## 1 Introduction
Full waveform LiDAR data, which can be acquired by installing a full waveform digitizer to the traditional discrete LiDAR system, provide a higher point density and additional information about the vertical characteristics of a tree which has been proved to apply to tree species classification [35; 36; 37]. Some attempts were made to fully use the structure information of the tree canopy for species classification, which needs to decompose the waveform data to obtain the high-density point cloud data and to retrieve the more vertical structure information of individual tree crowns, e. g., [PERSON], [PERSON] et al. [34; 38]. However, full waveform decomposition is complex and time-consuming, and different decomposition algorithms may achieve different parameters, which hinder their application to large scale tree species classification. Accuracy can be improved by using the LiDAR intensity values through a multiple band LiDAR system [39], both for individual tree segmentation and tree species classification [40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51], two main aspects in terms of applying LiDAR data for automatic forest inventory. For example, [PERSON] et al. in 2009 [47] and 2011 [52] employed the mean intensity values of individual tree crowns to identify the leaf-on and leaf-off tree species; also, [PERSON] et al. [38] applied supervised and unsupervised classification to classify fir and spruce by combining intensity values with other attributes from full waveform LiDAR data. Other strategies to improve both segmentation and classification accuracy include point clouds and spectral image fusion, and the application of recently developed machine learning algorithms such as deep neural networks [42; 43]. Though promising experimental results were achieved, the former largely depended on the registration accuracy of point clouds and image data, while the latter required a large quantity of training samples. Based on the above, the classification and recognition of tree species based on remote sensing technology has been highly developed, which enables determination of the key driving factors affecting forest biomass from complex predictors to obtain the quantitative estimation of above ground biomass [53; 54]. Also, some tree species results from LiDAR points have been successfully applied to cultivated or invasive tree species above ground biomass estimation [55; 56; 57; 58]. However, in summary, tree species classification based on remotely sensed data still shows the following challenges:(1) It is difficult to obtain tree structure information by solely using optical images or SAR data, hence it is not easy to achieve high accuracy tree species classification at the crown level. (2) More precise tree structure information can be retrieved from full waveform data than from the point cloud, but the identification results rely heavily on the precision of waveform decomposition. Moreover, to acquire full waveform data, more budget and more storage resources are required. (3) The LiDAR point cloud shows promising results in terms of tree species identification by the parameters describing the structural information of tree crowns. However, the parameters depend on the quality of the point cloud in general, and the point cloud density in particular. A better strategy for accurate tree species identification or classification requires the integration of structural and spectral information from the optical images and point cloud, respectively. Bearing the above-mentioned challenges in mind, the paper attempts to match the segmented tree crown with specific geometric shapes, that is, based on the extraction of the outer contour of a tree crown, a specific shape or the combination of several shapes are used to represent it to avoid the impact of point cloud density on the tree species classification. After the crown segmentation, it is possible to select a limited number of basic shapes, namely triangles, rectangles, or arcs as the basic geometric elements to fit the tree crown shape.