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

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.

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Publication Details

Journal
Land
Published
2024-12-20
DOI
https://doi.org/10.3390/land13122239
Citations
4
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
1.13

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article

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

Baoying Ye, Wei Zhou, Yanan He, Hongyu Wang et al.
4 citations
Land
Land Use and Ecosystem Services
1.13
article

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

Baoying Ye, Wei Zhou, Yanan He, Hongyu Wang, Juan He
article en
4 citations

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.

LandVol. 13(12)
Ministry of Natural Resources (CN), China University of Geosciences (Beijing) (CN), Land Consolidation and Rehabilitation Center (CN)
National Natural Science Foundation of China
Life in Land
Openalex Percentile: Top 22%
Land Use and Ecosystem Services
1.13
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