Predicting bark thickness with one- and two-stage regression models for three hardwood species in the southeastern US

Yang, Sheng-I; Radtke, Philip J.

doi:10.1016/j.foreco.2021.119778

Cited by 9 publications

(7 citation statements)

References 27 publications

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“…The variation in the profiles of heartwood, sapwood, and bark along the stem was analyzed to select predictor variables and model forms. The results (Figure 3) suggested that the heartwood radius and bark thickness decreased from base to top for the trees of Korean larch among various ages and sites, which agreed with the results of studies for other species [15,[34][35][36]. In contrast to previous findings [2,4,9,37], sapwood width was higher at the tree base, increased above the tree base to the maximum at the living branch height, and then decreased with tree height.…”

Section: Discussionsupporting

confidence: 87%

“…Predictor variables such as diameter over bark, heartwood radius at breast height, and sapwood width at breast height [8,19] did not consider destructive samples. In addition, many different forms are available as alternatives for bark thickness models, such as the quadratic regression equation, polynomial regression equation [38], segmented polynomial regression equation [39], variable exponent equation [40], and a combination of the stem taper function and bark thickness model by Yang and Radtke [15]. Relative height was the main predictor in these models, as in our study.…”

Section: Discussionmentioning

confidence: 52%

“…The prediction accuracy improved when additional variables were added to bark thickness models [12,13]. Recently, artificial neural networks [14] and a two-stage method (a method combining stem taper function and bark thickness model) [15] showed better performance in prediction bias and precision of bark thickness.…”

Section: Introductionmentioning

confidence: 99%

“…Although several statistical models for predicting the profiles of heartwood, sapwood, and bark have been examined, each component was often predicted by a separate model [8,[10][11][12][13][14][15][16][17][18][19][20][21]. The correlation among the models of the three stem components was not considered.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying the Profiles of Heartwood, Sapwood, and Bark Using a Seemingly Unrelated Mixed-Effect Model for Larix Olgensis in Northeast China

Yang

Hao

et al. 2023

Forests

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Heartwood, sapwood, and bark constitute the main components of the tree stem. The stem is the main component of the tree and plays an important role in supporting the tree and transporting nutrients and water. Therefore, quantifying the profiles of heartwood, sapwood, and bark is fundamental to understanding the different components of the tree stem. A seemingly unrelated mixed-effect model system was developed based on 179 destructively sampled trees for 31 permanent sample plots in Korean larch plantation in Northeast China. The heartwood radius and sapwood width were estimated and calibrated only by the observed bark thickness or by all response variables considering the correlations of submodel random effects. The results indicated that the model system achieved good fitting performance and prediction. In addition, after including one to ten bark thickness points and all response variables of sampling below the 2 m height of the tree, the estimated best linear predictor (EBLUP) for local calibration improved the prediction performance, indicating that the heartwood radius and sapwood width could be effectively calibrated by bark thickness while keeping intact the complete inner structure inside the stem. The results provided important information for forest managers and ecologists when selecting appropriate approaches for quantifying the profiles of heartwood, sapwood, and bark.

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Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 52%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantifying the Profiles of Heartwood, Sapwood, and Bark Using a Seemingly Unrelated Mixed-Effect Model for Larix Olgensis in Northeast China

Yang

Hao

et al. 2023

Forests

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“…New research shows that a combination of stem taper function and bark thickness model (called the two-stage method) is suggested to predict DBT, especially in upper and lower portions of the tree stem. [ 27 ]. The ANN recursive model also estimated DBT much better than the non-linear function in the ranges up to 1 m high at the trunk and also 1–2 meters from the top of the tree.…”

Section: Discussionmentioning

confidence: 99%

Modelling bark thickness for Scots pine (Pinus sylvestris L.) and common oak (Quercus robur L.) with recurrent neural networks

et al. 2022

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Variation of the bark depends on tree age, origin, geographic location, or site conditions like temperature and water availability. Most of these variables are characterized by very high variability but above of all are also affected by climate changes. This requires the construction of improved bark thickness models that take this complexity into account. We propose a new approach based on time series. We used a recurrent neural network (ANN) to build the bark thickness model and compare it with stem taper curves adjusted to predict double bark thickness. The data includes 750 felled trees from common oak and 144 Scots pine—trees representing dominant forest-forming tree species in Europe. The trees were selected across stands varied in terms of age and site conditions. Based on the data, we built recurrent ANN and calculated bark thickness along the stem. We tested different network structures with one- and two-time window delay and three learning algorithms—Bayesian Regularization, Levenberg-Marquardt, and Scaled Conjugate Gradient. The evaluation criteria of the models were: coefficient of determination, root mean square error, mean absolute error as well as graphical analysis of observed and estimated values. The results show that recurrent ANN is a universal approach that offers the most precise estimation of bark thickness at a particular stem height. The ANN recursive model had an advantage in estimating trees that were atypical for height, as well as upper and lower parts on the stem.

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An additive model system for heartwood, sapwood and bark diameter – A working example in Pinus koraiensis Siebold & Zucc. plantations

Sun,

Jia,

Saidahemaiti

2024

Computers and Electronics in Agriculture

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Predicting bark thickness with one- and two-stage regression models for three hardwood species in the southeastern US

Cited by 9 publications

References 27 publications

Quantifying the Profiles of Heartwood, Sapwood, and Bark Using a Seemingly Unrelated Mixed-Effect Model for Larix Olgensis in Northeast China

Quantifying the Profiles of Heartwood, Sapwood, and Bark Using a Seemingly Unrelated Mixed-Effect Model for Larix Olgensis in Northeast China

Modelling bark thickness for Scots pine (Pinus sylvestris L.) and common oak (Quercus robur L.) with recurrent neural networks

An additive model system for heartwood, sapwood and bark diameter – A working example in Pinus koraiensis Siebold & Zucc. plantations

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