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

Abstract

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.

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

“…This results in a function mismatch at the bottom of the tree, where the bark is thickest. Incorrect bark estimation at this point is equivalent to the greatest inaccuracy of the estimation of the bark volume [ 26 ]. 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.…”

Individual Tree Basal Area Increment Models for Brazilian Pine (Araucaria angustifolia) Using Artificial Neural Networks

“…ANNs form a subset of artificial intelligence (AI) which are efficient alternatives to estimate tree growth [25][26][27], the prognosis of tree diameter, height, and volume [28][29][30], survival and mortality [31], biomass and carbon [32,33]-applied with remote sensing data [34,35]-as well as species richness and composition mapping [36]. ANNs are used to improve estimates in mixed forests since modeling in this type of forest is complex and must consider species interactions, long dynamics of spatial or temporal gradients in resource availability, and climatic conditions.…”

High-throughput phenotyping of two plant-size traits of Eucalyptus species using neural networks

“…This technique has high generalization power and is better for generating nonlinear models unknown to the modeler, among other characteristics, in relation to the regression models (Vieira et al 2018). Recently, ANNs have been used to estimate the volume of wood using inputs such as DBH, Ht, among others (Soares et al 2012;Bhering et al 2015;Miguel et al 2016;Azevedo et al 2020). However, to the best of our knowledge, there are no reports of the use of a UAV multispectral sensor to obtain variables as input in ANNs to predict DBH and Ht of Eucalyptus trees.…”