## 4 Conclusions  
This study analyzed regenerated vegetation areas using multispectral images after a forest fire. The study asked whether these areas could be identified using pixel values. A hill in Okgye town, Gangneung city, where forest fires occurred, was set up as the research area.  
Multispectral image analysis was conducted using images taken from 26 March to 8 August 2019. On average, it takes five months to begin a restoration project after a fire breaks out in the Republic of Korea; however, in this study, vegetative regeneration was found to have sufficiently progressed in four months. This finding can save human and financial resources used in recovery projects by extracting the vegetative regeneration areas that occur over a short period.  
This study developed an analytical method that can be used to obtain primary data for the recovery plan after a forest fire using only the differences in vegetation index values. Assuming that the forest restoration budget for forest fires in Gangwon province in 2019 was 63.4 million USD, 0.2 million USD per 100,000 m\({}^{2}\) was spent. As the regeneration site in Gangneung is 881,000 m\({}^{2}\), the extraction of vegetative regeneration sites in Gangneung can lead to savings of approximately 1.9 million USD.  
Although satellite images are already used in the Republic of Korea to focus on disaster response, this study suggests a new methodology that can obtain a significant amount of data during the recovery phase. The study area consisted of a forest with very dense vegetation before the fire. The analysis was performed using NDVI, which is most commonly used; EVI to compensate for vegetative noise; and SAVI to eliminate noise due to increased soil exposure areas. Furthermore, this study applied clustering analysis to detect the boundary of regenerated vegetation areas accurately.  
This study focuses on pixel values in multispectral images. There are currently no criteria to determine regeneration using these vegetation indices. To this end, this study has defined criteria, such as the dNBR fire severity level, by providing a range of pixel values that indicate regrowth after a forest fire outbreak.  
Ultimately, future work should focus on localized factors for soil adjustment coefficient and atmospheric correction factor in vegetation indices, leading to improved satellite image noise correction. Moreover, if we can use commercial satellite images with better resolution, more precise boundaries can be extracted.  
Conceptualization, M.-H.J. and Y.K.; methodology, Y.K., M.Y. and J.K. ([PERSON]); software, M.-H.J.; validation, Y.K. and J.K. ([PERSON]); formal analysis, Y.K., M.Y. and M.-H.J.; investigation, J.K. ([PERSON]) and J.K. ([PERSON]); resources, M.-H.J. and Y.K.; data curation, Y.K.; writing--original draft preparation, Y.K. and M.-H.J.; writing--review and editing, M.-H.J., M.Y., Y.K., J.K. ([PERSON]) and J.K. ([PERSON]); visualization, Y.K. and J.K. ([PERSON]); supervision, M.-H.J. and Y.K.; project administration, M.-H.J. and Y.K.; funding acquisition, J.K. ([PERSON]) and M.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant (2020-MOIS31-014) from the Fundamental Technology Development Program for Extreme Disaster Response funded by Ministry of Interior and Safety (MOIS, Korea).  
Not applicable.  
The authors declare no conflict of interest.