Simple linear model reliably predicts bark thickness of radiata pine in the Australian capital territory

Johnson, Trisha; Wood, G. B.

doi:10.1016/0378-1127(87)90103-4

Cited by 16 publications

(12 citation statements)

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“…Most published studies that sampled bark thickness of coniferous tree species had sample sizes larger than the calculated number of required sample trees found in this analysis. Typically, more than 100 trees per species (e.g., Johnson 1966;Feduccia and Mann 1976;Gordon 1983;Cao and Pepper 1986;Johnson and Wood 1987;Persson and Downie 1992;Degenhardt 1999;Wilhelmsson et al 2002;McConnon et al 2004;Božić et al 2007;Li and Weiskittel 2011;Cellini et al 2012;Wehenkel et al 2012) and even more than 1000 trees per species (e.g., Kozak and Yang 1981;Laasasenaho et al 2005) have been used. However, these studies did not assess how many trees would have been needed to parameterize a sufficiently accurate bark thickness model, as was done for our data.…”

Section: Sample Size At Plot and Regional Levelsmentioning

confidence: 99%

Measurement and prediction of bark thickness in Picea abies: assessment of accuracy, precision, and sample size requirements

Stängle¹,

Weiskittel

Dormann

et al. 2016

Can. J. For. Res.

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Tree and log diameters are usually measured outside bark, but inside-bark diameters are of greater economic interest and are often derived with local or regional bark thickness equations. To date, the influence of measurement method, sampling design, and sample size on bark thickness equation accuracy and precision has received limited attention. The objectives of this study were to use an extensive regional bark thickness dataset for Norway spruce (Picea abies (L.) Karst) in southwestern Germany to (1) quantify the accuracy and precision of bark thickness measurements with a Swedish bark gauge, (2) determine the required number of measurements to assess the within-tree variation, and (3) estimate the required sample sizes per plot and per region to develop an accurate bark thickness prediction equation. Bark gauge readings were validated with measurements derived from X-ray computed tomography (CT) and indicate that Swedish bark gauges generally overestimated bark thickness by 13.6% ± 28.4% (mean ± standard deviation). Results suggested having at least one measurement location every 2 m along a tree bole and at least five bark thickness measurements per each of these locations to achieve an allowable error of <15%. For the study area, Monte Carlo simulations indicated that a total sample size of 50–250 trees was needed, depending on the complexity of the desired bark thickness model. Overall, this analysis indicated that there was relatively high within- and between-tree variation in bark thickness, but adequate sampling methods and sample sizes produced highly accurate bark thickness equations.

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Section: Sample Size At Plot and Regional Levelsmentioning

confidence: 99%

Measurement and prediction of bark thickness in Picea abies: assessment of accuracy, precision, and sample size requirements

Stängle¹,

Weiskittel

Dormann

et al. 2016

Can. J. For. Res.

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“…The models developed by Grosenbaugh ( 1967), later modified and used by many other American foresters (Mesavage 1969;Brickell 1970;Wiant and Koch 1974), were not tested for this study because Johnson and Wood (1987) and Cao and Pepper ( 1986) found them inferior to their models.…”

Section: Eucalyptusmentioning

confidence: 98%

“…However, with absence of variables such as dhh, H or h!H, then Equation 1 can be used. Johnson and Wood ( 1987) compared a number of possible equations, both linear and non-linear, using data for P. radiata. They found a simple linear model of relative bark thickness (DBTIDBT(h=I.30m)) to relative diameter (dob/dbh) raised to the fourth power to be the best model for predicting bark thickness for radiata pine.…”

Section: Eucalyptusmentioning

confidence: 99%

“…A considerable amount of work has been done on bark thickness measurement and prediction elsewhere (e.g., Grosenburgh 1967;Brickell 1970;Cochran 1982;Gordon 1983;Johnson and Wood 1987;Snowdon 1992; for both native and plantation forests. Apart from some work on Pinus radiata by , and some poorly documented work in the 1950s, not much work has been done on bark thickness prediction in New South Wales (NSW) for native eucalypt forests of the north coast of the State.…”

Section: Introductionmentioning

confidence: 99%

“…These problems have lead to the development and adoption of alternative methods of under bark diameter estimation. Such methods include the use of prediction models based on data sets measured carefully to minimise measurement errors (Gordon 1983; Johnson and Wood 1987).…”

Section: Introductionmentioning

confidence: 99%

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Bark thickness equations for five commercial tree species in regrowth forests of Northern New South Wales

Muhairwe¹

2000

Australian Forestry

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Bark thickness at any point along the stem of eucalypt regrowth tree species is related to over-bark diameter at that point, height above ground at point of measurement along the stem, total tree height and breast height diameter overbark. Equations have been derived for predicting bark thickness as a function of these variables and of over-bark diameter along the stem. Using these equations eliminates the need for routine bark-gauge measurements and their associated measurement errors and helps to accelerate the derivation of more precise stem volume functions. When used in conjunction with a tree or log taper function, the equations can provide estimates of the volume of bark resulting from harvesting and available for utilisation.Using data collected for taper and volume functions development for the Resource and Management Evaluation System at State Forests of New South Wales, nine different bark thickness equations were fitted for Eucalyptus pilularis, E. obliqua, E. andrewsii, E. saligna, and Corymbia maculata growing in northern coastal areas of New South Wales. Comparisons using fit statistics for all species and test statistics based an independent data set for E. pilularis, show that some equations were systematically biased for all species and it was recommended that they should not be used. The equation developed by Cao and Pepper ( 1986), labelled equation [8], was found to be the most consistent for all species.

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Bibliography

2011

Forest Growth and Yield Modeling

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Simple linear model reliably predicts bark thickness of radiata pine in the Australian capital territory

Cited by 16 publications

References 0 publications

Measurement and prediction of bark thickness in Picea abies: assessment of accuracy, precision, and sample size requirements

Measurement and prediction of bark thickness in Picea abies: assessment of accuracy, precision, and sample size requirements

Bark thickness equations for five commercial tree species in regrowth forests of Northern New South Wales

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