## 5 Results and Discussion
### Evaluation of Area Consistency  
Table 6 compares the \(R\) values of IGBP, UMD, LAI/FPAR, NPP and PFT in Wanbei, Wanzhong, Wannan and Anhui Province, respectively. Significantly, all the \(R\) values of IGBP, UMD and PFT are more than 97% in the three sub-regions. Their consistency shows pretty good on the whole. Considering the \(R\) values vary slightly from 98.22% to 99.61% in the three sub-regions and the whole Anhui Province, the compatibility and robustness of IGBP is best, indicating that the consistency of IGBP is best on a regional scale. The obtained result is the same with the reference [42], which shows that the area of land cover types of IGBP are more close to the land cover classification schemes based on the terrestrial ecosystem features and remote sensing (TEFRS).  
On the other hand, the \(R\) of LAI/FPAR and NPP has poor performance, which shows that the regional variation is obvious. For the LAI/FPAR, the \(R\) decreases from 56.40% of Wannan to \(-\)17.78% of Wanbei. The \(R\) of the NPP scheme varies from \(-\)31.47% of Wanbei to 69.26% of Wannan. To analyze the high regional variation of LAI/FPAR and NPP, \(PD\) is used to provide the correlation degree on each type in the five schemes. Combining the \(R\) and \(PD\), more detailed consistency evaluation of the five schemes can be presented.  
Figure 4 shows the comparison of area consistency of five types derived from the five schemes. As shown in blue and orange columns, the \(PD\) of IGBP and UMD are smaller than that of the other three schemes on each type in the three sub-regions, showing their consistency are better. The \"woodland\" and \"grassland\" (orange column) in the three sub-regions show particularly the best performance among the five schemes, which highlights that the consistency of UMD is best for \"woodland\" and \"grassland\". The study [58] shows the same result that UMD is best to reflect the temporal and spatial distribution of grassland.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Region** & **Land Cover Type** & **Area (km\({}^{2}\))** & **Yearbook Statistics** & **Fractional Error (\%)** \\ \hline \multirow{4}{*}{Anhui Province} & Woodland & 36711.25 & 38042.20 & 3.50 \\  & Cropland & 82294.50 & 90865.91 & 9.43 \\  & Wetland & 7346.50 & 10418.00 & 29.48 \\ \hline \multirow{4}{*}{Wanbei} & Woodland & 106.25 & 5628.60 & 98.11 \\  & Cropland & 25786.00 & 35316.03 & 26.98 \\  & Wetland & 326.00 & 1230.47 & 73.51 \\ \hline \multirow{4}{*}{Wanzhong} & Woodland & 15549.50 & 14104.50 & 10.24 \\  & Cropland & 44284.50 & 45209.74 & 2.05 \\  & Wetland & 5610.25 & 6271.95 & 10.55 \\ \hline \multirow{4}{*}{Wannan} & Woodland & 21056.75 & 18309.10 & 15.01 \\  & Cropland & 12239.50 & 10340.14 & 18.37 \\ \cline{1-1}  & Wetland & 1416.00 & 2915.58 & 51.43 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy validation for “woodland”, “cropland” and “wetland” of GlobeLand30.  
The grey column illustrates the _PD_ of LAI/FPAR and it shows that the classification results of \"woodland\" in the three sub-regions are worst among the five schemes, particularly in Wanbei with less forest resources. The result is also similar with the conclusion of the reference [58]. Moreover, the consistency of \"artificial surfaces\" in the three sub-regions is also worst among the five schemes.  
Due to the lack of \"cropland\" of NPP, the consistency of this type is obviously poor, as shown in green column. Similarly, the consistency of \"grassland\" is also poor. Conversely, the _PD_ of \"woodland\", \"wetland\" and \"artificial surfaces\" has a similar performance to UMD and PFT.  
The purple column shows that the consistency of \"grassland\" of PFT is worse than the other four land-cover types. The regional variation is obviously high due to the big difference of _PD_ in the three sub-regions, while the values of the PD of all the other land-cover types present to be low in the three sub-regions. Specifically, the _PD_ in Wannan is the best among the five schemes but it is the worst in Wanzhong.