Abstract
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.
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References
Andrew R L, Guan H D, Batelaan O, 2017. Large-scale vegetation responses to terrestrial moisture storage changes. Hydrology and Earth System Sciences, 21(9): 4469–4478.
Baez S, Collins S L, Pockman W T et al., 2013. Effects of experimental rainfall manipulations on Chihuahuan Desert grassland and shrubland plant communities. Oecologia, 172(4): 1117–1127.
Brohan P, Kennedy J J, Harris I et al., 2006. Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850. Journal of Geophysical Research-Atmospheres, 111(D12): 121–133.
Brown S, Versace V L, Laurenson L et al.}, 2012. Assessment of spatiotemporal varying relationships between rainfall, land cover and surface water area using geographically weighted regression. Environmental Modeling and Assessment, 17(3): 241–254.
Brunsdon C, Fotheringham A S, Charlton M E, 1996. Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis, 28(4): 281–298.
Del Grosso S, Parton W, Stohlgren T et al., 2008. Global potential net primary production predicted from vegetation class, precipitation, and temperature. Ecology, 89(8): 2117–2126.
Ding Yongjian, Zhou Chenghu, Shao Mingan et al., 2013. Studies of earth surface processes: Progress and prospect. Advances in Earth Science, 28(4): 407–419. (in Chinese)
Du Jiaqiang, Shu Jianmin, Zhang Linbo et al., 2011. Responses of vegetation to climate change in the headwaters of China’s Yellow River Basin based on zoning of dry and wet climate. Chinese Journal of Plant Ecology, 35(11): 1192–1201. (in Chinese)
Duo A, Zhao W, Qu X et al., 2016. 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: 103–117.
Fang J Y, Piao S L, He J S et al.}, 2004. Increasing terrestrial vegetation activity in China, 1982–1999. Science in China Series C: Life Sciences, 47(3): 229–240.
Fang J Y, Tang Y H, Son Y, 2010. Why are East Asian ecosystems important for carbon cycle research? Science China-Life Sciences, 53(7): 753–756.
Feng X M, Fu B J, Piao S L et al., 2016. Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nature Climate Change, 6(11): 1019–1022.
Fu Bojie, Yu Dandan, Lü Nan, 2017. An indicator system for biodiversity and ecosystem services evaluation in China. Acta Ecologica Sinica, 37(2): 341–348. (in Chinese)
Gao J B, Jiao K W, Wu S H et al., 2017. Past and future effects of climate change on spatially heterogeneous vegetation activity in China. Earth’s Future, 5(7): 679–692.
Han Ya, Zhu Wenbo, Li Shuangcheng, 2016. Modelling relationship between NDVI and climatic factors in China using geographically weighted regression. Acta Scientiarum Naturalium Universitatis Pekinensis, 52(6): 1125–1133. (in Chinese)
Hoover D L, Knapp A K, Smith M D, 2014. Resistance and resilience of a grassland ecosystem to climate extremes. Ecology, 95(9): 2646–2656.
Jiang L L, Jiapaer G, Bao A M et al., 2017. Vegetation dynamics and responses to climate change and human activity in Central Asia. Science of the Total Environment, 599: 967–980.
Kong Dongdong, Zhang Qiang, Huang Wenlin et al., 2017. Vegetation phenology change in Tibetan Plateau from 1982 to 2013 and its related meteorological factors. Acta Geographica Sinica, 72(1): 39–52. (in Chinese)
Krishnaswamy J, John R, Joseph S, 2014. Consistent response of vegetation dynamics to recent climate change in tropical mountain regions. Global Change Biology, 20(1): 203–215.
Levine J M, 2015. Ecology: A trail map for trait-based studies. Nature, 529(7585): 163–164.
Li Hengkai, Liu Xiaosheng, Li Bo et al., 2014. Vegetation coverage variations and correlation with geomorphologic factors in red soil region: A case in south Jiangxi Province. Scientia Geographica Sinica, 34(1): 103–109. (in Chinese)
Lü Y H, Zhang L W, Feng X M et al., 2015. Recent ecological transitions in China: Greening, browning, and influential factors. Scientific Reports, 5: 8732.
Mao D H, Wang Z M, Luo L et al., 2012. 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): 528–536.
Michaletz S T, Chen g D, Kerkhoff A J et al., 2014. Convergence of terrestrial plant production across global climate gradients. Nature, 512(7512): 39–43.
Peng S S, Piao S L, Ciais P et al., 2013. Asymmetric effects of daytime and night-time warming on Northern Hemisphere vegetation. Nature, 501(7465): 88–92.
Piao S L, Nan H J, Huntingford C et al., 2014. Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity. Nature Communications, 5: 5018.
Piao S L, Wang X H, Ciais P et al., 2011. Changes in satellite-derived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006. Global Change Biology, 17(10): 3228–3239.
Reyer C P O, Leuzinger S, Rammig A et al., 2013. A plant's perspective of extremes: terrestrial plant responses to changing climatic variability. Global Change Biology, 19(1): 75–89.
Seddon A W R, Macias-Fauri a M, Long P R et al., 2016. Sensitivity of global terrestrial ecosystems to climate variability. Nature, 531(7593): 229–243.
Urban M C, 2015. Accelerating extinction risk from climate change. Science, 348(6234): 571–573.
Wang J M, Wang H D, Cao Y G et al., 2016. Effects of soil and topographic factors on vegetation restoration in opencast coal mine dumps located in a loess area. Scientific Reports, 6: 22058.
Wang Q, Ni J, Tenhunen J, 2005. Application of a geographically-weighted regression analysis to estimate net primary production of Chinese forest ecosystems. Global Ecology and Biogeography, 14(4): 379–393.
Wang Q, Zhang Q P, Zhou W, 2012. Grassland coverage changes and analysis of the driving forces in Maqu County. Physics Procedia, 33: 1292–1297.
Wright C K, de Beurs K M, Henebry G M, 2012. Combined analysis of land cover change and NDVI trends in the Northern Eurasian grain belt. Frontiers of Earth Science, 6(2): 177–187.
Wu Shaohong, Zhao Yan, Tang Qiuhong et al., 2015. Land surface pattern study under the framework of Future Earth. Progress in Geography, 34(1): 10–17. (in Chinese)
Zeppel M J B, Wilks J V, Lewis J D, 2014. Impacts of extreme precipitation and seasonal changes in precipitation on plants. Biogeosciences, 11(11): 3083–3093.
Zhang Xuemei, Wang Kelin, Yue Yuemin et al., 2017. Factors impacting on vegetation dynamics and spatial non-stationary relationships in karst regions of southwest China. Acta Ecologica Sinica, 37(12): 4008–4018. (in Chinese)
Zhao M S, Running S W, 2010. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science, 329(5994): 940–943.
Zhao Yufei, Zhu Jiang, Xu Yan, 2014. Establishment and assessment of the grid precipitation datasets in China for recent 50 years. Journal of the Meteorological Sciences, 34(4): 414–420. (in Chinese)
Zhou Guangsheng, He Qijin, Yin Xiaojie, 2015. Adaptability and Vulnerability of Chinese Vegetation/Terrestrial Ecosystems under Climate Change. Beijing: China Meteorological Press. (in Chinese)
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Foundation: National Key R&D Program of China, No.2018YFC1508900, No.2018YFC1509003, No.2018YFC1508801; National Natural Science Foundation of China, No.41671098; Strategic Priority Research Program of the Chinese Academy of Sciences, No.XDA19040304, No.XDA20020202
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Gao, J., Jiao, K. & Wu, S. Investigating the spatially heterogeneous relationships between climate factors and NDVI in China during 1982 to 2013. J. Geogr. Sci. 29, 1597–1609 (2019). https://doi.org/10.1007/s11442-019-1682-2
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DOI: https://doi.org/10.1007/s11442-019-1682-2