Estimating biomass of terrestrial vegetation in tropical forest is not only a rapidly expanding research issue, but also a subject of high interest for reducing carbon emissions associated with deforestation and forest degradation (REDD+). The above-ground carbon density estimates (ACD) based on field inventories and airborne sensors, especially LiDAR sensors have led to a substantial progress in large-scale mapping of forest carbon stocks. However, these carbon maps have uncertainties generally associated with the calibration of the regression model used to produce these maps. This work establishes a methodology for calibrating and validating a general ACD estimation model using LiDAR in Ecuador’s Yasuní National Park. The size and location of the plots are considered in the model calibration phase as well as the influence of topography and spatial distribution of biomass. For the adjustment and validation of the model a stratified sampling scheme by topographic positions (valley, slope and ridge) is proposed. The validation of the general model for the study area showed values of RMSE= 5.81 Mg C ha-1, R2= 0.94 and bias= 0.59, while considering the topographical positions, the model showed values of RMSE= 1.67 Mg C ha-1, R2 = 0.98 and bias= 0.23 for the valley; RMSE= 3.13 Mg C ha-1, R2 = 0.98 and bias=-0.34 for the slope; and RMSE= 2.33 Mg C ha-1, R2= 0.97 and bias= 0.74 for the ridge. The results show that the stratified sampling methodology taking into account topographic positions, effectively calibrates the general model with field estimates of ACD, reducing RMSE and bias. The results show the potential of LiDAR data to characterize the vertical structure of vegetation in a highly diverse forest, allowing accurate estimates of ACD, and knowing continuous spatial patterns of biomass distribution and carbon stocks in the study area.