Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/48587
Título: Reduction of sampling intensity in forest inventories to estimate the total height of eucalyptus trees
Título(s) alternativo(s): Reducción de la intensidad de muestreo en inventarios forestales para estimar la altura total de eucaliptos
Palavras-chave: Artificial neural network
Machine learning
Stem volume
Schumacher and Hall
Redes neuronales artificiale
Altura dominante
Rede neural artificial
Inventário florestal
Data do documento: Dez-2020
Editor: Universidad Austral de Chile, Facultad de Ciencias Forestales
Citação: DANTAS, D. et al. Reduction of sampling intensity in forest inventories to estimate the total height of eucalyptus trees. Bosque, Valdivia, v. 41, n. 3, p. 353-364, dic. 2020. DOI: 10.4067/S0717-92002020000300353.
Resumo: This study aimed at evaluating the performance of different models based on Artificial neural networks (ANN) to estimate the total height of eucalyptus trees (Eucalyptus spp.), reducing the number of measurements in the field. Forty-eight ANN were tested, different from each other by the number of trees used as training sample, number of trees used to calculate the dominant height and use of variables (a) categorical, (b) categorical and continuous and (c) continuous, except for the diameter at 1.30 meters above the ground (DBH), used in all combinations. Estimates of height obtained by ANN were compared with values observed and estimates obtained by a hypsometric model. The ANN that showed the best results were used for the height estimation in forest inventory data for further application in the Schumacher and Hall volumetric model. The proposed models were efficient to estimate the total height of eucalyptus trees and allowed the expressive reduction of the number of trees to be measured in forest inventory. The best model found is composed of five trees as training sample, one as test sample and one as validation sample; dominant height coming from the height of the tallest tree in the plot; categorical variable Clone and continuous variables DBH, DBH dominant and basal area of the plot.
URI: http://repositorio.ufla.br/jspui/handle/1/48587
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