Individual Growth Model forEucalyptusStands in Brazil Using Artificial Neural Network

Renato Vinícius Oliveira Castro,Agostinho Lopes De Souza,Fabrina Bolzan Martins,Carlos Pedro Boechat Soares,Helio Garcia Leite,Gilciano Saraiva Nogueira

doi:10.1155/2013/196832

Abstract

This work aimed to model the growth and yield ofEucalyptusstands located in northern Brazil, at the individual tree level, by using artificial neural networks (ANNs). Data from permanent plots were used for training the neural networks to predict tree height and diameter as well as mortality probability. Once trained, the networks were evaluated using an independent data set. The first group was composed of 33 plots (11 in each productive capacity class) and was used for artificial neural network training. In five measurements, this group totaled 8,735 cases (measurements of individual trees), as each plot had 53 trees on average throughout this evaluation. The second group was composed of 30 plots (10 in each productive capacity class) and was used for model validation. This group totaled 7,756 cases. Were tested different network architectures Multilayer Perceptron (MLP). Results revealed an underestimation bias for number of surviving trees. However, estimates of diameter, height, and volume per hectare were found to be accurate. This indicates that artificial neural networks are a viable alternative to the traditional growth and yield modeling approach in the forestry sector.

Highlights

Individual tree models are constituted by a set of submodels that estimate diameter and height growth as well as mortality probability through tree- and stand-related variables and through competition data [1,2,3]
In Brazil, few studies have been conducted that used these types of growth model for Eucalyptus [8, 9], a genus whose planted area intended to supply the processing industry amounts to 4.75 million hectares [10]
Data were obtained from 63 permanent plots, approximately 500 m2 in area, containing clonal stands of Eucalyptus grandis × Eucalyptus urophylla, with initial spacing of 3 × 3 m between plants (Table 1)

Summary

Introduction

Individual tree models are constituted by a set of submodels that estimate diameter and height growth as well as mortality probability through tree- and stand-related variables and through competition data [1,2,3]. According to Munro [4], these models may be categorized according to how they consider the competition among trees, as represented by distance-dependent and distance-independent models. Since Newnham [5], many studies have been conducted worldwide in an attempt to improve growth models at the individual tree level and the relevant submodels. Methods to estimate parameters as well as different explanatory variables have been evaluated in an attempt to produce accurate, unbiased estimates of diameter and height growth and tree mortality [6, 7]. Some models have been developed and fitted for some species, including canjerana (Cabralea canjerana), black cinnamon (Nectandra megapotamica), cedar (Cedrela fissilis), and araucaria (Araucaria angustifolia), in natural forest conditions, yet without computing all the submodels that compose an individual tree model [11,12,13,14]

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Methods

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Conclusion