Bitemporal analysis of burnt areas in the Atlantic Forest

• Autor(a) principal: Sacramento, Iorrana Figueiredo
• Data de Publicação: 2020
• Outros Autores: Michel, Roberto Ferreira Machado, Siqueira, Rafael Gomes
• Tipo de documento: Artigo
• Idioma: por; eng
• Título da fonte: Sociedade & natureza (Online)
• Texto Completo: https://seer.ufu.br/index.php/sociedadenatureza/article/view/53339
• topic: Classificação de imagem; Segmentação de imagem; Razão de bandas; Seleção de variáveis; Incêndios florestais.; Image classification; Image segmentation; Band ratio; Variables selection; Forest fire

The study of burned areas is used as a subsidy for fire control and monitoring in the protected areas. In face of the challenges of the spectral signature characterization of burned areas, this study aimed to apply the object-oriented classification method and to evaluate the performance of spectral indices subsets for mapping burned areas in the Atlantic Forest. For that, we performed a bitemporal analysis between 2014 and 2016, considering the difference of each spectral indices among two LANDSAT 8 images: pre-and post-fire. The object-oriented classification was performed automatically by segmentation, supervised classification and optimization algorithms in the GIS environment. The “weak” burn severity class was the most expressive, with 13.65% of the mapped area, while the “severe” burn severity class occupied 0.3%. The burned areas presented an increase of reflectance in the red and shortwave infrared bands and a decrease in the near infrared band. The ΔNBR was the best discriminator of burned area and the ΔNBR, ΔNBR2, ΔNDMI, ΔSAVI, ΔNDVI, ΔGEMI and ΔMSAVI set presented the highest separation threshold. The validation of the classification by the Kappa agreement coefficient obtained a good outcome (0.72). The selection of the variables showed efficiency in determining the spectral indices’ subset with the best performance for detecting the classes of burned areas, improving the classification accuracy and reliability. The segmentation was also important for the effectiveness of the object-oriented classification, being directly influenced by the image spatial resolution.