*3.2. Canopy Cover Estimation Using ITD-Based Method*

To guarantee the independence of the canopy cover estimates from different data sources and to minimize the influences of laser scanning data processing methods, individual tree delineations were manually conducted in the ULS and TLS point clouds separately. Automatic tree detection and modeling methods were not used in this study. Thus, the canopy cover evaluation results revealed the capacities of the applied laser scanning data while excluding the influence of the data processing approach, such as the individual tree detection. Then, each individual tree crown in a plot was accumulated and subtracted from the overlap area to calculate the total crown area. The ITD-based canopy cover was calculated as the percentage of the total crown area to the plot area. The ITD-based canopy cover estimation results from the ULS and TLS data are denoted as ULS\_ITD and TLS\_ITD, respectively, in the following sections.

#### *3.3. Comparison Scheme and Accuracy Assessment*

First, the four canopy cover estimates (ULS\_CHM, TLS\_CHM, ULS\_ITD, and TLS\_ITD) were compared with the reference data. Then, the agreement and disagreement in the canopy cover estimations from ULS and TLS were quantified in detail with respect to the CHM-based and ITD-based methods. Finally, the influence of the pixel size on canopy cover estimation in the CHM-based method from ULS and TLS was analyzed.

In this study, the accuracy of the estimated canopy cover was evaluated using the coefficient of determination (*R*2) and root mean squared error (*RMSE*), which were calculated using the following equations:

$$R^2 = 1 - \frac{\sum\_{i=1}^{n} (x\_i - y\_i)^2}{\sum\_{i=1}^{n} (x\_i - \overline{x})^2} \tag{2}$$

$$RMSE = \sqrt{\frac{\sum\_{i=1}^{n} (x\_i - y\_i)^2}{n}} \tag{3}$$

where *xi* and *yi* are the values from the *i*th reference and estimated canopy cover values, *x* is the mean of the reference canopy cover, and *n* is the number of plots.

#### **4. Results**

#### *4.1. Comparison of LiDAR Estimations and Reference*

A comparison between the canopy cover estimations from the four methods (i.e., ULS\_CHM, ULS\_ITD, TLS\_CHM, and TLS\_ITD) and the reference data are shown in Figure 4 (for all the plots) and Figure 5 (for the GG plots alone). As illustrated in Figure 4, there was an overall moderate to high agreement between the LiDAR-estimated canopy cover and the reference data for all the plots, with *R*<sup>2</sup> values of 0.541–0.996, and *RMSE* values of 0.591–6.297%. Among the four methods, the ULS\_CHM method showed the highest accuracy, with an *R*<sup>2</sup> of 0.996 and an *RMSE* of 0.591% for canopy cover estimation, while the ULS\_ITD method had the second highest accuracy, with an *R*<sup>2</sup> of 0.992 and an *RMSE* of 0.820%, followed by the TLS\_ITD method (*R*<sup>2</sup> = 0.846, *RMSE* = 3.642%) and the TLS\_CHM method (*R*<sup>2</sup> = 0.541, *RMSE* = 6.297%).

**Figure 4.** Comparison of canopy cover estimations derived from the reference data and LiDAR estimations for all the plots. Scatter plots with *R*2, *RMSE*, and regression equations between reference data (y) and LiDAR-based estimations (x) are indicated for the (**a**) ULS\_CHM, (**b**) ULS\_ITD, (**c**) TLS\_CHM, and (**d**) TLS\_ITD estimations.

**Figure 5.** Comparison of canopy cover estimations derived from the reference data and LiDAR estimations for the GG plots alone. Scatter plots with *R*2, *RMSE*, and regression equations between reference data (y) and LiDAR-based estimations (x) are shown for the (**a**) ULS\_CHM, (**b**) ULS\_ITD, (**c**) TLS\_CHM, and (**d**) TLS\_ITD estimations.

As illustrated in Figure 5, the results from the GG plots alone showed a similar tendency, with the ULS\_CHM method showing the highest accuracy (*R*<sup>2</sup> of 0.996, *RMSE* of 0.476%), followed by the ULS\_ITD method (*R*<sup>2</sup> = 0.992, *RMSE* = 0.685%) and the TLS\_ITD method (*R*<sup>2</sup> = 0.986, *RMSE* = 0.915%). The TLS\_CHM method had the lowest accuracy (*R*<sup>2</sup> = 0.737, *RMSE* = 3.959%). For different forest conditions, the GG plots had a better performance (*R*<sup>2</sup> = 0.737–0.996, *RMSE* = 0.476–3.959%) than all other plots (*R*<sup>2</sup> = 0.541–0.996, *RMSE* = 0.591–6.297%).

The overestimation and underestimation of the four methods against the reference data are summarized in Table 1, which shows the difference between the ULS/TLS estimated canopy cover and the reference data for all the plots, the GG plots only, and the QZ plots only. Overall, the ULS produced smaller deviations than TLS. The mean deviation for the ULS was 2.1% for all the plots and 7.46% for TLS. The ULS\_CHM had the smallest deviations, followed by the ULS\_ITD, TLS\_ITD, and TLS\_CHM. In addition, the ULS canopy cover estimations were more robust across different stand conditions and different methods than the TLS estimations. The ULS produced similar deviations between the GG and QZ plots (2.08% vs. 2.13%), whereas significant differences were observed between the corresponding deviations of TLS (4.93% and 13.03%).


**Table 1.** Canopy cover differences (%) calculated as ULS/TLS estimations minus reference data for all the plots, the GG plots only, and QZ plots only, where |mean| represents the mean of the absolute values of the difference.

#### *4.2. The Agreement and Disagreement in the Estimations from ULS and TLS*

A more detailed comparison was conducted directly between the ULS and TLS estimations with respect to different forest conditions and estimation methods. The *R*2, *RMSE* and differences between the ULS and TLS estimations were summarized. The disagreement between the ULS and TLS estimations increased with increasing complexity of the forest stand with respect to these metrics.

A moderate agreement was observed between the ULS and TLS estimations when the CHM-based method was used. As illustrated in Figure 6a, the *R*<sup>2</sup> and *RMSE* between the ULS\_CHM and TLS\_CHM estimations were 0.554 and 6.288% for all the plots. In the case of the ITD method, the *R*<sup>2</sup> and *RMSE* between the ULS\_ITD and TLS\_ITD estimations were *R*<sup>2</sup> 0.859 and 3.600% for all the plots (Figure 6b). For different forest conditions, the GG plots had a higher agreement between the ULS and TLS estimations than all the plots. Figure 6c,d illustrates the comparison on the GG plots, where the ULS and TLS produced an *R*<sup>2</sup> of 0.745 and an *RMSE* of 3.913% for the CHM-based method, and *R*<sup>2</sup> of 0.985 and an *RMSE* of 0.919% for the ITD method.

Table 2 summarizes the difference between the ULS and TLS estimations for all the plots, the GG plots only, and the QZ plots only. The number of all plots, GG plots, and QZ plots were 16, 11 and 5 respectively. The ULS estimations were larger overall than the TLS estimations for the CHM-based method, with an averaged difference of 11.15% for all the plots. The overestimations in the GG plots were lower than those in the QZ plots. The average overestimation was 8.19% for the GG plots and 17.65% for the QZ plots (Table 2). For the ITD-based method, the TLS estimations tended to be larger in the GG plots and lower in the QZ plots than the ULS estimations (Figure 6b). Only two plots had lower TLS\_ITD estimations than the ULS\_ITD estimations (Figure 6b). In the QZ plots, the average TLS\_ITD estimations were 5.97% lower than ULS\_ITD estimations.

**Table 2.** Canopy cover differences (%) calculated as ULS estimations minus TLS estimations for all the plots, GG plots only, and QZ plots only, where |mean| represents the mean of the absolute values of the difference.


**Figure 6.** Comparisons of canopy cover estimations derived from ULS and TLS: (**a**,**b**) for all the plots, and (**c**,**d**) for only the GG plots.

#### *4.3. Estimation Results of CHM-Based Canopy Cover with Different Pixel Size*

Changes in *R*<sup>2</sup> and *RMSE* between the CHM-based estimations using different pixel sizes and the reference data are presented in Figure 7. For the ULS\_CHM method, the *R*<sup>2</sup> between the ULS\_CHM estimations and the reference data decreased with an increase in the pixel size (Figure 7a). The *R*<sup>2</sup> decreased slowly from 0.996 to 0.959 with an increase in the pixel size range from 0.07 m (raw pixel size) to 1.2 m. The decrease rate of *R*<sup>2</sup> was significantly larger after the pixel size exceeded 1.2 m. Conversely, the *RMSE* values increased as the pixel size increased.

For the TLS\_CHM method, *R*<sup>2</sup> initially increased and then decreased with increasing pixel size (Figure 7b). The *R*<sup>2</sup> between the TLS\_CHM estimations and reference data increased from 0.541 to 0.871 when the pixel size range increased from 0.01 m (raw pixel size) to 1.0 m. After the pixel size surpassed 1.0 m, *R*<sup>2</sup> rapidly decreased. The *RMSE* values first decreased and then increased as the pixel size increased.

**Figure 7.** Changes in *R*<sup>2</sup> and *RMSE* between two CHM-based canopy cover estimations using different pixel sizes and reference data. (**a**) *R*<sup>2</sup> and *RMSE* between ULS\_CHM estimations and reference data and (**b**) *R*<sup>2</sup> and *RMSE* between TLS\_CHM estimations and reference data.

#### **5. Discussion**

#### *5.1. Differences between LiDAR-Derived Canopy Cover and Reference Data*

In this study, we compared four LiDAR-estimated canopy covers (ULS\_CHM, ULS\_ITD, TLS\_CHM, and TLS\_ITD) with the reference data. The ULS\_CHM produced the highest accuracy, followed by ULS\_ITD, TLS\_ITD, and TLS\_CHM. The results demonstrated that the canopy covers obtained using the ULS\_CHM method were slightly higher than the reference data, and the canopy covers obtained from the other three methods were lower than the reference data. The higher canopy cover estimations obtained from the ULS\_CHM method could be partly attributed to the following aspects: (i) some small between-crown gaps with similar size of within-crown gaps were also filled as canopy pixels, and (ii) the crown boundaries that adjoined the open ground in the horizontal plane tended to expand after the interpolation procedure of the pit-free method.

Since the original CHM method would underestimate the canopy cover owing to the existence of within-crown gaps, we utilized the pit-free method proposed by [38] to fill the within-crown gaps to mitigate the underestimation. Our results showed that the pit-free method could effectively remove the within-crown gaps (c1 and c2 in Figure 8a,b). However, the small between-crown gaps with similar within-crown gap size were also interpolated as canopy pixels (d1 and d2 in Figure 8a,b), and the crown boundaries adjacent to the open ground expanded after the pit-free method was applied (e1 and e2 in Figure 8a,b). It was difficult to distinguish between the within-crown gaps and the between-crown gaps with similar sizes and fill the within-crown gaps while ensuring that the between-crown gaps remained unchanged in the CHM smoothing process. The crown boundaries adjacent to the open ground expanded because there were height jumps between the crown boundaries and the adjoining ground. The pit-free method interpolated the pixel values of the adjoining ground and increased their height to reduce the height difference. Thus, several ground pixels were classified as canopy, and the canopy cover was slightly magnified.

In the TLS\_CHM method, the canopy cover estimation was lower than the reference, which could be attributed to the incomplete tree crown structure generated from the TLS point clouds. Although the multi-scan mode was used in the TLS data collection, the crowns further away remained occluded when the laser beam was interrupted by stems or branches, and the upper crown of the higher trees was incomplete due to the limited field of view in the vertical direction (−40◦–60◦). These situations produced within-crown gaps, and these gaps could not be completely removed by the pit-free method, resulting in the underestimation of the TLS\_CHM method.

**Figure 8.** The pit-free method used in the ULS\_CHM canopy cover estimation, where (**a**,**b**) represent the original CHM and the pit-free CHM, respectively, while (c1) (c2), (d1) (d2), and (e1) (e2) represent the within-crown gaps, between-crown gaps, and crown boundaries adjacent to the open ground in the original CHM and the pit-free CHM.

Both the ULS\_ITD and TLS\_ITD canopy cover estimations were smaller than the reference data, because the reference data were produced from the fused ULS and TLS point clouds. The TLS\_ITD canopy cover estimations were lower when the crown boundaries were incomplete in the TLS point clouds. The slightly lower ULS\_ITD estimations could be partly attributed to the lower point density of the ULS point clouds when compared with the fused point clouds.

#### *5.2. Difference between ULS-Derived and TLS-Derived Canopy Cover Estimations*

In the CHM method, our results demonstrated that the ULS estimations were larger than the TLS estimations for all the plots. The differences between the ULS\_CHM and TLS\_CHM estimations increased with the increased forest complexity. In the ITD method, the ULS estimations were smaller than the TLS estimations in the simple plots with little understory vegetation and low stem density (GG plots), and the ULS estimations were larger than the TLS estimations in the relatively complex plots with abundant understory growth and higher stem density (QZ plots).

Overall, the ULS tree crowns were more comprehensive than the TLS tree crowns, even when the multi-scan mode was used. Similar results were reported by [28], where ALS produced slight overestimation of canopy cover and TLS underestimated the canopy cover. TLS was vulnerable to the radial occlusion due to the side view perspective and produced gaps within the crowns. These gaps were large and difficult to fill by the CHM smooth method (pit-free), resulting in lower TLS estimations in the CHM-based method. This underestimation grew with increased forest complexity due to the increased occlusion. The QZ plots had denser understory vegetation and higher stem density than the GG plots, resulting in more occlusions in TLS than the GG plots. Therefore, the difference between the ULS\_CHM and TLS\_CHM estimations for the GG plots (Figure 9) was smaller than that of the QZ plots (Figure 10).

**Figure 9.** Difference between ULS\_CHM and TLS\_CHM in GG plot: (**a**,**c**) normalized ULS and TLS point clouds, and (**b**,**d**) corresponding pit-free CHMs.

**Figure 10.** Difference between ULS\_CHM and TLS\_CHM in QZ plot: (**a**,**c**) normalized ULS and TLS point clouds and (**b**,**d**) corresponding pit-free CHMs.

In the ITD method, the TLS estimations were larger than the ULS estimations in simple plots, which can be partly attributed to the denser point density of the TLS point clouds and the fact that the incomplete tree crowns could be recovered as long as crown boundaries existed in the ITD method. The crown boundaries in the simple plots with little understory vegetation and low stem density were more likely to be collected than those in the relatively complex plots with abundant understory growth and higher stem density. Moreover, compared with the ULS crowns, the TLS crowns represented more details and larger areas (as illustrated in Figure 11e). The TLS crown boundaries were more compact, and their between-crown gaps were smaller than those of the ULS (Figure 11b,d). Therefore, the TLS\_ITD produced slightly higher estimations than ULS in the simple plots. However, there were two GG plots that produced lower TLS\_ITD estimations because their tree crown boundaries were incomplete and the ITD method could not recover the correct crowns areas.

**Figure 11.** Difference between ULS\_ITD and TLS\_ITD in GG plot: (**a**,**c**) normalized ULS and TLS point clouds, (**b**,**d**) corresponding individual tree crown boundaries, and (**e**) the local detail for the overlapped ULS and TLS crown points.

For the QZ plots, the TLS estimations were lower than the ULS estimations in the ITD method. As illustrated in Figure 12b,d, the QZ plots had denser understory vegetation and higher stem density than the GG plots, which led to more severe occlusion and incomplete tree crowns in the TLS point clouds. The crown boundaries were incomplete and the ITD method cannot recover the correct crowns areas, resulting in the underestimation.

**Figure 12.** Difference between ULS\_ITD and TLS\_ITD in QZ plot: (**a**,**c**) the normalized ULS and TLS point clouds and (**b**,**d**) corresponding individual tree crown boundaries.
