Abstract
During the last years, many regions in North of Morocco have suffered from the spread of wildfires in summer such as Mediouna forest. Indeed, the need for Remote sensing data became more and more evident, since it provides huge regional scale data in short window time and with less cost and human resources. The challenge of this paper is to study the behavior of the Normalized Burn Ratio (NBR) index over time to determine the relationship between its variation and the Mediouna forest biomass evolution using Landsat 8 images. Moreover, we classify the burned areas, and estimate the burn severity based on spectral signatures. The monitoring of the burned areas was performed using the NBR and Burn area severity is performed using Differential Normalized Burn Ratio (dNBR). The correction of the satellite images, the calculation of the NBR index, the analysis and the mapping of the results were carried out using QGIS software. We deduced that NBR and dNBR indices are effective in monitoring Mediouna forest fires.
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Acknowledgements
The authors would like to thank all the collaborators within this work, from the writing manuscript team. El Khalil Cherif would like to mention the financial support by FCT with the LARSyS—FCT project UIDB/50009/2020 and FCT project VOAMAIS (PTDC/EEIAUT/31172/2017, 02/SAICT/2017/31172).
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Badda, H. et al. (2023). Landsat 8 data for forest fire monitoring: case of Mediouna forest in Tangier, Morocco. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13644. Springer, Cham. https://doi.org/10.1007/978-3-031-37742-6_12
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DOI: https://doi.org/10.1007/978-3-031-37742-6_12
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