## 2 Materials and Methods  
### Satellite Data  
Satellite-based NBR and NDVI were used to detect the area affected by forest fire and evaluate post-fire recovery [32]. The NBR was calculated using the reflectance of the NIR and SWIR wavelengths (\(\rho\)) as shown in Equation (1):  
\[\text{NBR}=\frac{\rho_{\text{NIR}}-\rho_{\text{SWIR}}}{\rho_{\text{NIR}}+\rho_{ \text{SWIR}}} \tag{1}\]  
The NIR band is an effective spectral band for vegetation monitoring, and the SWIR spectral band effectively represents moisture in soil and vegetation. Sudden changes can occur in the NBR of burned areas because of alterations to the canopy structure and moisture content by forest fires [33], while NBR change is close to zero for unburned areas [32]. Thus, areas affected by the forest fire were identified using the difference between pre-fire and post-fire NBR:  
\[\text{dNBR}=\text{NBR}_{\text{pre-fire}}-\text{NBR}_{\text{post-fire}} \tag{2}\]  
The NBR was computed using Terra/MODIS (Moderate Resolution Imaging Spectroradiometer) surface reflectance data (MOD09 collection 6). The spatial and temporal resolution for MODIS NBR indices was 500 m and 1 day, respectively. MODIS Band 2 (NIR, 0.86 \(\upmu\)m) and Band 7 (SWIR, 2.1 \(\upmu\)m) were used to calculate the NBR (NBR\({}_{2.1}\)), and Band 6 (SWIR, 1.6 \(\upmu\)m) and Band 2 were also used to calculate another NBR that was defined as NBR\({}_{1.6}\). In other research fields, NBR\({}_{1.6}\) is called the Normalized Difference Water Index (NDWI) or NDII [29; 34]. NBR\({}_{2.1}\) is similar to the NBR originally used by [PERSON] and [PERSON] [35]. Although 1.6 \(\upmu\)m is not commonly used to estimate NBR, this study compared NBR\({}_{1.6}\) with NBR\({}_{2.1}\) for monitoring the post-fire forest. Vegetation health can be represented by the NDVI, a widely used vegetation index [36; 37]. The NDVI was computed using NIR and red wavelengths as follows:  
\[\text{NDVI}=\frac{\rho_{\text{NIR}}-\rho_{\text{Red}}}{\rho_{\text{NIR}}+ \rho_{\text{Red}}} \tag{3}\]  
Reflectance of red wavelengths is low when vegetation is healthy and has vital chlorophyll elements [38]. Contrarily, reflectance of NIR wavelengths is higher under such vegetative conditions. The NDVI was calculated from the MOD13A3 data collected by Terra/MODIS from 2000 to 2017. The Terra/MODIS NDVI data's spatial (temporal) resolution was 1 km (1 month). However, there was no Terra/MODIS data available for 1999 to represent the vegetative conditions before the forest fire damage in 2000. Thus, SPOT/Vegetation data from 1999 was used to estimate the NDVI. SPOT/Vegetation NDVI is known be in good agreement with Terra/MODIS NDVI [39]. Weekly SPOT/Vegetation NDVI data was collected in July-August of 1999, with a spatial resolution of 1 km. To understand the photosynthetic activity of the post-fire forest, the data obtained from Terra/MODIS GPP (MOD17A2H collection 6) was used. Spatial and temporal resolutions were 500 m and 8 days, respectively. MODIS GPP is useful for explaining seasonal vegetation patterns, but it exhibits a slight overestimation when compared with the GPP of flux sites in South Korea [40]. [PERSON] et al. [41] showed that the correlation coefficient of MODIS GPP of three flux sites with forests and croplands was 0.55-0.60. RGB composite images was calculated using Landsat-5 TM (Thematic Mapper) and Landsat-8/OLI (Operational Land Imager) surface reflectance data. Landsat-5 Band 1 (Blue, 0.49 \(\upmu\)m), Band 2 (Green, 0.56 \(\upmu\)m), Band 3 (Red, 0.66 \(\upmu\)m), and Landsat-8/OLI Band 2 (Blue, 0.48 \(\upmu\)m), Band 3 (Green, 0.56 \(\upmu\)m), Band 4 (Red, 0.65 \(\upmu\)m) were used to compute RGB composite images. The spatial and temporal resolution of the index was 30 m and 8 days. Forest pixels were extracted from the MODIS Land Cover product. Forest, crop, city, and other land types were divided according to the IGBP (International Geosphere-Biosphere Program) land cover classification scheme (Figure 1c).