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• The nNBR vary considerably between Landsat 8 and Sentinel 2 within the study areas. • The agreement or disagreement in the classification is not related to any particular burn severity class. • The difference in biogeographical regions and the vegetation types might be a major factor of disagreement. Material and methods Comparison of forest fire severity mappping using Landsat 8 and Sentinel 2 around the Mediterranean basin Mustapha Lateb 1,2 , Chariton Kalaitzidis 1 and Ioannis Gitas 3 1 Mediterranean Agronomic Institute of Chania (MAICh), Greece 2 Mouloud Mammeri University based Tizi-Ouzou (UMMTO), Algeria 3 Aristotle University of Thessaloniki, Greece UMMTO Sumforest Conference “Bridging research, policy and practice for sustainable forest management” (17-18 October 2017, Barcelona, Spain) Introduction Mapping wild fire effect on the forest ecosystem using Landsat image is an advanced topic. Many authors have used different combination of bands to measure the fire severity and assess the forest resilience in broad scale. The revolution comes with the Normalized Burn Ratio (NBR). It was used first time the US land management agencies to derive severity maps. Having the same formula with the normalized difference vegetation index (NDVI), the NBR uses mid-infrared (MIR) band in place of the red band. It integrates the two Landsat TM/ETM+ bands (4 and 7) that respond most, but in opposite ways to burning. Many improvements were made to the NBR by different authors; Key and Benson (2006) subtracted a post-fire NBR from a pre-fire NBR and created the differenced NBR (dNBR). It allows mapping the absolute vegetation change. The different sensitivity of the near-infrared (NIR) and MIR Landsat bands to pre and post-fire vegetation state showed high correlation with field data. The launch of Landsat 8 (L8) and Sentinel 2 (S2) satellites offer the opportunity for the continuity of dNBR use. Both satellites show spectral and radiometric differences that might affect the NBR values. Aim The aim of this study is to test the performance of NBR for forest fire severity mapping using the Landsat 8 and Sentinel 2 sensors, when assessing forest fire severity in different biogeographical regions and vegetation types. Mediterranean sea Class 1 Class 2 Class 3 Class 4 Class 5 Class 6 Class 7 Sum Class 1 0 0 3 4 5 2 0 14 Class 2 0 0 1 0 0 0 0 1 Class 3 27 1104 23015 1214 73 14 1 25448 Class 4 4 68 3733 12830 3379 190 9 20213 Class 5 1 3 163 3242 12754 2678 19 18860 Class 6 0 0 5 67 1458 6726 690 8946 Class 7 0 0 2 4 9 156 679 850 Sum 32 1175 26922 17361 17678 9766 1398 74332 dNBR Sentinel 2 dNBR Landsat8 Confusion matrix, Greece Conclusion Results Figure2: Sentinel 2, ESA Figure3: Landsat8, NASA = − + = − + References and Image credits • Key CH, Benson NC (2006) ‘Landscape assessment: sampling and analysis methods.’ USDA Forest Service, Rocky Mountain Research Station General Technical Report RMRS-GTR-164-CD. (Ogden, UT) • Figure 1: https://landsat.gsfc.nasa.gov/wp-content/uploads/2015/06/Landsat.v.Sentinel-2.png • Figure 2: https://cnes.fr/fr/le-monde-agricole-face-la-revolution-sentinel-2 • Figure 3 : https://www.nasa.gov/ Study sites For each study site, pre-fire and post-fire images are selected, NBR is computed for on each of them, so preNBR and postNBR are obtained. Based on the latter, dNBR is computed. dNBR of both Sentinel2 and Landsat8 are compared. = preNBR - posNBR Seven (7) forest fires from seven different Mediterranean countries occurred in summer 2016 have been selected from European Forest Fire Information System (EFFIS) using “Size Class = Major” as criteria since the NBR is designed for big forest fire events. Figure 1 Figure 4 Figure 5
