## 4 Discussion
### Tree Species Classification  
The test areas in this paper are in the northeast of China where there are mainly coniferous forests. Unlike the tree species in the southern part of China, the canopy is relatively separable and rarely grows staggered. Compared with the trees in the south of China, the growth of trees in the northeast region is relatively discrete, and the results of tree crown segmentation can truly reflect the profile geometry of trees. It is inevitable that many trees grow together and snuggle up to each other, causing some difficulties in the classification of trees. Section 3.2 indicated that, using the geometric information of the tree crown, the shape fitting method is slightly better than the LiDAR Metrics method proposed by [PERSON] and [PERSON] [34; 38]. The shape fitting method can more effectively eliminate the random error caused by the semi random dispersion characteristics of point clouds, making the tree species fit more specifically, which leads to better classification results. For a single tree growing naturally, each tree species has unique geometric characteristics, which have the following advantages for tree species classification:  
(1) Leaves have little impact on the tree species classification result, which is an important advantage of using geometric morphology over using spectral information;  
(2) Despite being affected by natural disasters, most trees can maintain their geometric shape well, and the general characteristics of the geometric shape of each tree species are relatively prominent and easy to distinguish. (3) In Figure 10, only a few typical tree geometries are listed. There are more than these tree geometries in the world. With the development of LiDAR technology, the density of point clouds is getting higher, and more details on trees can be obtained. More in-depth methods to use geometric forms need to be further studied and applied. (4) Each basic geometric feature can be expressed parametrically, taking triangular shape, for example, which has side length and included angle. Therefore, in addition to using the geometric composition for classification, for trees with similar shapes, the value range of different parameters can be used for further classification. However, the performance of the parallel-line shape fitting method mainly depends on the segmentation results, and Tables 5 and 6 indicate that the pines, birches, and cedars are easily confused or indistinguishable. There is also a sizable proportion of misclassification in the \"other \"class. The reasons are summarized as follows:  
(1) From the perspective of classified objects, trees with similar geometric shapes are easier to be misclassified, such as pines, birches and cedars in the test site. (2) The result of classification depends on the result of tree crown segmentation. The result of tree crown segmentation is affected by many factors, e.g., lightning or rocks can cause changes in the geometric shape of trees, and that is the main reason many tree species are wrongly classified into the \"other \" class. (3) If many trees are growing close together, the segmentation results as well as the classification of tree species will be affected. In this survey area, if pines or birches grow together, they are easily identified as cedars, because the segmentation algorithm easily classifies the trees' points next to each other equally, which makes the triangular shape become a rectangular section structure. (4) For tree species with single basic geometric structure, such as triangular, rectangular or arc-shaped tree species, the classification accuracy can reach 90%, such as pines, shrubs, etc. From above, it is clear that in Northeast China, the growth of trees is relatively sparse, the types of tree species are relatively simple, which are mainly coniferous forests and some deciduous forests. Therefore, the method proposed in this paper can obtain better classification results and save manual participation greatly in the forestry survey.