Evaluation and comparison of Sentinel-2 MSI, Landsat 8 OLI, and EFFIS data for forest fires mapping. Illustrations from the summer 2017 fires in Tunisia

This study aims to assess the performance of the Sentinel-2 and Landsat 8 sensors to map forest fires. We choose two fire events, the Haddad fire and the Sidi Ferdjani fire, in northwestern Tunisia in 2017. Several spectral indices were derived from each sensor and the performance of each spectral index was assessed. A validation exercise was undertaken for each fire to compare the spatial matching between the burned area retrieved from each spectral index and its homologue obtained from the Emergency Management Service (EMS). Our results indicate that Delta NBR and its relativized version RBR derived from both sensors exhibit the highest discrimination power (M-statistic values >2.5). The Sentinel sensor is slightly more efficient than the Landsat 8 in mapping burned scars, but both sensors produce acceptable results. We conclude that both sensors could be a good alternative to EFFIS data, particularly when there is a need to detect details inside the burned areas.

Automated summary

The study evaluated the performance of Sentinel-2 and Landsat 8 sensors in mapping forest fires, both sensors showed acceptable results with slight superiority of Sentinel sensor in mapping burned scars. This suggests that both sensors could be a good alternative to EFFIS data for detecting details inside the burned areas.