## 3 Results
### Spatial and Temporal Characteristics  
The spatial distribution of forest types from 2005 to 2020 is shown in Figure 8, where (a) is forest/non-forest and (b) is needleaf/broadleaf forest. The forest type changes information is shown in Table 5. The forest cover ratio in 2020 is 52.30%, which is 12.4% higher than that in 2005. During the evolution of forest types from 2005 to 2020, 11.12% of non-forest was converted to forest, while 7.97% changed from forest to non-forest. The area with unchanged forest and non-forest was 4757.23 km\({}^{2}\), accounting for 80.9%, of which 2421.32 km\({}^{2}\) (41.18%) was unchanged by forest. Analysis of spatial distribution characteristics indicated that the areas where non-forests converted to forests were scattered throughout the study area. Conversely, the areas where forests transformed into non-forests were concentrated and primarily located in the northern, central, and southwestern parts of Huize County. The change in the type of needleleaf and broadleaf forest contains nine statuses. In the past 15 years, the area with no change in type is 4797.41 km\({}^{2}\) (81.46%). The proportion of needleleaf forests was 32.40%, which was 31.15 times higher than that of broadleaf forests. Other to needleleaf and broadleaf forest is 551.15 km\({}^{2}\) (9.35%), of which needleleaf forest is 6.93%, which is 2.86 times more than that of broadleaf forest. During the evolution, 424.75 km\({}^{2}\) of needleleaf and broadleaf forests were converted to Other, accounting for 7.21%, and the needleleaf forests were 7.61 times more than the broadleaf forests. The area of needleleaf and broadleaf forest interconversion is 116.32 km\({}^{2}\), accounting for 1.97%, and they are relatively close. Among all the statuses with alteration, the largest area of conversion between needleleaf forest and Other was 783.79 km\({}^{2}\), with a total of 13.30%. Among the spatial distribution characteristics, the conversion of Other to needleleaf forest was mainly distributed in the north, central, and south, while the conversion of needleleaf forest to Other was mainly distributed in the west and central, and the rest of the statuses were relatively scattered. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**MIoU (\%)**} & \multirow{2}{*}{**OA (\%)**} & \multirow{2}{*}{**Kappa (\%)**} & \multicolumn{3}{c}{**Precision (\%)**} & \multicolumn{3}{c}{**IoU (\%)**} \\ \cline{5-10}  & & & & & **Other** & **Needleleaf** & **Broadleaf** & **Other** & **Needleleaf** & **Broadleaf** \\ \hline \multirow{10}{*}{GF-1} & RF & 42.57 & 77.59 & 36.92 & 86.13 & 44.10 & 25.65 & 79.52 & 31.75 & 16.43 \\  & SVM & 42.12 & 75.13 & 36.21 & 82.15 & 47.74 & 31.72 & 77.15 & 32.68 & 16.51 \\  & ML & 39.82 & 68.06 & 31.69 & 73.22 & 46.04 & 45.68 & 70.82 & 33.07 & 15.57 \\  & U-Net & 41.44 & 71.65 & 33.45 & 79.23 & 65.55 & 18.06 & 61.97 & 51.77 & 10.58 \\  & Pix2 Pix & 40.88 & 70.95 & 34.21 & 81.22 & 61.39 & 21.85 & 62.39 & 49.76 & 10.48 \\ \cline{2-10}  & PCA-RF & 44.56 & 79.09 & 40.16 & **87.38** & 46.82 & 27.61 & **80.77** & 35.97 & 16.94 \\  & PCA-SVM & 45.74 & 78.22 & 41.69 & 84.82 & 52.90 & 35.40 & 79.53 & 39.85 & 17.85 \\  & PCA-ML & 43.50 & 73.25 & 36.81 & 79.02 & 48.70 & 47.78 & 75.36 & 38.06 & 17.07 \\  & PCA-U-Net & 63.02 & 78.57 & 60.43 & 78.65 & 88.37 & 37.19 & 68.07 & 78.24 & 22.29 \\  & PCA-Pix2 Pix & 69.13 & 80.46 & 66.42 & 79.34 & 88.52 & 59.53 & 68.97 & **78.49** & 41.61 \\ \cline{2-10}  & F-U-Net & 69.13 & 83.65 & 73.98 & 78.37 & **92.37** & 67.04 & 69.85 & 76.63 & 60.92 \\  & Ours & **72.01** & **84.85** & **75.87** & 78.35 & 88.22 & **82.99** & 71.73 & 77.36 & **66.93** \\ \hline GF-2 & Ours & 77.43 & 87.22 & 80.34 & 87.47 & 89.83 & 83.77 & 80.16 & 80.70 & 71.44 \\ \hline Landsat OLI & Ours & 72.18 & 84.18 & 75.27 & 84.40 & 88.52 & 77.29 & 75.80 & 78.49 & 62.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy evaluation information of each model.