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.

Artificial Neural Networks and Linear Regression Reduce Sample Intensity to Predict the Commercial Volume of Eucalyptus Clones

“…The neuron number in the hidden layer varied from 3-25 in scenario (a) and from 1-30 in (b). In scenario (a), the number of input variables of ANNs with the general data was higher than the other approaches of this same scenario and, in scenario (b), the number of input variables in the intensity of the one tree per diameter class was lower (13)(14)(15)(16)(17)(18)(19).…”

“…In forestry, ANNs have been applied in several studies [18]. For instance, for modelling forest growth and dynamics [14], predicting height of individual trees [19], diametric distribution [20], prediction of biomass above ground [21], prognosis of diameter [22], volume of stems and branches [23], and modeling of survival and mortality [24].…”

Investigation of parametric and artificial neural network modeling approaches for total tree height prediction in cedar plantations

“…Until now, in the field of forest modeling, the implemented research led to the conclusion that artificial neural network models (ANNs) can be considered as a significant alternative modeling technique for many characteristics of trees against to standard modeling methods (Liu et al 2003;Özçelik et al 2008;Soares et al 2013;Imada 2014;Ercanli et al 2018;Socha et al 2020). Moreover, according to the same research field, ANN models have gained publicity because they have successfully been applied for classification (Schmoldt et al 1997;Sarigul et al 2003;Cosenza et al 2017) and estimation and prediction problems (Pertsen et al 2010;Leite et al 2011;Reis et al 2016;Özçelik et al 2017;Monteiro da Silva et al 2018;Zhou et al 2018;Socha et al 2020). Despite the fact that ANNs need effort for avoiding over or under-fitting the data during their training phase, artificial intelligence has been successfully used for total tree height models construction (Li and Jiang 2010;Diamantopoulou and Özçelik 2012;Özçelik et al 2013;Vieira et al 2018;Thanh et al 2019; Ercanli 2020), as well.…”

Site Index for Prosopis Alba Plantations in the Semi-Arid Chaco Through Mixed Models

“…According to Skovsgaard and Vanclay (2008); Cosenza et al, (2017), the methods for assessing the productivity of a site can be divided into dendrocentric and geocentric, the first based on information from the stands themselves and the second using environmental variables related to the site.…”