Résumé
Volume and biomass are estimated mostly using tree diameter at breast height (BDH), which is not reachable using light detection and ranging (lidar) data. In this paper, we compare volume and biomass estimations performed using lidar metrics derived from a traditional canopy height model (CHM), such as total height and crown area, with estimations using additional metrics derived from fullwaveform (FW) lidar signals related to the vertical distribution of vegetation. The study is performed on a selection of black pine trees (Pinus nigra ssp. nigra [Arn.]) in the French Alps. Predictive models are built on CHM-only and CHM+FW metrics, automatically selected using a stepwise algorithm, and the accuracy of the different models is compared. We find that FW metrics improve volume estimation of 3% and biomass estimations from 5% to 7%, with a slightly less spread distribution error.