Arboriculture & Urban Forestry 48(2): March 2022 acquire 3D information of the forest and can provide substantial benefits to urban forest management (Zhang and Qiu 2012; Alonzo et al. 2016; Iqbal et al. 2017; Estornell et al. 2018; Ciesielski and Sterenczak 2019; Kwong and Fung 2019). This research on roadside tree measurements in Kinmen, Taiwan, is separated into 2 portions: (1) the tree coordinates and DBH are directly measured by a point cloud, and (2) the tree heights are extracted from the normalized digital surface model (NDSM). The LiDAR data can be used to digitize the roadside tree database with related photos after the adjustment has been corrected. Then, the NDSM values, which are extracted from the point cloud data, can be used to calculate the tree height with forward results. After the measurement and correction of tree data, the trees along the roadside are numbered in sequential order from left to right, and the trees with reflections are recorded in the panoramic photos. The tree records still must be transferred into the vector records along with input attribute data to finish digitizing the road- side information. The new records will be utilized by a future management system. The system samples 15 main types of roadside trees to measure their DBHs and heights. These data are compared with the same data observed by in-vehicle LiDAR to analyze the measurement error. The mean absolute error (MAE) and standard deviation of absolute error are used in this research. The MAE is calculated by the mean of LiDAR and the absolute measured value, and the latter parameter is calculated using the standard deviation of LiDAR and the absolute measured value. The MAE of DBH of these trees is between 1.31 and 2.43 cm, the mean error is 1.82 cm, and the standard deviation of absolute error is 1.68 cm. These errors are all in the acceptable range. Therefore, we can conclude that LiDAR measurement is a feasible method to measure the DBH (Figure 3). The MAE of the height of these trees is between 0.65 and 2.74 m, the mean error is 1.10 m, and the standard deviation of absolute error is 1.42 m. These errors are all in the acceptable range. Therefore, we can conclude that in-vehicle LiDAR measurements can measure the DBH, height, and coordinate data of a tree. The efficiency of the use of in-vehicle LiDAR measurements is indicated in the example of the Kin- men street tree survey, in which vehicle-mounted radar was used to scan (back and forth once each) a total of 20,906 trees at a speed of 40 km/hour, with a total 53 road length of 70 km. Without considering other fac- tors, such as the control point placement, traffic sig- nal stops, and the distance between street trees, among others, the in-vehicle LiDAR measurement can scan 5,973 trees per hour, which is a very high field efficiency. However, there is no estimation of the subsequent data processing time in this study, as it only examined the efficiency of the field operation. Calders et al. (2020) pointed out that more ways need to be developed for the use of tripod-based ground light sources in urban forests, and the data and parameters collected through tripod-based ground light sources are used as training samples to estimate urban forest structure at this stage. However, new 3D measurement techniques, such as Structure from Motion (SfM), can be integrated to effectively obtain Figure 3. (A) LiDAR data of protected tree; (B) LiDAR data of roadside trees; (C) photograph of site sampled by LiDAR. ©2022 International Society of Arboriculture
March 2022
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