# The Response of Vegetation Dynamics to Climate in Xinjiang from 1991 to 2018
## 3 Results
### The Response of Vegetation to Climate Change
#### 3.3.3 The Correlation Between Vegetation and Temperature  
The proportion of positive and negative correlations between temperature and the three indices showed little variation. The positive correlation proportions for NDVI, LAI, and GPP were 55.5%, 54.6%, and 51.2%, respectively. However, the spatial distribution of correlations for each index exhibited remarkable heterogeneity (Figure 10). Regions with negative correlations between NDVI and temperature were mainly located in basin areas, including the Junggar Basin and its surroundings (the Turpan Basin, the Hami Basin, and the Tarim Basin). The dominant vegetation type in these regions was desert vegetation. In the eastern Tarim Basin and parts of the central areas of other basins, the correlation coefficient was less than \(-0.4\). As temperatures increased, plants enhanced transpiration to minimize water loss. However, excessively high temperatures could easily cause plants to die due to water stress. Therefore, these regions exhibited moderate to strong negative correlations. In high-altitude areas, rising temperatures made conditions more suitable for vegetation growth, leading to positive correlations. Areas with correlation coefficients greater than 0.6 were all distributed in high-altitude mountainous regions. In contrast, the cultivated areas around basins, which had sufficient water and sunlight, presented weak positive correlations.  
The correlation between temperature and LAI showed an interactive distribution of positive and negative correlations. Areas with positive correlations were mainly in regions with increasing temperature, while negative correlation areas were primarily located in high-altitude mountainous regions and regions with decreasing temperature. Regions with correlation coefficients greater than 0.4 accounted for 10.5%, mainly distributed in grasslands and parts of cropland. Regions with correlation coefficients less than \(-0.4\) accounted for 7.4%, primarily distributed in the Kunlun Mountains.  
Figure 10: The correlation between air temperature and different vegetation indicators (**a**) for NDVI, (**b**) for LAI, and (**c**) for GPP.  
The spatial distribution of the correlation between GPP and temperature was similar to that between LAI and temperature; however, it showed a weaker correlation on the whole. Areas with correlation coefficients between \(-0.4\) and \(0.4\) accounted for 95.8%. Regions with rising temperatures were still the main areas with positive correlations. Compared to the correlation with precipitation, the spatial distribution of the correlation between temperature and GPP in the Kunlun Mountains was reversed. Different types of vegetation responded differently to changes in temperature and precipitation, particularly alpine vegetation, which was more sensitive to climate change. Therefore, in high-altitude areas, opposite correlations between GPP and climate indicators may occur. In the Iii River Valley, negative correlations dominated, as rising temperatures were unfavorable for vegetation growth. Positive correlation areas around the Tarim Basin and southern Junggar Basin were mainly regions where crops and economic plants were grown. These areas were more severely affected by human activities.  
The response of vegetation to rising temperatures was more complex. In high-altitude regions like the Tianshan and Altai Mountains, higher temperatures prolonged the growing season, promoting vegetation growth. However, in desert regions like the Tarim Basin, higher temperatures may exacerbate water stress, leading to negative correlations between temperature and vegetation indices. These findings indicated that while rising temperatures may benefit vegetation activity, they could also pose challenges for arid regions where water resources were already limited.