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Research
An Approximate Point-based Alternative for the Estimation of Variance under Big BAF Sampling
Thomas B. Lynch
Oklahoma State University
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
A new variance estimator is derived and tested for big BAF (Basal Area Factor) sampling which is a forest inventory system that utilizes two BAF sizes, a small BAF for tree counts and a larger BAF on which tree measurements are made usually including \dbh s and heights needed for volume estimation.
Methods
The new estimator is derived using the \Dm\ from an existing formulation of the big BAF estimator as consisting of three sample means. The new formula is compared to existing big BAF estimators including a popular estimator based on Bruce's formula.
Results
Several computer simulation studies were conducted comparing the new variance estimator to all known variance estimators for big BAF currently in the forest inventory literature. In simulations the new estimator performed well and comparably to existing variance formulas.
Conclusions
A possible advantage of the new estimator is that it does not require the assumption of negligible correlation between basal area counts on the small BAF factor and volume-basal area ratios based on the large BAF factor selection trees, an assumption required by all previous big BAF variance estimation formulas. Although this correlation was negligible on the simulation stands used in this study, it is conceivable that the correlation could be significant in some forest types, such as those in which the \dbh-height relationship can be affected substantially by density perhaps through competition. We also mathematically derived expressions for bias in the big BAF estimator that can be used to show the bias approaches zero in large samples on the order of 1/n where n is the number of sample points.
Keywords
Bitterlich Sampling, Delta Method, Double Sampling, Forest Inventory, Horizontal Point Sampling, Variance of a Product
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Peer Review Timeline
CURRENT STATUS: Under Revision
Version 1
Posted 28 Dec, 2020
No community comments so far
Editorial decision: Minor Revision
On 25 Jan, 2021
Review #1 received
Received 24 Jan, 2021
Review #2 received
Received 23 Jan, 2021
Reviewer #2 agreed
On 06 Jan, 2021
Reviewer #1 agreed
On 30 Dec, 2020
Reviewers invited
Invitations sent on 29 Dec, 2020
Editor assigned
On 21 Dec, 2020
Submission checks complete
On 21 Dec, 2020
Editor invited
On 21 Dec, 2020
First submitted
On 16 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Forestry
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This preprint is under consideration at Forest Ecosystems. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
An Approximate Point-based Alternative for the Estimation of Variance under Big BAF Sampling
Thomas B. Lynch
Oklahoma State University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Revision
Version 1
Posted 28 Dec, 2020
No community comments so far
Editorial decision: Minor Revision
On 25 Jan, 2021
Review #1 received
Received 24 Jan, 2021
Review #2 received
Received 23 Jan, 2021
Reviewer #2 agreed
On 06 Jan, 2021
Reviewer #1 agreed
On 30 Dec, 2020
Reviewers invited
Invitations sent on 29 Dec, 2020
Editor assigned
On 21 Dec, 2020
Submission checks complete
On 21 Dec, 2020
Editor invited
On 21 Dec, 2020
First submitted
On 16 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Forestry
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
A new variance estimator is derived and tested for big BAF (Basal Area Factor) sampling which is a forest inventory system that utilizes two BAF sizes, a small BAF for tree counts and a larger BAF on which tree measurements are made usually including \dbh s and heights needed for volume estimation.
Methods
The new estimator is derived using the \Dm\ from an existing formulation of the big BAF estimator as consisting of three sample means. The new formula is compared to existing big BAF estimators including a popular estimator based on Bruce's formula.
Results
Several computer simulation studies were conducted comparing the new variance estimator to all known variance estimators for big BAF currently in the forest inventory literature. In simulations the new estimator performed well and comparably to existing variance formulas.
Conclusions
A possible advantage of the new estimator is that it does not require the assumption of negligible correlation between basal area counts on the small BAF factor and volume-basal area ratios based on the large BAF factor selection trees, an assumption required by all previous big BAF variance estimation formulas. Although this correlation was negligible on the simulation stands used in this study, it is conceivable that the correlation could be significant in some forest types, such as those in which the \dbh-height relationship can be affected substantially by density perhaps through competition. We also mathematically derived expressions for bias in the big BAF estimator that can be used to show the bias approaches zero in large samples on the order of 1/n where n is the number of sample points.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.