Comparison of volume and stand table estimates with alternate methods for selecting measurement trees in point samples

Yang, Sheng-I; Burkhart, Harold E.

doi:10.1093/forestry/cpy029

Cited by 3 publications

(8 citation statements)

References 11 publications

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“…"In general, the large-factor BAF is four to five times that of the small-factor BAF" (Yang & Burkhart, 2018) and corresponds to a proportion of approximately 1 VBAR tree would be chosen out of 5 counted trees (Iles, 2012).…”

Section: The Big Baf Sampling Methodsmentioning

confidence: 99%

“…The separation of basal area (tree counts) and VBAR as separate issues, led to the invention of Big BAF method, in which VBARs with lower variability than DOI: 10.4236/ojf.2019.93013 244 Open Journal of Forestry counting trees are subsampled (Iles, 2017). Some alternative methods before the proposed Big BAF are: 1) the double sampling, instead of measuring all "in" trees, we record the count trees on every Bitterlich point (first phase) and then we subsample the VBARs from every nth sample point (second phase) (Desmarais, 2001;Iles, 1989Iles, , 2012; usually we measure all of the trees on every fourth (less third) sample point cluster of trees (Brooks, 2006;Iles, 1989;Yang & Burkhart, 2018), 2) the two-stage sampling in which measurements are taken in systematic manner, every 10th (nth) tree "in" on the second stage (Iles, 1989) or 3)…”

Section: The Big Baf Sampling Methodsmentioning

confidence: 99%

“…randomly selecting two trees on every point of the second stage (Iles, 1989;Yang & Burkhart, 2018). It is not acceptable the subjective selection of closest trees to the sample point, because in that case smaller trees oversampled and usually the stand volume underestimated; all "in" trees must have an equal probability of being sampled (Iles, 1989).…”

Section: The Big Baf Sampling Methodsmentioning

confidence: 99%

“…References can be found for softwood forests like fir (Abies × borisii-regis Mattf) (Samiotis, 2008), redwood/Douglas-fir (Corrin, 1998;Marshall et al, 2004;Opalach, 2017), white pine in New Hampshire (Desmarais, 2003), spruce-fir forests in Maine (Lindemuth, 2007), hemlock (Desmarais, 2002), Jeffery pine (Marshall et al, 2004), in hardwoods, like Appalachian of West Virginia (Brooks, 2006), Northern red oak (Desmarais, 2002) and Aspen in Minnesota (Deegan, 2011). Big BAF has been applied also in mixed species forest structures (Yang et al, 2017), with uneven-age and multistory oak-pine stands (Lindemuth, 2007) and in plantations, like loblolly pine with spatial heterogeneity (Yang & Burkhart, 2018). Geographically, the Big BAF applied in Canada and the western United States (Brooks, 2006;Chen, Yang, Hsu, Kershaw, & Prest, 2019;Corrin, 1998;Desmarais, 2002;Marshall et al, 2004), while studies have been done for potential applicability in northeastern North America (Brooks, 2006;Burk, 2004;Deegan, 2011;Lindemuth, 2007;Yang et al, 2017) and in Europe (Samiotis & Stamatellos, 2011).…”

Section: The Big Baf Sampling Methodsmentioning

confidence: 99%

“…The sample points can be spread randomly or systematically (Marshall et al, 2004;Rennie, Wood, Schreuder, & Lund, 1991) in the forest area. The measurements of the first stage include the number of trees (of various species) and give the basal area from the Bitterlich sample using a BAF angle gauge (Lindemuth, 2007;Marshall et al, 2004;Rennie et al, 1991;Yang & Burkhart, 2018). Many applications of both methods, Big BAF and Point-3P, involved operational FIs (Marshall et al, 2004;Opalach, 2017;Rice et al, 2014;West, 2011;Yang & Burkhart, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Two Contemporary and Efficient Two-Stage Sampling Methods for Estimating the Volume of Forest Stands: A Brief Overview and Unified Mathematical Description

Georgakis¹,

Stamatellos²

2019

OJF

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Big Basal Area Factor (Big BAF) and Point-3P are two-stage sampling methods. In the first stage the sampling units, in both methods, are Bitterlich points where the selection of the trees is proportional to their basal area. In the second stage, sampling units are trees which are a subset of the first stage trees. In the Big BAF method, the probability of selecting trees in the second stage is made proportional to the two BAFs' ratio, with a basal area factor larger than that of the first stage. In the Point-3P method the probability of selecting trees, in the second stage, is based on the height prediction and use of a specific random number table. Estimates of the forest stands' volume and their sampling errors are based on the theory of the product of two random variables. The increasing error in the second stage is small, but the total cost of measuring the trees is much smaller than simply using the first stage, with all the trees measured. In general, the two sampling methods are modern and cost-effective approaches that can be applied in forest stand inventories for forest management purposes and are receiving the growing interest of researchers in the current decade.

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Section: The Big Baf Sampling Methodsmentioning

confidence: 99%

Section: The Big Baf Sampling Methodsmentioning

confidence: 99%

Section: The Big Baf Sampling Methodsmentioning

confidence: 99%

Section: The Big Baf Sampling Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Two Contemporary and Efficient Two-Stage Sampling Methods for Estimating the Volume of Forest Stands: A Brief Overview and Unified Mathematical Description

Georgakis¹,

Stamatellos²

2019

OJF

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Preference of trees for nest building by critically endangered white‐rumped vultures (Gyps bengalensis) in Nepal

Gautam,

Baral,

Sharma

2024

Ecology and Evolution

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White‐rumped vultures (Gyps bengalensis) are critically endangered species, and protecting their habitats, particularly the nesting trees, may have a positive impact on their reproductive success. For a better understanding of vultures' habitat needs, the characteristics of nesting trees should be accounted. In this paper, we compare the characteristics of the trees that have vultures' nests and that do not by randomly select a control tree within a 10 m radius of the nesting tree. We extensively searched and monitored the white‐rumped vultures' nests, nesting trees, and nesting tree species in Nepal between 2002 and 2022, and measured the characteristics of sampled trees such as their height, girth, canopy spread, branching orders, and whorls. We recorded 1161 nests of white‐rumped vulture in total on 194 trees belonging to 19 species over the past two decades. White‐rumped vultures preferred the kapok trees (Bombax ceiba) for nest construction than other tree species (χ2 = 115.38, df = 1, p < .001) as 66.49% of nests were built on them. In the logistic regression model, the number of whorls on a tree, canopy spread, and the height of the first branch determined whether a nest was present or absent on a tree. These results help to prioritize the tree attributes in a habitat conservation plan for vultures.

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An approximate point-based alternative for the estimation of variance under big BAF sampling

et al. 2021

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Background A new variance estimator is derived and tested for big BAF (Basal Area Factor) sampling which is a forest inventory system that utilizes Bitterlich sampling (point sampling) with two BAF sizes, a small BAF for tree counts and a larger BAF on which tree measurements are made usually including DBHs and heights needed for volume estimation. Methods The new estimator is derived using the Delta method 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 derived a formula that can be used to estimate the covariance between estimates of mean basal area and the ratio of estimates of mean volume and mean basal area. 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 $\frac {1}{n}$ 1 n where n is the number of sample points.

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Comparison of volume and stand table estimates with alternate methods for selecting measurement trees in point samples

Cited by 3 publications

References 11 publications

Two Contemporary and Efficient Two-Stage Sampling Methods for Estimating the Volume of Forest Stands: A Brief Overview and Unified Mathematical Description

Two Contemporary and Efficient Two-Stage Sampling Methods for Estimating the Volume of Forest Stands: A Brief Overview and Unified Mathematical Description

Preference of trees for nest building by critically endangered white‐rumped vultures (Gyps bengalensis) in Nepal

An approximate point-based alternative for the estimation of variance under big BAF sampling

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