## 4 Conclusions  
In this study, we successfully developed an algorithm to estimate the leaf angle with LiDAR-derived point cloud data. The study focused on an efficient solution for leaf segmentation and estimation of the leaf angle. A leaf segmentation method based on \(k\)-means coupled with an octree structure was proposed. In addition, the issue of possible segmentation errors, which has been omitted in the majority of previous studies but which potentially affects the estimation of leaf angle distribution, was discussed. As was shown by the presented results, using our approaches to estimate the leaf angle shows high potential. A strong agreement in the relation and fewer potential errors between the LiDAR-derived and manual measurements were discovered. High leaf curvature within the leaf classification region was considered to affect the accuracy of leaf angle estimation. Fortunately, even if such a problem presents itself, our approach can flexibly change the extent of the size of the classification region based on an automatic adjustment of the \(k\)-value to achieve more accurate results. It seems likely that depending on the range of the leaf classification region, the level of effect of leaf curvature on the leaf angle estimation is different, but there was not enough evidence to prove this here. Thus, in future work, we would like to collect data from sampling trees with different curvatures to discuss them for achieving higher leaf angle estimation. Furthermore, due to the location of the target tree, it is difficult to use data from more than one location of LiDAR surrounding the target tree to prevent effect of leaf occlusion on reconstruction of 3D model for LAD estimation. To achieve more accurate results of LAD estimation, future improvements will also focus on field experimental design for data collection.  
Looking at our study, automatic classification was achieved by specifying a different initial parameter (\(L\)) to find the \(k\)-value. We considered that this approach is also possible for other issues related to unsupervised classification. Besides, the LiDAR-derived leaf angle method may also be useful in retrieving another leaf structural parameter, such as the leaf area index (LAI) [35; 36]. Aside from the remote sensing field, this method may also prove to be effective in the measurement of the leaf angle distribution in agriculture-relevant fields. A method for accurate leaf inclination estimation in small plants has already been reported [18; 36]. The estimation could lead to leaf area estimation and plant structural information monitoring. In combination with our proposed method, we expect that more  
Figure 10: Leaf angle distribution in the mochi tree: (**a**) LAD graph for the tree’s top (3–1.5 m) and bottom (1.5–0 m); (**b**) LAD image of the whole tree.  
accurate leaf inclination angle estimation can be accomplished for leaf edges, even in small plants. For these reasons, the presented method has the potential to contribute to various fields, including remote sensing and agriculture.  
Conceptualization and methodology, K.K., F.H. and K.I.; data collection, K.K. and K.I.; analysis, validation, and visualization, K.K.; Writing--Original draft preparation, review and editing, K.K.; supervision and funding acquisition, F.H.  
This research was supported by JSPS KAKENHI, Grant Number JP 17H03898.  
Field data collection was supported by [PERSON].  
The authors declare no conflicts of interest.