This article presents an airborne Light Detection and Ranging (LiDAR)-based method to extract interesting stand attributes for forest management in high-density Eucalyptus globulus Labill. plantations. An adaptive morphological filter (AMF) for classifying terrain LiDAR points in forested areas is used to classify LiDAR points; canopy cover (CC), number of LiDAR-detected trees per hectare (N LD) and individual tree height (h tree) were calculated using the canopy height model (CHM); and several statistics and metrics extracted from the CHM and the normalized height of the LiDAR data cloud (NHD) were incorporated into the linear and multiplicative models for estimating mean height (H m), dominant height (H d), mean diameter (d m), quadratic mean diameter (d g), number of stems per hectare (N), basal area (G) and volume (V). The height accuracy results of the LiDAR-derived digital terrain model (DTM), root mean square error (RMSE) = 0.303 m, revealed that the developed filter behaved well. The values of the RMSE for CC, N LD and h tree were 13.2%, 733.3 stems ha–1 and 1.91 m, respectively. The regressions explained 78% of the variance in ground-truth values for H m (RMSE = 1.33 m); 92% for H d (RMSE = 1.18 m); 71% for d m (RMSE = 1.68 cm); 73% for d g (RMSE = 1.66 cm); 49% for N (RMSE = 667 stems ha–1); 78% for G (RMSE = 5.30 m2 ha–1); and 81% for V (RMSE = 53.6 m3 ha–1).