WETLAND CLASSIFICATION USING MACHINE LEARNING MODELS IN THE BRAZILIAN PANTANAL

Autores

DOI:

https://doi.org/10.5380/raega.v63i2.100193

Resumo

The Pantanal is the largest tropical wetland in the world and covers the countries of Bolivia, Brazil, and Paraguay. Some regions within the Pantanal are part of the Convention on Wetlands or are recognized as Biosphere Reserves and World Heritage Sites by UNESCO. This work's objective is to classify surface water bodies into wetlands using machine learning techniques, through clustering techniques. Initially, the (Normalized Difference Vegetation Index) was used from a mosaic of images covering the study area from the Sentinel 2-A satellite processed on the Google Earth Engine platform. From this index, a threshold is established empirically, and the water bodies are segmented. The clustering technique is then applied to the morphological characteristics of each segmented object. The results obtained show that it is possible to categorize water bodies with unsupervised learning techniques.

Biografia do Autor

Natalia Verónica Revollo, Universidad Nacional del Sur

Ph.D. in Engineering from UNS, Buenos Aires, Argentina, and Computer Engineer from the National University of Jujuy. She is currently a professor in the Department of Electrical and Computer Engineering at UNS and an adjunct researcher at the Institute of Computer Science and Engineering (ICIC-CONICET) in the area of technological and social development projects.

Edinéia Santos Galvanin, Universidade Estadual Paulista (Unesp)

PhD in Cartographic Sciences from São Paulo State University (UNESP) (2007), is an Associate Professor at FCTE/UNESP, Ourinhos Campus, and a permanent faculty member of the Professional Master's Program in Geography.

Carlos Enrique Berger , Universidad Provincial del Sudoeste

PhD in Control Systems and Electronic Engineer from the National University of the South (UNS), located in Bahía Blanca, Argentina. He is a full professor at the Faculty of Micro, Small, and Medium-Sized Enterprises at the Provincial University of the Southwest (UPSO), and at the Department of Electrical and Computer Engineering (DIEC-UNS). His work focuses on the implementation of innovative teaching methodologies in computer programming, and his main line of research involves the application of software solutions to multidisciplinary fields of study.

Verónica Gil, Universidad Nacional del Sur /

Doctorate in Geography in the area of Physical Geography from UNS. Independent Researcher at CONICET in the area of Social Sciences and Adjunct Professor in the area of Physical Geography at UNS.

Sandra Mara Alves da Silva Neves, University of Mato Grosso State

PhD in Sciences (Geography) from the Federal University of Rio de Janeiro (UFRJ) (2006). She is an adjunct professor in the undergraduate and stricto sensu graduate programs in Geography and Environmental Sciences at UNEMAT.

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Publicado

2025-08-20

Como Citar

Revollo, N. V., Galvanin, E. S., Berger , C. E., Gil, V., & Neves, S. M. A. da S. (2025). WETLAND CLASSIFICATION USING MACHINE LEARNING MODELS IN THE BRAZILIAN PANTANAL. Ra’e Ga: O Espaço Geográfico Em Análise, 63(2), 19–34. https://doi.org/10.5380/raega.v63i2.100193

Edição

v. 63 n. 2 (2025): VIII - Geopantanal

Seção

Artigos

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