## 3 Results  
### Accuracy of the PL-Based and the Other Five Forest Maps  
The spatial distribution of the PL-based and the other five forest maps are shown in Figure 6. The overall accuracy and the Kappa coefficient of the PL-based forest map are 91.27% and 0.77, respectively (Table 3). The user accuracy and producer accuracy are 91.07% and 76.41%, respectively, in forest area. In the non-forest areas, the user accuracy and producer accuracy are 91.33% and 97.07%, respectively. In addition, the forest map of FROM-GLC showed the accuracy second to that of the PL forest map in the Loess Plateau, with an overall accuracy of 87.96% and Kappa coefficient of 0.68, and the user accuracy and producer accuracy of forest areas are 81.26% and 71.52%, respectively. In non-forest areas, the user accuracy and producer accuracy are 90.02% and 93.40%, respectively. In non-forest areas, there is a large commission error. The JAXA forest map relies on the PALSAR data. The overall accuracy and Kappa coefficient are 83.57% and 0.5236, respectively. The user accuracy and producer accuracy in forest areas are 59.01% and 67.28%, while those in non-forest areas are 91.15% and 87.82%, respectively. JAXA forest map only approximates the producer accuracy of the PL forest map in the non-forest areas, and other precisions are obviously lower than the PL forest map. The OU-FDL forest \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Forest Maps** & **Forest Cover Types** & **Resolution** & **Algorithms** & **Data Sources** & **Major References** \\ \hline \multirow{3}{*}{FROM-GLC} & Tree cover \(\geq\) 15\%, & \multirow{3}{*}{30 m} & Automatic classification & \multirow{3}{*}{Landsat} & \multirow{3}{*}{[48]} \\  & Tree height \(\geq\) 3 m & & algorithms & & \\ GlobeLand30 & Tree cover \(\geq\) 10\% & 30 m & & POX(pixel-object- & Landsat, \\  & & & knowledge)-based method & HJ-1 & \\ GLCF-VCF & Tree cover \(\geq\) 10\%, & 30 m & Supervised classification & Landsat & [50] \\ \multirow{3}{*}{JAXA} & Tree cover \(\geq\) 10\%, & \multirow{3}{*}{25 m} & \multirow{3}{*}{Rule-based} & \multirow{3}{*}{PALSAR} & \multirow{3}{*}{[22]} \\  & Tree height \(\geq\) 5 m & & & & \\ \multirow{3}{*}{OU-FDL} & Tree cover \(\geq\) 10\%, & \multirow{3}{*}{30 m} & \multirow{3}{*}{Decision Tree} & \multirow{3}{*}{MODIS} & \multirow{3}{*}{[26]} \\  & Tree height \(\geq\) 5 m & & & & \\ PALSAR/Landsat & Tree cover \(\geq\) 10\%, & \multirow{3}{*}{30 m} & \multirow{3}{*}{Rule-based} & \multirow{3}{*}{Landsat} & \multirow{3}{*}{This study} \\  & Tree height \(\geq\) 5 m & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of forest products for the comparison in the Loess Plateau in 2010. Figure 6: Spatial distributions of the PL-based and the other five forest maps. map integrated PALSAR and MODIS data sources. The overall accuracy and Kappa coefficient are 84.40% and 0.5622, respectively. The user accuracy and producer accuracy in forest areas are 65.30% and 67.47%, while those in non-forest areas are 90.29% and 89.41%. Compared with JAXA, the accuracy in the forest areas has been improved. On the pixel scale, these forest maps have a good consistency in the areas with non-forest coverage and greater uncertainties in the areas with forest coverage (Figure 7). Throughout the entire state area, about 1.5% and 80.6% of the pixels were identified by these forest maps as consistent forest and non-forest.