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

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Title: Appraising the effects of tree height prediction uncertainty on large-scale estimates for mean wood volume per unit area for a subtropical population.
Authors: Oliveira, Laio Zimermann1,2 (AUTHOR) laiozoliveira@gmail.com, McRoberts, Ronald Edward3,4 (AUTHOR), Liesenberg, Veraldo5 (AUTHOR), Vibrans, Alexander Christian1 (AUTHOR)
Source: Canadian Journal of Forest Research. 2/20/2026, Vol. 56, p1-16. 16p.
Subject Terms: *Tree height, *Biomass estimation, *Forest surveys, *Forests & forestry, Monte Carlo method, Parameter estimation, Sampling errors
Abstract: 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 SE ̂ s as much as 11%, while those from the H-D models alone increased SE ̂ s as much as 9%. SE ̂ s 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 SE ̂ s than model prediction uncertainty; and (ii) the effects of H prediction uncertainty on SE ̂ s 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. [ABSTRACT FROM AUTHOR]
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Abstract: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 SE ̂ s as much as 11%, while those from the H-D models alone increased SE ̂ s as much as 9%. SE ̂ s 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 SE ̂ s than model prediction uncertainty; and (ii) the effects of H prediction uncertainty on SE ̂ s 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. [ABSTRACT FROM AUTHOR]
ISSN:00455067
DOI:10.1139/cjfr-2025-0247