## 1 Introduction  
Forests are a major component of terrestrial ecosystems and are closely connected to human living environments, playing an irreplaceable role in water conservation and biodiversity protection [1, 2]. Accurately identifying forest plants and obtaining plant information can establish the foundation for the research and utilization of forest ecosystems [3]. The traditional method of identifying tree species relies primarily on human surveys, which are difficult, inefficient, and imprecise due to the complex conditions in the forest understory [4]. Therefore, the rapid and accurate acquisition of forest plant information to unleash the multiple functions of forests is a prerequisite for achieving the multi-objective sustainable management of forest ecosystems. Remote sensing technology has long been considered a good solution to the problem of tree species classification. Remote sensing data acquired from different platforms and sensors are extensively used in this field of research [5]. The emergence of high-resolution satellite and airborne remote sensing systems has provided image data with rich spatial, color, and texture information, leading to the improved fine classification of tree species [6]. Different tree species possess unique spectral information due to their distinct structures and morphologies [7]. Even under the same environmental conditions, trees in different growth stages or health states can differ in spectral information [8]. [PERSON] et al. [9]. classified eight species of tropical forest trees by quantifying the spectral differences of their canopies. [PERSON] et al. [10] used only UAV multi-spectral features extracted from spectral image data to classify eight dominant tree species in the southern part of the Ronggu Turnip Nature Reserve, Yunnan Province, China, using a random forest classifier and proved that the spectral features of remote sensing images are useful for tree species classification. [PERSON] et al. [11] selected the canopy spectra of five tree species from the USGS spectral library and extracted 11 vegetation indices for spectral difference analysis, showing that this method can identify tree species. However, hyperspectral data contain excess information, leading to the \"Hughes phenomenon\", which can affect classification results. Therefore, it is necessary to process hyperspectral data for dimensionality reduction [12]. Although aerial remote sensing images provide rich spectral and texture information, they are insufficient for quantifying the vertical structural attributes of tree species. Airborne light detection and ranging (LiDAR) technology is characterized by fast data acquisition efficiency and high accuracy and has unparalleled advantages for measuring the vertical structure and physical and chemical characteristics of the forest canopy [13]. This technology can also be used as a new data source for tree species classification [14]. In recent years, researchers have explored many applications for monitoring forests using airborne LiDAR data. [PERSON] et al. [15] combined airborne LiDAR and backpack LiDAR data to extract single wood parameters and fit volumetric models for tropical plantation forests. The results showed that the combination of the two data sources had a strong ability to estimate single wood parameters and fit models. [PERSON] et al. [16] inferred single-wood tree heights with the help of aerial imagery and airborne LiDAR data. The average estimation accuracy was as high as 74.89% compared to the actual measured tree height. Despite the advantages of LiDAR data in quantifying the structural parameters of forest species and single-wood segmentation, a study showed that the results are susceptible to the influence of the point cloud data density [17]. Furthermore, due to its working principle, LiDAR technology is unable to provide vegetation-level spectral information, which presents a significant disadvantage in the application of tree species classification.