Combining Machine Learning and Remote Sensing for Online Environmental Quality Monitoring and Cause Analysis in Xiamen, China - Yang, Honghui; Xu, Weizhen; Chen, Zhengyan; Yu, Jiao (Transactions on Environment, Energy and Earth Sciences, 2024)
This study focuses on Xiamen, China, and uses the Google Earth Engine (GEE) platform to calculate the city's Remote Sensing Ecological Index (RSEI) from 2000 to 2020, analyzing the temporal and spatial changes in environmental quality. Additionally, it integrates the eXtreme Gradient Boosting (XGBoost) model and SHapley Additive exPlanations (SHAP) algorithms to analyze the driving factors affecting changes in the environmental quality of Xiamen. The results of the study show that: (1) From 2000 to 2020, the environmental quality in Xiamen City showed a trend of "first increase and then decrease", with the average value of RSEI increasing from 0.55 to 0.57 and then slightly decreasing to 0.56. (2) During 2000 to 2020, the majority of Xiamen's environment quality improved, covering 44.75% of the area. However, regions where the environment deteriorated were primarily located along the coast in Jimei district and Xiang'an district, accounting for 32.58% of the total area. (3) Elevation, slope, percentage of construction area, annual average precipitation, and percentage of cropland area are the five factors with the greatest impact on environmental quality. The effects of elevation and slope on environmental quality exhibit a quadratic function relationship, while percentage of construction area, annual average precipitation, and percentage of cropland area show linear relationships.