# Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar
## 3 Results
### Comparison of Forest Area  
We discussed the differences between these datasets by analyzing the distribution and proportion of forest areas in 14 states of Myanmar in eight datasets. Figure 4 shows the distribution of forest area for all forest datasets. Overall, the eight datasets have a variable distribution of forest areas in Myanmar. The CCI-LC has the least forest area and FROM-GLC10 has the most forest area. Among the three datasets in 2015, the differences in forest area between CCI-LC and GLC-FCS are small, while the forest area of FROM-GLC exceeds the other two datasets by about 100,000 km\({}^{2}\). Since they are from the same year, the effect on the change in forest area due to the expansion or reduction of the actual forest area is small. The reason for such a large difference is mainly related to the objective conditions, such as different remote sensing data sources, classification systems, and production methods. The two datasets of 2017 are essentially the same product FROM-GLC. The difference in forest areas is about 40,000 km \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Year** & **Dataset** & **Land Type** & **UA (\%)** & **PA (\%)** & **Commission (\%)** & **Omission (\%)** & **OA (\%)** & **Kappa** \\ \hline \multirow{2}{*}{2010} & \multirow{2}{*}{trecover} & forest & 81.65 & 91.33 & 18.35 & 8.67 & \multirow{2}{*}{82.23} & \multirow{2}{*}{0.62} \\ \cline{3-4} \cline{6-8}  & & non forest & 84.28 & 68.29 & 15.72 & 31.71 & & \\ \hline \multirow{4}{*}{2015} & \multirow{2}{*}{CCI-LC} & forest & 84.76 & 73.29 & 15.24 & 26.71 & \multirow{4}{*}{75.59} & \multirow{4}{*}{0.51} \\ \cline{3-4} \cline{6-8}  & & non forest & 65.68 & 79.18 & 34.32 & 20.82 & & \\ \cline{2-8} \cline{2-8} \cline{3-4} \cline{6-8}  & \multirow{2}{*}{FROM-GLC2015} & forest & 80.72 & 90.51 & 19.28 & 9.49 & \multirow{2}{*}{80.94} & \multirow{2}{*}{0.59} \\ \cline{3-4} \cline{6-8}  & & non forest & 81.97 & 66.03 & 18.03 & 33.87 & & \\ \cline{2-8} \cline{3-4} \cline{6-8}  & \multirow{2}{*}{GLC-FCS2015} & forest & 82.03 & 68.19 & 17.97 & 31.81 & \multirow{2}{*}{71.41} & \multirow{2}{*}{0.43} \\ \cline{3-4} \cline{6-8}  & & non forest & 60.92 & 76.44 & 39.08 & 23.56 & & \\ \hline \multirow{4}{*}{2017} & \multirow{2}{*}{FROM-GLC2017} & forest & 87.44 & 87.00 & 12.56 & 13.00 & \multirow{2}{*}{83.51} & \multirow{2}{*}{0.64} \\ \cline{3-4} \cline{6-8}  & & non forest & 77.01 & 77.25 & 22.99 & 22.75 & & \\ \cline{2-8} \cline{3-4} \cline{6-8}  & \multirow{2}{*}{FROM-GLC10} & forest & 84.55 & 93.00 & 15.45 & 7.00 & \multirow{2}{*}{84.48} & \multirow{2}{*}{0.65} \\ \cline{3-4} \cline{6-8}  & & non forest & 84.93 & 69.16 & 15.07 & 30.84 & & \\ \hline \multirow{4}{*}{2020} & \multirow{2}{*}{GlobeLand30} & forest & 87.52 & 87.38 & 12.48 & 12.62 & \multirow{4}{*}{83.51} & \multirow{2}{*}{0.64} \\ \cline{3-4} \cline{6-8}  & & non forest & 76.47 & 76.23 & 23.53 & 23.77 & & \\ \cline{2-8} \cline{3-4} \cline{6-8}  & \multirow{2}{*}{GLC-FCS2020} & forest & 84.83 & 74.26 & 15.17 & 25.74 & & \\ \cline{1-1} \cline{3-4} \cline{6-8}  & & non forest & 60.96 & 74.69 & 39.04 & 25.31 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix for eight forest land datasets. as seen in Figure 4, and is mainly due to different spatial resolutions. The FROM-GLC10 not only has higher mapping accuracy but also has clearer spatial details, more forests can be identified. The difference in forest area between the two datasets in 2020 is about 60,000 km\({}^{2}\). We further computed the forest proportion in 14 states of Myanmar in eight datasets. As shown in Figure 5, Kayin, Tanintharyi, Mon, and Rakhine have a larger variation in the proportion of different forest datasets. For example, the difference between the maximum and minimum percentages of forest area amounts to 40.34% in Rakhine. There is a difference of 37.11% between the three datasets of the same year in 2015, and 28.03% in the two datasets in 2020. In contrast, the difference between the two same products in 2017 is significantly reduced to 1.79%.