Different approaches for modeling Swietenia macrophylla commercial volume in an Amazon agroforestry system

Dolácio, Cícero Jorge Fonseca; Oliveira, Thiago Wendling Gonçalves de; Oliveira, Rudson Silva; Cerqueira, Clebson Lima; Costa, Luiz Rodolfo Reis

doi:10.1007/s10457-019-00468-3

Cited by 3 publications

(3 citation statements)

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Section: Methodsmentioning

confidence: 99%

“…Among the many existing models for expressing wood volume as a function of diameter and height. The model proposed by Schumacher and Hall [8] is one of the most widespread in the forestry area due to its statistical properties since it almost always results in unbiased estimates [29,37,43]. TVb and TVw = total volume with and without bark; V5b and V5w = volume with and without bark up to the commercial diameter of 5 cm; V10b and V10w = volume with and without bark up to the commercial diameter of e 10 cm; V15b and V15w = volume with and without bark up to the commercial diameter of 15 cm; V20b and V20w = volume with and without bark up to the commercial diameter of 20 cm.…”

Section: Schumacher and Hall Modelmentioning

confidence: 99%

“…RNAs provide a specific approach for developing predictive models, offering a powerful method to analyze complex relationships between variables, without having to make assumptions about the data, which creates significant advantages over the use of conventional regression models [35]. Therefore several studies have shown advantages in employing ANNs in forestry measurement [26,[36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

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Multi-volume modeling of Eucalyptus trees using regression and artificial neural networks

et al. 2020

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The stem volume of commercial trees is an important variable that assists in decision making and economic analysis in forest management. Wood from forest plantations can be used for several purposes, which makes estimating multi-volumes for the same tree a necessary task. Defining its exploitation and use potential, such as the total and merchantable volumes (up to a minimum diameter of interest), with or without bark, is a possible work. The goal of this study was to use different strategies to model multi-volumes of the stem of eucalyptus trees. The data came from rigorous scaling of 460 felled trees stems from four eucalyptus clones in high forest and coppice regimes. The diameters were measured at different heights, with the volume of the sections obtained by the Smalian method. Data were randomly separated into fit and validation data. The single multi-volume model, volume-specific models, and the training of artificial neural networks (ANNs) were fitted. The evaluation criteria of the models were: coefficient of determination, root mean square error, mean absolute error, mean bias error, as well as graphical analysis of observed and estimated values and distribution of residuals. Additionally, the t-test (α = 0.05) was performed between the volume obtained in the rigorous scaling and estimated by each strategy with the validation data. Results showed that the strategies used to model different tree stem volumes are efficient. The actual and estimated volumes showed no differences. The multi-volume model had the most considerable advantage in volume estimation practicality, while the volumespecific models were more efficient in the accuracy of estimates. Given the conditions of this study, the ANNs are more suitable than the regression models in the estimation of multivolumes of eucalyptus trees, revealing greater accuracy and practicality.

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Section: Methodsmentioning

confidence: 99%

Section: Schumacher and Hall Modelmentioning

confidence: 99%