Aboveground biomass (AGB) estimates are required to improve our knowledge on carbon cycle and for ecosystem modelling. Suitable mapping of AGB also supports the implementation of sustainable management strategies and practices that will contribute to forest ecosystem preservation and climate change mitigation. The potential of Lidar to assess AGB at plot level is widely acknowledge [Nelson et al. 1988, Næsset 2004, Van Leeuwen and Nieuwenhuis 2010]. In most studies, biomass estimation are estimated from statistical relationships linking biomass values measured in field inventory, to Lidar metrics extracted from the point cloud data. In general, several height percentiles are selected to describe tree height distribution, and only a few of them remain in the final model. In such approaches, biomass estimations do not take into account horizontal heterogeneity of canopy. The aim of this study is to improve AGB estimation for mono-layer stands by including indicators of the spatial heterogeneity of tree height distribution derived from Airborne Laser Scanning (ALS) data. As part of the FORESEE project (www.fcba.fr/foresee/), a 70 km2 area covered mainly by Maritime Pine (Pinus pinaster) in the Landes forest (South-Western France) was sampled by an ALS system. This ALS has a small footprint and a full-waveform signal. Sixty circular plots (0.1 ha or 0.7 ha each depending on tree heights) were inventoried by traditional field measurements and/or using a terrestrial laser scanner (TLS). Reference biomasses were derived from tree height and diameter at breast height (DBH) measurements using allometric equations. The current estimates of AGB from statistical relationships do not provide satisfactory results. It is hypothesized that spatial metrics describing local heterogeneity will allow improving AGB in mono-layer stands. Therefore, we investigated field data in order to determine key parameters that could complement those usually derived from Lidar. We identified new parameters allowing to correct the bias due to the extrapolation at plot-level of allometric equations that are valid for individual trees. Combining local stand density with tree mean height was insufficient to correct this bias primarily due to heterogeneity in tree heights. Conversely, adding the kurtosis and the skewness of the tree height distribution to the mean height values turned out useful to correct this bias. Consequently we defined new Lidar metrics aiming at quantifying spatial heterogeneity and skewness of tree height distribution. These metrics were then used to build a new AGB estimation model. The obtained model is compared to biomass estimation calculated from reference measurements. This model reduces error significantly (6%) compared to model based only on mean height. Further studies will be required to investigate the capacity of such kind of model to predict AGB in complex forest stands, especially in multi-layered forests. Full-waveforms data should also be analyzed to improve estimations.