The aerial biomass of Pinus radiata plantations in the Región del Maule, Chile, was estimated from linear models using databases of LiDAR and multispectral LANDSAT ETM+. Six descriptive height variables were obtained from the LiDAR point cloud; the 25%, 50%, 75%, 95% and 100% percentiles and the mean height. Two variables associated with the density of points were also obtained, which relate the returns between fixed weighted intervals calculated as a function of the observed biomass. For multispectral variables we used NDVI, corrected NVDI (NDVIc) and the “Tasseled Cap”components brilliance, greenness and humidity. The results showed coefficients of determination (R2) between 0.801 and 0.814, with errors between 36.07 and 36.11 ton ha-1 for the models generated using height percentiles, and R2 from 0.807 to 0.823 with errors between 36.06 and 36.84 ton ha-1 for transformed LiDAR data. Finally, the stepwise model using all available variables had R2 of 0.821-0.835 with errors of 34.28 - 36.31ton ha-1.Key words:ALS, forest above ground biomass, point cloud density, LiDAR, NDVIc.