YAO Kaixuan; Abudureheman HALIKE; CHEN Limei; WEI Qianqian
Abstract: The rapid economic development that the Hotan Oasis in Xinjiang Uygur Autonomous Region, China has undergone in recent years may face some challenges in its ecological environment. Therefore, an analysis of the spatiotemporal changes in ecological environment of the Hotan Oasis is important for its sustainable development. First, we constructed an improved remote sensing-based ecological index (RSEI) in 1990, 1995, 2000, 2005, 2010, 2015 and 2020 on the Google Earth Engine (GEE) platform and implemented change detection for their spatial distribution. Second, we performed a spatial autocorrelation analysis on RSEI distribution map and used land-use and land-cover change (LUCC) data to analyze the reasons of RSEI changes. Finally, we investigated the applicability of improved RSEI to arid area. The results showed that mean of RSEI rose from 0.41 to 0.50, showing a slight upward trend. During the 30-a period, 2.66% of the regions improved significantly, 10.74% improved moderately and 32.21% improved slightly, respectively. The global Moran's I were 0.891, 0.889, 0.847 and 0.777 for 1990, 2000, 2010 and 2020, respectively, and the local indicators of spatial autocorrelation (LISA) distribution map showed that the high-high cluster was mainly distributed in the central part of the Hotan Oasis, and the low-low cluster was mainly distributed in the outer edge of the oasis. RSEI at the periphery of the oasis changes from low to high with time, with the fragmentation of RSEI distribution within the oasis increasing. Its distribution and changes are predominantly driven by anthropologic factors, including the expansion of artificial oasis into the desert, the replacement of desert ecosystems by farmland ecosystems, and the increase in the distribution of impervious surfaces. The improved RSEI can reflect the eco-environmental quality effectively of the oasis in arid area with relatively high applicability. The high efficiency exhibited with this approach makes it convenient for rapid, high frequency and macroscopic monitoring of eco-environmental quality in study area.