## 3 Result and Discussion
### Multispectral Data Processing
#### 3.1.2 Verification of Regeneration Areas Using NBR  
NBR was used to derive images of the affected area before and after the fire, and negative images were extracted using dNBR. Figure 4 presents the NBR images; the details are shown in Table 4.  
Figure 4 presents an image obtained on 26 March 2019, before the fire; it consists mostly of positive (+) pixel values, which indicates a healthy distribution of vegetation. Only a few negative (\(-\)) pixel values were observed due to the forests' natural state. The average value was 0.41816, indicating that the distribution of vegetation was healthy. Figure 4 depicts an image obtained on 20 April 2019, immediately after the fire, clearly demonstrating the destruction. Furthermore, the average pixel value of \(-\)0.12467 indicates that most areas were affected by the fire. The image presented in Figure 4c was obtained on 8 August 2019, four months after the fire; compared to the image from April 20, limited vegetation recovery can be observed. Additionally, the mean pixel value was 0.21387. Compared to the average value in April, this indicates that vegetation regenerated.  
The post-fire images were subtracted from the pre-fire images to conduct a quantitative assessment of the vegetative changes over time. Figure 5a presents an image of the difference between 26 March and 20 April 2019, with an average value of 0.5428. According  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **Date** & **Min** & **Max** & **Avg.** \\ \hline \multirow{4}{*}{NBR} & pre-fire & 2019.03.26 & \(-\)0.14002 & 0.71666 & 0.41816 \\  & post-fire & 2019.04.20 & \(-\)0.52600 & 0.61567 & \(-\)0.12467 \\  & & 2019.08.08 & \(-\)0.3391 & 0.73291 & 0.21387 \\ \cline{1-1}  & difference in values & between pre-fire and right after & \(-\)0.50708 & 1.13161 & 0.54284 \\ \cline{1-1}  & between pre-fire and four month later & \(-\)0.81507 & 1.03147 & 0.20429 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Result of NBR pixel values.  
Figure 4: Normalized Burn Ratio (NBR) images: (**a**) pre-fire, (**b**) right after the fire, and (**c**) four months later.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Vegetation Index** & **Date** & **Min** & **Max** & **Avg.** \\ \hline \multirow{4}{*}{NDVI} & pre-fire & 2019.03.26 & \(-\)0.00819 & 0.96009 & 0.66461 \\  & post-fire & 2019.04.20 & \(-\)0.06359 & 0.91523 & 0.27663 \\  & & 2019.08.08 & \(-\)0.43527 & 0.9264 & 0.57097 \\  & difference in values & \(-\)0.62056 & 0.79164 & 0.29665 \\ \hline \multirow{4}{*}{EVI} & pre-fire & 2019.03.26 & \(-\)0.00442 & 0.64319 & 0.31495 \\  & post-fire & 2019.04.20 & \(-\)0.04932 & 0.83207 & 0.10769 \\  & & 2019.08.08 & \(-\)0.3153 & 0.90822 & 0.29134 \\  & difference in values & \(-\)0.42895 & 0.78493 & 0.18365 \\ \hline \hline \multirow{4}{*}{SAVI} & pre-fire & 2019.03.26 & \(-\)0.01229 & 1.43972 & 0.99672 \\  & post-fire & 2019.04.20 & \(-\)0.09537 & 1.37272 & 0.41484 \\ \cline{1-1}  & post-fire & 2019.08.08 & \(-\)0.65269 & 1.38944 & 0.85306 \\ \cline{1-1}  & difference in values & \(-\)0.9306 & 1.18736 & 0.43821 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Result of vegetation index pixel values.  
to the fire severity classification in Table 2, the average value corresponds to a moderate-high severity level. The average value in Figure 5b is 0.2043, which is significantly lower than the average value in Figure 5a; this numerical decrease corresponds to a low fire severity classification.