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Exploring Ecological Quality and Its Driving Factors in Diqing Prefecture, China, Based on Annual Remote Sensing Ecological Index and Multi-Source Data

Author

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  • Chen Wang

    (College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
    The Center of Southern Modern Forestry Cooperative Innovation, Nanjing Forestry University, Nanjing 210037, China
    Research Center for Digital Innovation Design, Nanjing Forestry University, Nanjing 210037, China
    Jin Pu Research Institute, Nanjing Forestry University, Nanjing 210037, China)

  • Qianqian Sheng

    (College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
    The Center of Southern Modern Forestry Cooperative Innovation, Nanjing Forestry University, Nanjing 210037, China
    Research Center for Digital Innovation Design, Nanjing Forestry University, Nanjing 210037, China
    Jin Pu Research Institute, Nanjing Forestry University, Nanjing 210037, China)

  • Zunling Zhu

    (College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
    The Center of Southern Modern Forestry Cooperative Innovation, Nanjing Forestry University, Nanjing 210037, China
    Research Center for Digital Innovation Design, Nanjing Forestry University, Nanjing 210037, China
    Jin Pu Research Institute, Nanjing Forestry University, Nanjing 210037, China)

Abstract

The interaction between the natural environmental and socioeconomic factors is crucial for assessing the dynamics of plateau ecosystems. Therefore, the remote sensing ecological index (RSEI) and CatBoost-SHAP model were employed to investigate changes in the ecological quality and their driving factors in the Diqing Tibetan Autonomous Prefecture, China, from 2001 to 2021. The results showed an increase from 0.44 in 2001 to 0.71 in 2021 in the average RSEI for the Diqing Prefecture, indicating an overall upward trend in the ecological quality. Spatial analysis shows the percentage of the area covered by different levels of RSEI and their temporal changes. The results revealed that “good” ecological quality accounted for the largest proportion of the study area, at 42.77%, followed by “moderate” at 21.93%, and “excellent” at 16.62%. “Fair” quality areas accounted for 16.11% and “poor” quality areas only 2.57%. The study of ecological and socioeconomic drivers based on the CatBoost-SHAP framework also indicated that natural climate factors have a greater impact on ecological quality than socioeconomic factors; however, this effect differed significantly with altitude. The findings suggest that, in addition to strengthening climate monitoring, further advancements in ecological engineering are required to ensure the sustainable development of the ecosystem and the continuous improvement of the environmental quality in the Diqing Prefecture.

Suggested Citation

  • Chen Wang & Qianqian Sheng & Zunling Zhu, 2024. "Exploring Ecological Quality and Its Driving Factors in Diqing Prefecture, China, Based on Annual Remote Sensing Ecological Index and Multi-Source Data," Land, MDPI, vol. 13(9), pages 1-20, September.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:9:p:1499-:d:1478909
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    References listed on IDEAS

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    1. Shilong Piao & Philippe Ciais & Yao Huang & Zehao Shen & Shushi Peng & Junsheng Li & Liping Zhou & Hongyan Liu & Yuecun Ma & Yihui Ding & Pierre Friedlingstein & Chunzhen Liu & Kun Tan & Yongqiang Yu , 2010. "The impacts of climate change on water resources and agriculture in China," Nature, Nature, vol. 467(7311), pages 43-51, September.
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