Modelling wood property variation among Tasmanian Eucalyptus nitens plantations

Vega, Mario; Harrison, Peter A.; Hamilton, Matthew; Musk, Rob; Adams, P.; Potts, Brad M.

doi:10.1016/j.foreco.2021.119203

Cited by 9 publications

(5 citation statements)

References 68 publications

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“…Basic density and stiffness were consistently lower at Gads (700 m elevation and with the highest rainfall), than at Urana and Florentine at 400 m. The pattern of decreasing basic density with increasing elevation and its associated factors, such as colder temperature and higher precipitation, for E. nitens in Tasmania was also observed in previous studies [22,35,36,81]. Similarly, softwoods have been found to have lower basic density on sites grown at higher elevations [46,[82][83][84][85][86].…”

Section: Discussionsupporting

confidence: 78%

“…Thus, basic density appeared to be inversely related to elevation in this study; however, there was only a single site at this elevation. Similarly, stiffness or MOE might also be related to elevation where in previous studies with E. nitens, MOE decreased with increasing elevation [36,81], and plantations grown at lower elevations and rainfall had higher stiffness (MOE) compared to higher elevations and rainfall [22]. Similarly in softwoods, there was an observed negative relationship between elevation and MOE for Picea abies [84] and Picea sitchensis [82,83].…”

Section: Discussionmentioning

confidence: 88%

“…While the above-mentioned studies were undertaken at sites ranging from 120 to 340 m above sea level, it remains unclear how higher elevations may interact with thinning treatments to affect wood properties, as well as the consistency of the effects of thinning treatments on wood properties across sites of contrasting environments. This is particularly important given the diverse environments and broad elevation range across which E. nitens is planted and the impact that site characteristics, such as elevation and precipitation, have on wood properties [35,36].…”

Section: Introductionmentioning

confidence: 99%

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Thinning Influences Wood Properties of Plantation-Grown Eucalyptus nitens at Three Sites in Tasmania

Gendvilas

Downes

Neyland

et al. 2021

Forests

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Thinning of forestry plantations is a common silviculture practice to increase growth rates and to produce larger dimension logs. The wood properties, basic density and stiffness, are key indicators of the suitability of timber for particular purposes and ultimately determine timber value. The impact of thinning operations on wood properties is, therefore, of considerable interest to forest growers and timber producers. To date, studies examining the impact of thinning on wood properties have produced variable results and understanding of the consistency of the effects of thinning treatments across various sites for important plantation species is limited. Two non-destructive assessment techniques, drilling resistance and acoustic wave velocity, were used to examine the impact of thinning on basic density and stiffness in 19–21-year-old plantation grown Eucalyptus nitens across three sites. Commercial thinning to 300 trees ha−1 decreased the stiffness of standing trees and this effect was consistent across the sites. Reduction in stiffness due to thinning ranged from 3.5% to 11.5%. There was no difference in wood properties between commercially and non-commercially thinned trees to 300 trees ha−1 and no difference in wood properties when thinned to 500 trees ha−1. Basic density was not affected by thinning. The site had significant effects on both basic density and stiffness, which were lowest at the highest precipitation and highest elevation site. The results indicate that wood properties are influenced both by silviculture and site environmental differences. This knowledge can be used for the better management of E. nitens resources for solid wood production.

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Section: Discussionsupporting

confidence: 78%

Section: Discussionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Thinning Influences Wood Properties of Plantation-Grown Eucalyptus nitens at Three Sites in Tasmania

Gendvilas

Downes

Neyland

et al. 2021

Forests

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“…The traditional way of estimating both tree growth and productivity is through regression methods [7]. In this way, the use of machine learning (ML) algorithms are tools with the potential to assist in forest inventory [3], characterization, and future property planning [8]. Oliveira et al [5] found high accuracy in identifying different eucalyptus species based on their growth, by applying the random forest (RF) method.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Methods for Woody Volume Prediction in Eucalyptus

et al. 2023

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Machine learning (ML) algorithms can be used to predict wood volume in a faster and more accurate way, providing reliable answers in forest inventories. The objective of this work was to evaluate the performance of different ML techniques to predict the volume of eucalyptus wood, using diameter at breast height (DBH) and total height (Ht) as input variables, obtained by measuring DBH and Ht of 72 trees of six eucalyptus species (Eucalyptus camaldulensis, E. uroplylla, E. saligna, E. grandis, E. urograndis, and Corymbria citriodora). The trees were cut down in two different epochs, rendering 48 samples at 24 months and 24 samples at 48 months, and the volume of each tree was measured using the Smailian method. This research explores five machine learning models, namely artificial neural networks (ANN), K-nearest neighbor (KNN), multiple linear regression (LR), random forest (RF) and support vector machine (SVM), to estimate the volume of eucalyptus wood using DBH and Ht. Artificial neural networks achieved higher correlations between observed and estimated wood volume values. However, the RF outperformed all models by providing lower MAE and higher correlations between observed and estimated wood volume values. Therefore, RF is the most accurate for predicting wood volume in eucalyptus species.

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“…Wood properties (e.g., physical, chemical, anatomical, and other mechanical parameters) are essential for forest cultivation and resource use (Vega et al 2021, Villa et al 2021). However, these property parameters are influenced by many factors.…”

mentioning

confidence: 99%

Determination of Geographical Origin and Tree Species Using Vis-NIR and Chemometric Methods

Liu¹,

Via²,

Li³

et al. 2022

Forest Products Journal

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The variation of wood properties between different geographical origin and tree species has an important influence on end use applications. This study aimed to investigate the feasibility of wood origin and species classification based on visible and near infrared spectroscopy and chemometric methods. The influence of geographical origin on tree species identification also was analyzed. A total of 530 samples with 2 origins and 5 tree species were collected for analysis. The raw reflectance spectra were preprocessed by spectral transformation technique, and nonlinear discrimination models were built by support vector machine (SVM) using various spectral forms. Three algorithms—grid search (GS), genetic algorithm (GA), and particle swarm optimization (PSO)—were applied to optimize the parameters of SVM models, respectively. Regardless of spectral forms and optimization techniques, the prediction accuracy was lower than that of the calibration set for wood origin and tree species identification. Except for reflectance spectra, prediction accuracy of 100 percent was obtained based on SVM in combination with three algorithms for origin discrimination. However, SVM in combination with reflectance spectra and GS technique achieved the best prediction accuracy (93.18%) for tree species identification. These results demonstrated that visible and near infrared spectroscopy combined with chemometric techniques can be used for geographical origin and tree species determination.

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Modelling wood property variation among Tasmanian Eucalyptus nitens plantations

Cited by 9 publications

References 68 publications

Thinning Influences Wood Properties of Plantation-Grown Eucalyptus nitens at Three Sites in Tasmania

Thinning Influences Wood Properties of Plantation-Grown Eucalyptus nitens at Three Sites in Tasmania

Machine Learning Methods for Woody Volume Prediction in Eucalyptus

Determination of Geographical Origin and Tree Species Using Vis-NIR and Chemometric Methods

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