Revealing the climatic impacts on spatial heterogeneity of NDVI in China during 1982-2013

被引:0
|
作者
Gao J. [1 ]
Jiao K. [1 ,2 ]
Wu S. [1 ,3 ]
机构
[1] Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing
[2] Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, CAS, Shenyang
[3] University of Chinese Academy of Sciences, Beijing
来源
Dili Xuebao/Acta Geographica Sinica | 2019年 / 74卷 / 03期
基金
中国国家自然科学基金;
关键词
China; Climate change; GWR; NDVI; Spatial heterogeneity;
D O I
10.11821/dlxb201903010
中图分类号
学科分类号
摘要
Climate change is a major driver of vegetation activity, and thus its complex processes become a frontier and difficulty in global change research. To understand the complex relationship between climate change and vegetation activity, the spatial distribution and dynamic characteristics of the response of NDVI to climate change from 1982 to 2013 in China were investigated by the geographically weighted regression (GWR) model. The GWR was run based on the combined datasets of satellite vegetation index (GIMMS NDVI) and climate observation (temperature and moisture) from meteorological stations nationwide. The results noted that the spatial non-stationary relationship between NDVI and surface temperature has appeared in China. The significant negative temperature-vegetation relationship was distributed in northeast, northwest and southeast parts of the country, while the positive correlation was more concentrated from southwest to northeast. And then, by comparing the normalized regression coefficients for different climate factors, regions with moisture dominants for NDVI were observed in North China and the Tibetan Plateau, and regions with temperature dominants for NDVI were distributed in the East, Central and Southwest China, where the annual mean maximum temperature accounts for the largest areas. In addition, regression coefficients 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, there was a positive correlation between vegetation dynamics and climate variability. However, the continuous and dynamic responding process of vegetation activity to climate change will be determined by spatially heterogeneous conditions in climate change and vegetation cover. Furthermore, the 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. © 2019, Science Press. All right reserved.
引用
收藏
页码:534 / 543
页数:9
相关论文
共 40 条
  • [21] Reyer C.P.O., Leuzinger S., Rammig A., Et al., A plant's perspective of extremes: terrestrial plant responses to changing climatic variability, Global Change Biology, 19, 1, pp. 75-89, (2013)
  • [22] Wu S., Zhao Y., Tang Q., Et al., Land surface pattern study under the framework of Future Earth, Progress in Geography, 34, 1, pp. 10-17, (2015)
  • [23] Han Y., Zhu W., Li S., Modelling Relationship between NDVI and Climatic Factors in China Using Geographically Weighted Regression, Acta Scientiarum Naturalium Universitatis Pekinensis, 52, 6, pp. 1125-1133, (2016)
  • [24] Zhao Y., Zhu J., Xu Y., Establishment and assessment of the grid precipitation datasets in China for recent 50 years, Journal of the Meteorological Sciences, 34, 4, pp. 414-420, (2014)
  • [25] Wright C.K., De Beurs K.M., Henebry G.M., Combined analysis of land cover change and NDVI trends in the Northern Eurasian grain belt, Frontiers of Earth Science, 6, 2, pp. 177-187, (2012)
  • [26] Mao D.H., Wang Z.M., Luo L., Et al., Integrating AVHRR and MODIS data to monitor NDVI changes and their relationships with climatic parameters in Northeast China, International Journal of Applied Earth Observation and Geoinformation, 18, 1, pp. 528-536, (2012)
  • [27] Kong D., Zhang Q., Huang W., Et al., Vegetation phenology change in Tibetan Plateau from 1982 to 2013 and its related meteorological factors, Acta Geographica Sinica, 72, 1, pp. 39-52, (2017)
  • [28] Duo A., Zhao W., Qu X., Et al., Spatio-temporal variation of vegetation coverage and its response to climate change in North China plain in the last 33 years, International Journal of Applied Earth Observation and Geoinformation, 53, pp. 103-117, (2016)
  • [29] Brunsdon C., Fotheringham A.S., Charlton M.E., Geographically weighted regression: A method for exploring spatial nonstationarity, Geographical Analysis, 28, 4, pp. 281-298, (1996)
  • [30] Brown S., Versace V.L., Laurenson L., Et al., Assessment of spatiotemporal varying relationships between rainfall, land cover and surface water area using geographically weighted regression, Environmental Modeling and Assessment, 17, 3, pp. 241-254, (2012)