## 6 Conclusions  
Forest fires are occurring frequently not just in Korea but all around the world. For the rapid restoration of forests, a more accurate burned area detection map is required. Satellite remote sensing effectively provides the accurate extent of burned area and damaged map from forest fire because it can provide spatiotemporal images with a low cost. In this study, three machine learning models (LightGBM, RF, and U-Net) were used to detect burned areas and three Schemes were tested to investigate how the input data composition affects the performance of each model. Through the qualitative and quantitative assessment, a model adequate for detecting burned areas was identified. Among the three machine learning approaches, U-Net, a deep learning-based model, showed the robust and high accuracy in detecting burned areas. Ensemble-based approaches, such as LightGBM and RF, performed poorly and showed noise in Site A showing the heterogeneous land covers. When using satellite images before and after forest fires as well as NDVI and NBR indices, the burned area detection performance was the best regardless of the input variables in both Sites A and B. It was also found that obtaining the accurate burned area detection results was difficult by using one image acquiring after the forest fire event. Although machine learning approaches performed significantly worse due to a lack of information, the U-Net had the highest accuracy. It is considered that the U-Net can be used even with limited data, i.e., only satellite images after occurring the forest fires. Post-disaster images are generally easy to collect, while prior images are more challenging. This benefit is regarded as an important feature in monitoring disasters, such as the detection of burned areas from forest fires. The results of the qualitative and quantitative studies lead to the conclusion that the deep learning-based model outperforms the ensemble learning-based machine learning models in identifying burned regions from forest fires.  
This study tested three Schemes using different combinations of SR and two indices. In addition to used indices, various satellite-based factors that are related to forest fires, such as the soil index or water index, can be used to detect the burned area. It is expected that by using more information, it will be feasible to detect burned areas with greater accuracy. Additionally, the distribution of land covers was not properly accounted for the training data, especially in Site A where the land covers are heterogeneous. Future research should thus consider topographical variables such as land covers and broaden the variety of data by including information directly impacted by forest fires. Furthermore, beyond usingthe well-known model like U-Net itself, future works will develop the model to improve the performance of the forest fire burned area detection. Finally, study sites occurring in large-scale forest fires, e.g., in the United States or Australia, will be considered to confirm whether the demonstrated results from this study are applicable, such as in wide areas as well.  
Conceptualization, C.L., M.N.M.R. and S.P.; methodology, C.L., J.O. and S.P.; software, T.K. and C.L.; validation, C.L. and S.P.; formal analysis, C.L. and S.P.; investigation, Y.H.; resources, C.L. and S.P.; data curation, C.L. and T.K.; writing--original draft preparation, C.L., S.P. and Y.H.; writing--review and editing, S.L., M.N.M.R., J.O. and Y.H.; visualization, C.L. and S.P.; supervision, S.L. and Y.H.; funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Research Program funded by the SeoulTech (Seoul National University of Science and Technology).  
Not applicable.  
The authors declare no conflict of interest.