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Dynamic characteristics and driving factors of vegetation greenness under changing environments in Xinjiang, China

Environmental Science and Pollution Research volume 28, pages 42516–42532 (2021)Cite this article

Abstract

Global environment changes rapidly alter regional hydrothermal conditions, which undoubtedly affects the spatiotemporal dynamics of vegetation, especially in arid and semi-arid areas. However, identifying and quantifying the dynamic evolution and driving factors of vegetation greenness under the changing environment are still a challenge. In this study, gradual trend analysis was applied to calculate the overall spatiotemporal trend of the normalized difference vegetation index (NDVI) time series of Xinjiang province in China, the abrupt change analysis was used to detect the timing of breakpoint and trend shift, and two machine learning methods (boosted regression tree and random forest) were used to quantify the key factors of vegetation change and their relative contribution rate. The results have shown that vegetation has experienced overall recovery over the past 20 years in Xinjiang, and greenness increased at a rate of 17.83 10−4 year−1. Cropland, grassland, and sparse vegetation were the main biome types where vegetation restoration is happening. Nearly 10% of the pixels (about 166000 km2) were detected to have breakpoints from 2004 to 2016 of the monthly NDVI, and most of the breakpoints were concentrated in the ecotone of various biomes. CO2 concentration was the most prevalent environmental factor to increase vegetation greenness, because continuous emission of CO2 greatly enhanced the fertilization effect, further promoted vegetation growth. Besides, cropland expansion and desertification control were the vital anthropogenic factors to vegetation turning “green” in Xinjiang, and most areas under anthropogenic were mainly in oasis areas. These findings provide new insights and measures for the regional response strategies and terrestrial ecosystem protection.

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Data availability

The datasets and codes used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We thank Yipu Wang in School of Earth and Space Sciences, University of Science and Technology of China and Xixi Yang in School of Geography and Planning, Sun Yat-Sen University for significant help in data analysis and language services in this work.

Funding

This research was funded by the Science and Technology Basic Resources Investigation Program of China (2017FY100200) and National Natural Science Foundation of China (32060408, 41601181).

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Affiliations

  1. Ministry of Education Key Laboratory for Western Arid Region Grassland Resources and Ecology, College of Grassland and Environment Sciences, Xinjiang Agricultural University, Urumqi, 830052, China

    Panxing He, Zongjiu Sun, Yiqiang Dong & Huixia Liu

  2. Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute of Biodiversity Science, Fudan University, Shanghai, 200438, China

    Panxing He & Jun Ma

  3. State Key Laboratory Base of Eco-Hydraulic Engineering in Arid Area, Xi’an University of Technology, Xi’an, 710000, China

    Zhiming Han

  4. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China

    Xiaoyu Meng

  5. University of Chinese Academy of Sciences, Beijing, 100049, China

    Xiaoyu Meng

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  1. Panxing He
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  2. Zongjiu Sun
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  3. Zhiming Han
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  7. Jun Ma
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Contributions

The authors’ contributions to this article are as follows: conceptualization, Z.S. and J.M.; methodology, P.H. and X.M.; validation, P.H. and Z.H.; formal Analysis, J.M. and H.L.; investigation, Z.S., Y.D., and J.M.; resources, P.H. and X.M.; data curation, P.H. Z.H. and X.M.; writing (original draft preparation), P.H., Z.H., and H.L.; writing (review and editing), Z.S., J.M., and Y.D.; visualization, P.H.; supervision, J.M. and Z.S.; project administration, Z.S.; and funding acquisition, Z.S. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Zongjiu Sun.

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Highlights

The Sen’s and BFAST analysis were jointly used to explore the vegetation dynamics of various biomes in Xinjiang, China.

The growth rate of vegetation in Xinjiang accelerated significantly after 2010.

In space, most of the breakpoint pixels concentrate in the ecotone of various biomes.

CO2 fertilization effect was the primary driving factors of vegetation greening.

Land-use management directly leads to the vegetation turn “green” around the oasis.

Responsible Editor: Philippe Garrigues

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He, P., Sun, Z., Han, Z. et al. Dynamic characteristics and driving factors of vegetation greenness under changing environments in Xinjiang, China. Environ Sci Pollut Res 28, 42516–42532 (2021). https://doi.org/10.1007/s11356-021-13721-z

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Keywords

  • Vegetation greenness
  • Abrupt change
  • Gradual change
  • Environmental factors
  • Land-use management
  • Xinjiang