Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for burnt and unburnt scars

Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for burnt and unburnt scars

AbstractThis research compares the use of the SAR (Sentinel-1) and Optical (Sentinel-2) sensors in identifying and mapping burnt and unburnt scars are rising during a bushfire in southeastern Australia and Margalla Hills, Islamabad, Pakistan, in 2019 and 2020. In order to evaluate the backscatter st...

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Bibliographic Details
Main Authors: Aqil Tariq, Yan Jiango, Linlin Lu, Ahsan Jamil, Ibrahim Al-ashkar, Muhammad Kamran, Ayman El Sabagh
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:Geomatics, Natural Hazards & Risk
Subjects:
Synthetic Aperture Radar (SAR)
Grey Level Co-occurrence Matrix (GLCM)
differential Normalized Burnt Ratio (dNBR)
machine learning
F-score
H-a plane
Online Access:https://www.tandfonline.com/doi/10.1080/19475705.2023.2190856

Internet

https://www.tandfonline.com/doi/10.1080/19475705.2023.2190856

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