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The authors concluded that the most reliable vegetation indices for monitoring post-fire vegetation recovery were the Leaf Chlorophyll Content Index (LCCI) and Normalized Difference Red-Edge and SWIR2 (NDRESWIR). The complexity of vegetation fires in South America is observed from two recent fire crises: the 2019 Amazonia fire crisis, which was mainly associated with deforestation [[1]], and the 2020 Pantanal fire crisis [[2]], which was triggered by unprecedented drought and heat wave events [[3], [5]]. Vegetation fires represent a major disturbance in the tropics, with South America notable for having both fire-sensitive (e.g., Amazonia and Atlantic Forest) and fire-prone (e.g., Cerrado and Pantanal) biomes. In a similar study also conducted in southern Ecuador, Maxwald et al. [[9]] identified the most reliable vegetation indices for post-fire vegetation monitoring, and analyzed vegetation recovery across different classes of fire severity. [Extracted from the article]

MATOPIBA is an agricultural frontier, where fires are essential for its biodiversity maintenance. However, the increase in its recurrence and intensity, as well as accidental fires can lead to socioeconomic and environmental losses. Due to this dual relationship with fire, near real-time (NRT) fire management is required throughout the region. In this context, we developed, to the best of our knowledge, the first Machine Learning (ML) algorithm based on the GOES-16 ABI sensor able to detect and monitor Active Fires (AF) in NRT in MATOPIBA. To do so, we analyzed the best combination of three ML algorithms and how long it takes to consider a historical time series able to support accurate AF predictions. We used the most accurate combination for the final model (FM) development. The results show that the FM ensures an overall accuracy rate of approximately 80%. The FM potential is remarkable not only for single detections but also for a consecutive sequence of positive predictions. Roughly, the FM achieves an accuracy rate peak after around 20 h of consecutive AF detections, but there is an important trade-off between the accuracy and the time required to assemble more fire indications, which can be decisive for firefighters in real life. [ABSTRACT FROM AUTHOR]