Abstract
Abstract: The objective of this work was to compare methods of obtaining the site index for eucalyptus (Eucalyptus spp.) stands, as well as to evaluate their impact on the stability of this index in databases with and without outliers. Three methods were tested, using linear regression, quantile regression, and artificial neural network. Twenty-two permanent plots from a continuous forest inventory were used, measured in trees with ages from 23 to 83 months. The outliers were identified using a boxplot graphic. The artificial neural network showed better results than the linear and quantile regressions, both for dominant height and site index estimates. The stability obtained for the site index classification by the artificial neural network was also better than the one obtained by the other methods, regardless of the presence or the absence of outliers in the database. This shows that the artificial neural network is a solid modelling technique in the presence of outliers. When the cause of the presence of outliers in the database is not known, they can be kept in it if techniques as artificial neural networks or quantile regression are used.
Highlights
Understanding growth and yield processes in forests is important to their rational management (Cosenza et al, 2015)
When this procedure is used for the classification of site productive capacity, its evaluation generally includes the stability analysis (Machado et al, 2011), which deals with the estimation of the number of plots that remain in the same site index class over time
Databases used for dominant height modelling in forest stands are obtained from permanent or temporary plots, during a forest inventory, or from stem analyses (Scolforo, 1997)
Summary
Understanding growth and yield processes in forests is important to their rational management (Cosenza et al, 2015). The guide-curve is the most commonly used, whose application includes the adjustment of regression models that relate dominant height and forest age data (Scolforo, 2006) When this procedure is used for the classification of site productive capacity, its evaluation generally includes the stability analysis (Machado et al, 2011), which deals with the estimation of the number of plots (or samples) that remain in the same site index class over time. Databases used for dominant height modelling in forest stands are obtained from permanent or temporary plots, during a forest inventory, or from stem analyses (Scolforo, 1997) These measurements, simple, must be done carefully in order to avoid non-sampling errors (Soares et al, 2011), which can impact the analysis when one or more values are out of the general trend of the data and are considered as outliers. There are several ways to identify them, among which stands out the boxplot graphic (Schwertman et al, 2004)
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