Burned area detection using Sentinel-1 SAR data: A case study of Kangaroo Island, South Australia

Hosseini, Maryamsadat; Lim, Samsung

doi:10.1016/j.apgeog.2022.102854

Cited by 8 publications

(2 citation statements)

References 32 publications

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“…In our future work, we aim to (i) explore the potential of the proposed framework, still using SAR time series, in addressing other environmental issues such as floods, landslides, urban expansion, deforestation, and more; (ii) investigate alternative probability modeling approaches; and (iii) analyze additional SAR indices, possibly with a bitemporal approach (e.g. Hosseini and Lim, 41 Goodenough et al 39 …”

Section: Discussionmentioning

confidence: 99%

“…The results were validated according to a reference ΔNBR image from Sentinel-2, showing results with an F1-Score >0.8. Similarly, Hosseini and Lim 41 adopted a reference image with the ΔNBR index from Sentinel-2 as ground-truth data; however, the indices used and the classification method are different. The authors applied the Random Forest algorithm for burned area detection, obtaining results that adhere to the validation database (F1-Score = 0.87), surpassing the MODIS MCD64 product (F1-Score = 0.83).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%