**1. Introduction**

Forest canopy cover, which is defined as the proportion of the forest floor covered by the vertical projection of tree crowns [1,2], is directly related to the forest floor microclimate and light conditions [3–5] and is commonly used for biophysical and natural resource management applications. The spatially accurate mapping of canopy cover plays a critical role in forest stand structure classification [6], biomass production [7], wildfire behavior simulation [8], and wildlife habitat assessment [9,10].

Traditionally, canopy cover has been obtained from field measurements using sighting tubes [11], line intersect sampling [12], canopy photography [13], and the portable station field-map [14], which are laborious and time consuming. In addition, the field measurements obtained via these methods may be inaccurate because the crown boundaries can

**Citation:** Dai, W.; Guan, Q.; Cai, S.; Liu, R.; Chen, R.; Liu, Q.; Chen, C.; Dong, Z. A Comparison of the Performances of Unmanned-Aerial-Vehicle (UAV) and Terrestrial Laser Scanning for Forest Plot Canopy Cover Estimation in *Pinus massoniana* Forests. *Remote Sens.* **2022**, *14*, 1188. https://doi.org/10.3390/rs14051188

Academic Editors: Changhui Jiang, Yuwei Chen, Qian Meng, Panlong Wu, Bing Xu, Lianwu Guan, Wang Gao and Zeyu Li

Received: 25 January 2022 Accepted: 25 February 2022 Published: 28 February 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

be difficult to distinguish in practice, and some subjectivity remains in the field measurement [2]. Compared with field measurements, remote sensing techniques can provide spatially continuous observations with a higher efficiency and at a lower cost. Light detection and ranging (LiDAR) is a promising tool for quantifying forest structural parameters because of its ability to assess 3D information with high precision [15–17]. LiDAR has the potential to replace field measurements or even be used to assess the quality of field measurements [5,18,19].

The potential of airborne laser scanning (ALS) from manned aircraft for estimating canopy cover has been investigated since commercial systems have become available. The simplest way to estimate canopy cover from ALS is to calculate the proportion of canopy hits above a specified height threshold [2]. However, this method is slightly biased because the ALS pulses are characterized by oblique observations and are not precisely vertical. Moreover, the sides of crowns are also observed at a certain scan angle, and this bias increases with the scan angle, becoming significant at approximately 40◦ (20◦ from the zenith) [13]. To eliminate the effect of oblique pulses on canopy cover estimations, several studies have utilized the rasterized canopy height model (CHM)-based method to estimate the canopy cover from ALS point clouds [2,20–22]. In these studies, canopy cover was calculated as the ratio of the number of canopy pixels to the total number of pixels. Allocating the canopy echoes to a grid based on XY coordinates was assumed to reduce this effect.

Several studies have also used mathematical models to estimate canopy cover from ALS. Mathematical models were constructed by investigating the correlations between airborne laser metrics and field-measured canopy cover [19,23,24]. For example, Holmgren et al. [23] utilized proportions of laser returns at certain height intervals derived from ALS data as explanatory variables in simple linear regression models for crown coverage estimation in southern Sweden. They reported a root-mean-square error (*RMSE*) of 4.9% for the tree crown coverage estimation. Melin et al. [24] compared with different remote sensing materials for predicting boreal forest canopy cover and observed a high correlation between the field-measured canopy cover and the selected LiDAR predictor.

The potential of terrestrial laser scanning (TLS) for mapping canopy gaps and structures has also been investigated [25–27]. However, compared with ALS canopy cover estimation studies, these studies are relatively fewer. The estimation of vertical canopy cover using TLS has not been studied as intensively. One Study [28] created a raster map of the canopy from TLS, and canopy cover was estimated as the proportion of canopy pixels with *RMSE* of 8.0–17.9% and bias of 6.8–13.1%.

In recent years, improvements in the convenience and miniaturization of unmanned aerial vehicles (UAVs) have made it a powerful platform for forestry mapping. Combined with LiDAR, UAV laser scanning (ULS) provides higher data acquisition efficiency and flexibility at a lower cost than ALS [29,30] and provides detailed data comparable to that of TLS [31–34]. By providing a distinct combination of high spatial and temporal resolution, ULS narrows the gap between ALS and TLS systems and provides a new type of high-quality point cloud for forest investigations [33].

Previous studies have examined ULS as an alternative technology to ALS, which digitizes forests in a similar manner with higher altitude [30]. Previous ULS studies have predominantly focused on replicating existing forest attributes from ALS point clouds, such as forest height [35], tree crown diameter [29], stem volume [36], and aboveground biomass [30]. More recently, ULS has gained interest for its potential as an alternative technology to TLS owning to its near-ground perspective and dense point cloud characteristics. The first strict evaluation of ULS on in situ observations was provided by [33] and compared the DBH, tree height, and tree position results with those from TLS in a boreal forest. Similarly, [31] compared the ULS and TLS systems for canopy height and DBH estimation in a forest in the Netherlands, while [32] compared the performance of ULS and TLS with respect to explicit tree modeling and tree volume estimation in a Dutch temperate forest.

There is a growing tendency for foresters to use ULS instead of TLS in forest plot inventories, and this development will dramatically improve the efficiency and reduce costs for plot inventories. Accurate forest plot canopy cover estimations are crucial since mapping the distribution of canopy cover over large areas relies on the plot canopy cover estimations for model calibration and validation. It is necessary to study the performances of ULS and TLS in forest plot canopy cover estimations and how different methods may influence canopy cover estimations.

The main objectives of the present study were, therefore, threefold: (1) to investigate the performances of the recent rapidly developed ULS and the current widely used TLS techniques for plot canopy cover estimation under different forest stand conditions with respect to the manual references; (2) quantify the agreement and disagreement in the canopy cover estimations from ULS and TLS with respect to the CHM-based method and individual tree delineation (ITD)-based method; (3) clarify the influence of the pixel size on canopy cover estimation in the CHM-based method from ULS and TLS. The results from this study can provide practical guidance for the selection of data sources and estimation methods in plot canopy cover mapping.

### **2. Study Area and Materials**
