2015
DOI: 10.3390/rs70810243
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Responses of Natural Vegetation Dynamics to Climate Drivers in China from 1982 to 2011

Abstract: This study investigated the spatiotemporal variation of vegetation growth and the influence of climatic drivers from 1982 to 2011 across China using datasets from the Normalized Difference Vegetation Index (NDVI) and climatic drivers. Long term trends, significance and abrupt change points of interannual NDVI time series were analyzed. We applied both simple regression and multi-regression models to quantify the effects of climatic drivers on vegetation growth and compare their relative contributions. Results … Show more

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Cited by 93 publications
(63 citation statements)
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“…MODIS image time series provide a critically important resource for understanding both the dynamics and the evolution of environmental phenomena (Eastman et al 2013). MODIS data have been widely used in analyses of land cover change over large areas (Zhan et al 2002;Galford et al 2008), vegetation dynamic change and its driving forces in arid and semiarid regions (CampoBescós et al 2013;Li et al 2013;Bao et al 2014;Liu and Lei 2015;Qu et al 2015), and crop mapping and productivity estimation (Wardlow and Egbert, 2008;Mishra and Chaudhuri, 2015). Furthermore, MODIS NDVI time series data have been used to describe regional-scale temporal trends in vegetation by quantifying interannual and seasonal vegetation dynamics (Wardlow and Egbert, 2008;Mishra and Chaudhuri, 2015).…”
Section: Introductionmentioning
confidence: 99%
“…MODIS image time series provide a critically important resource for understanding both the dynamics and the evolution of environmental phenomena (Eastman et al 2013). MODIS data have been widely used in analyses of land cover change over large areas (Zhan et al 2002;Galford et al 2008), vegetation dynamic change and its driving forces in arid and semiarid regions (CampoBescós et al 2013;Li et al 2013;Bao et al 2014;Liu and Lei 2015;Qu et al 2015), and crop mapping and productivity estimation (Wardlow and Egbert, 2008;Mishra and Chaudhuri, 2015). Furthermore, MODIS NDVI time series data have been used to describe regional-scale temporal trends in vegetation by quantifying interannual and seasonal vegetation dynamics (Wardlow and Egbert, 2008;Mishra and Chaudhuri, 2015).…”
Section: Introductionmentioning
confidence: 99%
“…Ecological modelling suggested that LUC plays a more significant role in vegetation greenness in China [21,22]. Liu and Lei [62] indicated that NDVI variations in China are affected by external factors such as drought and afforestation. Liu et al [63] showed a strong correlation between the cumulative afforestation area and NDVI during the period from 2001-2012.…”
Section: Uncertainties In the Attribution Of Observed Ndvi Changesmentioning
confidence: 99%
“…At present, due to the dual influences of natural and human factors on vegetation change, it is difficult to quantitatively identify the contribution ratio of human activities, especially population migration, to vegetation greenness change. According to the existing research, studies on the factors influencing vegetation change can be divided into two aspects, natural factors (primarily temperature and precipitation) Duo, Zhao, Qu, Jing, &Xiong, 2016Gessner et al, 2013He, Guo, Dixon, & Wilmshurst, 2012;Liu & Lei, 2015;Liu, Pan, Zhu, & Li 2015;Liu, Zhao, et al, 2015;Piao et al, 2012;Sun, Yang, Zhang, & Wang, 2015) and human activities (Aide & Grau, 2004;Cao, Ma, Yuan, & Wang, 2014;Feng, Ma, Jiang, Wang, & Cao, 2015;Gartzia, Pérez-Cabello, Bueno, & Alados, 2016;Li, Wu, & Huang, 2012;Lu et al, 2015;Sun et al, 2015;Tousignant, Pellerin, & Brisson, 2010). The natural factors have received extensive attention.…”
mentioning
confidence: 99%