Utilização de redes neurais artificiais em inventário de florestas comerciais

Abstract

The objective of this study was to analyze the performance of artificial neural networks (ANN) to obtain estimates of eucalyptus and pine dendrometric variables in different growing conditions in order to analyze your learning ability and generalization in estimating variables commonly used in inventory of commercial forests and is divided into three chapters. Chapter I is to examine the ability of RNA to estimate the total height of trees of different kinds in different growth conditions and compare the results with designs commonly used for forest companies. For this, we used data of total height and diameter of 1.30 m in height from a sample of the population and registration information such as age, location, farm, age, gender and spacing. Chapter 2 aims to estimate the volume of trees of different species and growing conditions through artificial neural networks, comparing the results with a model commonly used by forestry companies. To do so, they were obtained cubed data eucalyptus and pine sample collected in different growing conditions, forming several layers with representative samples being fundamental basis for analyzing the ability of learning and generalization of a RNA into precise estimate variables and exact a heterogeneous population. Finally, chapter III aims to analyze the ability of RNA to describe the shaft profile, estimating the diameter at different positions along the trunk of trees of different species in different growing conditions, and compare its performance with models commonly used in by forestry companies. The data were related to the cubed of eucalyptus and pine trees, collected in different growing conditions, forming several layers with representative samples being fundamental basis for analyzing the ability of learning and generalization of a RNA in describing the bole profile precise and exact a heterogeneous population. To generate the estimates of the variables in this study we used the free system NeuroForest 3.0. Given the above, the use of artificial intelligence through artificial neural networks proved effective and efficient assimilation capacity and generalization of data from different species, are able to recommend its use in inventory of commercial forests, with excellent results.