Fire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterogeneity due to the spatial and temporal variability of the burned areas, distinct persistence of the fire signal, increase in cloud and smoke cover surrounding burned areas, and difficulty in detecting understory fire signals. To produce a large-scale time-series of burned area, a robust number of observations and a more efficient sampling strategy is needed. In order to overcome these challenges, we used a novel strategy based on a machine-learning algorithm to map monthly burned areas from 1985 to 2020 using Landsat-based annual quality mosaics retrieved from minimum NBR values. The annual mosaics integrated year-round observations of burned and unburned spectral data (i.e., RED, NIR, SWIR-1, and SWIR-2), and used them to train a Deep Neural Network model, which resulted in annual maps of areas burned by land use type for all six Brazilian biomes. The annual dataset was used to retrieve the frequency of the burned area, while the date on which the minimum NBR was captured in a year, was used to reconstruct 36 years of monthly burned area. Results of this effort indicated that 19.6% (1.6 million km²) of the Brazilian territory was burned from 1985 to 2020, with 61% of this area burned at least once. Most of the burning (83%) occurred between July and October. The Amazon and Cerrado, together, accounted for 85% of the area burned at least once in Brazil. Native vegetation was the land cover most affected by fire, representing 65% of the burned area, while the remaining 35% burned in areas dominated by anthropogenic land uses, mainly pasture. This novel dataset is crucial for understanding the spatial and long-term temporal dynamics of fire regimes that are fundamental for designing appropriate public policies for reducing and controlling fires in Brazil.

Savannas play a key role in estimating emissions. Climate change has impacted the Cerrado savanna carbon balance. We used the burned area product and long-term field inventories on post-fire vegetation regrowth to estimate the impact of the fire on greenhouse gas emissions and net carbon dioxide (CO₂) emissions in the Cerrado savanna between 1985 and 2020. We estimated the immediate emissions from fires, CO₂ emissions by plant mortality, and CO₂ removal from vegetation regrowth. The burned area was 29,433 km²; savanna fires emitted approximately 2,227,964 Gg of CO₂, 85,057 Gg of CO, 3010 Gg of CH₄, 5,103 Gg of NO_x, and 275 Gg of N₂O. We simulated vegetation regrowth according to three fire regime scenarios: extreme (high fire frequency and short fire interval), intermediate (medium fire frequency and medium fire interval), and moderate (low fire frequency and long fire interval). Under the extreme and intermediate scenarios, the vegetation biomass decreased by 2.0 and 0.4% (ton/ha-year), while the biomass increased by 2.1% under a moderate scenario. We converted this biomass into CO₂ and showed that the vegetation regrowth removed 63.5% of the total CO₂ emitted (2,355,426 Gg), indicating that the Cerrado savanna has been a source of CO₂ to the atmosphere.

The semi-arid Caatinga biome is particularly susceptible to fire dynamics. Periodic droughts amplify fire risks, while anthropogenic activities such as agriculture, pasture expansion, and land-clearing significantly contribute to the prevalence of fires. This research aims to evaluate the effectiveness of a fire detection model and analyze the spatial and temporal patterns of burned areas, providing essential insights for fire management and prevention strategies. Utilizing deep neural network (DNN) models, we mapped burned areas across the Caatinga biome from 1985 to 2023, based on Landsat-derived annual quality mosaics and minimum NBR values. Over the 38-year period, the model classified 10.9 Mha (12.7% of the Caatinga) as burned, with an average annual burned area of approximately 0.5 Mha (0.56%). The peak burned area reached 0.89 Mha in 2021. Fire scars varied significantly, ranging from 0.18 Mha in 1985 to substantial fluctuations in subsequent years. The most affected vegetation type was savanna, with 9.8 Mha burned, while forests experienced only 0.28 Mha of burning. October emerged as the month with the highest fire activity, accounting for 7266 hectares. These findings underscore the complex interplay of climatic and anthropogenic factors, highlighting the urgent need for effective fire management strategies.