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Characteristics of NDVI Changes in the Altay Region from 1981 to 2018 and Their Relationship to Climatic Factors

Author

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  • Yang Yan

    (College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
    Xinjiang Key Laboratory of Soil and Plant Ecological Processes, Xinjiang Agricultural University, Urumqi 830052, China)

  • Junhui Cheng

    (College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
    Xinjiang Key Laboratory of Soil and Plant Ecological Processes, Xinjiang Agricultural University, Urumqi 830052, China)

  • Yongkang Li

    (College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
    Xinjiang Key Laboratory of Soil and Plant Ecological Processes, Xinjiang Agricultural University, Urumqi 830052, China)

  • Jie Fan

    (College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
    Xinjiang Key Laboratory of Soil and Plant Ecological Processes, Xinjiang Agricultural University, Urumqi 830052, China)

  • Hongqi Wu

    (College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
    Xinjiang Key Laboratory of Soil and Plant Ecological Processes, Xinjiang Agricultural University, Urumqi 830052, China)

Abstract

Vegetation growth and its response to climatic factors have become one of the most pressing issues in ecological research. However, no consensus has yet been reached on how to resolve this problem in arid areas with a high-elevation gradient and complex underlying surface. Here, NOAA CDR AVHRR NDVI V5 for 1981–2018 and China’s regional surface meteorological faction-driven datasets were used. General linear regression, the Mann-Kendall test and sliding t -test, Pearson correlations, and the Akaike information criterion (AIC), on a grid-scale, were applied to analyze the annual normalized difference vegetation index (NDVI) and its relationship with temperature and precipitation in the Altay region. Results revealed that the temporal trend of NDVI for most grid cells was non-significant. However, mountains, coniferous forests, grasslands, and meadows in the high-elevation zone displayed a slow increasing trend in NDVI. Further, NDVI was positively correlated with the mean annual temperature and total annual precipitation, the latter playing a more significant role. Yet, for desert and shrub vegetation and coniferous forest, their NDVI had insignificant negative correlations with the mean annual temperature. Hence, both the trends and drivers of NDVI of high elevation are highly complex. This study’s findings provide a reference for research on vegetation responses to climate change in arid areas having a high-elevation gradients and complex underlying surfaces.

Suggested Citation

  • Yang Yan & Junhui Cheng & Yongkang Li & Jie Fan & Hongqi Wu, 2023. "Characteristics of NDVI Changes in the Altay Region from 1981 to 2018 and Their Relationship to Climatic Factors," Land, MDPI, vol. 12(3), pages 1-18, February.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:3:p:564-:d:1080830
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    References listed on IDEAS

    as
    1. Baixue Wang & Weiming Cheng & Keyu Song & Suiji Wang & Yichi Zhang & Hao Li & Jiayin Deng & Ruibo Wang, 2022. "Application of Ecology-Geomorphology Cognition Approach in Land Type Classification: A Case Study in the Altay Region," Sustainability, MDPI, vol. 14(7), pages 1-22, March.
    2. Roman J. Dial & Colin T. Maher & Rebecca E. Hewitt & Patrick F. Sullivan, 2022. "Sufficient conditions for rapid range expansion of a boreal conifer," Nature, Nature, vol. 608(7923), pages 546-551, August.
    3. Haochen Yu & Zhengfu Bian & Shouguo Mu & Junfang Yuan & Fu Chen, 2020. "Effects of Climate Change on Land Cover Change and Vegetation Dynamics in Xinjiang, China," IJERPH, MDPI, vol. 17(13), pages 1-25, July.
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