Revealing the Eco-Environmental Quality of the Yellow River Basin: Trends and Drivers

Abstract

The Yellow River Basin (YB) acts as a key barrier to ecological security and is an important experimental region for high-quality development in China. There is a growing demand to assess the ecological status in order to promote the sustainable development of the YB. The eco-environmental quality (EEQ) of the YB was assessed at both the regional and provincial scales utilizing the remote sensing-based ecological index (RSEI) with Landsat images from 2000 to 2020. Then, the Theil–Sen (T-S) estimator and Mann–Kendall (M-K) test were utilized to evaluate its variation trend. Next, the optimal parameter-based geodetector (OPGD) model was used to examine the drivers influencing the EEQ in the YB. Finally, the geographically weighted regression (GWR) model was utilized to further explore the responses of the drivers to RSEI changes. The results suggest that (1) a lower RSEI value was found in the north, while a higher RSEI value was found in the south of the YB. Sichuan (SC) and Inner Mongolia (IM) had the highest and the lowest EEQ, respectively, among the YB provinces. (2) Throughout the research period, the EEQ of the YB improved, whereas it deteriorated in both Henan (HA) and Shandong (SD) provinces. (3) The soil-available water content (AWC), annual precipitation (PRE), and distance from impervious surfaces (IMD) were the main factors affecting the spatial differentiation of RSEI in the YB. (4) The influence of meteorological factors (PRE and TMP) on RSEI changes was greater than that of IMD, and the influence of IMD on RSEI changes showed a significant increasing trend. The research results provide valuable information for application in local ecological construction and regional development planning.

Evolution and Spatiotemporal Response of Ecological Environment Quality to Human Activities and Climate: Case Study of Hunan Province, China

“…Some scholars use multiple regression methods to mathematically model various natural factors and EEQ and use residuals to represent human impacts [60]. Some scholars also analyze the spatial relationship between EEQ and discrete data such as land cover/use, economy, and population [18]. These methods have contributed to the construction of a comprehensive driving model of EEQ changes.…”

“…The RSEI provides comprehensive coverage and quantifiable data and is objective and easy to use. Therefore, the RSEI is widely used in many research fields [11][12][13][14][15][16][17], such as ecological watershed [18] and mine [19] monitoring, urban environmental evaluation [20], and ecological risk assessment of projects [21]. In addition, the RSEI has often been used as a parameter in the research on ecological resilience [22], human landscape changes [22], and urban development strategies [23].…”