## 4 Results
Figure 4: Boxplots for each verification measures (**a**) Omission Error (OE); (**b**) Comission Error (CE); (**c**) Bias (BIAS); (**d**) Dice Coefficient (DICE) and (**e**) Critical Success Index (CSI) for 13 Landsat 8 scenes based on _Area Queimada_—Project for On-Board Autonomy-Vegetation burned area product (AQM-PROBA) (gray boxes) and Moderate Resolution Imaging Spectroradiometer direct broadcast (DB) burned area product (MCD64A1) for each Landsat 8 scene. Rather than presenting just a single value for each verification measure, a confidence interval can be calculated and presented as part of the classification skill. The confidence interval is comprised by the range, which is the lower and upper limit of each metric that can be expected from the classification approach, and by the probability that the verification measures of the classification approach will fall within the range. Accordingly, Table 5 indicates the expected range of each metric using a confidence interval of 95%, which means that there is a likelihood of 95% that the confidence interval (lower-upper limits) encompasses the true classification metric on unseen data. The confidence interval results (Table 5) indicate that if the verification measure were calculated for different reference datasets, the average value of each metric would fall between the lower and upper limits of the interval, with high probability. Thus, the AQM-PROBA yielded better results for the OE, BIAS, DC, CSI verification measures than MCD64A1, which, in turn, has a lower CE. Comparing the total burned area obtained from reference data, AQM-PROBA and MCD64A1 for each Landsat scene it is clear that AQM-PROBA displayed results closer to the reference data than MCD64A1, with the exception of the 220/68, 221/67, 222/67 and 222/68 scenes. Figure 5 shows different fire patterns among the thirteen analyzed scenes. Three scenes (218/072, 219/071 and 219/072) present less than 400 km\({}^{2}\) of burned area, whereas another three scenes contain approximately 600-750 km\({}^{2}\) of burned area (220/068, 222/068, and 221/071). Finally, the majority (seven scenes, 219/068, 219/070, 220/066, 220/067, 221/067, 221/070 and 222/067) display more than 1200 km\({}^{2}\) of area burned each. While all three BA products show similar spatial behavior, there are significant differences regarding the magnitude of the amount of burned area. On average, the AQM-PROBA and MCD64A1 provide underestimation of BA compared to the reference Landsat. The AQM-PROBA (MCD64A1) product provides greater burned area amount in 8 (5) of the analyzed scenes than MCD64A1 (AQM-PROBA), corroborating the BIAS behavior shown in Table 4.  
A better insight into the fire pattern of each analyzed scene may be obtained by analyzing the distribution of fire scars size by the corresponding fractions of total number of scars and of total burned area. Accordingly, Figure 6 shows the reference fire scar size distribution by number of scar categorized into four classes: (1) very small (0-25 ha); (2) small (25-100 ha); (3) medium (100-1000 ha); and (4) large (>1000 ha). Figure 6 reveals that the region presents a predominance of very small fire scars (class 1), however these scars accounts for less than 5% of the total amount of burned area. Conversely, the highest amount of burned area belongs to large fire scars (class 4, larger than 1000 ha), which in turn, accounts for the lowest number of scars. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Verification Measures** & **Lower Limit** & **Upper Limit** & **Fire Product** \\ \hline OE & 0.27 & 0.42 & AQM-PROBA \\  & 0.31 & 0.5 & MCD64A1 \\ \hline CE & 0.16 & 0.27 & AQM-PROBA \\  & 0.11 & 0.2 & MCD64A1 \\ \hline BIAS & 0.73 & 0.94 & AQM-PROBA \\  & 0.6 & 0.81 & MCD64A1 \\ \hline DC & 0.64 & 0.75 & AQM-PROBA \\  & 0.6 & 0.76 & MCD64A1 \\ \hline CSI & 0.48 & 0.61 & AQM-PROBA \\  & 0.45 & 0.61 & MCD64A1 \\ \hline \hline \end{tabular}
\end{table}