VEGETATION SUCCESSIONAL STRATA CLASSIFICATION IN THE AMAZON ESTUARY THROUGH QUANTILE REGRESSION

Authors

DOI:

https://doi.org/10.5380/rf.v56i1.100425

Keywords:

robust method, Stoffels equation, modeling

Abstract

Vegetation successional strata classification is essential for guiding forest management and conservation, particularly in Amazonian floodplain environments. This study sought to classify successional stages in a floodplain forest of the Amazon estuary by adjusting height–diameter models using quantile regression. Data were collected from a permanent forest inventory in Gurupá, Pará. Two models (Curtis and Stoffels) were tested, and their performance was evaluated using root mean square error (RMSE), Pearson’s correlation coefficient, Akaike information criterion (AIC), bias, and mean absolute deviation (MAD). Residual distribution and trend line plots were also analyzed. Quantile regression proved to be an efficient method for fitting the models, with the Stoffels equation showing the best performance. By adjusting its coefficients, this equation enabled the classification of species across different successional strata, demonstrating the potential of quantile regression as a robust tool for analyzing forest structure in heterogeneous environments.

Author Biographies

Jadson Coelho de Abreu, Universidade do Estado do Amapá

He holds a degree in Forest Engineering from the State University of Amapá UEAP (2010), a degree in Mathematics from Estácio de Sá University (2021), a Master's degree in Forest Sciences from the Federal Rural University of Pernambuco UFRPE (2012), and a PhD in Forest Science from the Federal University of Viçosa UFV (2019). He is currently an Adjunct Professor at the State University of Amapá UEAP. He leads the research group on quantitative methods applied to forest resources. He works as a permanent professor in the Postgraduate Program in Natural Resources of the Amazon (RENAmazon-UEAP). He has experience in Forest Resources and Forest Engineering, with an emphasis on Forest Management and Measurement, working mainly on the following topics: Dendrometry and Forest Inventory, statistical models (linear and non-linear), mixed models, robust regression, and machine learning (Artificial Neural Networks, Support Vector Machine, Decision Tree, and Random Forest).

Robson Borges de Lima, Universidade do Estado do Amapá

He holds a degree in Forestry Engineering from the State University of Amapá (2011), a Master's degree (2012-2014) and a PhD (2014-2017) in Forestry Sciences from the Postgraduate Program in Forestry Sciences at the Federal Rural University of Pernambuco. He is currently a professor in the Forestry Engineering program at the State University of Amapá. He has experience in the area of ​​Forest Resources and Forestry Engineering, with an emphasis on Statistical Methods, Measurement, Inventory and Forest Management.

Perseu da Silva Aparício , Universidade do Estado do Amapá

He holds a degree in Forest Engineering (2006), a Master's degree in Forest Sciences (2008), and a PhD in Tropical Biodiversity (2013). He is a Professor at the State University of Amapá. He has experience in Forest Resources and Forest Engineering, with an emphasis on Forest Management, working mainly on the following topics: Dendrometry, Forest Statistics and Experimentation, and Forest Inventory. He has experience in Project Assembly and Execution, Laboratory Procedures, and Teaching.

Wegliane Campelo Silva, Universidade Federal do Amapá

She holds a degree in Forest Engineering (2003) and a PhD in Forest Sciences (2011) from the Federal Rural University of Pernambuco. She is currently a Professor/Associate Researcher at the Federal University of Amapá - UNIFAP. Curator of the Herbarium of the Federal University of Amapá - HUFAP. Coordinator of the MAFLOR Research Group - Management and Characterization of Forest Essences/CNPq/UNIFAP. She has experience in the area of ​​Forest Resources and Forest Engineering, with an emphasis on Botany and Silviculture, working mainly in the following themes: Plant Morphology and Taxonomy, Dendrology, Plant Ecology, Inventory and Forest Management.

Lucas Sergio De Sousa Lopes, Universidade Federal Rural da Amazônia

Adjunct Professor at the Federal Rural University of the Amazon (UFRA), Capitão Poço Campus. He holds a degree in Forestry Engineering from the Federal University of Western Pará (UFOPA) (2017) and a master's and doctorate in Forestry Science from the Federal University of Viçosa (UFV) (2024). He has experience in Forest Management, working mainly on the following topics: statistical methods applied to forestry sciences, forest inventory and measurement, machine learning, and statistical modeling.

Carlos Pedro Boechat Soares , Universidade Federal de Viçosa

He holds a bachelor's degree in Forest Engineering from UFV; a master's and doctorate in Forest Science from UFV; and a postdoctoral degree from the University of Florida. He is currently a Full Professor at the Federal University of Viçosa. He is a reviewer for scientific journals. He teaches the undergraduate Forest Engineering program at UFV. He advises students in the graduate program in Forest Science (UFV). He develops research in the areas of Forest Measurement and Management, Dendrometry, and Forest Inventory. He is a CNPq researcher/scholar (level 2).

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Published

2026-01-16

How to Cite

Abreu, J. C. de, Lima, R. B. de, Aparício , P. da S., Silva, W. C., Lopes, L. S. D. S., & Soares , C. P. B. (2026). VEGETATION SUCCESSIONAL STRATA CLASSIFICATION IN THE AMAZON ESTUARY THROUGH QUANTILE REGRESSION . Floresta, 56(1), e100425. https://doi.org/10.5380/rf.v56i1.100425

Issue

Vol. 56 No. 1 (2026)

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Artigos

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Copyright (c) 2026 Jadson Coelho de Abreu; Robson Borges de Lima; Perseu da Silva Aparício , Wegliane Campelo Silva, Lucas Sergio De Sousa Lopes, Carlos Pedro Boechat Soares

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