SITE CLASSIFICATION FOR EUCALYPT STANDS USING ARTIFICIAL NEURAL NETWORK BASED ON ENVIRONMENTAL AND MANAGEMENT FEATURES

ABSTRACT Several methods have been proposed to perform site classification for timber production. However, there is frequent need to assess site productive capacity before forest establishment. This has motivated the application of Artificial Neural Networks (ANN) for site classification. Hereby, the traditional guide curve (GC) procedure was compared to the ANN with no stand measures as input. In addition, different ANN settings were tested to assess the best setting. The variables used to train the ANN were: climatic variables, soil types, spacing and genetic material. The results from the ANN and the GC methods were compared to the observed classes, which were defined using the observed dominant high at the age of seven years. The comparison was performed using the Kappa coefficient (K) and descriptive analysis. The results showed that the cost function “Cross Entropy” and the output activation function “Softmax” were the best for this purpose. The ANN classification resulted in substantial agreement with the observed indices against a moderate agreement of the GC procedure. The change in growth patterns throughout the rotation may have hindered the proper classification by the CG method, which does not happen with the ANN. Moreover, the GC method shows efficiency on classification in cases which data from stands at the age close to the reference age are available. Also, it could be possible to improve its accuracy if another advanced regression techniques were applied. However, the ANN method presented here is not sensible to growth instability and allows classifying sites with no plantation history.

Bibliographic Details

Main Authors:	Cosenza,Diogo Nepomuceno, Soares,Alvaro Augusto Vieira, Alcântara,Aline Edwiges Mazon de, Silva,Antonilmar Araujo Lopes da, Rode,Rafael, Soares,Vicente Paulo, Leite,Helio Garcia
Format:	Digital revista
Language:	English
Published:	UFLA - Universidade Federal de Lavras 2017
Online Access:	http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-77602017000300310
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