## 4 Results  
Figure 1 illustrates the mean reflectance and spectral index values of the unburned (first image acquisition) and burned (second image acquisition) pixel samples at each site. As discussed in the introduction, the impact of burning is predominantly to reduce NIR and SWIR reflectance. Among the spectral indices the NBR index derived from bands 8a and 12 (denoted b8a/12 in Figure 1) has the greatest mean unburned to burned reduction. Among the four NDVI band variants the greatest reductions occur for the 10m NDVI band (denoted 8/4 in Figure 1). These results should be treated with some caution as although a large number of pixels were considered (Table 4, middle column) the burned and unburned data were not normally distributed. This is illustrated in Figure 2 which shows the results of the Shapiro-Wilk normality tests. Only the unburned NDVI red edge band values (denoted 8a/6 and 8a/7 in Figure 2) for the Australia and Canada sites were significantly normally distributed, and only the burned NDVI red edge band derived from bands 8a and 7 for the Australia and Cambodia sites were significantly normally distributed. The pattern of results in Figures 1 and 2 was similar for the atmospherically corrected (i.e., surface reflectance) data. To illustrate the effect of atmospheric correction, and provide context for the separability results, Figure 3 shows TOA and surface reflectance for the second image acquisition near Fort McMurray, Alberta, Canada, illustrating true color 10 m (b04, b03, b02) and false color 20 m (b12, b11, b8a) TOA and surface reflectance. The impact of the atmospheric correction is most apparent comparing the true color TOA reflectance (a) and surface reflectance (b). The atmospheric correction increases the true color image spatial contrast and reduces the blue appearance, which is expected from comparable Landsat atmospheric correction experiments [64; 65]. The distinct smoke plume is not well corrected, which as noted earlier is a problem for most atmospheric correction methods. The impact of the atmospheric correction is visually less apparent in the longer wavelength false color bands, i.e., comparing (c) and (d), which is expected as atmospheric effects are smaller at longer wavelengths [65; 66]. The false color longer wavelength bands are more sensitive to the effects of fire on vegetation and consequently the extensive burned area in the east side of the image has more contrast with the surrounding unburned vegetation than is apparent in the true color images. These qualitative results illustrate the need for careful band selection for burned area mapping. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & & \multicolumn{2}{c}{**Reference Data**} \\ \cline{3-4}  & & **Class 1** & **Class 2** \\ \hline \multirow{2}{*}{**Classified Data**} & **Class 1** & \(X_{11}\) & \(X_{12}\) \\  & **Class 2** & \(X_{21}\) & \(X_{22}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cross-tabulation of the counts of correctly classified and misclassified classes. Figure 1: Mean unburned and burned MSI band (Table 2) and spectral index (Table 3) top of atmosphere (TOA) values. Figure 2: Shapiro-Wilk normality test \(p\)-values for the top of atmosphere (TOA) unburned and burned MSI band (Table 2) and spectral index values (Table 3). High \(p\)-values indicate that the null hypothesis that the data came from a normally distributed population cannot be rejected, i.e., that the data are normally distributed. Figure 4 illustrates the TD separability values for each of the TOA band reflectances and spectral indices. The blue bars illustrate the control unburned-unburned TD values and the red bars illustrate the unburned-burned TD values. Figure 5 shows the same results for the SEN2 COR atmospherically corrected data. The impact of the atmosphere at visible wavelengths, i.e., for b02, b03 and b04, is apparent with greater differences between the TOA control (blue) and the unburned-burned (red) separability values (Figure 4) than for the equivalent TD surface (Figure 5) values.