Assessment of spatial–temporal changes of ecological environment quality based on RSEI and GEE: A case study in Erhai Lake Basin, Yunnan province, China

The Erhai Lake Basin is an area with the active economic and social development of agriculture and tourism, facing increasingly prominent environmental problems with rapid urbanization. Assessing spatial–temporal changes in ecological environment quality objectively and quantitatively in a timely fashion is crucial for environmental protection and policymaking. First, we selected the high-quality Landsat imagery acquired at the same time phase in the years of 1999, 2004, 2009, 2014, and 2019 respectively. Second, the remote sensing-based ecological index (RSEI) was constructed by using Landsat 5 TM and Landsat 8 OLI/TIRS imagery based on Google Earth Engine (GEE) platform. Third, we assessed the spatial–temporal changes and spatial autocorrelation of ecological environment quality in our study area based on five RSEI maps. The mean values of RSEI in 1999, 2004, 2009, 2014, and 2019 were 0.513 0.457, 0.462, 0.506, and 0.509, respectively, which indicated that the overall ecological environment quality of the Erhai Lake Basin degraded from 1999 to 2009 and promoted from 2009 to 2019. The worst degradation occurred between 1999 and 2004, about 27.12% of the total area was degraded, and the greatest improvement occurred between 2009 and 2014, about 26.42% of the total area was improved. The Globalmoran's I value ranged from 0.662 to 0.783 in 1999–2019, which indicated that the spatial distribution of ecological environment quality was positively correlated. The cluster map of local indicator of spatial association of RSEI show that the high-high points were mainly located in the western and southern high-altitude area of the study area, and the low-low points were mainly distributed in lakeside area, where populations were dense and human activities were frequent. This study provides a promising approach to assess the spatial–temporal changes in ecological environment quality based on RSEI and GEE, which is critical to investigate the interactions between human activities and ecosystem services in basin systems.