Appraising the effects of tree height prediction uncertainty on large-scale estimates for mean wood volume per unit area for a subtropical population

This study evaluated the effects of uncertainty in predictions of height-diameter ( H- D) models on large-area estimates for mean wood volume ( V) per unit area for a subtropical population. In addition to the uncertainty due to sampling variability associated with the forest inventory dataset, uncertainty in model parameter estimates and residual variability of V and H- D models were propagated into standard errors (SEs) of the estimated mean through a Monte Carlo scheme. Uncertainty arising from the V models alone increased [Formula: see text] as much as 11%, while those from the H- D models alone increased [Formula: see text] as much as 9%. [Formula: see text] increased only marginally when correlation among tree observations on the same sample location was considered during the estimation of H- D models. Key findings include: (i) sampling variability associated with the inventory dataset had a greater effect on [Formula: see text] than model prediction uncertainty; and (ii) the effects of H prediction uncertainty on [Formula: see text] depended on the mathematical form of the V model. These results generally apply to scenarios where models are estimated using large datasets (e.g., n > 400), where uncertainty due to model parameter estimates is reduced. Future research using model calibration datasets of varying sizes and multiple H- D functions are strongly encouraged.