## 2 Study Area and Data
### Data Preparation  
Forest fires in Korea are small scale compared to those in Australia or in the United States. Therefore, satellites with low spatial resolution were considered unsuitable due to these characteristics. In this study, the Multi Spectral Instrument (MSI) sensor of the Sentinel-2 satellite operated by the European Space Agency (ESA) was used because it has suitable spatiotemporal resolution to monitor forest fires at a small scale. The Sentinel-2 satellites are made up of two satellites having the same chematistics (i.e., 2A and 2B) that are moving on the same orbit with 180 degrees apart. The temporal resolution of a single Sentinel-2 satellite is ten days; the combined constellation revisit of the two satellites is thus five days. Table 1 shows the data acquisition date of the study sites. Sentinel-2 satellite images are available at various product levels. Among them, Level 2A products, which have been geometrically and atmospherically corrected by ESA's 'Sen2 Cor' process, were used in this study due to their suitability for forest burned area detection studies that require the SR data. There are three spatial resolutions accessible at the Sentinel-2 (10 m, 20 m, and 60 m), and each resolution offers different kind of bands. A total of nine bands with 20 m spatial resolution data--B2 (Blue), B3 (Green), B4 (Red), B5--7 (Red Edge 1-3), B8a (Narrow NIR), B11 (SWIR 1), and B12 (SWIR 2)--were used to classify the forest fire damaged area. To increase the accuracy of the forest fire detection, NDVI, dNDVI, NBR, and dNBR were employed in addition to the nine bands given by Sentinel-2 satellites in this work. NDVI is the most widely used vegetation index in the field of the agriculture and the environment studies. dNDVI was calculated to estimate fire burn damage by subtracting post-fire image from pre-fire image. The NBR is calculated using the NIR band, which is sensitive to vegetation, and the SWIR band, which is sensitive to soil moisture content (Equation (1)) [36]. NBR uses the magnitude of the spectral difference between NIR and SWIR bands normalized by dividing by the sum of the two. A higher NBR value means \begin{table}
\begin{tabular}{c c c c c} \hline
**Sites** & **Event Date** & **Acquisition Date** & **Size** & **Resolution \& Product Level** \\ \hline Site A & 4 April 2019 & Pre-event & 26 March 2019 & 494 \(\times\) 655 pixels & \\ Site B & 24 April 2020 & Pre-event & 14 April 2020 & 886 \(\times\) 976 pixels & Spatial Resolution: 20 m \\  & & Post-event & 29 April 2020 & 886 \(\times\) 976 pixels & Product Level: Level-2A \\ \hline \end{tabular}
\end{table} 
Table 1: Description of satellite image collection. Figure 1: Study area (true color and false color composition of Sentinel-2 image). (**a**) True color image collected on April 2019 of Site A, (**b**) False color image collected on April 2019 of Site A, (**c**) True color image collected on April 2020 of Site B, and (**d**) False color image collected in April 2020 of Site B.  
healthy vegetation, while a lower NBR value suggests a burned-out area or bare soil. It is possible to distinguish bare soil from the damaged region since it tends to have a value near to 0. In the case of dNBR, it is determined by subtracting the pre-fire and post-fire images in the same way as dNDVI, calculated as Equation (2). The higher the dNBR value in the positive range, the greater the damage; the region where the dNBR value is negative may grow trees and vegetation again after affecting the forest fire. To establish objectivity of the relationship between the dNBR value and the fire burn severity, the United States Geological Survey (USGS) proposed a taxonomy to evaluate forest fire burn severity according to the dNBR value [37]. \[NBR=\frac{NIR-SWIR}{NIR+SWIR} \tag{1}\]  
\[dNBR=PrefireNBR-PostfireNBR \tag{2}\]  
Ground reference data were collected from satellite images by visual interpretation with the help of the forest map given by the Korea Forest Service.