Granger Causality Analysis of Grass Response to Climatic Changes Over Tibetan Plateau

  • Hua Wang
  • Yuke ZhouEmail author
  • Chenghu Zhou
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1228)


Due to the complex natural environment, vegetation on the Tibetan Plateau (TP) has a sensitive response to climatic changes. Thus, it is of great importance to explore the cause effect of climatic shifts on vegetation. Based on the long-term satellite NDVI dataset during 1982–2012, we analysed the causal relationship of vegetation greenness with temperature and precipitation by using the Granger causality test at monthly and seasonal temporal scale for each pixel. The results show that (1) the proportion of pixels with stationary time series for NDVI vs. temperature and NDVI vs. precipitation is greater at month scale than at seasonal scale, which is 99% and 98% at monthly scale, 64% and 71% at seasonal scale, respectively. (2) At month scale, the lagging time period of the average temperature and total precipitation on NDVI is around 12–13 months at monthly scale and show similar temporal profile across various vegetation types. At seasonal scale, the lagging time period is mainly occurred in 3, 4 and 6 quarter and very different in desert steppe, steppe and meadow. (3) For 98% area of the TP, average temperature change is found to granger cause of NDVI. While for 89% of the TP, except for the south-eastern TP, NDVI is supposed to granger cause of average temperature change at month scale. At seasonal scale, average temperature change is granger cause of changes in NDVI approximately accounting for 92% area of TP, where the central part of TP is excluded. However, in the eastern and western TP (about 50% of TP), NDVI is interpreted as granger cause of average temperature changes. (4) In the north-eastern and north western parts (about 98% of TP), precipitation is the granger cause of NDVI changes. While for 94% of the plateau, except for a few areas in the southeastern TP, NDVI is supposed to the granger cause of precipitation change at month scale. The precipitation change is considered as granger cause of NDVI in the south-eastern part of the TP (approximate 61% TP) at seasonal scale. In the central and eastern (about 48% of the plateau), NDVI is granger cause of precipitation change. Overall, climate factors have an interactive relationship with vegetation changes. Climatic factors and vegetation greenness can compose a Grainger causality relationship to each other, but climatic factors have stronger Grainger cause effect on vegetation than vegetation’s Grainger effect on climatic factors. There is more Granger cause effect region at month scale than seasonal scale over the TP.


Tibetan plateau Climatic changes Granger causality effect NDVI 



This work is supported by National Natural Science Foundation of China (Grant No. 41601478), National Key Research and Development Program (Grant No. 2018YFB0505301, 2016YFC0500103), Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), (Grant No. GML2019ZD0301).


  1. 1.
    Nemani, R.R., et al.: Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300(5625), 1560–1563 (2003)CrossRefGoogle Scholar
  2. 2.
    Piao, S., Mohammat, A., Fang, J., Cai, Q., Feng, J.: NDVI-based increase in growth of temperate grasslands and its responses to climate changes in China. Global Environ. Change 16(4), 340–348 (2006)CrossRefGoogle Scholar
  3. 3.
    Sun, Y., et al.: Recent progress in studies of climate change detection and attribution in the globe and China in the past 50 years. Clim. Change Res. 9, 235–245 (2013)Google Scholar
  4. 4.
    Ichii, K., Kawabata, A., Yamaguchi, Y.: Global correlation analysis for NDVI and climatic variables and NDVI trends: 1982–1990. Int. J. Remote Sens. 23(18), 3873–3878 (2002)CrossRefGoogle Scholar
  5. 5.
    He, B., Chen, A., Jiang, W., Chen, Z.: The response of vegetation growth to shifts in trend of temperature in China. J. Geogr. Sci. 27(7), 801–816 (2017)CrossRefGoogle Scholar
  6. 6.
    Diks, C., Panchenko, V.: A new statistic and practical guidelines for nonparametric Granger causality testing. J. Econ. Dyn. Control 30(9–10), 1647–1669 (2006)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Chen, Y., Rangarajan, G., Feng, J., Ding, M.: Analyzing multiple nonlinear time series with extended Granger causality. Phys. Lett. A 324(1), 26–35 (2004)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Pinzon, J., Tucker, C.: A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens. 6, 6929–6960 (2014)CrossRefGoogle Scholar
  9. 9.
    Peng, D., Zhang, B., Liu, L., Chen, D., Fang, H., Hu, Y.: Seasonal dynamic pattern analysis on global FPAR derived from AVHRR GIMMS NDVI. Int. J. Digit. Earth 5, 439–455 (2012)CrossRefGoogle Scholar
  10. 10.
    De Jong, R., Verbesselt, J., Zeileis, A., Schaepman, M.: Shifts in global vegetation activity trends. Remote Sens. 5, 1117–1133 (2013)CrossRefGoogle Scholar
  11. 11.
    Stow, D., et al.: Variability of the seasonally integrated normalized difference vegetation index across the north slope of Alaska in the 1990s. Int. J. Remote Sens. 24, 1111–1117 (2003)CrossRefGoogle Scholar
  12. 12.
    Yang, K., Wu, H., Qin, J., Lin, C., Tang, W., Chen, Y.: Recent climate changes over the Tibetan Plateau and their impacts on energy and water cycle: a review. Global Planet. Change 112, 79–91 (2014)CrossRefGoogle Scholar
  13. 13.
    Yang, K., et al.: Response of hydrological cycle to recent climate changes in the Tibetan Plateau. Clim. Change 109, 517–534 (2011)CrossRefGoogle Scholar
  14. 14.
    Cheung, Y.W., Lai, K.S.: Lag order and critical values of the augmented Dickey-Fuller test. J. Bus. Econ. Stat. 13(3), 277–280 (1995)Google Scholar
  15. 15.
    Yi, H.W.: Discussion on how to do granger causality test. J. Postgrad. Zhongnan Univ. Econ. Law 5, 34–36 (2006)Google Scholar
  16. 16.
    Ferrarini, L.: On the reachability and reversibility problems in a class of Petri nets. IEEE Trans. Syst. Man Cybern. 24(10), 1474–1482 (2002)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Aybar, A., Iftar, A.: Overlapping decompositions and expansions of Petri nets. IEEE Trans. Autom. Control 47(3), 511–515 (2002)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Zhou, J.: The researches on the test power and features on the lagging number selecting criteria about the time series models. System Eng. Theory Pract. 25, 20–27 (2005)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Institute of Geographic Sciences and Nature Resources ResearchChinese Academy of SciencesBeijingChina
  2. 2.Chinese People’s Liberation Army 31009BeijingChina

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