## 3 Results
### Needleleaf and Broadleaf Forest Identification Results  
RF, SVM, U-Net, and Pix2 Pix were used to compare with the model proposed in this paper (F-Pix2 Pix). The segmentation accuracy of needleleaf and broadleaf forests is shown in Table 4. In all experiments, the accuracy of using only the original spectral information was the lowest. However, the OA was still higher than 65%, comparable to that of existing product data. Nonetheless, a significant class imbalance persisted, resulting in an IoU of less than 20% for broadleaf forest, rendering it unrecognizable. RF, SVM, and ML had comparable performance effects with the deep learning models of U-Net \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Model (\%)**} & \multirow{2}{*}{**MIoU (\%)**} & \multirow{2}{*}{**AoA (\%)**} & \multicolumn{2}{c}{**Precision (\%)**} & \multicolumn{2}{c}{**F1 Score (\%)**} & \multicolumn{2}{c}{**IoU (\%)**} \\ \cline{5-10}  & & & & & **Non-Forest** & **Forest** & **Non-Forest** & **Forest** & **Non-Forest** & **Forest** \\ \hline \multirow{5}{*}{GF-1} & RF & 61.45 & 83.13 & 48.75 & 87.38 & 64.91 & 89.36 & 59.28 & 80.77 & 42.13 \\  & SVM & 61.41 & 82.29 & 49.37 & 84.82 & 71.45 & 88.60 & 60.43 & 79.53 & 43.29 \\  & ML & 58.54 & 79.05 & 46.03 & 79.02 & 79.22 & 85.95 & 58.87 & 75.36 & 41.71 \\  & U-Net & 76.24 & 88.11 & 72.71 & 87.51 & 89.56 & 91.28 & 81.32 & 83.95 & 68.52 \\  & Pix2 Pix & 80.22 & 89.48 & 78.00 & 88.24 & 91.59 & 91.34 & 86.62 & 84.05 & 76.39 \\ \cline{2-10}  & F-U-Net & 80.39 & 89.78 & 78.17 & 88.60 & 91.98 & 91.86 & 86.26 & 84.95 & 75.84 \\  & Ours & **82.93** & **91.25** & **81.26** & **90.28** & **93.05** & **93.06** & **88.17** & **87.02** & **78.85** \\ \hline GF-2 & Ours & 84.28 & 92.05 & 82.85 & 91.95 & 92.23 & 93.74 & 89.10 & 88.22 & 80.34 \\ \hline Landsat OLI & Ours & 81.32 & 90.13 & 79.32 & 89.52 & 91.15 & 91.88 & 87.42 & 84.98 & 77.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy evaluation information of each model. Notes: the data are GF-1, and the imaging time is the summer of 2020. RF = random forest; ML = maximum likelihood; SVM = support vector machine. Bold is the best for GF-1, and underline is the second. Figure 6: Comparison of the segmentation results for forest and non-forest areas. and Pix2 Pix, all of which were more affected by the class imbalance, and even the IoU of broadleaf forest was still significantly lower than that of the three machine learning models, with U-Net performing slightly better than Pix2 Pix. Regarding the segmentation of images after PCA feature extraction, U-Net and Pix2 Pix were significantly better than the remaining three models (RF, SVM, and ML), which showed the advantage of the transfer-learning-based domain adaptation method of multi-source image for semantic segmentation. Although the advantages appeared, they were still not significant, the IoU of broadleaf forest was still significantly lower than others, and the accuracy of Pix2 Pix for broadleaf forest identification was higher than U-Net. Overall, the identification of needleleaf forest was better than broadleaf forest, and the local features of the results are shown in Figure 7.  
The accuracy values of needleleaf and broadleaf forest were effectively improved, but the accuracy of Other was lost. However, we utilized vegetation index and high-accuracy forest/non-forest segmentation results to filter the segmentation in the application, resulting in enhanced utility of the results. The F-Pix2 Pix results with the highest accuracy were used to calculate the area of needleleaf and broadleaf forests. The area of needleleaf forests in the study area was 2612.92 km\({}^{2}\), and the area of broadleaf forests was 228.90 km\({}^{2}\). The needleleaf forests in the forest management inventory data were 2376.35 km\({}^{2}\), and the broadleaf forests were 256.44 km\({}^{2}\), with relative errors of 7.73% and 10.16%, and the mean relative error was 8.94%.