Spatial−temporal variation of ecological environment quality and driving factors from 2000 to 2020 in Wuliangsu Lake Basin, Northern China

Abstract

Due to global climate change and the intensification of human activities, the ecological function of Wuliangsu Lake Basin has been seriously degraded. Obtaining accurate spatial–temporal dynamics of regional ecological environment quality is essential for the evaluation of ecological management and restoration effects. This study assessed the trend changes and drivers of the Remote Sensing Ecological Index (RSEI) in the Wuliangsu Lake Basin from 2000−2020. Firstly, the trend analysis method and hurst index were used to analyze the temporal and spatial variation of RSEI. Then the main factors of RSEI variation were analyzed using meteorological data, integrated nighttime lighting data, and population density data. Overall, the RSEI shows an increasing trend from the west to the east with a rate of 0.0034 year−1 over the last 21 years. The area change of RSEI was 54.22%, 63.80% and 52.43% for 2000−2006, 2007−2013 and 2014−2020, respectively, which indicates that most areas have a stable ecological environment. However, the overall Future Improvement Trend (FIT) area of RSEI is 42.21%, mainly in Dengkou area, Urad Qianqi and central area. This indicates that the RSEI remains stable locally and shows an overall improving trend. The results of the correlation analysis showed that the areas influenced by meteorological and human factors were highly coincident, mainly in Dengkou and northern Linhe areas and Urad Qianqi. Considering the lagging effect of ecological engineering, the sustainable development status of RSEI in the western and eastern regions will maintain an improving trend in the future. Our study confirms the complex relationship between RSEI and meteorological and human activities, which is crucial for the scientific management of watershed ecosystems under the influence of anthropogenic factors.

Analysis of Eco-Environmental Quality of an Urban Forest Park Using LTSS and Modified RSEI from 1990 to 2020—A Case Study of Zijin Mountain National Forest Park, Nanjing, China

“…Forest vegetation conditions, climate factors, and the forest landscape structure are the main drivers affecting the long-term EEQ of the study area [62,63]. Since the forest cover of the study area is as high as 78.2%, the EEQ is closely related to the conditions of the forest growth conditions [24].…”