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. RESUMO Vários métodos têm sido propostos para realizar a classificação de sítio para produção de madeira. No entanto, há necessidade frequente de se obter a capacidade produtiva do local antes mesmo do estabelecimento da floresta. Isto motivou a aplicação das Redes Neurais Artificiais (RNA) para classificação de sítio. Desta forma, o método tradicional da curva guia (CG) foi comparado com a RNA sem medidas do povoamento como variáveis preditivas. Além disso, diferentes configurações de RNA foram testadas. As variáveis utilizadas para treinamento da RNA foram: variáveis climáticas, tipos de solo, espaçamento e material genético. Os resultados obtidos pela RNA e CG foram comparados com as classes de referência, definidas utilizando altura dominantes de povoamentos observadas no sétimo ano de idade. A comparação foi realizada utilizando o coeficiente Kappa (K