Characterization of Fire Severity in the Moroccan Rif Using Landsat-8 and Sentinel-2 Satellite Images

Issam Eddine Zidane (1), Rachid Lhissou (2), Maryem Ismaili (3), Yassine Manyari (4), Abdelali Bouli (5), Mustapha Mabrouki (6)
(1) Ecology and Sustainable development Laboratory, Sultan Moulay Slimane University, Faculty of Science and Technology, B.O.523, Beni Mellal, Morocco
(2) Environmental and Northern Remote Sensing Research Team, Institut National de la Recherche Scientifique, 490, de la Couronne, Québec (Québec) G1K 9A9 Canada.
(3) Team of Remote Sensing and GIS Applied to Geosciences and Environment, Sultan Moulay Slimane University, Faculty of Science and Technology, B.O.523, Beni Mellal, Morocco
(4) Team of Remote Sensing and GIS Applied to Geosciences and Environment, Sultan Moulay Slimane University, Faculty of Science and Technology, B.O.523, Beni Mellal, Morocco
(5) Ecology and Sustainable development Laboratory, Sultan Moulay Slimane University, Faculty of Science and Technology, B.O.523, Beni Mellal, Morocco
(6) Industrial Engineering Laboratory, Sultan Moulay Slimane University, Faculty of Science and Technology, B.O.523, Beni Mellal, Morocco
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How to cite (IJASEIT) :
Zidane, Issam Eddine, et al. “Characterization of Fire Severity in the Moroccan Rif Using Landsat-8 and Sentinel-2 Satellite Images”. International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 1, Feb. 2021, pp. 72-83, doi:10.18517/ijaseit.11.1.10342.
Forest ecosystems are exposed increasingly to a variety of human activities and accentuated by climate change. With its Mediterranean climate, Northern Morocco is very hot, which exposes forests to widespread fires. This work aims at the delineation of wildfires and the spectral characterization of burnt vegetation as well as the characterization of the fire severity in the North of Morocco by using Landsat-8, Sentinel-2 spectral data, and topographic data. The methods used include the derivation of wildfires spectral indices and the computation of topographic parameters (elevation, slope, exposure) from SRTM and PALSAR digital elevation models. Then, the Spectral Angle Mapper (SAM) classification was used to map forest fires' severity. Furthermore, we have compared the severity classes obtained from the SAM method applied to Landsat 8 and Sentinel 2 data, with different spectral indices specialized in detecting wildfires, on the one hand, and topographic data, on the other hand. Results showed that MIRBI and NBR indices allow a better characterization of burned areas than BAI index. For its part, SAM classification provides a fair characterization of the severity classes of burnt forests. It has also been shown that the MIRBI index and sun exposure are strongly correlated with severity classes. The obtained maps show the spatial heterogeneity of burns severity and how they interact with topography. These maps may help land resource managers and fire officials predict areas of potential fire hazards and study vegetation regrowth areas after fires.

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