# Automatic Methodology for Forest Fire Mapping with SuperDove Imagery
## 4 Discussion  
This research tackled the critical challenge of accurately delimiting burned areas, particularly for smaller fires. This task becomes increasingly important as the alarming trend of global warming intensifies forest fires. By precisely mapping burned zones, including even small fires, it is possible to gain a deeper understanding of fire-climate relationships. This includes how longer fire seasons and drier conditions, fueled by rising temperatures, are impacting the extent and severity of fires globally. This information is vital for developing effective fire management strategies and mitigating the escalating consequences of global warming on world forests. To address this point, this work explores the potential of vegetation indices derived from CubeSat imagery, focusing specifically on the SuperDove sensors in the PlanetScope constellation. The advantage of these sensors lies in their fast revisit time, which allows an almost daily monitoring of fires. However, challenges remain. Notably, the SuperDove sensor lacks an SWIR band, crucial for the commonly used NBR-based fire rating. Additionally, radiometric problems inherent to the sensor can introduce noise into the data, as noted in Section 2.1. Despite these limitations, the SuperDove constellation presents a valuable opportunity for fire monitoring and a chance to improve fire analysis, particularly in relation to the fast-moving dynamics of smaller fires. To prove the validity of this assumption, a methodology was developed and applied to four recent fires in Greece and Spain that combined a series of characteristics, namely small fire size, areas of mountainous relief with orography pushing the functioning of vegetation indices to the limit, and the activation of the CEMS service, providing a report and geodata associated with each event. In these areas, the performance of nine different vegetation indices was compared for fire-extent mapping purposes. The results showed that the combination of the NDVI and the YNDVI provides accurate data about burned areas. These choices were motivated by their ability to detect significant average differences in burned areas in the four affected regions after a detailed quantitative and qualitative analysis. Finally, an automatic methodology based on hierarchical clustering algorithms was used to classify the pixels within the images. This methodology diverges from the classic classification approach to this type of problem based on fixed thresholds. In this sense, it appears that bisecting K-means clustering effectively separates pixels into burned and unburned categories, while K-means clustering provides greater granularity by delineating fire severity levels within burned areas. However, this last aspect requires additional research, the results of which must be compared with field data. By combining frequent satellite imagery, informative vegetation indices, and powerful automatic clustering techniques, the proposed methodology offers a robust approach to the delineation of burned areas, especially for smaller fires, which is critical to understanding the evolution of fire-climate relationships in a warming world. The burned-area maps synthesized with the proposed methodology largely reflect those prepared by the CEMS for three of the four studied areas. There was only one case where a slight difference arose. Similarly, fire-severity maps aligned well with the CEMS reports in identifying burned areas, although some variation emerged in the specific percentages of land categorized within each severity level. Additionally, with respect to the estimation of the burned area, it seems that the reports derived from the CEMS tend to overestimate the burned area. This discrepancy becomes more significant in larger fires. Also, this misalignment could be explained by the limitations of the proposed methodology, particularly when dealing with various fire-affected regions. Variations in soil type in these regions can significantly influence the spectral reflectance captured by the SuperDove sensor.