## 4 Results
### Spatially Varying Relationships between FVC and Driving Factors Explored by SGWR  
#### 4.3.1 The Results of Local Parameters on a County Level  
The parameter estimates of local and global model variable are mapped for each of the four periods. However, only those local parameter estimates that were significant are depicted. In this case, significance is defined using the adjusted critical t value equated with an original significance level of 0.05, which addresses the issue of multiple hypothesis testing and multiple testing issue in GWR [36; 37].  
Figures 3-6 show the local estimates produced by SGWR for the major driving factors of fractional vegetation cover, including sunshine duration, rainfall, urban land, and population. According to the results of the SGWR model, local sunshine duration and rainfall estimates for the four periods featured significant geospatial heterogeneity. Although all the local sunshine duration estimates indicate a positive association between fractional vegetation cover and sunshine intensity, the strength of the relationship varies across space. For example, the sunshine duration in 2000 greatly impacted the vegetation coverage of Qingyuan city. However, the estimated value of the sunshine duration decreased significantly after 2010, indicating the recent weakening influence of sunshine intensity on FVC in most counties in Qingyuan City.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **2000** & **2005** & **2010** & **2015** \\ \hline OLS AIC & 930.521 & 900.004 & 878.957 & 862.096 \\ GWR AIC & 872.605 & 860.799 & 821.530 & 803.515 \\ SGWR AIC & 844.831 & 817.562 & 770.801 & 749.462 \\ OLS RSS & 391.056 & 411.024 & 387.734 & 369.250 \\ GWR RSS & 316.632 & 269.814 & 248.466 & 236.924 \\ SGWR RSS & 301.748 & 241.942 & 206.329 & 181.205 \\ OLS Adjusted R\({}^{2}\) & 0.527 & 0.549 & 0.581 & 0.621 \\ GWR Adjusted R\({}^{2}\) & 0.678 & 0.736 & 0.757 & 0.765 \\ SGWR Adjusted R\({}^{2}\) & 0.781 & 0.803 & 0.820 & 0.829 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of Akaike information criterion (AIC), residual sum of squares, and adjusted R\({}^{2}\) values for the OLS, GWR, and SGWR models.  
Figure 4: Local estimates for Rainfall factor of Fractional Vegetation Cover (FVC) calculated by the SGWR model.  
Figure 3: Local estimates for Sunshine duration factor of Fractional Vegetation Cover (FVC) calculated by the SGWR model.  
In contrast, the alternating positive and negative effects of the rainfall coefficient and the large proportion of positive effects reveal that rainfall is the principal factor affecting FVC (Figure 4); coastal rainfall promotes plant growth, whereas rainfall at typhoon landfall locations negatively impacts vegetation cover. With regard to the impact of urban land, Figure 5 indicates that the local influence of most urban land on FVC in Guangdong Province is negatively correlated, primarily because of accelerated urbanisation from 2000-2015 and the consequential increase in the demand for cultivated and forested land.  
Increasing population decreases vegetation coverage area because of the negative correlation between population and FVC. Population growth in the study area has led to rapid economic and real estate development, urban expansion, and demand for residential land. In contrast, the inland mountainous areas of northern Guangdong (such as Shaoguan City and Heyuan City) have low population density. The outflow of population from this region leads to a marked difference to the Pearl River Delta and explains the low sensitivity of this region to the population factor.  
Figure 5: Local estimates for Urban land factor of Fractional Vegetation Cover (FVC) calculated by the SGWR model.