## 2 Materials and Methods
### Data Collection/Preprocessing  
The scanner was mounted on a 1.5-m-high tripod and placed 3 m in front of the target (Figure 1b). Before scanning, 10 sampling leaves from both of the species were marked using colored tape. The leaf angle was measured with a protractor for subsequent validation purposes [23; 24; 25]. In order to prevent unreliable data from being taken from the measurement of the leaf angle, the measurements of leaf angle in each leaf sample were performed cautiously in a windless state and were repeated three times to obtain an average value. Each scanning angular resolution was 0.009, and the scanner spent approximately 15 min completing each scan. After scanning, PCDs were collected, and the target leaf PCD parts were manually extracted using CloudCompare software. Except for the leaf PCDs, the nonphotosynthetic parts, such as wood trunks and branches in the canopy, were pre-deleted by grouping the images based on established classification methods for estimating the leaf angle without effects from untargeted materials [26]. Research studies use either geometric- or intensity-based classification methods to group leaf and wood PCDs, but the methods are slightly complicated in terms of procedures [27; 28; 29]. In contrast, considering that the recently developed laser scanner had its own digital camera, color information could also be used as a basis for classification. In fact, the color components of wood and leaves are intuitively different; therefore, it is not difficult to distinguish between them and classify them according to color difference.  
The digital number (DN) in the RGB channel (for red, blue, green three colors) shows the absolute magnitude of the color component in each channel. However, if only the DN is considered, the classification between wood and leaf PCDs is not always successful. We could not guarantee that the DN of leaves in the green channel would always be higher than that of wood even though the leaves looked greener, because the wood material could maintain a higher \"brightness\", resulting in a  
Figure 1: (a) Location of study area (red hot spot; source: ©2019 Imagery, ©2019 NASA, TerraMetrics). (b) Set-up of terrestrial LiDAR.  
higher wood DN in the green channel. However, if compared under the same brightness conditions, the DN of the wood PCDs in the green channel is relatively lower than the leaf. Therefore, the DN of the PCDs was rescaled via normalization to allow for a comparison under the same brightness conditions. A simple method of doing this is to obtain the value in the target channel and divide it by the sum of the DNs in the R, G, and B channels [20]. However, in this study, another simple way was proposed to emphasize the comparison in the green channel by converting the RGB color space into an L*a*b* color space (is the color space defined by International Commission on Illumination), which contained three representative values: L* for brightness from black (0) to white (100), a* for green (-) to red (+), and b* for blue (-) to yellow (+). Generally, the leaf is greener, which results in mostly negative a* values in the L*a*b* color space, while the a* value of wood is positive. Thus, a threshold a* value was defined to classify the wood and leaf PCDs by averaging the a* values of randomly selected PCDs. Then, the wood PCDs were removed according to the classification results. The accuracy of removal classification was calculated as given in Reference [27]:  
\[Accuracy_{r}=\ \frac{\text{Tw}+\text{Tl}}{\text{Tw}+\text{Tl}+\text{Fw}+ \text{Fl}}. \tag{1}\]  
Here, \(T_{w}\) is the number of points correctly classified as wood, \(F_{w}\) is the number of points incorrectly classified as wood, \(T_{l}\) is the number of points correctly classified as a leaf, and \(T_{w}\) is the number of points incorrectly classified as a leaf.