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-mi...

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Published in:Land
Main Authors: Yanan He, Baoying Ye, Juan He, Hongyu Wang, Wei Zhou
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Subjects:
Online Access:https://www.mdpi.com/2073-445X/13/12/2239
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author Yanan He
Baoying Ye
Juan He
Hongyu Wang
Wei Zhou
author_facet Yanan He
Baoying Ye
Juan He
Hongyu Wang
Wei Zhou
author_sort Yanan He
collection DOAJ
container_title Land
description 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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spelling doaj-art-9f6094e5c6f54fc4bee8b61ba14c7ece2025-08-20T02:53:38ZengMDPI AGLand2073-445X2024-12-011312223910.3390/land13122239Long 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 EngineYanan He0Baoying Ye1Juan He2Hongyu Wang3Wei Zhou4School of Land Science and Technology, China University of Geosciences, Beijing 100083, ChinaSchool of Land Science and Technology, China University of Geosciences, Beijing 100083, ChinaSchool of Land Science and Technology, China University of Geosciences, Beijing 100083, ChinaSchool of Land Science and Technology, China University of Geosciences, Beijing 100083, ChinaSchool of Land Science and Technology, China University of Geosciences, Beijing 100083, ChinaHealthy 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.https://www.mdpi.com/2073-445X/13/12/2239upper-middle Fen River Basinecosystem environment qualityremote sensing ecological indexGoogle Earth Enginedriving factorsgeographically weighted regression
spellingShingle Yanan He
Baoying Ye
Juan He
Hongyu Wang
Wei Zhou
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
upper-middle Fen River Basin
ecosystem environment quality
remote sensing ecological index
Google Earth Engine
driving factors
geographically weighted regression
title 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic upper-middle Fen River Basin
ecosystem environment quality
remote sensing ecological index
Google Earth Engine
driving factors
geographically weighted regression
url https://www.mdpi.com/2073-445X/13/12/2239
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