## 4 Results
### Spatial Autocorrelation Analysis of Vegetation Dynamics in the ARB
#### 4.3.2 Regional Impacts of Land Use Changes on the Vegetation  
In addition to climatic factors, anthropogenic activities that result in land use changes, such as farming, urbanization, and reforestation, also obviously affect the vegetation dynamics in the ARB. The PLSR model results (see Table 5) revealed that at the basin scale, the proportions of forest and pasture are the most important drivers for vegetation changes. Overall, the forest area posed a positive effect on all three indexes, while an increase in pasture area most likely resulted in the decreases in all three indexes. The upper part of Figure 7 presents the spatial distribution of the regression coefficients for forest with the LAI, FVC and GPP GTWR models for the studied four periods. From the perspective of the temporal and spatial distribution of the regression coefficients, the spatial distributions of the impacts of forest on LAI, FVC and GPP were very similar, and the spatial pattern varied slightly in the four studied periods. Regression coefficients for the forest over all sub-basins were greater than zero, and low values occurred in Huma County and Tahe County in China and Amur Oblast in Russia. According to the spatial distributions of the regression coefficients for pasture with the LAI, FVC and GPP GTWR models, as shown in the lower part of Figure 7, the negative values of the regression coefficients were mainly concentrated near the Songnen Plain in China. In addition, areas of high pasture influence on the three vegetation indexes have decreased in size and intensity over the four studied periods. Pasture had the strongest impact on vegetation in the northeastern ARB in Period 1, i.e., 1982-1990. 3.3 Spatial-Temporal Heterogeneity of the Relationships between Vegetation Dynamics and Hydrological Variables  
Hydroological and ecological processes are interactive: on the one hand, the water cycle strongly influences the component structure, spatial distribution, and dynamics of plant communities; on the other hand, vegetation influences hydrological processes directly through root water uptake and stomatal transpiration; while indirectly through affecting water infiltration, slope runoff and evaporation processes via the vertical canopy structure and horizontal community distribution [95]. As shown in Table 5, the PLSR models predicted that SURQ, SW and SM were more closely related to vegetation growth than the other hydrological processes considered, and had similar levels of importance. Only SW was positively correlated with all three indexes, LAI, FVC, and GPP at the global scale. The spatial distribution of the regression coefficients for SURQ with the LAI, FVC and GPP GTWR models for the four studied periods are presented in Figure 8. Comparing the distributions of regression coefficients for SURQ with different vegetation indexes, it is clear that SURQ has the smallest range of positive regression coefficients in the FVC model. However, both the range and value of the positive coefficients in the FVC model showed an overall increasing trend during the study period. Figure 7: The spatial distribution of the regression coefficients for forest and pasture with LAI ((**a**–**d**) display the coefficients for Periods 1–4, respectively), FVC ((**e**–**h**) show the coefficients for Periods 1–4, respectively), and GPP ((**i**–**I**) present the coefficients for Periods 1–4, respectively) GTWR models for the four studied periods. The spatial distribution of the regression coefficients for SW with the LAI, FVC and GPP GTWR models for the four studied periods are presented in Figure 9. In the region near the Argun River Basin in the western part of the study area, the relationship between SW and LAI, FVC and GPP generally showed a roughly positive to negative trend for the four studied periods.