## 5 Results and Discussion
### Assessment of Landscape Diversity  
Figure 6 illustrates the landscape diversity indices of _MSIDI_ and _MSIEI_ of the five MCD12Q1 data layers and GlobeLand30 in Wanbei, Wanzhong and Wannan, respectively. The values of diversity \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Land cover type**} & \multicolumn{2}{c}{**IGBP**} & \multicolumn{2}{c}{**UMD**} & \multicolumn{2}{c}{**LAI/FPAR**} & \multicolumn{2}{c}{**NPP**} & \multicolumn{2}{c}{**PFT**} \\ \cline{3-11}  & **(\%)** & \(p_{A_{i}}\) & \(p_{u_{i}}\) & \(p_{A_{i}}\) & \(p_{u_{i}}\) & \(p_{A_{i}}\) & \(p_{u_{i}}\) & \(p_{A_{i}}\) & \(p_{u_{i}}\) & \(p_{u_{i}}\) \\ \hline \multirow{8}{*}{**MODEL**} & Woodland & 1.75 & 0.74 & 1.46 & 0.74 & 9.38 & 0.74 & 1.49 & 0.74 & 1.40 & 0.74 \\  & Grassland & 2.02 & 0.29 & 10.00 & 0.29 & 0.50 & 78.46 & 0.56 & 97.29 & 0.90 & 0.14 \\  & Cropland & 81.99 & 98.19 & 82.00 & 98.20 & 80.00 & 11.92 & 0 & 0 & 82.01 & 98.18 \\  & Wetland & 57.78 & 2.10 & 64.00 & 1.30 & 64.00 & 1.37 & 64 & 1.29 & 64.00 & 1.29 \\  & Artificial & & & & & & & & & & \\  & Surfaces & & & & & & & & & & \\  & & \(OA=80.82\%\) & \(OA=80.86\%\) & \(OA=10.23\%\) & \(OA=1.61\%\) & \(OA=80.80\%\) \\  & & & \(K=6.88\) & \(K=6.73\) & \(K=-0.30\) & \(K=0.66\) & \(K=6.91\) \\ \hline \multirow{8}{*}{**MODEL**} & Woodland & 74.50 & 66.40 & 71.20 & 69.38 & 78.27 & 56.06 & 70.17 & 75.89 & 70.03 & 74.78 \\  & Grassland & 5.02 & 4.47 & 5.22 & 4.95 & 2.49 & 52.28 & 2.82 & 65.49 & 5.07 & 2.15 \\  & Cropland & 77.26 & 91.1 & 78.15 & 91.02 & 71.60 & 11.98 & 0 & 0 & 78.09 & 90.84 \\  & Wetland & 71.65 & 42.25 & 88.60 & 37.10 & 88.60 & 37.88 & 88.60 & 36.25 & 88.04 & 36.53 \\  & Artificial & & & & & & & & & \\  & Surfaces & & & & & & & & & & \\  & & \(OA=73.76\%\) & \(OA=74.15\%\) & \(OA=23.97\%\) & \(OA=21.68\%\) & \(OA=74.89\%\) \\  & & & \(K=49.11\) & \(K=49.86\) & \(K=13.62\) & \(K=15.33\) & \(K=51.25\) \\ \hline \multirow{8}{*}{**MODEL**} & Woodland & 80.89 & 85.86 & 79.98 & 87.30 & 79.60 & 58.43 & 77.55 & 92.56 & 77.73 & 91.62 \\  & Grassland & 4.10 & 15.69 & 3.98 & 16.72 & 3.01 & 48.02 & 2.84 & 41.36 & 4.29 & 6.52 \\  & Cropland & 72.49 & 58.1 & 74.34 & 56.81 & 68.96 & 8.49 & 0 & 0 & 74.23 & 57.10 \\  & Wetland & 53.64 & 36.99 & 84.34 & 29.72 & 84.34 & 30.95 & 84.34 & 28.39 & 83.36 & 28.86 \\  & Artificial & & & & & & & & & \\  & Surfaces & & & & & & & & & & \\  & & \(OA=71.77\%\) & \(OA=71.99\%\) & \(OA=39.32\%\) & \(OA=56.23\%\) & \(OA=74.29\%\) \\  & & & \(K=48.33\) & \(K=47.92\) & \(K=16.36\) & \(K=25.72\) & \(K=49.74\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of the classification accuracy of five classification results. indices of GlobeLand30 are significantly larger in comparison with those of five MCD12Q1 data layers. Since the GlobeLand30 data present the finer spatial resolution, its increasing number of landscape patches and the continuous equalization of area proportions lead to the larger diversity indices. It indicates that the number of landscape patches of GlobeLand30 for the same area is higher than that of five MCD12Q1 data layers. Meanwhile, GlobeLand30 has a more balanced proportion of different patch types in the landscape. In comparison with other schemes, the LAI/FPAR shows the higher values and the difference between the results of the LAI/FPAR scheme and the GlobeLand30 data is the lowest in the three sub-regions, which reveals that the consistency of LAI/FPAR is the best on the number of landscape patches and the equalization of area proportions. More significantly, the values of diversity indices for the three sub-regions are ordered as follows: Wanbei \(<\) Wanzhong \(<\) Wannan. In Section 5.3, the same result can be found that the \(K\) values of Wanbei are the smallest in the three sub-regions (Table 8), which corresponds to the same rank of the values of diversity indices for the three sub-regions. Specifically, \"cropland\" is distinctly characterized by a large proportion in Wanbei, and the other types present a fragmentized distribution.