Accurately representing forest canopies in atmospheric models remains challenging because trees interact with airflow in complex ways and strongly modulate surface–atmosphere exchanges. Aerodynamic roughness is therefore a key control variable in models of air quality, meteorology, and atmospheric transport. In this study, we test a physically based, spatially resolved framework for estimating aerodynamic roughness length from remote sensing observations. Using AHN (Actueel Hoogtebestand Nederland) airborne laser scanning data over a coniferous forest in Loobos, within the Veluwe Natura 2000 region in the central Netherlands, we derive geometric roughness parameters and compare them with eddy-covariance (EC) tower measurements. To further evaluate the approach, the LiDAR-derived roughness field is aggregated within sector-specific tower footprint climatologies and compared with tower-derived roughness estimates across 12 wind-direction sectors. Results show that LiDAR-based roughness captures strong directional and structural variability driven by forest stand height and canopy heterogeneity, closely aligning with the anisotropy observed in EC-derived displacement height and roughness length. The sector-wise comparison reproduces the main directional variability of tower-based aerodynamic roughness, although the LiDAR-derived values generally underestimate its magnitude, consistent with the distinction between structural and effective aerodynamic roughness. Seasonal differences between leaf-on and leaf-off conditions further highlight the role of canopy phenology in aerodynamic behaviour. The spatial patterns resolved by AHN demonstrate the potential of high-resolution laser scanning to capture fine-scale canopy–atmosphere interactions missed by traditional land-use-based roughness representations. This framework offers an observation-driven pathway for improving surface roughness parameterization in wind-flow and chemical transport models such as LOTOS–EUROS.