# Automatic Methodology for Forest Fire Mapping with SuperDove Imagery
## 5 Conclusions  
This study aimed to demonstrate the effectiveness of SuperDove imagery in wildfire detection and severity assessment. The results with the SuperDove images and the proposed methodology differed from the CEMS data on burned vegetation cover by less than 20%, considering the different approaches already described. The proposed methodology contributes to improving management strategies for such devastating events. In this sense, the following main conclusions can be listed regarding the methodology, sensor, vegetation indices, and fire mapping:  
1. This study presents an automatic methodology to map burned areas and assess fire severity using images from the SuperDove sensor of the PlanetScope constellation. The methodology leverages vegetation indices and a combination of clustering algorithms to precisely delineate burned areas and classify them according to severity levels.
2. The results highlight the importance of appropriately selecting vegetation indices to accurately assess burned areas and wildfire severity. The indices that performed best in this study were NDVI and YNDVI, as they provided a clear distinction between burned areas and unaffected vegetation and were less susceptible to radiometric problems.
3. The SuperDove sensor, despite lacking an SWIR band, demonstrated effective mapping of burned areas when combined with specific vegetation indices and clustering algorithms.
4. Regarding severity mapping, the results of the SuperDove sensor in conjunction with this methodology, in contrast to the common approach provided by the CEMS, are not compelling and imply the need for future research exploring ground truth.
5. The methodology developed in this study enables a temporally detailed monitoring of wildfires and has potential to improve current fire management strategies. The results were compared with CEMS data, indicating their usefulness in various scenarios.  
In summary, this work demonstrates the effectiveness of the images captured by the SuperDove sensor in the estimation of burned areas and severity levels, proposing an automatic methodology for mapping burned areas and fire severity.  
**Author Contributions:** Conceptualization, D.R.-E. and P.G.; methodology, D.R.-E.; software, D.R.-E.; validation, D.R.-E. and P.G.; formal analysis, D.R.-E., P.G. and J.M.; investigation, D.R.-E. and P.G.; resources, P.G.; data curation, D.R.-E.; writing--original draft preparation, D.R.-E.; writing--review and editing, D.R.-E., P.G. and J.M.; visualization, D.R.-E. and J.M.; supervision, D.R.-E., P.G. and J.M.; project administration, D.R.-E.; funding acquisition, D.R.-E., J.M. and P.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the SPIP2022-02897 project, funded by the Organisation Autonomo Parques Nacionales. [PERSON]'s work is supported through ULPGC and the financing of his stay at the University of Pavia by the Ministry of Universities was granted by Order UNI/501/2021 on 26 May. Financing was also provided by the European Union's NextGenerationEU Fund.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** PlanetScope imagery from Planets Lab PBC are used under the Education and Research Licensing of University of Pavia.  
**Conflicts of Interest:** The authors declare no conflicts of interest.