## 1 Introduction  
Tree species information is a basic parameter in vegetation monitoring, change detection, forest inventory, tree growth condition analyzing, and carbon stock predicting, to mention only a few [1, 2, 3, 4, 5]. In sustainable forest management, spatial and structure information of individual trees offer crucial information for management decisions. Tree species classification originally relied on expensive and time-consuming field investigations to measure structure attributes such as tree height, leaf area index, branch angle etc. of individual trees, then identified the tree species by comparing these parameters to a standard one, where visual inspection by a botanist is usually necessary [6]. With the development of the remote sensing technique, large-scale classification of tree species became possible. Most studies conducted in the past two decades tried to enhance the performance of extraction of structure parameters of individual trees from both optical and Synthetic Aperture Radar (SAR) images, then input them into a classifier to identify each individual tree species [7, 8]. Some earlier research also tried to establish the statistical, physical, and geometric relationships between electromagnetic scattering characteristics of various tree species and parameters of interest [9], leading to the intensive study of forest parameters retrieval, such as biomass, leaf and basal area, net and gross primary production (NPP/GPP), etc. [10, 11, 12, 13, 14] from remote sensing data. The existing studies show that conventional optical remote sensing data are applicable for large-scale forest monitoring and classification, because tree components such as water and chlorophyll show strong absorption in the visible and infrared spectral bands [15, 16]. Many machine learning methods have been developed for pixel-level classification based on spectral differences among tree species, which are generally caused by their differences in foliar properties [17; 18; 19]. SAR data, especially in L-band or P-band, have showed superiority in small scale forest monitoring and species identification due to its penetrability of the tree canopy [20; 21; 22; 23], But the inherent speckle noises in SAR data pose huge challenges for extracting fine structural information from them, and it is difficult to use SAR data alone for accurate classification of tree species [24]. As a result, for large-scale tree species identification, it is difficult, if not impossible, to extract structural information on individual trees from SAR data. Airborne LiDAR is another kind of active remote sensing technique developed in the early 1960s using pulsed lasers to detect and measure terrain and object surfaces, providing range data in the form of three-dimensional point clouds [25; 26]. The application of LiDAR data in forest inventory began in the early 1980s [27], though it had already been employed to generate the Digital Elevation Model (DEM) [28], because it acquires high precision 3D coordinate values of the Earth's surface. Most research on tree species classification using airborne LiDAR data pays the most attention to the extraction of structural parameters from the data, for example, [PERSON] and [PERSON] [29] classified Norway spruce and Scotch Pine with an overall accuracy of 95%, which applied tree height and average forest plot height, such as Lorey's mean height, and predominant tree height extracted from laser scanning point clouds put to supervised classifiers. In this aspect, differences in the definitions of tree structure parameters can change the classification accuracy [30]. For instance, mean tree height may be taken as the average height of dominant and co-dominant trees [31], whereas others may consider the contribution from suppressed trees [32; 33; 34].