## 4 Results and Discussion
### Mapping Results of HFRI and DWI  
Figure 9 shows the spatial distribution of two indices (i.e., HFRI and DWI) with a two-hour interval on 25 February 2016 with three forest fire cases. The difference in spatial distribution of two indices over time and the advantages of the fine spatial resolution of HFRI are described in Figure 7. Both HFRI and DWI had similar spatial distributions overall, and the high forest fire risk areas were well-matched with actual fire locations. This is because both HFRI and DWI are largely influenced by Rehu, which is a common factor.  
Figure 8: HFRI and DWI classes by season. The total sum of each time for each index is 100%, indicating how many forest fires fall into this class as a percentage form.  
Figure 9: HFRI (**a**–**f**) and DWI (**g**-**l**) maps with the two-hour interval in winter. The black dots are forest fire locations. There were three forest fires in February 26, 2016: (**A**) Gwangju during the 9:55–18:00 KST, (**B**) Chuncheon during the 14:43–15:35 KST, and (**C**) Yeongwol during the 15:40–16:15 KST. HFRI was masked using a forest land cover type to produce results only in forest regions in (**a**–**f**). If there are missing values in DWI, they are displayed as No data (white color).  
However, as mentioned in Figure 7, DWI was not able to detect the forest fire risk in the morning compared to HFRI. This is well illustrated on the map at 10:00 KST (Figure 9b,h). Despite the forest fire occurred in Gwangju at 9:55, that area was classified as Low class of DWI (Figure 9h), but the high class of HFRI (Figure 9b). Moreover, as mentioned in Figure 8, DWI was not able to detect the forest fires well in winter when compared to HFRI. Forest fires usually occurred in low class of DWI in winter, while HFRI well detected them in high class. This supports that HFRI is stable regardless of season, showing the advantages of more detailed spatial variations within administrative districts.  
Figure 10 shows the spatial distribution of HFRI and DWI from 30 to 31 March 2016 with a total of 7 forest fires. According to Figures 7 and 8, DWI has almost the same rate as the High class with HFRI from 10:00 to 20:00 in spring. Therefore, the spatiotemporal patterns between the two indices in spring are examined in Figure 10.  
On the map from 12:00 on March 30 to 2:00 on 31 March, the forest fire risk at the fire location matched a relatively high risk zone in both HFRI and DWI. On the other hand, paying attention to the  
Figure 10: HFRI (**a–h**) and DWI (**i–p**) maps in spring. The black dots are forest fires. There are seven forest fires. If there is no data due to the missing data from DWI, the next time data was used. Thus, the map was displayed as a time series at intervals of 2 or 4 h.  
circle with a dashed red line where the forest fire did not occur, HFRI showed a relatively lower risk distribution (low or moderate) on the red circle region than the location of the fires. However, the DWI has a nationally high class (high and very high) in the afternoon and early evening in the region with the red circle. In particular, the 20:00 maps (Figure 10f,n) show that the DWI still showed a high-risk across the country (Figure 10n), compared to HFRI that has a high risk mainly around the forest fire location and lower risk in other areas (Figure 10f). In spring, the forest fire risk naturally rises due to dry and warm weather so the DWI tended to overestimate the fire risk. Thus, the proportion of forest fires that occurred in high class rapidly increased in the afternoon in spring. On the other hand, HFRI can simulate the detailed forest fire risk in a single administrative district with fine resolutions so it did not show excessively high risk spatial distribution over a large area. Furthermore, the maps from 22:00 on 30 March and 2:00 on 31 March (Figure 10g,h,o,p) showed that HFRI had a relatively high risk at the forest fire location while the DWI had a low risk. This confirms that DWI did not work well at night, resulting in a low risk even in spring, as shown in Figure 7.