## 5 Conclusions  
In this paper, a rotating profile at a certain angle (RPAA) is used, and the initial segmentation of the crowns is complete; then the accuracy of tree crown segmentation has reached 95%, which provides excellent preparation for tree species classification. Assuming that the tree crown can be seen as the triangle, rectangle, arc, and other basic shapes, or as combinations of basic geometry, the parallel-line shape fitting method is performed to classify tree species; this type of species classification has an average classification accuracy of 90.9%, and the optimal classification accuracy was 95.9%. It is superior to the parametric crown classification method, which also uses the geometric information of the tree crown, in average accuracy of 87.2% and the highest accuracy of 93.8%; however, the latter often requires full waveform data. Table 7 shows the accuracy of different tree species classification. Method 1 is an SVM/RF classifier based on fusion data [61]. It works well in general macro-classes, but it is not very suitable for single tree species classification. Method 2 is CNN based on UVA images [49]. It is only applicable to palm classification and does not have universality. Method 3 is the linear discriminant function with a cross validation based on LiDAR intensity data [47]. Its classification rate is higher using leaf-off data (84.3%) than using leaf-on data (73.1%) and is at its highest (90.6%) when combining these two. Method 4 is unsupervised classification based on full waveform LiDAR data [38]. Its results are also different in the data set of leaf-off and leaf-on. Method 5 is DNN based on UAV LiDAR data [62]. It has satisfactory results in the classification of two tree species. Method 6 is the algorithm used in this paper and the method in this paper largely depends on the segmentation results of the tree crown. For trees whose crown geometry is similar to each other or grow too close, the tree species classification usually has poor results, because the original crown shapes are damaged by interwoven crowns. As a result, the method proposed in this paper can obtain better classification results in sparse forest areas. From Table 7, it is clear that the shape fitting method is suitable for tree species classification in sparse areas. In further work, more shapes should be tested, and the spectral information of the image can be combined, or the phenological information of multi-temporal data can be used to improve the accuracy of tree species classification. But, in the tropical rain forest where tree species grow staggered, tree species extraction and classification are still difficult. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**NO.** & **Accuracy** & **Method** & **Data** & **Species** & **Study Area** \\ \hline
1 & 76.5\% & SVM/RF & Fusion data & \
\begin{tabular}{c} 7 species and a \\ “non-forest” class \\ \end{tabular} &
\begin{tabular}{c} a mountain area in the \\ Southern Alps \\ \end{tabular} \\ \hline
2 & 98.6\% & CNN & UVA images & 3 palm species &
\begin{tabular}{c} 135 ha within an old-growth \\ Amazon forest \\ \end{tabular} \\ \hline
3 & 90.6\% & \begin{tabular}{c} linear discriminant \\ function with a \\ cross validation \\ \end{tabular} & LiDAR intensity & \
\begin{tabular}{c} 8 broadleaved and \\ 7 iferous \\ data \\ \end{tabular} &
\begin{tabular}{c} the Washington Park \\ Arboreum, Seattle, \\ Arbseis \\ \end{tabular} \\ \hline
4 & \begin{tabular}{c} 96\%(leaf-off) \\ 85\%(leaf-on) \\ \end{tabular} & \begin{tabular}{c} Unsupervised \\ classification \\ \end{tabular} & \begin{tabular}{c} Full waveform \\ LiDAR data \\ \end{tabular} & \
\begin{tabular}{c} Coniferous, \\ deciduous \\ \end{tabular} &
\begin{tabular}{c} in the Bavarian Forest \\ National Park \\ \end{tabular} \\ \hline
5 & 86.7\% & DNN & UAV LiDAR data & Birch and larch &
\begin{tabular}{c} Saihanba National Forest \\ Park \\ \end{tabular} \\ \hline
6 & 90.9\% & Shape fitting & LiDAR data & 6 species & the Hupao National Park \\ \hline \hline \end{tabular}
\end{table}