# Hierarchical Fine Extraction Method of Street Tree Information from Mobile LiDAR Point Cloud Data
## 4 Discussion
### Comparative Advantages of Our Extraction Method  
1) Problems oriented towards the bending of the trunk. There are many irregularly shaped tree trunks in the study area of this paper, and the inability to obtain complete trunk backside points due to vehicle-mounted LiDAR scanning along the road causes some of the trunks to appear circular in their horizontal projection. Model-based extraction methods such as those applied by [PERSON] et al. [14; 15] and [PERSON] et al. [16] to extract tree trunks using a 3D cylindrical model are influenced by the density and geometry of the trunk points, and such methods will not be able to be applied to the extraction of curved tree trunks in the study area of this paper. A single willow tree in the study area is shown in Figure 16, and the trunk of the tree has an irregular shape. When the distance threshold of the bent trunk point is greater than the fitted radius, all trunk points from that point onwards cannot be extracted but the algorithm in this paper can still extract the trunk in its entirety. In contrast to the model fitting approach, although the irregular trunk points cannot be extracted by applying only a single feature extraction method (e.g., vertical linear features in this paper), the extracted feature points can be used to locate the trunk points, and the remaining trunk points can be extracted by applying a GDRG algorithm to use the feature trunk points as seed points. At the same time, the rule of Gaussian distribution prevents the wrong extraction of the crown point tree from the original data. In the critical region between the trunk point and the crown point, although there are some crown points in the neighborhood of the trunk point, these crown points are not within the confidence interval of the Gaussian distribution, thus avoiding the seed point growing in the direction of the crown point. 2) Problems oriented towards the low canopy point density. In the case of poplar tree in Figure 17, for example, the method of selecting seed points based on local elevation differences and density by [PERSON] et al. [46] was only able to extract some of the canopy points that were less distant from the apex of the trunk due to the large spacing between the majority of canopy points and trunk points; [PERSON] et al. [47] selected seed points based on the criterion that the trunk was near the horizontal center of the crown and smaller in diameter than the crown, but poplar trees was unable to extract the trunk points due to missing or irregularly distributed canopy points. [PERSON] et al. [47] selected seed points based on the criterion that the trunk is near the horizontal center of the crown and smaller in diameter than the crown, but poplar trees have a large offset in the center due to missing or irregularly distributed canopy points, and in this case, [PERSON] et al. 's method was unable to extract the trunk points. Figure 16: Results of tree trunk bending (the red points represent tree trunk points extracted by our method). In this paper, the RG algorithm with optimized seed points using the extracted trunk vertices as initial points can solve most of the problems of large canopy gaps. 3) Problems oriented towards the severe trunk point missing. When trunk points are severely missing, the salient features of trunk points cannot be applied, so the three types of methods summarized above for extracting trunk points are unable to extract the trunk and extract the canopy points. The method in this paper mainly solves the extraction problem of missing trunk points of poplar trees, see the extraction results in Figure 18 (the points in the red box are the extraction results of non-trunk trees). Part of the top trunk points are hidden in the canopy, and the trunk is extracted in the canopy extraction step based on the trunk top points by applying the GDRG algorithm with optimized seed points.