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

Muhairwe, Charles K.

doi:10.1080/00049158.2000.10674811

Cited by 16 publications

(7 citation statements)

References 11 publications

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“…Our preliminary investigation of this model form suggested that it was more reasonable to allow b 3 to be an estimated parameter for different species. Muhairwe (2000) also identified Eq. 10 with a non-fixed b 3 as one of the best equations in terms of the least mean bias for five species in New Zealand.…”

Section: Datamentioning

confidence: 91%

Estimating and predicting bark thickness for seven conifer species in the Acadian Region of North America using a mixed-effects modeling approach: comparison of model forms and subsampling strategies

Weiskittel

2010

Eur J Forest Res

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In many situations, information on stem diameters inside bark (dib) are more desirable than on diameters outside bark (dob). However, obtaining dib measurements is usually expensive, time-consuming, and prone to significant measurement errors when done on standing trees. Many bark thickness equations have been proposed to estimate the dibs of standing trees. In this study, we compared several commonly used bark thickness equations for seven conifer species in the Acadian Region of North America. Mixed-effects modeling techniques were employed to fit linear and non-linear bark thickness equations. We found the equation proposed by Cao and Pepper (South J Appl Forestry 10:220-224, 1986; Eq. 5) performed significantly better than other equations for most of our study species. The Cao and Pepper (South J Appl Forestry 10:220-224, 1986) equation is a function of dob, relative height in the stem, tree height, and the ratio of dib to dob at breast height. The mean absolute bias was found to be reduced up to 74% compared with using a fixed ratio approach employed in the widely used Northeastern variant of the Forest Vegetation Simulator (FVS-NE) growth and yield model. Leave-one-out cross validation was further performed to determine the location of suitable prior measurements in the prediction process for three of the most well-behaved equations. Results show that no unified prior measurement can provide best predictive abilities across all species as the choice of prior dib measurements depends on both species and bark thickness equations.

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Section: Datamentioning

confidence: 91%

Estimating and predicting bark thickness for seven conifer species in the Acadian Region of North America using a mixed-effects modeling approach: comparison of model forms and subsampling strategies

Weiskittel

2010

Eur J Forest Res

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“…

Bark C = V \cdot RBA \cdot bark density \cdot carbon fraction \cdot BFI,

Bark C = BM \cdot RBA \cdot carbon fraction \cdot BFI,

Bark C = Tree C \cdot RBA \cdot BFI .

We analysed using Equation 5 the effect of tree size on total bark carbon of 1 m 3 wood for four hypothetical smooth‐barked trees (not measured) with 2, 10, 30 and 50 cm diameter over bark at breast height (DBH), correcting for BFI using values from E. albens . RBA was corrected using bark thickness calculated using an allometric bark thickness model for eucalypts (Muhairwe, 2000).…”

Section: Methodsmentioning

confidence: 99%

Quantifying carbon in tree bark: The importance of bark morphology and tree size

Neumann

Lawes

2021

Methods Ecol Evol

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Bark contributes approximately 20% to the total above‐ground biomass of trees, yet bark is not properly accounted for when estimating carbon sequestered by trees. Current allometric functions estimate tree volume from diameter measured over the bark, and derive bark density and carbon content from estimates for wood. As the bark density of hardwood species is 40%–50% lower than the wood density, but nearly equivalent in conifers, bark carbon is overestimated for most species. The latter is further exacerbated by variation in bark volume with bark surface morphology. Fissured bark volume is overestimated by diameter over bark measurements by up to 40%. The vacant space in fissures can be accounted for by a bark fissure index (BFI). We calculate bark carbon for Australian species from a non‐destructive and effective BFI using bark thickness measured in the field. Bark volume, and in turn bark carbon, scaled inversely with tree size (diameter) so that bark volume comprised 42% of small trees (10 cm diameter at breast height, DBH) but 23% of large trees (50 cm DBH). Our BFI method using a bark thickness gauge (BGM) yielded similar results than using the less time‐efficient contour gauge method (CM) to estimate BFI (bias BGM‐CM −1.3%, non‐significant at p = 0.72). Both BGM and CM had an error of <4% compared to digitized BFI from destructive sampled stem disks. An average of 15 bark gauge measurements per tree estimated bark thickness (and inconsequence BFI) for both fissured and unfissured bark with <20% error relative to the exact value. Using the bark gauge method, BFI can be rapidly measured from large numbers of trees needed for estimating bark carbon at the community level and modelling carbon uptake, storage and cycling in woody biomes.

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“…Muhairwe 2000) and their assumptions could replace this assumption in the present theory. (5.6-5.8).…”

Section: 4mentioning

confidence: 94%

“…Muhairwe (2000) has summarised and tested a variety of functions that have been developed from time to time for this purpose. An example of the type of method used to do this is given in Section 5.4.…”

Section: Sectionat Measurement Of Standing Treesmentioning

confidence: 99%

Tree and Forest Measurement

West¹

2004

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

Cited by 16 publications

References 11 publications

Estimating and predicting bark thickness for seven conifer species in the Acadian Region of North America using a mixed-effects modeling approach: comparison of model forms and subsampling strategies

Estimating and predicting bark thickness for seven conifer species in the Acadian Region of North America using a mixed-effects modeling approach: comparison of model forms and subsampling strategies

Quantifying carbon in tree bark: The importance of bark morphology and tree size

Tree and Forest Measurement

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