Long Time-Series Monitoring and Drivers of Eco-Quality in the Upper-Middle Fen River Basin of the Eastern Loess Plateau: An Analysis Based on a Remote Sensing Ecological Index and Google Earth Engine

Yanan He, Baoying Ye, Juan He, Hongyu Wang and Wei Zhou ()
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Yanan He: School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
Baoying Ye: School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
Juan He: School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
Hongyu Wang: School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
Wei Zhou: School of Land Science and Technology, China University of Geosciences, Beijing 100083, China

Land, 2024, vol. 13, issue 12, 1-21

Abstract: Healthy watershed environments are essential for socioeconomic sustainability. The long-term monitoring and assessment of watershed ecological environments provide a timely and accurate understanding of ecosystem dynamics, informing industry and policy adjustments. This study focused on the upper-middle Fen River Basin (UMFRB) in eastern China’s Loess Plateau and analyzed the long-term spatial and temporal characteristics of eco-quality from 2000 to 2023 by calculating a remote sensing ecological index (RSEI) via the Google Earth Engine (GEE) platform. In addition, this study also explored the trends and future consistency of the RSEI, as well as the impacts of natural and anthropogenic factors on RSEI spatial variations. The findings revealed that (1) the average RSEI value increased from 0.51 to 0.57 over the past 24 years, reflecting an overall improvement in eco-quality, although urban centers in the Taiyuan Basin exhibited localized degradation. (2) The Hurst index value was 0.468, indicating anti-consistency, with most regions showing trends of future decline or exhibiting stochastic fluctuations. (3) Elevation, temperature, precipitation, slope, and land use intensity are significantly correlated with ecological quality. Natural factors dominate in densely vegetated regions, whereas socioeconomic factors dominate in populated plains. These results provide valuable guidance for formulating targeted ecological restoration measures, protection policies, and engineering solutions.

Keywords: upper-middle Fen River Basin; ecosystem environment quality; remote sensing ecological index; Google Earth Engine; driving factors; geographically weighted regression (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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