# Vegetation Dynamics and Recovery Potential in Arid and Semi-Arid Northwest China
## 2 Results
### Response of Vegetation Restoration Potential to Climate Warming and Humidification
The percentage of areas showing significant and highly significant positive correlations (13.89%) is also higher than that of areas with a negative correlation (5.03%) at \(p<0.05\). There was an overall positive correlation between NDVI and SPEI-12 in the study area from 1982 to 2019 (59.2%). Figure 9: Changes in Standardized Precipitation Evapotranspiration Index (SPEI) at 12-month scale in northwest China from 1961 to 2019. The correlation analysis between the annual NDVI and SPEI-12 showed that the correlation between grassland and drought was the most significant, the positive correlation between farmland and drought was only lower than that of grassland, and the correlation between forest and drought was not significant or even negative, which indicated that forests were weakly affected by drought, that the sensitivity of vegetation to drought response varied among the different land cover types, and that the growth status of grassland was more indicative of drought status (Figure 10a,b). The larger the SPEI value, the more humid the conditions; conversely, the smaller the value, the drier the conditions. On an annual scale, the correlation between FVC and SPEI-12 revealed that in the eastern part of northwest China, namely eastern Inner Mongolia, central and southern Ningxia, and central Gansu, FVC was positively correlated with  
Figure 10: (**a,b**) represent the correlation and significance between NDVI and SPEI-12 in northwest China from 1982 to 2019; (**c,d**) represent the correlation and significance between FVC and SPEI-12 in northwest China from 1982 to 2019. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Indicator** & **Correlation** & \begin{tabular}{c} **Extremely** \\ **Significant** \\ **Positive** \\ \end{tabular} & \begin{tabular}{c} **Significant** \\ **Positive** \\ \end{tabular} & **Not Positive** & **Not Negative** & \
\begin{tabular}{c} **Significant** \\ **Negative** \\ \end{tabular} &
\begin{tabular}{c} **Extremely** \\ **Significant** \\ **Negative** \\ \end{tabular} \\ \hline NDVI and SPEI 12 & Proportion & 6.62\% & 7.27\% & 45.31\% & 35.77\% & 3.83\% & 1.2\% \\ \hline FVC and SPEI 12 & Proportion & 6.51\% & 8.68\% & 50.19\% & 31.30\% & 2.35\% & 0.97\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Correlation and significance test between NDVI/FVC and SPEI 12 for northwest China (unit %). SPEI-12. This means that higher SPEI values, indicating lower drought levels, corresponded to higher FVC. In contrast, FVC and SPEI-12 showed a significant negative correlation in northern Gansu and the northwest corner of Inner Mongolia. This may be due to the widespread distribution of underlying surfaces, where greater evapotranspiration leads to the loss of more soil moisture, thereby inhibiting vegetation growth (Figure 10c,d). In order to investigate the dynamic response of vegetation restoration potential to climate warming and humidification, spatial and temporal variations in maximum vegetation cover (FVC), or peak vegetation cover, were analyzed from 1982 to 2019. The process for obtaining the peak value of vegetation cover is to use the maximum synthesis method to obtain the maximum vegetation cover through the vegetation cover data from each year from 1982 to 2019 and then to use the maximum vegetation cover to subtract the raster of the vegetation cover of each year. Then, the number of rasters whose value is equal to 0 indicates the peak value of vegetation cover for each year, so as to obtain the temporal change in the peak value of vegetation cover. The latitude and longitude of a raster with a value equal to 0 were extracted to obtain the spatial variation in the peak value of vegetation cover. Based on the satellite observation data, the temporal changes in the maximum vegetation cover (FVC), i.e., the peak vegetation cover, in northwest China from 1982 to 2019 were obtained.