Evaluation of Spectral Indices for Mapping Burned Areas using Unsupervised Classification in Different Ecosystems using Spectral Indices from Sentinel-2 images

Conteúdo do artigo principal

Juarez Antonio da Silva Júnior
https://orcid.org/0000-0002-2898-0309
Admilson da Penha Pacheco
https://orcid.org/0000-0002-3635-827X

Resumo

Fire is one of the natural agents that has the greatest impact on the terrestrial ecosystem and plays an important ecological function in a huge portion of the Earth's surface. Remote sensing is an important source for mapping and monitoring forests as well as environmental damage to the landscape caused by fire.To estimate the severity of the fire, the temporal distinction between the spectral indices  before and after the fire is critical. This study examines the performance of spectral indices derived from Sentinel-2 Multi Spectral Instrument (MSI) bands for the detection of fire-affected areas in forest fires in regions with different ecosystems located in Brazil, the United States, and Portugal. Separability Index (M) and the Reed-Xiaoli Detection (RXD) Anomaly automatic classifier allowed us to assess the spectral separability and the thematic accuracy of the burned area for the different spectral indices. The analysis parameters were based on spatial dispersion with validation data, Commission Errors (CE), Omission Errors (OE), and Sorensen–Dice Coefficient (DC). The results indicated that the indices based exclusively on the longer shortwave infrared (SWIR1), shorter SWIR (SWIR2), and red-edge bands showed a high degree of separability and were more suitable for detecting the burned areas, although it was observed that the diversity of land use affects the performance of the indices. The evaluation of the performance of the spectral indices based on Sentinel-2 data was important in the analyzes of potentialities and limitations in the detection of burned areas in the face of the different global biomes.

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SILVA JÚNIOR, J. A. da; PACHECO, A. da P. Evaluation of Spectral Indices for Mapping Burned Areas using Unsupervised Classification in Different Ecosystems using Spectral Indices from Sentinel-2 images. Revista Brasileira de Cartografia, [S. l.], v. 75, 2023. DOI: 10.14393/rbcv75n0a-68307. Disponível em: https://seer.ufu.br/index.php/revistabrasileiracartografia/article/view/68307. Acesso em: 17 jul. 2024.
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Sensoriamento Remoto

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