Plant area density (PAD in m²·m⁻³) defines the total one-sided total plant surface area within a given volume. It is a key variable in characterizing exchange processes between the atmosphere and land surface. Terrestrial laser scanning (TLS) provides unprecedented detail of the 3D structure of forest canopies. Yet, signal occlusion and uneven sampling density of the TLS point clouds limit our capacity to characterize the 3D distribution of canopy components. Recent studies have made use of statistical estimators of PAD that are applied to TLS point clouds subdivided into three-dimensional (3D) cubes, or voxels. Computation of such metrics under actual field conditions with point clouds containing several millions of returns is challenging. Moreover, rigorous assessment of the estimated PAD and effects of occlusions in forests remain unclear due to laborious, time-consuming, and inaccurate field measurements. In the present study, we present L-Vox, a software that computes PAD per voxel for TLS scans acquired in forest environments, which is based upon recent development of unbiased estimators derived from maximum likelihood. Two applications are presented. First, the software is evaluated for virtual forest plots, which are detailed 3D models of individual trees with corresponding simulated TLS scans, for which reference data are known. Second, L-Vox is applied to actual scans that were acquired in hardwood and coniferous plots in New Brunswick and Newfoundland, Canada. Both test cases were used to investigate the effects of occlusion and the uneven sampling in estimating PAD. The test cases were also used to assess the influence of voxel size and the number of scans per plot on PAD estimates. Our results showed strong correlations between the estimated PAD profile from L-Vox and simulated PAD for virtual forest plots, with a mean R² = 0.98 and a mean coefficient of variation (CV) = 15.6%. We demonstrated that comparing multi-scan to single scan TLS acquisitions in real forest plots substantially reduced signal occlusion, resulting in an increase up to 50% in PAD values. Effects of voxel size on PAD estimates greatly depended upon the relative size of foliar and woody elements, with an optimal size around 10 cm in coniferous plots. L-Vox proved to be an efficient and accurate tool for computing 3D distributions of PAD from TLS measurements in natural forest environments.

种植面积密度（PAD in m ² ·m ^-3) 定义给定体积内的总单侧总植物表面积。它是表征大气和地表之间交换过程的关键变量。地面激光扫描 (TLS) 提供了前所未有的森林冠层 3D 结构细节。然而，TLS 点云的信号遮挡和不均匀的采样密度限制了我们表征冠层组件 3D 分布的能力。最近的研究利用了 PAD 的统计估计器，这些估计器应用于细分为三维 (3D) 立方体或体素的 TLS 点云。在实际现场条件下使用包含数百万个回报的点云计算此类指标具有挑战性。此外，由于费力、耗时、和不准确的现场测量。在本研究中，我们介绍了 L-Vox，这是一种计算在森林环境中获得的 TLS 扫描的每个体素 PAD 的软件，该软件基于最近开发的源自最大似然的无偏估计量。提出了两个应用程序。首先，该软件针对虚拟森林图进行评估，这些图是单个树木的详细 3D 模型，具有相应的模拟 TLS 扫描，其参考数据是已知的。其次，L-Vox 应用于在加拿大新不伦瑞克和纽芬兰的硬木和针叶林地块进行的实际扫描。两个测试用例都用于研究遮挡和不均匀采样在估计 PAD 时的影响。测试用例还用于评估体素大小和每个图的扫描次数对 PAD 估计值的影响。R ²  = 0.98，平均变异系数 (CV) = 15.6%。我们证明，在真实森林图中将多扫描与单扫描 TLS 采集进行比较可显着减少信号遮挡，导致 PAD 值增加高达 50%。体素大小对 PAD 估计的影响很大程度上取决于叶面和木本元素的相对大小，针叶林中的最佳大小约为 10 厘米。L-Vox 被证明是一种有效且准确的工具，可用于根据天然森林环境中的 TLS 测量值计算 PAD 的 3D 分布。