## 1 Introduction
With the development of remote sensing technology, fusing multi-source remote sensing data for tree species classification has become an important trend in the current study of vegetation classification [18]. High-resolution remote sensing image data and LiDAR data have their own data characteristics and advantages in tree species classification, respectively [19; 20]. Many studies have demonstrated that combining the characteristics of the two data sources for tree species classification has resulted in greater accuracy than using a single data source [21; 22]. In general, the two datasets combine information on the vertical and horizontal characteristics of tree species, and through this combination, the differences between tree species can be more easily identified, thus improving the accuracy of tree species classification [23]. Additionally, most aerial integrated remote sensing systems have both LiDAR and multispectral camera sensors, which can acquire point cloud and image data of the same area in real time, providing great convenience for later utilization. In existing studies, some scholars have achieved better classification results by combining machine learning classifiers after feature screening using both data sources [24]. [PERSON] et al. [25] used the above method to achieve the classification of five dominant tree species in Changshu National Forest Park, Jiangsu Province, with an accuracy of 84%. They showed a higher accuracy of 91.3% in classifying forest types using this method. Similarly, [PERSON] et al. [26] achieved the classification of five tree species in a subtropical forest based on this method, with a single wood segmentation accuracy of 82.9%, and the classification also provided relatively high accuracy. However, this method does not fully utilize the features of both data sources, and for denser forest types, it is difficult to perform accurate single-wood segmentation of vegetation using LiDAR point cloud data [27]. Therefore, adequate feature fusion and screening are necessary to improve the classification accuracy of tree species. Species diversity varies at different altitudes due to the environmental conditions and changes in the vegetation response [28]. In a study by [PERSON] et al., it was shown that the species richness and diversity of forests peaked at medium altitudes and exhibited a decreasing trend with an increasing altitude. The impact of altitude on classification accuracy is expressed through differences in the tree species among altitudes. Therefore, it is important to study the use of multi-source remote sensing data to classify forest vegetation at different altitudes and obtain vegetation information. To summarize the aforementioned study, it is evident that combining high-resolution remote sensing images and LiDAR data for tree species classification can provide higher accuracy in identifying tree species than using single data features alone. However, most existing studies have focused on single species or forests with simple stand structures. In multi-species forests with complex stand structures, the fine classification of tree species is challenging, and the classification accuracy is often low. Furthermore, classifying tree species in complex forest environments at different altitudes poses an even greater challenge. Therefore, the purpose of this study was to explore the responsiveness of different forest community tree species to multi-source remote sensing data features in different altitude regions and to analyze the effectiveness and differences of different machine learning classifiers combined with optimal features for tree species classification in three altitude regions. This study aimed to address the problem of the insufficient ability of a single data source in classification applications and to clarify the impact of different species diversity caused by altitude on tree species classification. Optimal classification features were selected for each altitude, and species classification was performed using the optimal machine learning classifier.