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Research on the Spatio-Temporal Changes of Vegetation and Its Driving Forces in Shaanxi Province in the Past 20 Years

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  • Ming Shi

    (Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
    Intelligent Agriculture Engineering Laboratory of Anhui Province, Hefei 230031, China
    College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Fei Lin

    (Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
    Intelligent Agriculture Engineering Laboratory of Anhui Province, Hefei 230031, China
    College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, 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)

  • Jingsha Qin

    (School of Resources and Environmental Engineering, Anhui University, Hefei 200601, China)

  • Manqi Wang

    (School of Resources and Environmental Engineering, Anhui University, Hefei 200601, China)

  • Yang Shi

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

Abstract

(1) Background: Vegetation is an important component of ecosystems. Investigating the spatio-temporal dynamic changes in vegetation in various Shaanxi Province regions is crucial for the preservation of the local ecological environment and sustainable development. (2) Methods: In this study, the KNDVI vegetation index over the 20-year period from 2003 to 2022 was calculated using MODIS satellite image data that was received from Google Earth Engine (GEE). Sen and MK trend analysis as well as partial correlation analysis were then utilized to examine the patterns in vegetation change in various Shaanxi Province regions. This paper selected meteorological factors, such as potential evapotranspiration (PET), precipitation (PRE), and temperature (TMP); human activity factors, such as land-use type and population density; and terrain factors, such as surface elevation, slope direction, and slope gradient, as the influencing factors for vegetation changes in the research area in order to analyze the driving forces of vegetation spatio-temporal changes. These factors were analyzed using a geo-detector. (3) Results: The vegetation in the research area presented a growth trend from 2003 to 2022, and the area of vegetation improvement was 189,756 km 2 , accounting for 92.15% of the total area. Among them, the area of significantly improved regions was 174,262 km 2 , accounting for 84.63% of the total area, and the area of slightly improved regions was 15,495 square kilometers, accounting for 7.52% of the total area. (4) Conclusions: The strengthening of bivariate factors and nonlinear enhancement were the main interaction types affecting vegetation changes. The combination of interaction factors affecting vegetation change in Shaanxi Province includes PRE ∩ PET as well as TMP ∩ PET. Therefore, climate conditions were the main driving force of KNDVI vegetation changes in Shaanxi Province. The data supported by this research are crucial for maintaining the region’s natural ecosystem.

Suggested Citation

  • Ming Shi & Fei Lin & Xia Jing & Bingyu Li & Jingsha Qin & Manqi Wang & Yang Shi & Yimin Hu, 2023. "Research on the Spatio-Temporal Changes of Vegetation and Its Driving Forces in Shaanxi Province in the Past 20 Years," Sustainability, MDPI, vol. 15(23), pages 1-25, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16468-:d:1291974
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    References listed on IDEAS

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    1. Penny, Jessica & Ordens, Carlos M. & Barnett, Steve & Djordjević, Slobodan & Chen, Albert S., 2023. "Small-scale land use change modelling using transient groundwater levels and salinities as driving factors – An example from a sub-catchment of Australia’s Murray-Darling Basin," Agricultural Water Management, Elsevier, vol. 278(C).
    2. Luobin Yan & Ruixiang He & Milica Kašanin-Grubin & Gusong Luo & Hua Peng & Jianxiu Qiu, 2017. "The Dynamic Change of Vegetation Cover and Associated Driving Forces in Nanxiong Basin, China," Sustainability, MDPI, vol. 9(3), pages 1-15, March.
    3. Bo Yao & Lei Ma & Hongtao Si & Shaohua Li & Xiangwen Gong & Xuyang Wang, 2023. "Spatial Pattern of Changing Vegetation Dynamics and Its Driving Factors across the Yangtze River Basin in Chongqing: A Geodetector-Based Study," Land, MDPI, vol. 12(2), pages 1-21, January.
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