## 3 Results
### Driving Factors for the Significant Increase in Vegetation  
Table 4 lists the proportions of various land use changes in places with significantly increased vegetation. Here, land use data with a spatial resolution of 30 m were used. The six main types of land use change included unchanged cropland, unchanged forest, unchanged grassland, other types to cropland, other types to forest, and other types to grassland. These six main types of land use change accounted for 97.21% and 96.79% in the areas with significantly increased LAI and GPP, respectively. The areas with unchanged land use accounted for 86.78% of the locations with significantly increased LAI. The proportions of different land use changes in the areas with significantly increased LAI can be sorted as follows: unchanged forest (34.27%), unchanged grassland (27.51%), unchanged cropland (23.07%), other types to forest (5.15%), other types to grassland (4.13%), and other types to cropland (3.08%). The areas with unchanged land use made up 86.89% of the areas with significantly increased GPP. In the regions with significantly increased GPP, the proportions of various land use changes can be ranked as follows: unchanged forest (33.85%), unchanged grassland (27.1%), unchanged cropland (23.8%), other types to forest (4.93%), other types to grassland (4.10%), and other types to cropland (3.01%).  
Table 5 illustrates the correlation coefficients between vegetation and different climatic indicators in the MHRB from 2001 to 2018. In contrast to other climate factors, vegetation had positive correlations with precipitation and humidity index in all sub-basins. There was a substantial association between precipitation and vegetation. The correlation coefficients between precipitation and LAI were significant in the MLHRB, the MBSHRB, and the MYDHRB (\(p<0.05\)). The correlation coefficients of GPP with precipitation and humidity index were above 0.42 in all sub-basins and were significant in the sub-basins except for the MLHRB and the MZWHRB. The adverse impact of VPD on vegetation was the most obvious. LAI and GPP had negative correlations with VPD in all sub-basins. In all the sub-basins except for the MDQHRB and the MZYHRB, the climate factors with the lowest correlation coefficients with vegetation were VPD. The sensitivity of GPP to climate change was stronger than that of LAI. In all the sub-basins except for the MLHRB, the positive effects of precipitation and humidity index on GPP were stronger than those on LAI. In all sub-basins, VPD had stronger negative effects on GPP than those on LAI. From 2001 to 2018, precipitation increased in all sub-basins, and the humidity index increased in the sub-basins except for the MLHRB (Table 6). VPD decreased in the sub-basins except for the MZWHRB. The increased precipitation and humidity index and the reduced VPD promoted the increase in vegetation in the MHRB from 2001 to 2018.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Land Use Change** & **LAI** & **GPP** \\ \hline Unchanged cropland & 23.07 & 23.80 \\ Unchanged forest & 34.27 & 33.85 \\ Unchanged grassland & 27.51 & 27.10 \\ Other types to cropland & 3.08 & 3.01 \\ Other types to forest & 5.15 & 4.93 \\ Other types to grassland & 4.13 & 4.10 \\ Six main types & 97.21 & 96.79 \\ Unchanged land use & 86.78 & 86.89 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Proportions of various land use changes between 2001 and 2018 in places with significantly increased vegetation (%).