Small footprint discrete return lidar data have already proved useful for providing information on forest areas. During the last decade, a new generation of airborne laser scanners, called full-waveform (FW) lidar systems, has emerged. They digitize and record the entire backscattered signal of each emitted pulse. Fullwaveform data hold large potentialities. In this study, we investigated the processing of raw full-waveform lidar data for deriving Digital Terrain Model (DTM) and Canopy Height Model (CHM). The main objective of this work was to compare geometric information derived from full-waveform and multi-echo data for various stands. An enhanced peak detection algorithm developed in a previous study was used to extract target positions from full-waveform data on plots under different stand characteristics. The resulting 3D point clouds were compared to the discrete return lidar observations provided by the lidar operator. Ground points were then identified using an original classification algorithm. They were used to derive DTMs which were compared to ground truth. Digital Surface Models were obtained from first echoes and canopy height models were then computed. Detecting weak echoes, when processing full-waveform data, enabled to better describe the canopy shape and to penetrate deeper into forest cover. However DTM was not significantly improved.