Computer-aided classification of successional stage in subtropical Atlantic Forest: a proposal based on fuzzy artificial intelligence - Bressane, Adriano [UNESP]; Gomes, Isadora Gurjon; da Rosa, Graziele Coraline Scofano [UNESP]; Brandelik, Caio Cesar Moraes; Silva, Mirela Beatriz [UNESP]; Siminski, Alexandre; Negri, Rogério Galante (Springer Nature, 2023)
Statement of problem. Due to the continuous variability of the forest regeneration process, patterns of indicator variables with membership in more than one successional stage may occur, making the classification of such stages a challenging and complex task. Purpose. This study aims at presenting a comparative analysis of artificial intelligence methods as an alternative for computer-aided classification of successional stages in subtropical Atlantic Forest. As a research hypothesis, the authors consider that a fuzzy inference system should provide the best performance due to its ability to deal with uncertainties inherent to complex processes. Material and methods. The analyses were carried out using a database of the forest inventory of Santa Catarina, Southern Brazil. The data are composed of 177 sampling units of subtropical Atlantic Forest (mixed ombrophilous forest), characterized according to eighth indicator variables verified from the field by experts. This database was employed to train several machine learning methods under a tenfold cross-validation process. The overall accuracy (θ) and kappa coefficient were used to compare the performance between FIS and neural networks, classifier committees and support vector machine. Then, to verify if the classification by the FIS differed from the one performed by experts, the Kappa index and a statistical significance analysis by Pearson’s χ2 test were determined. The hypotheses were verified with two-way tests at a significance level (α) 0.05, for a test power (1-β) 0.8 and minimum expected effect size between medium (ρ = 0.3). Results. Statistical significance tests confirmed the hypothesis that FIS achieved the highest performance, with θ = 98.3% and a kappa value equal to 0.93 (almost perfect agreement) and showed no significant difference (χ2 = 0.047, p = 0.976) in comparison with the classification by experts. Conclusions. The use of FIS represents a promising alternative as a tool applicable for computer-aided classification of successional stages in subtropical Atlantic Forest. Practical implications. The results and conclusions should substantially impact the guidelines and decision-making process for deforestation authorizations and applicable compensation measures, which are based on the forest succession stage.
Keywords: Artificial intelligence; Data-driven fuzzy; Forest regeneration; Successional stage