Journal of Geographical Sciences ›› 2019, Vol. 29 ›› Issue (10): 1597-1609.doi: 10.1007/s11442-019-1682-2

• Research Articles •     Next Articles

Investigating the spatially heterogeneous relationships between climate factors and NDVI in China during 1982 to 2013

GAO Jiangbo1, JIAO Kewei1,2, WU Shaohong1,3   

  1. 1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, CAS, Shenyang 110016, China
    3. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2019-01-10 Accepted:2019-03-20 Online:2019-10-25 Published:2019-12-09
  • About author:Gao Jiangbo, PhD, specialized in climate change risk and adaptation. E-mail:
  • Supported by:
    National Key R&D Program of China(No.2018YFC1508900);National Key R&D Program of China(No.2018YFC1509003);National Key R&D Program of China(No.2018YFC1508801);National Natural Science Foundation of China(No.41671098);Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA19040304);Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA20020202)


Climate change is a major driver of vegetation activity, and thus their complex processes become a frontier and difficulty in global change research. To understand this relationship between climate change and vegetation activity, the spatial distribution and dynamic characteristics of the response of NDVI to climate change were investigated by the geographically weighted regression (GWR) model during 1982 to 2013 in China. This model was run based on the combined datasets of satellite vegetation index (NDVI) and climate observation (temperature and moisture) from meteorological stations nationwide. The results showed that the spatial non-stationary relationship between NDVI and surface temperature has appeared in China: the significant negative temperature-vegetation relationship was located in Northeast, Northwest and Southeast China, while the positive correlation was more concentrated from southwest to northeast. By comparing the normalized regression coefficients from GWR model for different climate factors, it presented the regions with moisture dominants for NDVI were in North China and the Tibetan Plateau, and the areas of temperature dominants were distributed in East, Central and Southwest China, where the annual mean maximum temperature accounted for the largest areas. In addition, regression coefficients from GWR model between NDVI dynamics and climate variability indicated that the higher warming rate could result in the weakened vegetation activity through some mechanisms such as enhanced drought, while the moisture variability could mediate the hydrothermal conditions for the variation of vegetation activity. When the increasing rate of photosynthesis exceeded that of respiration, the positive correlation between vegetation dynamics and climate variability was reflected. However, the continuous and dynamic process of vegetation activity response to climate change will be determined by spatially heterogeneous conditions in climate change and vegetation cover. Furthermore, the dynamic description of climate-induced vegetation activity from its rise to decline in different regions is expected to provide a scientific basis for initiating ecosystem-based adaptation strategies in response to global climate change.

Key words: NDVI, climate change, spatial heterogeneity, GWR, China