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Study on the Spatial and Temporal Evolution of NDVI and Its Driving Mechanism Based on Geodetector and Hurst Indexes: A Case Study of the Tibet Autonomous Region

Author

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  • Jian Wang

    (Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
    Key Laboratory of Geospatial Information Integration Innovation for Smart Mines, Kunming 650093, China
    Spatial Information Integration Technology of Natural Resources, Universities of Yunnan Province, Kunming 650211, China
    These authors contributed equally to this work.)

  • Junsan Zhao

    (Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
    Key Laboratory of Geospatial Information Integration Innovation for Smart Mines, Kunming 650093, China
    Spatial Information Integration Technology of Natural Resources, Universities of Yunnan Province, Kunming 650211, China)

  • Peng Zhou

    (School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
    State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
    These authors contributed equally to this work.)

  • Kangning Li

    (Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
    Key Laboratory of Geospatial Information Integration Innovation for Smart Mines, Kunming 650093, China
    Spatial Information Integration Technology of Natural Resources, Universities of Yunnan Province, Kunming 650211, China)

  • Zhaoxiang Cao

    (College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China)

  • Haoran Zhang

    (Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
    Key Laboratory of Geospatial Information Integration Innovation for Smart Mines, Kunming 650093, China
    Spatial Information Integration Technology of Natural Resources, Universities of Yunnan Province, Kunming 650211, China)

  • Yang Han

    (Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
    Key Laboratory of Geospatial Information Integration Innovation for Smart Mines, Kunming 650093, China
    Spatial Information Integration Technology of Natural Resources, Universities of Yunnan Province, Kunming 650211, China)

  • Yuanyuan Luo

    (School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
    State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China)

  • Xinru Yuan

    (School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
    State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China)

Abstract

The Tibet Autonomous Region (TAR) is located in the mid-latitude and high-cold regions, and the ecological environment in most areas is fragile. Studying its surface vegetation coverage can identify the ecosystem’s development trends and provide a specific contribution to global environmental change. The normalized difference vegetation index (NDVI) can better reflect the coverage of surface vegetation. Therefore, based on remote sensing data with a resolution of 1 km 2 , air temperature, precipitation, and other data in the same period in the study area from 1998 to 2019, this paper uses trend analysis, F-significance tests, the Hurst index, and the Geodetector model to obtain the spatial distribution, change characteristics, and evolution trends of the NDVI in the TAR in the past 22 years. At the same time, the quantitative relationship between natural and human factors and NDVI changes is also obtained. The study results show that the NDVI in the southern and southeastern parts of the TAR is higher, with mean values greater than 0.5 showing that vegetation cover is better. The NDVI in the western and northwestern parts of the TAR is lower, with mean values less than 0.3, indicating vegetation cover is worse. NDVI in the TAR showed an overall increasing trend from 1998 to 2019 but a decreasing trend in ridgelines, snow cover, and glacier-covered areas. The areas where NDVI values show a trend of increasing and then decreasing in the future account for 53.69% of the total area of the TAR. The most crucial factor affecting NDVI changes in the TAR is soil type, followed by influencing factors such as vegetation cover type, average annual air temperature, and average annual precipitation. The influence of natural elements is generally more significant than anthropogenic factors. The influencing factors have synergistic effects, and combining anthropogenic factors and other factors will show mutual enhancement and non-linear enhancement relationships. This study provides a theoretical basis for natural resource conservation, ecosystem restoration, and sustainable human development strategies in the TAR.

Suggested Citation

  • Jian Wang & Junsan Zhao & Peng Zhou & Kangning Li & Zhaoxiang Cao & Haoran Zhang & Yang Han & Yuanyuan Luo & Xinru Yuan, 2023. "Study on the Spatial and Temporal Evolution of NDVI and Its Driving Mechanism Based on Geodetector and Hurst Indexes: A Case Study of the Tibet Autonomous Region," Sustainability, MDPI, vol. 15(7), pages 1-24, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:5981-:d:1111552
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    References listed on IDEAS

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    1. Chenlu Huang & Qinke Yang & Weidong Huang, 2021. "Analysis of the Spatial and Temporal Changes of NDVI and Its Driving Factors in the Wei and Jing River Basins," IJERPH, MDPI, vol. 18(22), pages 1-16, November.
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    Cited by:

    1. Yongkang Li & Qing He & Yongqiang Liu & Amina Maituerdi & Yang Yan & Jiao Tan, 2024. "Development and Evaluation of Machine Learning Models for Air-to-Land Temperature Conversion Using the Newly Established Kunlun Mountain Gradient Observation System," Land, MDPI, vol. 13(11), pages 1-26, November.

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