## 4 Result
### Comparison of Spatial Distributions of Forest Burned Area Detection Results  
Figures 5 and 6 illustrate the maps of the burned area for Site A and B, respectively. The area affected by forest fires is expressed as a 'burned area', and the area without forest fire damage is expressed as an 'unburned area'. According to the results in Tables 4 and 5, U-Net shows the best performance among three machine learning approaches in both sites. Pixel-based learning approaches, including LightGBM and RF, showed the noise in burned area detection, while there is little noise in U-Net results. As mentioned above, it is because U-Net could consider both the shape and pixel value of the burned area. The performance of the spatial distribution of burned area detection improved with the use of both pre- and post-fire images (Scheme 2) and additional indices including NDVI, NBR, dNDVI, and dNBR (Scheme 3). It produces less noises of burned area detection as more information is employed.  
Different types of land cover can be identified at Sites A and B. While Site A has a heterogeneous distribution of land cover, Site B has a homogeneous distribution. These features explain why Site B's accuracy outperforms than that of Site A (Tables 5 and 6). Similarly, Site B had a less noisy spatial distribution of the burned area. Sites A and B also differ in terms of their topography, which includes elevation and latitude/longitude. Site A has a lower latitude and lower elevation than Site B. This can be confirmed through the True Color Image (TCI) images of Sites A and B. Although the two images were acquired at a similar time, vegetation did not grow in the left area of Site A, and snow was distributed only in some areas at Site A only. On the other hand, it is not possible to check the snow-covered land at all areas of Site B, and it is assumed that vegetation is growing in a balanced manner in all areas by seeing that most of the mountain areas in the image are green. As a result, the forest class in Site A is mixed with snow, resulting to a significantly greater rate of false detection than the forest class in Site B. The spatial distribution of dNBR was compared with machine learning results. It performed poor with lots of noise. False detections in the water body were also identified at both sites when using the dNBR (Figures 5 and 6).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & - & \multicolumn{3}{c}{**Site A**} & \multicolumn{2}{c}{**Site B**} \\ \hline \multicolumn{2}{c}{**-**} & \multicolumn{1}{c}{**Accuracy**} & \multicolumn{1}{c}{**Training Time**} & \multicolumn{1}{c}{**Accuracy**} & \multicolumn{1}{c}{**Training Time**} \\ \hline \multirow{3}{*}{Scheme 1} & LightGBM & 0.88 & 175.5 s & 0.92 & 143.8 s \\  & RF & 0.78 & 17.5 s & 0.95 & 17.6 s \\  & U-Net & 0.91 & 173.2 s & 0.94 & 175.4 s \\ \hline \multirow{3}{*}{Scheme 2} & LightGBM & 0.89 & 190.4 s & 0.93 & 163.9 s \\  & RF & 0.84 & 18.2 s & 0.94 & 17.3 s \\  & U-Net & 0.90 & 177.2 s & 0.97 & 176.8 s \\ \hline \multirow{3}{*}{Scheme 3} & LightGBM & 0.90 & 196.2 s & 0.94 & 175.6 s \\  & RF & 0.88 & 18.5 s & 0.90 & 17.3 s \\ \cline{1-1}  & U-Net & 0.91 & 174.1 s & 0.97 & 179.2 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Time requirement for training to detect burned area.  
Figure 5: Spatial distribution of burned area detection results in Site A.