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Quantitative Analysis and Nonlinear Response of Vegetation Dynamic to Driving Factors in Arid and Semi-Arid Regions of China

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  • Shihao Liu

    (College of Resources and Environment, Shandong Agricultural University, Taian 271018, China
    Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Dazhi Yang

    (Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Xuyang Zhang

    (Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    School of Earth and Environment, Anhui University of Science and Technology, Huainan 232001, China)

  • Fangtian Liu

    (School of Public Administration, Hebei University of Economics and Business, Shijiazhuang 050061, China
    Hebei Collaborative Innovation Center for Urban-Rural Integrated Development, Shijiazhuang 050061, China)

Abstract

Vegetation dynamics are complexly influenced by multiple factors such as climate, human activities, and topography. In recent years, the frequency, intensity, and diversity of human activities have increased, placing substantial pressure on the growth of vegetation. Arid and semi-arid regions are particularly sensitive to climate change, and climate change and large-scale ecological restoration have led to significant changes in the dynamic of dryland vegetation. However, few studies have explored the nonlinear relationships between these factors and vegetation dynamic. In this study, we integrated trend analysis (using the Mann–Kendall test and Theil–Sen estimation) and machine learning algorithms (XGBoost-SHAP model) based on long time-series remote sensing data from 2001 to 2020 to quantify the nonlinear response patterns and threshold effects of bioclimatic variables, topographic features, soil attributes, and anthropogenic factors on vegetation dynamic. The results revealed the following key findings: (1) The kNDVI in the study area showed an overall significant increasing trend ( p < 0.01) during the observation period, of which 26.7% of the area showed a significant increase. (2) The water content index (Bio 23, 19.6%), the change in land use (15.2%), multi-year average precipitation (pre, 15.0%), population density (13.2%), and rainfall seasonality (Bio 15, 10.9%) were the key factors driving the dynamic change of vegetation, with the combined contribution of natural factors amounting to 64.3%. (3) Among the topographic factors, altitude had a more significant effect on vegetation dynamics, with higher altitude regions less likely to experience vegetation greening. Both natural and anthropogenic factors exhibited nonlinear responses and interactive effects, contributing to the observed dynamic trends. This study provides valuable insights into the driving mechanisms behind the condition of vegetation in arid and semi-arid regions of China and, by extension, in other arid regions globally.

Suggested Citation

  • Shihao Liu & Dazhi Yang & Xuyang Zhang & Fangtian Liu, 2025. "Quantitative Analysis and Nonlinear Response of Vegetation Dynamic to Driving Factors in Arid and Semi-Arid Regions of China," Land, MDPI, vol. 14(8), pages 1-23, August.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:8:p:1575-:d:1715934
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    References listed on IDEAS

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    1. Jun Li & Yao Zhang & Emanuele Bevacqua & Jakob Zscheischler & Trevor F. Keenan & Xu Lian & Sha Zhou & Hongying Zhang & Mingzhu He & Shilong Piao, 2024. "Future increase in compound soil drought-heat extremes exacerbated by vegetation greening," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. Zuopei Zhang & Yunfeng Hu & Batunacun, 2025. "Analysis of the Driving Mechanism of Grassland Degradation in Inner Mongolia Grassland from 2015 to 2020 Using Interpretable Machine Learning Methods," Land, MDPI, vol. 14(2), pages 1-19, February.
    3. A. L. Burrell & J. P. Evans & M. G. De Kauwe, 2020. "Anthropogenic climate change has driven over 5 million km2 of drylands towards desertification," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
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