Résumé
Close-range airborne LiDAR captures high-density, high-accuracy point clouds, offering strong potential for spatially explicit Leaf Area Index (LAI) estimation in complex forests. However, key challenges in the modelling process — particularly LiDAR detection rates and accounting for the wood component — remain inadequately explored. Using simulated UAV LiDAR data generated with the Discrete Anisotropic Radiative Transfer (DART) model and realistic forest mock-ups, we evaluate how these factors influence LAI estimates. Simulated point clouds are processed with the open-source ray-tracing tool AMAPVox to assess (1) the bias introduced by incomplete detection and (2) the effectiveness of using a wood mask to exclude woody contributions. Our results show that pulse fragmentation promotes incomplete detection, which biases LAI estimates; the magnitude of this bias depends on the return-weighting strategy. For example, removing returns with small reflectance, which collectively contribute less than 5% of the backscattered energy, results in an overestimation of LAI by 10% to 20%. An alternative very simple and highly scalable weighting strategy consisting of selecting the strongest return per pulse is also explored and is shown to be effective in most cases albeit slightly less accurate. Additionally, segmentation accuracy declines as the proportion of mixed leaf-wood points increases. These findings suggest that small footprint LiDAR systems are better suited for LAI mapping: their smaller footprints reduce beam fragmentation (mitigating echo-weighting uncertainties) and minimize mixed points while improving geolocation accuracy — critical for robust leaf/wood classification. These insights advance best practices for LiDAR-based forest monitoring.