An Approximate Point-based Alternative for the Estimation of Variance under Big BAF Sampling
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.
This is a list of supplementary files associated with this preprint. Click to download.
Posted 28 Dec, 2020
On 25 Jan, 2021
Received 24 Jan, 2021
Received 23 Jan, 2021
On 06 Jan, 2021
On 30 Dec, 2020
Invitations sent on 29 Dec, 2020
On 21 Dec, 2020
On 21 Dec, 2020
On 21 Dec, 2020
On 16 Dec, 2020
An Approximate Point-based Alternative for the Estimation of Variance under Big BAF Sampling
Posted 28 Dec, 2020
On 25 Jan, 2021
Received 24 Jan, 2021
Received 23 Jan, 2021
On 06 Jan, 2021
On 30 Dec, 2020
Invitations sent on 29 Dec, 2020
On 21 Dec, 2020
On 21 Dec, 2020
On 21 Dec, 2020
On 16 Dec, 2020
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.