study area exhibited initially improved and then deteriorated. In addition, the EEQ was categorized into five levels: excellent, good, moderate, fair, and poor. The regions with excellent EEQ were predominantly situated between the eastern and southern areas, while those categorized as poor were mainly concentrated in the central region. Moreover, the mean centers of each level underwent a series of changes and ultimately tended to regress towards the vicinity of the central region. The modeling and prediction performance of the DRSEI determined using stepwise regression was excellent. Additionally, this paper discusses the reasons for the changes in the regression coefficients of each indicator. The DRSEI has significant advantages in improving the accuracy of EEQ assessment. Based on our analyses, we suggest that the DRSEI may provide more accurate evaluations in large-scale regions above the county level and in areas with lighter air pollution.