## 3 Results
### Correlation Analysis Between Landscape Pattern and NPP  
As shown in Figure 3, the impact trend of patch layer indicators on NPP is basically similar. CONTAG, AI, and LPI are positively correlated with NPP, with CONTAG showing a highly significant positive correlation with NPP, indicating that the higher the degree of aggregation or extension trend of different land use patches in the landscape, the better the vegetation growth status. SPLIT, ED, IJI, LSI, which characterize the shape of patches, are negatively correlated with NPP, with SPLIT, IJI, and LSI showing a highly significant negative correlation, that is, the more complete and regular the shape of patches, the more \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Types** & **Year** & **TE** & **NP** & **JJI** & **PLAND** \\ \hline \multirow{3}{*}{Timberland} & 2000 & 32,113.8 & 40,821 & 24.25 & 11.81 \\  & 2010 & 32,745.51 & 43,440 & 23.85 & 11.05 \\  & 2020 & 34,043.97 & 42,312 & 21.81 & 11.92 \\ \hline \multirow{3}{*}{Grassland} & 2000 & 91,428.21 & 86,966 & 83.44 & 46.73 \\  & 2010 & 88,033.08 & 90,603 & 77.18 & 47.76 \\  & 2020 & 87,611.34 & 83,334 & 71.44 & 47.81 \\ \hline \multirow{3}{*}{Farmland} & 2000 & 41,672.22 & 34,073 & 45.3 & 12.66 \\  & 2010 & 40,790.94 & 36,046 & 44.5 & 11.72 \\ \cline{1-1}  & 2020 & 41,994.75 & 34,699 & 45.32 & 13.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Interannual variation in horizontal landscape pattern indices. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{6}{c}{**Landscape Pattern Index**} \\ \cline{2-10}  & **AI** & **COHESION** & **CONTAG** & **SPIT** & **DIVISION** & **ED** & **IJI** & **LPI** & **LSI** & **PAFRAC** \\ \hline
2000 & 87.119 & 99.895 & 49.520 & 4.543 & 0.779 & 8.551 & 63.050 & 40.109 & 236.998 & 1.527 \\
2010 & 87.869 & 99.9 & 51.417 & 4.238 & 0.763 & 8.056 & 58.211 & 41.607 & 224.650 & 1.531 \\
2020 & 87.469 & 99.895 & 53.734 & 4.564 & 0.780 & 8.322 & 54.727 & 40.769 & 230.560 & 1.511 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Landscape level index for 2000, 2010, and 2020. Figure 4 shows that the landscape-level metrics SPLIT, CONTAG, and JII show significant correlations with NPP. SPLIT and JII represent the separation and dispersion of landscape patches, both of which are strongly negatively correlated with NPP. This indicates that the more dispersed the patches are in space, the lower the carbon sequestration capacity. On the other hand, CONTAG shows a negative correlation with NPP, where lower CONTAG values indicate lower landscape fragmentation and higher patch connectivity, which helps enhance carbon sequestration. However, as patch fragmentation increases and surpasses a certain threshold, even with higher vegetation cover, carbon storage may not be effectively promoted. In contrast, the largest patch index (LPI), which represents the proportion of the landscape covered by the largest patch, shows a positive correlation with NPP. The larger the LPI value, the larger the area of the largest patch, which favors increased carbon sequestration. The scatter plots show a wide range of LPI values, corresponding to a large variation in NPP values, indicating that LPI has a significant impact on carbon storage. Overall, higher landscape connectivity and lower fragmentation levels contribute to increased carbon sequestration, while excessive landscape fragmentation weakens this positive effect. Figure 5 shows a positive correlation between ED and NPP, indicating that the more complex the landscape pattern, the better the carbon sequestration effect. ED represents the length of the edge of a landscape per unit area, directly reflecting the complexity of the patch edge; AI characterizes the spatial clustering degree of a certain type of landscape patch and neighborhood relationships. Landscape patches with low AI values have a high level of fragmentation, while large-value landscape patches have a good degree of connectivity within them. There is a significant positive correlation with NPP.