Enhancing site quality classification in eucalyptus plantations: A multivariate approach using self-organizing maps

Abstract

The classification of productive capacity is a fundamental component of forest management, but traditional univariate methods, such as the guide-curve, may oversimplify the complex interactions of factors determining site productivity. Therefore, this study addresses a research gap by assessing the potential of Kohonen Neural Networks (SOM), a multivariate artificial intelligence technique, as an alternative for stratifying commercial eucalyptus stands. The objective was to compare the classification obtained by the guide-curve method with the artificial neural network (ANN) approach, assessing the superiority of the latter. To this end, data from 750 eucalyptus stands in Minas Gerais, Brazil, were classified using both the guide-curve method (Schumacher's model) and an ANN (SOM) with subsequent hierarchical clustering. Validation and comparison were performed using discriminant analysis and a contingency matrix. The results indicated that the ANN provided a more accurate stratification, notably by reclassifying 26% of the stands from the guide-curve's Medium class to the Low class and refining the High-productivity class into a more elite group. The cohesion of the ANN-derived clusters was validated by discriminant analysis, which achieved an overall accuracy of 69.9%. It is concluded that the ANN is superior to the traditional method, providing a more realistic, multidimensional classification with greater sensitivity in detecting low-productivity areas and greater specificity in identifying elite stands. The methodology is thus established as a strategic tool for enhancing planning and optimizing operations in precision silviculture.

Keywords: Site quality; Kohonen network; Precision silviculture