The 3D distribution of plant material is a key parameter to describe vegetation structure, which influences several processes such as radiation interception and ecosystem functioning. Vegetation covers are often described using Leaf Area Index (LAI) or Plant Area Index (PAI) for monitoring or modeling purposes. Characterizing vegetation 3D structure at fine scale is increasingly required, notably in order to be able to apply radiative transfer simulations at scales consistent with the spatial resolution of recent remote sensing sensors. To assess 3D PAI of a vegetation plot, this paper evaluates the potential of a voxelization method using Terrestrial Laser Scanning (TLS) data, based on the Beer-Lambert transmittance computation law. The theoretical validation was performed using a simulation framework based on a radiative transfer model (DART). The framework allowed simulating TLS acquisition on a theoretical distribution of leaves and a realistic representation (single tree), for which all characteristics are well known. Hence, a sensitivity analysis was performed to study the influence of instrument parameters (i.e. single- or multi-echo, beam divergence), scanning configuration (scan angle step), vegetation characteristics (leaf size and density, leaf angle distribution), and voxel parameters (cubic versus spherical geometry, at different resolutions, with and without occlusion) on the estimation of PAI. For a theoretical distribution of leaves, results showed good accuracy of the voxelization method (R-2 = 0.91 and RMSE = 20% for a mean case, at voxel level) with a high resolution multi-echo TLS scan, cubic voxels over 0.5-m resolution, low inter-voxel occlusion, small leaves, and up to a surface density of 2 m(2).m(-3). Error increased with a larger scan angle resolution, single echo TLS systems, and vegetation density. Also, without clumping, error increased with smaller voxels or larger leaves. Best results were obtained with multi-echo TLS scans (angular resolution of 0.05), cubic voxels at 1-m resolution when occlusion is low (voxel sampling higher than 50% of maximum sampling at 15 m) and small leaves (e.g. 10 cm(2)), which provided very good agreement (RMSD = 7.6%, R-2 = 0.98, p = 0.99). On a realistic isolated tree, PAI was correctly assessed with cubic voxels at 0.25 m resolution. A method to merge voxelized scans was proposed to deal with inter-voxel occlusion effects. (C) 2017 Elsevier Inc. All rights reserved.