Strategies for Stem Measurement Sampling: A Statistical Approach of Modelling Individual Tree Volume

David, Hassan Camil; Miranda, Rodrigo Otávio Veiga de; Welker, John; Fiorentin, Luan Demarco; Ebling, Ângelo Augusto; Belavenutti, Pedro

doi:10.1590/01047760201622032155

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…For Model 5, at least three trees per class are better for predicting v, while for ANN, at least four trees per class are better for prediction, based on the increase in RMSE% in the intensities of two and three trees per class, respectively. This result was similar to that found by David et al [53], in which they affirmed that it is possible to predict stem volume of Eucalyptus grandis W. Hill ex Maiden × Eucalyptus urophylla ST Blake by measuring at least two trees per class. However, these authors used sampling proportional to the frequency of trees by diameter class; the present study used the same number of trees per class, except for the intensity with only one tree in the class.…”

Section: Discussionsupporting

confidence: 91%

Artificial Neural Networks and Linear Regression Reduce Sample Intensity to Predict the Commercial Volume of Eucalyptus Clones

Júnior

Rocha

Ebling

et al. 2019

Forests

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Equations to predict Eucalyptus timber volume are continuously updated, but most of them cannot be used for certain locations. Thus, equations of similar strata are applied to clonal plantations where trees cannot be felled to fit volumetric models. The objective of this study was to use linear regression and artificial neural networks (ANN) to reduce the number of trees sampled while maintaining the accuracy of commercial volume predictions with bark up to 4 cm in diameter at the top (v) of Eucalyptus clones. Two methods were evaluated in two scenarios: (a) regression model fit and ANN training with 80% of the data (533 trees) and per clone group with 80% of the trees in each group; and (b) model fit and ANN training with trees of only one clone group at ages two and three, with sample intensities of six, five, four, three, two, and one tree per diameter class. The real and predicted v averages did not differ in sample intensities from six to two trees per diameter class with different methods. The frequency distribution of individuals by volume class by the two methods (regression and ANN) compared to the real values were similar in scenarios (a) and (b) by the Kolmogorov–Smirnov test (p-value > 0.01). The application of ANN was more effective for total data analysis with non-linear behavior, without sampled environment stratification. The Prodan model also generates estimates with accuracy, and, among the regression models, is the best fit to the data. The volume with bark up to 4 cm in diameter at the top of Eucalyptus clones can be predicted with at least three trees per diameter class with regression (root mean square error in percentage, RMSE = 12.32%), and at least four trees per class with ANN (RMSE = 11.73%).

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Section: Discussionsupporting

confidence: 91%

Artificial Neural Networks and Linear Regression Reduce Sample Intensity to Predict the Commercial Volume of Eucalyptus Clones

Júnior

Rocha

Ebling

et al. 2019

Forests

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“…and black spruce (Picea mariana (Mill.) B.S.P), respectively across the boreal forest of Northern Ontario when estimating coefficients for an existing stem taper model, whereas in [38] reliable stem-volume estimates were obtained with 36 eucalyptus trees from~559 ha. Our results are thus in line with these as we obtained relatively stable RMSE of stem-volume estimates with a sample size of ≥46 trees, which corresponds approximately 0.003% of the Scots pine population within our 2000 ha study area.…”

Section: Discussionmentioning

confidence: 90%

“…Stem volume can be obtained by integrating the taper curve function or using diameters at multiple heights to calculate volume of stem sections with geometrical shapes (e.g., cylinder or cone) and aggregating them [29,34,35]. Taper equations commonly rely on allometric relationships and include traditionally easily measurable attributes as predictors (e.g., DBH and tree height) [29,[36][37][38]. These stem-volume estimates could then be utilized in developing new and/or updating existing stem-volume equations that are more intuitive to use compared to taper curve models [29].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Effects of Sample Size on Parametrizing a Taper Curve Equation and the Resultant Stem-Volume Estimates

Saarinen

Kankare

Pyörälä

et al. 2019

Forests

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Large and comprehensive datasets, traditionally based on destructive stem analysis or other labor-intensive approaches, are commonly considered as a necessity in developing stem-volume equations. The aim here was to investigate how a decreasing number of sample trees affects parametrizing an existing taper curve equation and resultant stem-volume estimates. Furthermore, the potential of terrestrial laser scanning (TLS) in producing taper curves was examined. A TLS-based taper curve was derived for 246 Scots pines (Pinus sylvestris L.) from southern Finland to parametrize an existing taper curve equation. To assess sensitivity of the parametrization regarding sample size, the number of Scots pines included in the parametrization varied between full census and 1 Scots pine at a time. Root mean square error of stem-volume estimates remained ≤20.9% and the mean absolute difference was relatively constant (≤9.0%) between stem-volume estimates when the sample size included ≥46 Scots pines. Thus, it can be concluded that, with a rather small sample size, a taper curve equation can be re-parametrized for local conditions using point clouds from TLS to produce consistent stem-volume estimates.

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“…Thus, 7-12 trees were sampled on each plot depending on the range of tree diameters in the forest stand. To represent trees of different diameters the number of sample trees was proportional to the frequency of 4-cm diameter class which usually has better performance in tree volume estimation (David et al 2016).…”

Section: Data Collectionmentioning

confidence: 99%

Semi-empirical estimation of log taper using stem profile equations

Bilous¹,

Myroniuk²,

Svynchuk³

et al. 2021

J. For. Sci.

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In January 2019 the forest industry in Ukraine adopted European standards for measuring and grading of round wood based on mid-point diameters, which caused major discrepancies from traditionally used estimates of timber volume using top diameters. To compare methods of merchantable wood volume estimation, we investigated the stem form inside bark for two dominant tree species in Ukraine, i.e. Scots pine (Pinus sylvestris L.) and common oak (Quercus robur L.). We used tree stem measurements to fit stem profile equations, whereas simulation was applied to derive log taper. We found that Newnham's (1992) variable-exponent taper equation performed well for predicting stem taper for both tree species. Then, we simulated the structure of harvested wood, so that it replicated annual distribution of logs by their length and diameters. As a result, the average log taper was estimated at 0.836 ÷ 0.855 cm·m<sup>–1</sup> and 1.180 ÷ 0.121 cm·m<sup>–1</sup> for pine and oak, respectively. The study also indicated that log taper varied along stems. The higher rates of diameter decrease were found for butt logs, for which the taper was 2.5–3.5 times higher than its average for the whole stem. The results of our study ensure the stacked round wood volume conversion between estimates obtained using top and mid-point diameters.

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Strategies for Stem Measurement Sampling: A Statistical Approach of Modelling Individual Tree Volume

Cited by 5 publications

References 9 publications

Artificial Neural Networks and Linear Regression Reduce Sample Intensity to Predict the Commercial Volume of Eucalyptus Clones

Artificial Neural Networks and Linear Regression Reduce Sample Intensity to Predict the Commercial Volume of Eucalyptus Clones

Assessing the Effects of Sample Size on Parametrizing a Taper Curve Equation and the Resultant Stem-Volume Estimates

Semi-empirical estimation of log taper using stem profile equations

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