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Over the past two decades spaceborne LiDAR systems have gained momentum in the remote sensing community with their ability to accurately estimate canopy heights and aboveground biomass. This article aims at using the most recent global ecosystem dynamics investigation (GEDI) LiDAR system data to estimate the stand-scale dominant heights (${{\boldsymbol{H}}_{{\rm{dom}}}}$), and stand volume (V) of Eucalyptus plantations in Brazil. These plantations provide a valuable case study due to the homogenous canopy cover and the availability of precise field measurements. Several linear and nonlinear regression models were used for the estimation of ${{\boldsymbol{H}}_{{\rm{dom}}}}$ and V based on several GEDI metrics. ${{\boldsymbol{H}}_{{\rm{dom}}}}$ and V estimation results showed that over low-slopped terrain the most accurate estimates of ${{\boldsymbol{H}}_{{\rm{dom}}}}$ and V were obtained using the stepwise regression, with an root-mean-square error (RMSE) of 1.33 m (R2 of 0.93) and 24.39 m3.ha−1 (R2 of 0.90) respectively. The principal metric explaining more than 87% and 84% of the variability (R2) of ${{\boldsymbol{H}}_{{\rm{dom}}}}$ and V was the metric representing the height above the ground at which 90% of the waveform energy occurs. Testing the postprocessed GEDI metric values issued from six available different processing algorithms showed that the accuracy on ${{\boldsymbol{H}}_{{\rm{dom}}}}$ and V estimates is algorithm dependent, with a 16% observed increase in RMSE on both variables using algorithm a5 vs. a1. Finally, the choice to select the ground return from the last detected mode or the stronger of the last two modes could also affect the ${H_{{\rm{dom}}}}$ estimation accuracy with 12 cm RMSE decrease using the latter.