## 3 Results and Discussion
### Responses of Vegetation Dynamics to Climatic Drivers  
#### 3.3.1 Relationships between NDVI and Single Climate Driver  
Mean NDVI within the growing season, spring, summer and autumn were calculated for the following regression analysis. At a national scale, the growing season NDVI was significantly and positively correlated with temperature (\(R^{2}=0.382,p<0.01\)) and precipitation (\(R^{2}=0.103,p<0.05\)), but not significantly correlated with radiation over the period of 1982-2010 (Table 1). Seasonally, NDVI was significantly and positively correlated with temperature in spring (\(R^{2}=0.525,p<0.01\)) and autumn (\(R^{2}=0.291,p<0.01\)), especially in spring, when the high regression coefficient indicated a strong linkage between spring NDVI and temperature. No significant correlation between NDVI and precipitation was found in any of the other three seasons. The regression analysis at a national scale suggested that vegetation was greatly affected by temperature, consistent with previous studies [55, 56]. Since radiation showed little correlation with NDVI and the influence directions were unstable, for simplification, it won't be further discussed below. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Period** & \(\beta_{T}(\times 10^{-3}/\mathrm{o}\mathrm{C})\) & \(\beta_{P}(\times 10^{-6}/mm)\) & \(\beta_{R}(\times 10^{-3}/W/m^{2})\) \\ \hline Growing season & 9.321 ** & 8.349 * & \(-3.107\) \\ Spring & 15.935 ** & 7.826 & 8.241 \\ Summer & 3.977 & 7.078 & 5.334 \\ Autumn & 8.903 ** & 4.554 & \(-6.949\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Regression coefficients between NDVI and temperature (\(\beta_{T}\)), NDVI and precipitation (\(\beta_{P}\)) and NDVI and radiation (\(\beta_{R}\)) during the growing season, spring, summer and autumn from 1982 to 2010. (** \(p<0.01\); * \(p<0.05\)). Figure 7: Box plot for the trends of growing season NDVI of Evergreen Needle Forest (ENF), Evergreen Broadleaf Forest (EBF), Deciduous Broadleaf Forest (DBF), Mixed Forest (MF), Shrubs and Grasses from 1982 to 2011. The edges of the boxes are 25 th and 75 th percentiles [54]. Figure 8 showed the spatial patterns of regression coefficients between temperature and NDVI, and precipitation and NDVI, respectively, in the growing season. Over the period of 1982-2010, NDVI was significantly and positively correlated with temperature in about 26.6% of the area (Figure 8a). Particularly, in southern China, where the main vegetation types are evergreen broadleaf forest and deciduous broadleaf forest (Figure 1), the high regression coefficients indicated that temperature rise has strongly promoted vegetation growth in these areas. In 17.7% of the area, NDVI was significantly and positively correlated with precipitation (Figure 8b), mainly in arid or semi-arid areas in northern China where the growth of grass is under water stress. In northeast Inner Mongolia, NDVI decreased (Figure 6a) due to severe water limitation with decreasing precipitation (Figure 4b) while in the grassland of northern Qinghai, NDVI increased (Figure 6a) with more water supply from precipitation (Figure 4b). Our results indicated that NDVI was significantly and positively correlated with temperature in large amount of area in China, which is consistent with previous studies [2, 19, 28, 32]. However, compared to the previous study [19] suggesting little significant correlations between annual NDVI and precipitation in these areas, the significant correlations in the growing season highlighted the strong effect of the growing season precipitation on the growth of grasses in arid and semi-arid areas. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Periods** & **Growing Season** & **Spring** & **Summer** & **Autumn** \\ \hline \(\beta_{\mathrm{NDVI}-T}\) & 0.583 ** & 0.726 ** & 0.176 & 0.560 ** \\ \(\beta_{\mathrm{NDVI}-P}\) & 0.228 * & \(-\)0.080 & 0.128 & 0.177 \\ \hline \hline \end{tabular}
\end{table}