*5.3. Effect of Pixel Size on the CHM-Based Canopy Cover Estimation Accuracy*

The canopy cover estimation increased as the pixel size increased for both the ULS and TLS. This aligned with the results of [38]. When a larger pixel size was used, each within-crown gap was more likely to be assigned to a mixed pixel with crown points, and the gaps were neglected because only the highest point was selected. In addition, the crown boundaries expanded as the pixel size increased, which contributed to an increase in canopy cover values.

For the ULS point clouds, the raw pixel size of the CHM produced slightly overestimated estimations. With increasing pixel size, the overestimation increased when compared with the reference data. Therefore, *R*<sup>2</sup> decreased as the pixel size increased. The *R*<sup>2</sup> decreased slowly and remained relatively constant (0.959–0.996) when the pixel size increased from 0.07 m to 1.2 m, and then decreased significantly again when the pixel size exceeded 1.2 m. The increasing pixel size caused more gaps to be filled as crowns, and the tree crown edges were gradually expanded, resulting in an increase in canopy cover estimation. Reasonable and similar canopy cover estimations could be achieved at pixel sizes ranging from 0.07 m to 1.2 m, which could explain over 95% of the variations in the reference data.

For the TLS point clouds, the raw pixel size of the CHM was underestimated owing to the incomplete tree crown structure. The differences between the CHM estimations and reference data were gradually narrowed at first and then gradually increased with increasing pixel size owing to the increase in canopy cover values. The best agreement was achieved at a pixel size of 1.0 m with an *R*<sup>2</sup> of 0.871 and an *RMSE* of 3.333%. Reasonable and similar canopy cover estimations could be achieved at a pixel size range of 0.07 m to 1.5 m, which could explain over 80% of the variations in the reference data.

#### **6. Conclusions**

Forest canopy cover plays a fundamental role in forest assessment and management. The Sample plot inventories are currently experiencing changes driven by the rapid development of UAV. This study provided a comprehensive cross-comparison of plot canopy cover from the recent rapidly developed ULS and current widely used TLS point clouds over 16 plots in *Pinus massoniana* forests with different stand conditions in Guangxi, China. Both the CHM- and ITD-based methods were used to estimate the canopy cover for both the ULS and TLS point clouds. Our results illustrated that, compared with the reference data, the ULS\_CHM method was the most accurate, with an *R*<sup>2</sup> of 0.996 and *RMSE* of 0.591%, followed by the ULS\_ITD method (*R*<sup>2</sup> = 0.992, *RMSE* = 0.820%), TLS\_ITD method (*R*<sup>2</sup> = 0.846, *RMSE* = 3.642%), and TLS\_CHM method (*R*<sup>2</sup> = 0.541, *RMSE* = 6.297%). When the ULS estimations were directly compared against the TLS estimations, most ULS estimations were larger than the TLS estimations, with an average difference of 6.91%, and the disagreement increased as the forest complexity increased. The ULS estimations were lower than the TLS estimations; this occurred when the crown boundaries were complete in the ITD method in the simple plots due to the more detailed crowns in the intermediate and suppressed layer than the ULS. In the CHM-based method, the reasonable CHM pixel sizes for the canopy cover estimations were 0.07–1.2 m for ULS and 0.07–1.5 m for TLS. In these ranges, the estimations were marginally influenced by the pixel size. Further work should investigate the estimation performance of canopy cover over large areas from different sources and extend the forest types.

**Author Contributions:** Conceptualization, W.D. and Z.D.; methodology, W.D. and S.C.; software, W.D. and S.C.; validation, Q.G., R.L., R.C. and Q.L.; formal analysis, W.D., S.C. and C.C.; writing, W.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China (Grant 42101456).

**Data Availability Statement:** The data that support the findings of this study are available on request from the author, (R.C.). The data are not publicly available because they contain information that could compromise the privacy of research participants.

**Acknowledgments:** The authors would like to acknowledge Guangxi Zhuang Autonomous Region Institute of Natural Resources Remote Sensing for providing the datasets for this study.

**Conflicts of Interest:** The authors declare no conflict of interest.
