## 4 Discussion  
### Comparison of Segmentation Methods  
#### 4.1.1 Feature Extraction  
With PCA for multi-feature dimensionality reduction, this process may lose the most useful information for this task [33], and the PCA transform does not target better retention of data information when the data detail is completely unknown. Decentralization and standardization are needed in data pre-processing, especially for data contaminated by noise, where the standard deviation of the noise amplifies the data more significantly, while data not contaminated by noise amplify less in the process of standardization, resulting in unsatisfactory segmentation results after feature extraction. It is simple and feasible to directly select the feature combinations that have obvious differences between needleleaf and broadleaf forest areas for segmentation.  
In semantic image segmentation tasks, deep learning models have higher accuracy than other models [17; 34; 35]. However, for simple tasks, the advantages of deep learning have not been manifested, such as for the identification of forests and non-forests. Although the segmentation accuracy of the deep learning model is still slightly higher than that of  
Figure 8: Spatial distribution of forest types from 2005 to 2020.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **States** & **Total Pixels** & **Area (km\({}^{2}\))** & **Ratio (\%)** \\ \hline \multirow{4}{*}{Forest/Non-forest} & Forest\(\rightarrow\)Non-forest & 4096 & 468.94 & 7.97 \\  & Non-forest\(\rightarrow\)Forest & 5714 & 654.19 & 11.12 \\  & Non-forest unchanged & 20,403 & 2335.91 & 39.72 \\  & Forest unchanged & 21,149 & 2421.32 & 41.18 \\ \hline \multirow{4}{*}{Needleleaf/Broadleaf} & Broadleaf\(\rightarrow\)Other & 431 & 49.34 & 0.84 \\  & Broadleaf\(\rightarrow\)Needleleaf & 577 & 66.06 & 1.12 \\  & Broadleaf unchanged & 533 & 61.02 & 1.04 \\  & Needleleaf\(\rightarrow\)Other & 3279 & 375.41 & 6.37 \\  & Needleleaf\(\rightarrow\)Broadleaf & 439 & 50.26 & 0.85 \\  & Needleleaf unchanged & 16,665 & 1907.95 & 32.40 \\  & Other\(\rightarrow\)Broadleaf & 1247 & 142.77 & 2.42 \\  & Other\(\rightarrow\)Needleleaf & 3567 & 408.38 & 6.93 \\  & Other unchanged & 24,705 & 2828.44 & 48.02 \\ \hline \hline \end{tabular}
\end{table}
Table 5: State transition of forest types from 2005 to 2020. Note: the calculation method for forest state change is the difference between the identification results in 2005 and 2020.  
other models, neither the system overhead nor the running process is advantageous. For complex tasks such as needleaf and broadleaf forest identification, the advantages of deep learning models can be demonstrated, but the optimization of their performance still needs to be explored in depth.  
Forest types are highly correlated with topographic features [36; 37], and single-temporal images combined with topographic features can effectively be identified [38; 39]. However, the terrain of the study area is complex, with a large range in elevation (695\(\sim\)4017 m). The experiments show that adding topographic features does not effectively distinguish between needleaf and broadleaf forests, and this method is not effective in this study area.