Discriminating Vegetation Cover Classes of na Amazon/Cerrado Transition Region in the Mato Grosso State Using ALOS-2/PALSAR-2 Satellite Images

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Vanessa Souza Silva
https://orcid.org/0000-0002-1726-7401
Edson Eyji Sano
https://orcid.org/0000-0001-5760-556X
Tati de Almeida
https://orcid.org/0000-0002-6387-8254
Humberto Navarro de Mesquita Júnior
https://orcid.org/0000-0002-1718-9204

Abstract

The transition region between Amazonia and Cerrado, especially in the Mato Grosso State, is environmentally sensitive because of the high levels of biodiversity and high grain and cattle beef productions. The objective of this study was to discriminate representative land use and land cover (LULC) classes found in the Sinop/Mato Grosso region based on the ALOS-2/PALSAR-2 satellite images. The following thematic classes were considered: primary forest, secondary forest, croplands, and cultivated pasturelands. The scenes were obtained in February (rainy season) and September (dry season) of 2017, with spatial resolution of 6.25 m, HH and HV polarizations, and L-band (wavelength of 23 cm) and processed by the Random Forest (RF) and Support Vector Machine (SVM) classifiers. Training and validation samples were obtained in the field (65 samples) and complemented based on the LULC maps produced by the MapBiomas and TerraClass projects (135 samples). It was possible to discriminate two groups of thematic classes: primary forest and secondary forest; and croplands and pasturelands. Although the overall accuracy of RF was higher than that from SVM, both classifiers presented statistically similar performances.

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How to Cite
SILVA, V. S. .; SANO, E. E. .; ALMEIDA, T. de; MESQUITA JÚNIOR, H. N. de. Discriminating Vegetation Cover Classes of na Amazon/Cerrado Transition Region in the Mato Grosso State Using ALOS-2/PALSAR-2 Satellite Images. Brazilian Journal of Cartography, [S. l.], v. 73, n. 1, p. 1–16, 2021. DOI: 10.14393/rbcv73n1-48516. Disponível em: https://seer.ufu.br/index.php/revistabrasileiracartografia/article/view/48516. Acesso em: 4 dec. 2024.
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