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Ecological Environment Quality Assessment of Arid Areas Based on Improved Remote Sensing Ecological Index—A Case Study of the Loess Plateau

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  • Ming Shi

    (Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
    College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China
    Hefei Institutes of Collaborative Research and Innovation for Intelligent Agriculture, Hefei 231131, China)

  • Fei Lin

    (Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
    Hefei Institutes of Collaborative Research and Innovation for Intelligent Agriculture, Hefei 231131, China)

  • Xia Jing

    (College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Bingyu Li

    (College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Yang Shi

    (Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
    Hefei Institutes of Collaborative Research and Innovation for Intelligent Agriculture, Hefei 231131, China)

  • Yimin Hu

    (Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
    Hefei Institutes of Collaborative Research and Innovation for Intelligent Agriculture, Hefei 231131, China)

Abstract

Ecosystems in arid and semi-arid areas are delicate and prone to different erosive effects. Monitoring and evaluating the environmental ecological condition in such areas contribute to the governance and restoration of the ecosystem. Remote sensing ecological indices (RSEIs) are widely used as a method for environmental monitoring and have been extensively applied in various regions. This study selects the arid and semi-arid Loess Plateau as the research area, in response to existing research on ecological monitoring that predominantly uses vegetation indices as monitoring indicators for greenness factors. A fluorescence remote sensing ecological index (SRSEI) is constructed by using monthly synthesized sun-induced chlorophyll fluorescence data during the vegetation growth period as a new component for greenness and combining it with MODIS product data. The study generates the RSEI and SRSEI for the research area spanning from 2001 to 2021. The study compares and analyzes the differences between the two indices and explores the evolution patterns of the ecosystem quality in the Loess Plateau over a 21-year period. The results indicate consistent and positively correlated linear fitting trend changes in the RSEI and SRSEI for the research area between 2001 and 2021. The newly constructed ecological index exhibits a higher correlation with rainfall data, and it shows a more significant decrease in magnitude during drought occurrences, indicating a faster and stronger response of the new index to drought in the research area. The largest proportions are found in the research area’s regions with both substantial and minor improvements, pointing to an upward tendency in the Loess Plateau’s ecosystem development. The newly constructed environmental index can effectively evaluate the quality of the ecosystem in the research area.

Suggested Citation

  • Ming Shi & Fei Lin & Xia Jing & Bingyu Li & Yang Shi & Yimin Hu, 2023. "Ecological Environment Quality Assessment of Arid Areas Based on Improved Remote Sensing Ecological Index—A Case Study of the Loess Plateau," Sustainability, MDPI, vol. 15(18), pages 1-25, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13881-:d:1242605
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    References listed on IDEAS

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    5. Jiayu Xia & Duyuzheng Ren & Xuhui Wang & Bo Xu & Xingyao Zhong & Yajiang Fan, 2023. "Ecosystem Quality Assessment and Ecological Restoration in Fragile Zone of Loess Plateau: A Case Study of Suide County, China," Land, MDPI, vol. 12(6), pages 1-32, May.
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    1. Lingda Zhang & Quanhua Hou & Yaqiong Duan & Sanbao Ma, 2024. "Spatial and Temporal Heterogeneity of Eco-Environmental Quality in Yanhe Watershed (China) Using the Remote-Sensing-Based Ecological Index (RSEI)," Land, MDPI, vol. 13(6), pages 1-20, May.
    2. Manya Luo & Xia Jia & Yonghua Zhao & Huanyuan Wang & Chunyang Chen & Dongqian Li & Shuyuan Yang & Juan Li, 2024. "The Temporal and Spatial Characteristics of Ecological Security Pattern in the Loess Plateau, China," Land, MDPI, vol. 13(5), pages 1-16, April.

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