Spatial-temporal evolution and motivation of ecological vulnerability based on RSEI and GEE in the Jianghan Plain from 2000 to 2020

Abstract

Recent urbanization and growing food consumption have had a severely detrimental effect on the ecological environment of the Jianghan Plain. The ecological fragility of the Jianghan Plain must be continually monitored for environmental conservation and sustainable development. This study utilized principal component analysis (PCA) to quantitatively assess the ecological vulnerability of the Jianghan Plain based on the remote sensing ecological index (RSEI) and analyzed the space-time changes and drivers in the Jianghan Plain from 2000 to 2020 using the Google Earth Engine Platform (GEE). The findings of this research indicated that the ecological vulnerability of the Jianghan Plain from 2000 to 2020 was predominantly Moderate or Strong level. But still, the EVI displayed a changing decreasing trend, revealing a small development towards a healthier ecological environment. The most significant ecological vulnerability deterioration occurred between 2005 and 2010, accounting for roughly 44.90 percent, whereas the highest improvement occurred between 2000 and 2005, occupying approximately 37.52% of the area. Moran’s I of EVI was greater than 0 in Jianghan Plain and displayed a growing and subsequently a falling trend, representing that the spatial distribution of regional ecological vulnerability was strongly correlated and aggregated and that the degree of aggregation has declined. The effects of heat, greenness, wetness, and dryness on the ecological vulnerability of Jianghan Plain were all significant, with greenness and wetness being the primary determinants of the change in Jianghan Plain’s ecological vulnerability. The results of this study can offer a theoretical and scientific foundation for ecological protection and restoration in the Jianghan Plain. Meanwhile, this study also provides a practical and rapid method for monitoring regional ecological vulnerability using RSEI, GEE, and PCA, which can be applied elsewhere for ecological vulnerability evaluation.

Landsat series images for evaluating ecological restoration effect from multi-time scale based on an ideal reference

“…The integrated requirements of ecosystems can only be fulfilled by conducting detailed studies that incorporate ecosystem status assessments (Qiao et al, 2021;Zhou et al, 2021). The remote sensing ecological index (RSEI) was established by integrating four indicators (i.e., humidity, greenness, temperature, and aridity) into principal component analysis (Xu, 2013); it can be calculated based on remotely sensed bands from identical satellite sensors, without weight determination (Yi et al, 2023). Notably, a dynamic change in the RSEI is generally correlated to environmental pressures caused by anthropogenic activities (e.g., urbanization and industrialization) (Boori et al, 2021), ecosystem change (e.g., changes in vegetation cover) (Yang et al, 2022), and climatic fluctuations (e.g., changes in temperature and humidity) (Zheng et al, 2022); thus, the RESI can be employed for improving the continuous dynamic assessment of the ecological restoration effects in a region (Zhang et al, 2022;Xiao et al, 2023).…”

Spatiotemporal Pattern, Evolutionary Trend, and Driving Forces Analysis of Ecological Quality in the Irtysh River Basin (2000–2020)

“…It replaces the traditional desktop processing platforms that require significant time and hardware resources for pre-processing such as acquisition, stitching, atmospheric correction, and cloud and shadow removal of massive remote sensing data [40,41]. Researchers have combined GEE with the RSEI for EQ assessment and monitoring in regions such as the Jianghan Plain (Yi et al, 2023), the Loess Plateau (Gong et al, 2023), northern Anhui, China (Wang et al, 2022), the Erhai Lake Basin in Yunnan Province, China (Xiong et al, 2021), and the Yellow River Basin (Yang et al, 2022) [42][43][44][45][46]. This also demonstrates the convenience and efficiency of using the GEE platform for the RSEI assessment on a large scale and over extended time series of imagery.…”