## 4 Discussion
### Effects of Different Remote Sensing Features on Classification Results  
Many studies have shown that the raw spectral features of high-resolution multispectral images can be used to classify tree species with high accuracy [44]. In this study, it was also observed from the selected optimal features at different altitudes (Figure 3a-c) that four original spectral bands were important for tree species classification at each altitude. However, the accuracy of tree species classification in the 2000 m altitude study area with fewer species was the highest, while for areas with more species, the classification accuracy was relatively lower. Using raw band features alone is insufficient to reflect the forest structure on the different altitude gradients, and the accuracy of tree species classification for different altitudes is also somewhat unstable.  
The six vegetation indices used in this study primarily expressed differences in the interleaf and canopy structure among the tree species at each altitude and were shown to be important for tree species classification in a previous study [10]. This is consistent with the results of the current study, where vegetation indices played an important role in identifying the tree species at the three different altitudes. However, relying solely on vegetation indices cannot be applied to classify all types of forests. For instance, in this study, for the study area at 2000 m above sea level, where the forest structure is relatively simple, the extracted vegetation index feature values were relatively similar when the tree species were similar, leading to confusion and subsequently affecting the overall accuracy of tree species classification. Therefore, it is necessary to extract other remote sensing features to supplement the classification [45].  
Studies have shown that the involvement of texture features in tree species classification using high-resolution aerial imagery determines whether the overall accuracy is improved or not [46], which is consistent with the results of this study. The optimal results at each altitude indicate that different texture feature metrics calculated in the NIR band are highly important for tree species identification in this study area and play a significant role in distinguishing the variability between species, thereby improving the accuracy of tree species classification. Therefore, it has been suggested that the vertical structure features of tree species should be incorporated to increase the classification accuracy of tree species [19; 47].  
LiDAR features can reflect differences in the vertical structure of forests. [PERSON] et al. [48] demonstrated that LiDAR data can be used to extract tree height information, which can be used to stratify forest stands into different layers and classify tree species in each layer, thus improving the overall tree species classification accuracy. However, in this study, the results of the optimal feature screening showed that the importance of the extracted LiDAR features for tree species classification at each altitude was low. This was mainly due to the low density of the LiDAR data points obtained in this study. Previous research has shown that the quality of the feature raster generated from LiDAR data has a direct impact on the accuracy of tree species classification, and point cloud density has a direct impact on the quality of the generated raster [49; 50]. Therefore, for more accurate tree species refinement classification, higher point density LiDAR data should be used.