Geotechnologies as decision support strategies for the identification of fire-susceptible areas in Rio de Janeiro State

Camargo, Leandro de Souza; Silva, Corbiniano; Pimentel, Leonardo Duarte; Silva, Rodrigo Werner da; Sobrinho, Marco Antonio Basques; Landau, Luiz

doi:10.1007/s10661-022-10227-0

Cited by 2 publications

(1 citation statement)

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“…To evaluate the accuracies of the methods analyzed in this study, we utilized the DBFires product, 54 which is part of a well-established governmental project that utilizes remote sensing data and climatic services to detect fire foci and burned areas 55 – 58 It is important to inform that the use of the fire product described above as a reference for the occurrence of fires is due to the lack of in situ data. The method proposed in De Luca et al., 40 herein referred to as DL, was assumed as a baseline.…”

Section: Methodsmentioning

confidence: 99%

Unsupervised burned areas detection using multitemporal synthetic aperture radar data

Simões,

Negri,

Souza

et al. 2024

J. Appl. Rem. Sens.

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Climate change is a critical concern that has been greatly affected by human activities, resulting in a rise in greenhouse gas emissions. Its effects have far-reaching impacts on both living and non-living components of ecosystems, leading to alarming outcomes such as a surge in the frequency and severity of fires. This paper presents a data-driven framework that unifies time series of remote sensing images, statistical modeling, and unsupervised classification for mapping fire-damaged areas. To validate the proposed methodology, multiple remote sensing images acquired by the Sentinel-1 satellite between August and October 2021 were collected and analyzed in two case studies comprising Brazilian biomes affected by burns. Our results demonstrate that the proposed approach outperforms another method evaluated in terms of precision metrics and visual adherence. Our methodology achieves the highest overall accuracy of 58.15% and the highest F1 score of 0.72, both of which are higher than the other method. These findings suggest that our approach is more effective in detecting burned areas and may have practical applications in other environmental issues such as landslides, flooding, and deforestation.

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