The need for fine scale description of vegetation structure is increasing as Leaf Area Density (LAD, m²/m³) becomes a critical parameter to understand ecosystem functioning and energy and mass fluxes in heterogeneous ecosystems. Terrestrial Laser Scanning (TLS) has shown great potential for retrieving the foliage area at stand, plant or voxel scales. Several sources of measurement errors have been identified and corrected over the past years. However, measurements remain sensitive to several factors, including, 1) voxel size and vegetation structure within voxels, 2) heterogeneity in sampling from TLS instrument (occlusion and shooting pattern), the consequences of which have been seldom analyzed at the scale of forest plots. In the present paper, we aimed at disentangling biases and errors in plot-scale measurements of LAD with TLS in a simulated vegetation scene. Two negative biases were formerly attributed to (i) the unsampled voxels and to (ii) the subgrid vegetation heterogeneity (i.e. clumping effect), and then quantified, thanks to a the simulation experiment providing known LAD references at voxel scale, vegetation manipulations and unbiased point estimators. We used confidence intervals to evaluate voxel-scale measurement accuracy.

We found that the unsampled voxel effect (i) led to underestimations with the “mean layer” method –commonly used to fill unsampled voxels- for small voxels (0.1–0.2 m) and/or low number of scans (<4). It was explained by the spatial correlations in vegetation, which induced that dense voxels were more often occluded by dense neighbors than light voxels. The distribution of the bias was heterogeneous in canopy, the bias being stronger at mid canopy where occlusion started, but smaller in highly-occluded upper layers. This somehow counterintuitive result was explained by a more random sampling of upper layers, but could highly depend on vegetation structure.

The subgrid vegetation heterogeneity effect (ii) was confirmed to increase with voxel size, yet, the magnitude of this bias -quantified with vegetation manipulation- was found to be more homogeneously-distributed than the unsampled voxel effect.

Overall, we found that no scenario was unbiased. However, an intermediate voxel size (0.5 m) was the best option, because the relatively homogeneous subgrid effect could be handled with a single correction factor and voxel-scale measurements errors were reasonable. On the contrary, smaller voxels led to poor voxel-scale measurements and variable biases in magnitude and spatial distribution with sampling design. However, more similar research in other context is required to adapt these conclusions to other forest plots.

随着叶面积密度（LAD，m ² / m ³）成为了解异质生态系统中生态系统功能以及能量和质量通量的关键参数。陆地激光扫描（TLS）已显示出在林分，植物或体素尺度上检索树叶区域的巨大潜力。在过去的几年中，已经发现并纠正了几种测量误差源。但是，测量仍然对几个因素敏感，包括：1）体素大小和体素内的植被结构； 2）TLS仪器采样中的异质性（遮挡和射击方式），其后果很少在林地范围内进行分析。在本文中，我们旨在消除模拟植被场景中带TLS的LAD地块规模测量中的偏差和误差。先前有两个负偏差可归因于（i）未采样的体素和（ii）亚网格植被异质性（即结块效应），然后进行了量化，这要归功于模拟实验，该实验提供了已知的LAD参考（体素尺度，植被操纵和无偏见）。点估计量。我们使用置信区间来评估体素尺度的测量准确性。

我们发现，对于小体素（0.1–0.2 m）和/或扫描次数较少（<4），未采样体素效应（i）导致“均值层”方法（通常用于填充未采样体素）的估计不足。植被的空间相关性可以解释这一点，这导致密集的体素比轻的体素更常被密集的邻居遮挡。偏向的分布在冠层中是不均匀的，偏斜在开始遮盖的中冠层中较强，而在高度遮盖的上层较小。这种不符合直觉的结果可以通过对上层进行更随机的采样来解释，但可能高度依赖于植被结构。

确认亚栅格植被异质性效应（ii）随着体素大小的增加而增加，但是，该偏差的幅度（通过植被处理量化）比未采样的体素效应分布更均匀。

总体而言，我们发现没有任何情况是公正的。但是，中等体素大小（0.5 m）是最佳选择，因为可以使用单个校正因子来处理相对均匀的子网格效果，并且体素尺度的测量误差是合理的。相反，较小的体素导致采样设计不佳的体素标度测量以及大小和空间分布的可变偏差。但是，需要在其他情况下进行更多类似的研究，才能使这些结论适用于其他森林地。