Diameter Increment Modeling in an Araucaria Forest Fragment Using Cluster Analysis

Roik, M.; Machado, Sebastião do Amaral; Filho, Afonso Figueiredo; Sanquetta, Carlos Roberto; Roveda, Marcelo; Stepka, Thiago Floriani

doi:10.1590/2179-8087.062517

Cited by 2 publications

(2 citation statements)

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“…Unlike their findings, in our study the species composition (species diversity index) was not effective for all species groups. In general, the BAL variable has been used as the most important competition variable in previous growth and increment modeling studies (e.g., [60][61][62][63][64]). In this study, we incorporated all the measured independent variables, i.e., DBH, BAL, H S, H d, BA, number of trees per hectare, slope, aspect, and altitude into the modeling process.…”

Section: Discussionmentioning

confidence: 99%

“…The second reason might be the autocorrelation of data used in increment models due to both time and spatial components [26]. Although permanent sample plots are the most popular method for estimating forest growth and yield and have been used in numerous studies (e.g., [60][61][62][63]), they are not immune from the autocorrelation. The autocorrelation created within the sample plots was greater than the autocorrelation between the sample plots, because the sample plots are separated by more distance than the trees within a sample plot [26].…”

Section: Discussionmentioning

confidence: 99%

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Machine Learning for the Estimation of Diameter Increment in Mixed and Uneven-Aged Forests

et al. 2022

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Estimating the diameter increment of forests is one of the most important relationships in forest management and planning. The aim of this study was to provide insight into the application of two machine learning methods, i.e., the multilayer perceptron artificial neural network (MLP) and adaptive neuro-fuzzy inference system (ANFIS), for developing diameter increment models for the Hyrcanian forests. For this purpose, the diameters at breast height (DBH) of seven tree species were recorded during two inventory periods. The trees were divided into four broad species groups, including beech (Fagus orientalis), chestnut-leaved oak (Quercus castaneifolia), hornbeam (Carpinus betulus), and other species. For each group, a separate model was developed. The k-fold strategy was used to evaluate these models. The Pearson correlation coefficient (r), coefficient of determination (R2), root mean square error (RMSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were utilized to evaluate the models. RMSE and R2 of the MLP and ANFIS models were estimated for the four groups of beech ((1.61 and 0.23) and (1.57 and 0.26)), hornbeam ((1.42 and 0.13) and (1.49 and 0.10)), chestnut-leaved oak ((1.55 and 0.28) and (1.47 and 0.39)), and other species ((1.44 and 0.32) and (1.5 and 0.24)), respectively. Despite the low coefficient of determination, the correlation test in both techniques was significant at a 0.01 level for all four groups. In this study, we also determined optimal network parameters such as number of nodes of one or multiple hidden layers and the type of membership functions for modeling the diameter increment in the Hyrcanian forests. Comparison of the results of the two techniques showed that for the groups of beech and chestnut-leaved oak, the ANFIS technique performed better and that the modeling techniques have a deep relationship with the nature of the tree species.

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