2.2.2. Terrestrial Laser Scanning Data

TLS data acquisition was completed on 28 and 29 May 2020, using the RIEGL VZ-400 laser scanner system. The RIEGL VZ-400 laser scanner system has a 360◦ field of view in the horizontal direction and 100◦ in the vertical direction, a maximum range of 600 m, and a measurement rate of 120,000 points per second. At the Guigang site, five scans were performed with an average scanning spacing of 35 m to complete a full coverage of the plots, since the plots in the Guigang site were characterized by clear visibility, little understory vegetation, and sparse tree density. Five scans were performed at the Qingzhou site with an average scanning spacing of 15 m to accommodate the high tree density and abundant understory vegetation in this region. Additional retro-reflective targets were set up during the data collection campaign for the co-registration of scans. The average point density of registered TLS data was 29,471 points per m2, with a point spacing of approximately 6 mm.

#### *2.3. Establishment of Reference Data*

To evaluate the performance of the canopy cover estimation from TLS and ULS, the reference data for the 16 plots were generated by manual measurements from the co-registered TLS and ULS point clouds. The co-registered TLS and ULS point clouds combined the observations from under and above canopy view perspectives with high spatial resolution and were assumed to provide a more reliable canopy cover measurement compared to the use of either TLS or ULS point clouds alone. The ULS point clouds of the plots were manually co-registered to the multi-scan TLS point clouds by selecting common points from the crown boundary and stems. Manual fine-tuning was implemented when a discrepancy between the point clouds remained observable from the top view and two side views. Then, the digital terrain models (DTMs) were created at a 0.2 m resolution from the fused TLS and ULS point clouds using the cloth simulation filter (CSF) method [37]. The fused point cloud height was normalized by subtracting the ground surface height from the DTMs. Finally, the canopy boundaries in each plot were manually delineated from the fused and normalized point clouds, and the canopy cover for each plot was calculated as the ratio of the area of tree crown to the area of the plot. The canopy cover of all 16 plots ranged from 63.37% to 96.28%. Details of the canopy cover distribution are shown in Figure 3.

**Figure 3.** Canopy cover reference for the 16 plots analyzed in the study.

#### **3. Methods**

#### *3.1. Canopy Cover Estimation Using CHM-Based Method*

LiDAR-derived CHMs have been widely used to estimate canopy cover [2,21]. To guarantee independence of the canopy cover estimates from different data sources, CHM creations and canopy cover estimations were carried out separately in ULS and TLS point clouds. First, for each sample plot, the ground points and off-ground points were classified from the ULS and TLS point clouds separately, and their DTMs were generated using the CSF method [37] with a 0.2 m × 0.2 m resolution. The TLS and ULS data were normalized with respect to their corresponding DTMs. Then, the normalized point clouds were gridded, and the highest point in each grid was selected to construct the CHMs. To simultaneously describe the tree crown in as much detail as possible and reduce data redundancy, a raw pixel size that was slightly larger than the mean point spacing of the point cloud was used for CHM construction, as reported by [38,39]. In this study, 7 cm and 1 cm were used for the ULS and TLS point clouds, respectively, for CHM construction.

The CHM-based canopy cover was calculated as the percentage of pixels with a CHM value larger than a specified height threshold (canopy pixels):

$$\text{ConopyCover} = \sum \text{CHM}\_{\text{canopy}} / \sum \text{CHM}\_{\text{total}} \tag{1}$$

where *CHMcanopy* represents the number of canopy pixels (above a specific height threshold) in CHM, and *CHMtotal* represents the total number of CHM pixels.

Considering the different understory vegetation arrangements in the plots of the two sites (as illustrated in Figure 2), different height thresholds were used to separate the crowns from the background for canopy cover estimation. In the GG plots, since these plots represented clear visibility with sparse understory vegetation, a distance of 2 m was used to separate the crown pixels. In the QZ plots, a distance of 5 m was used to extract crown pixels because of the relatively dense and high shrubs. The within-crown gaps would lead to the underestimation of canopy cover because their CHM values were relatively small (smaller than the height threshold) and were likely to be classified as non-canopy pixels. This situation often occurs in the crown where laser pulses penetrate the gap and reach the ground surface, resulting in a small height value in data collection. Therefore, we utilized the pit-free CHM method proposed by [38] to fill the within-crown gaps of the CHM before canopy cover estimation. This method works by simulating cloth sticking to the CHM surface and filling the within-crown gaps using the hardness of the simulated cloth. The pit-free CHM-based method was capable of filling within-crown gaps while keeping the original CHM pixel values unchanged. The CHM-based canopy cover estimation results from the ULS and TLS data are denoted as ULS\_CHM and TLS\_CHM, respectively, in the following sections.

We also explored the sensitivity of canopy cover estimation to different CHM pixel sizes for the ULS and TLS point clouds. The original CHMs of ULS and TLS were created under the raw pixel size, which was assumed to describe the crown structure in the most detail. Then, the pixel sizes were increased, from 0.07 to 4.8 m for the ULS and from 0.01 to 2.5 m for the TLS for CHM construction. The canopy cover estimations were then calculated using these CHMs. The canopy cover accuracy was evaluated by comparison with the reference data.
