# Automatic Methodology for Forest Fire Mapping with SuperDove Imagery
## 3 Results
### Severity Maps  
Finally, Figure 8 and Table 4 show the severity maps for each of the study zones, as well as the quantitative data on the percentage of surface area over the total fire-affected zone for each severity class. Quantitative analysis by visual inspection shows the coherence of the severity maps obtained with the proposed methodology and those observed in the CEMS reports. However, the quantitative analysis shows a substantial discrepancy in the surface percentages assigned to each of the four categories used by the Copernicus Service: negligible to slightly damaged, moderately damaged, highly damaged, and destroyed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Zone**} & **Estimated** & **CEMS Area** & \multirow{2}{*}{**Difference**} \\  & **Burned Area** & **Delimitation Data** & \\ \hline North Attica & 4766.90 & 6076.21 & 1309.31 (21.55\%) \\ Portbou & 450.13 & 500.04 & 49.91 (9.98\%) \\ Euboea & 496.11 & 526.22 & 30.11 (5.72\%) \\ Sierra de los Guajares & 4131.95 & 5141.4 & 1009.45 (19.63\%) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of burned-area estimation and CEMS area delimitation.  
Figure 7: Detailed views of discrepancy in burned-area edges. The red line delineates the fire’s extent as per CEMS data, contrasting with the yellow line that maps the area using the specified methodology: (**a**) pre-fire, (**b**) post-fire.  
To explain this discrepancy, the mechanisms of each approach should be considered. Indeed, the automatic approach proposed in this work uses a clustering algorithm that is inherently adaptive, while the procedure used in the CEMS reports is based on NDVI  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Zone** & **Severity** & **Estimated Severity** & **CEMS Reports Severity** \\ \hline \multirow{4}{*}{North Attica} & Negligible to slight damage & 25\% & 13\% \\  & Moderately damaged & 15\% & 48\% \\  & Highly damaged & 30\% & 38\% \\  & Destroyed & 30\% & 1\% \\ \hline \multirow{4}{*}{Portbou} & Negligible to slight damage & 34\% & 83\% \\  & Moderately damaged & 32\% & 16\% \\  & Highly damaged & 24\% & 1\% \\  & Destroyed & 10\% & 0\% \\ \hline \multirow{4}{*}{Euboea} & Negligible to slight damage & 28\% & 4\% \\  & Moderately damaged & 32\% & 0\% \\  & Highly damaged & 17\% & 49\% \\  & Destroyed & 23\% & 47\% \\ \hline \multirow{4}{*}{Sierra de los Guajares} & Negligible to slight damage & 32\% & 12\% \\  & Moderately damaged & 31\% & 14\% \\ \cline{1-1}  & Highly damaged & 25\% & 54\% \\ \cline{1-1}  & Destroyed & 12\% & 20\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of percentages of severity estimation between the proposed approach and CEMS report data.  
Figure 8: Synthetized severity maps computed using the proposed methodology: (**a**) north Attica, (**b**) Portbou, (**c**) Euobea, and (**d**) Sierra de los Guajères.  
thresholding, for which the same thresholds are used in all scenarios. Indeed, if the percentages in Table 4 are analyzed, it is observed that there is a certain balance between the groupings generated by the proposed methodology and a lack of representativeness of degrees of severity in the CEMS data. For example, in the fire that occurred in north Attica, only 1% of the area was considered as destroyed. Unfortunately, establishing the validity of either one of these approaches requires field data, which is not available.