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. |
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| 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] |
| Copyright of Canadian Journal of Forest Research is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | GreenFILE |
| FullText | Text: Availability: 0 |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 191632948 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Appraising the effects of tree height prediction uncertainty on large-scale estimates for mean wood volume per unit area for a subtropical population. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Oliveira%2C+Laio+Zimermann%22">Oliveira, Laio Zimermann</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> laiozoliveira@gmail.com</i><br /><searchLink fieldCode="AR" term="%22McRoberts%2C+Ronald+Edward%22">McRoberts, Ronald Edward</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liesenberg%2C+Veraldo%22">Liesenberg, Veraldo</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Vibrans%2C+Alexander+Christian%22">Vibrans, Alexander Christian</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Canadian+Journal+of+Forest+Research%22">Canadian Journal of Forest Research</searchLink>. 2/20/2026, Vol. 56, p1-16. 16p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Tree+height%22">Tree height</searchLink><br />*<searchLink fieldCode="DE" term="%22Biomass+estimation%22">Biomass estimation</searchLink><br />*<searchLink fieldCode="DE" term="%22Forest+surveys%22">Forest surveys</searchLink><br />*<searchLink fieldCode="DE" term="%22Forests+%26+forestry%22">Forests & forestry</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Sampling+errors%22">Sampling errors</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Canadian Journal of Forest Research is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1139/cjfr-2025-0247 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1 Subjects: – SubjectFull: Tree height Type: general – SubjectFull: Biomass estimation Type: general – SubjectFull: Forest surveys Type: general – SubjectFull: Forests & forestry Type: general – SubjectFull: Monte Carlo method Type: general – SubjectFull: Parameter estimation Type: general – SubjectFull: Sampling errors Type: general Titles: – TitleFull: Appraising the effects of tree height prediction uncertainty on large-scale estimates for mean wood volume per unit area for a subtropical population. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Oliveira, Laio Zimermann – PersonEntity: Name: NameFull: McRoberts, Ronald Edward – PersonEntity: Name: NameFull: Liesenberg, Veraldo – PersonEntity: Name: NameFull: Vibrans, Alexander Christian IsPartOfRelationships: – BibEntity: Dates: – D: 20 M: 02 Text: 2/20/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00455067 Numbering: – Type: volume Value: 56 Titles: – TitleFull: Canadian Journal of Forest Research Type: main |
| ResultId | 1 |
