## 5 Results and Discussion
### Analysis of Spatial Consistency  
The spatial consistency of \"woodland\" is just considered in this study. Firstly, the spatial distribution of woodland shows highly different in the three sub-regions. It occupies only a little in Wanbei; but accounts for nearly one fifth in Wanzhong and most of Wannan is covered by this type. The classification \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Region** & **IGBP (\%)** & **UMD (\%)** & **LAI/FPAR (\%)** & **NPP (\%)** & **PFT (\%)** \\ \hline Wanbei & 98.44 & 98.43 & \(-\)17.78 & \(-\)31.47 & 98.43 \\ Wanzhong & 99.61 & 99.58 & \(-\)28.02 & \(-\)42.55 & 99.80 \\ Wannan & 98.22 & 97.27 & 56.40 & 69.26 & 97.98 \\ Anhui Province & 99.35 & 99.27 & \(-\)27.89 & \(-\)38.72 & 99.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of \(R\) values of the five classification schemes of MCD12Q1. Figure 4: Comparison of percentage disagreement of the five schemes in (**a**) Wanbei, (**b**) Wanzhong, and (**c**) Wannan. results indicate the there is a significant difference for \"woodland\" in the five schemes. It shows that the \(O\) of LAI/FPAR is the worst of 58.17% (Table 7), indicating that the spatial consistency is weak for \"woodland\". The \(O\) of NPP reaches up to 86.70%, followed by 85.68% of PFT, which shows that NPP has the best spatial consistency of \"woodland\" among the five schemes. Figure 5 compares the spatial consistency of \"woodland\" between the five data layers of MCD12Q1 and GlobeLand30 at provincial and regional scales (Anhui Province and three sub-regions). It illustrates the change of land cover types in the same area from the GlobeLand30 data to five data layer of MCD12Q1, respectively. According to the pixel-by-pixel comparison of the results, the classification errors of \"woodland\" appear mainly in Wannan and the south of Wanzhong with most forest area. As shown in Figure 5, \"cropland\" is misclassified into \"woodland\" in many areas, which results in serious influence on the consistency of these schemes. In the reference [42], the same reason is investigated that the forest area is increasing of NPP, PFT and LAI/FPAR, while the forest areas show a decreasing trend of IGBP and UMD from the year of 2001 to 2009, in Gansu Province, China. Moreover, Figure 5 reveals that NPP eliminates the potential causes of the misclassification from \"woodland\" to \"cropland\" due to the lack of \"cropland\" in NPP, but \"woodland\" is categorized into \"grassland\" in some certain places. Consequently, another driving factor affecting the consistency of \"woodland\" are analyzed. The \"woodland\" of IGBP, UMD, LAI/FPAR and PFT are categorized into \"cropland\" in Wanbein and Wanzhong to a great degree. Considering both the factors, it reveals that the misclassification between \"woodland\" and \"cropland\" exhibits a big influence on the consistency of \"woodland\" in IGBP, UMD, LAI/FPAR and PFT. Moreover, other inconsistent areas of IGBP, UMD, NPP and PFT of \"woodland\" are mainly concentrated around the Yangtze River and Huaihe River Basin - two natural dividing lines of the three sub-regions of Anhui Province. The \"wetland\" of these four schemes is classified into \"woodland\" in the area. As shown in Table 7, the consistency of \"woodland\" of LAI/FPAR is the weakest in accordance with Figure 5. Significantly, in addition to the above driving factors, the misclassification between \"others\" and \"woodland\" is also the main cause leading to the worst consistency of LAI/FPAR. Since the above Section 4.2 has presented the harmonization of land cover classification by treating \"barren land\" as \"others\", the \"woodland\" type is distinctly misclassified into the \"barren land\" and it accounts for the main cause leading to the poor performance of LAI/FPAR. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification Schemes** & **IGBP** & **UMD** & **LAI/FPAR** & **NPP** & **PFT** \\ \hline Spatial Similarity \(O\) (\%) & 78.66 & 80.62 & 58.17 & 86.70 & 85.68 \\ \hline \hline \end{tabular}
\end{table}