## 5 Conclusions  
NBRs, NDVI, and GPP in terms of properties of moisture condition, vegetation biomass, and photosynthetic activity were applied to monitor the temporal patterns of forest recovery and identify the recovery-time point after the worst forest fire in South Korean history, according to three levels of fire severity. Further, the change of moisture condition was separately evaluated by two NBR types of 2.1 and 1.6 \(\upmu\)m bands, and those NBRs were also used to detect the burned areas. These four remote-sensing variables on the forest recovery progress had similar temporal patterns representing ecological functions. However, their recovery rate was different in the region of each fire severity level.  
In NBR2.1 (NBR1.6), the burned area became close to the unburned reference area after 4, 6, and 8 (6, 8, and 15) years in low, moderate, and high levels (Table 3). In NDVI, 9 years were required to recover in the low and moderate levels, but the burned area in the higher level needed more time. On the other hand, the GPP in the burned area continuously increased during our whole study period, and excessed the value in the reference area after 15 years. Thus, we concluded that the application using those indices of different properties could be suitable for evaluating the progress of forest recovery from a variety of perspectives. A single remote-sensing indictor should be not necessary for decision-making in forest management.  
GPP is the outcome of complex biogeochemical processes of a forest ecosystem. Thus, it is commonly considered as a useful indicator of the ecological condition of a forest. However, the ground-based GPP in the post-fire region is not well appropriately interpreted because spatio-temporal data are not rarely produced. Satellite-based GPP is useful to monitor photosynthetic activity over a large area, but it is relatively difficult to estimate, unlike the simple calculations for NBR and NDVI from the satellite sensors. A process-based biogeochemical model for GPP estimation might be one of the effective ways to understand the cause and effect of the forest recovery processes in a time series. In addition, the satellite derived NBR and NDVI observational data should contribute to the model performance. In this study, we identified the possibility of using NBR\({}_{2.1}\), NBR\({}_{1.6}\), NDVI, and GPP to evaluate the recovery of burned areas. In future work, an integrated approach of satellite observation and biogeochemical modeling will be necessary and further long-term monitoring will be required.  
The following are available online at [[http://www.mdpi.com/2072-4292/10/6/918/s1](http://www.mdpi.com/2072-4292/10/6/918/s1)]([http://www.mdpi.com/2072-4292/10/6/918/s1](http://www.mdpi.com/2072-4292/10/6/918/s1)), Figure S1: Forest fire areas detected using dNBR of Landsat and MODIS.  
All authors contributed this paper. [PERSON], the main author, designed the research, analyzed the remote sensing data, and wrote the manuscript; [PERSON], [PERSON], [PERSON] and [PERSON], the co-authors, reviewed the paper and contributed to the discussion; [PERSON], corresponding author, designed the research and wrote the manuscript.  
We are also grateful to the three anonymous reviewers for their constructive comments. This work was supported by \"Development of Hydrology, wildfire, and statistical Applications\" project, funded by ETRI, which is a subproject of \"Development of Geostationary Meteorological Satellite Ground Segment (NMSC-2017-01)\" program funded by NMSC (National Meteorological Satellite Center) of KMA (Korea Meteorological Administration).  
The authors declare no conflict of interest.