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.

The use of optical remote sensing to monitor short-term postfire dynamics in rapidly recovering ecosystems is a complex task, requiring strong spectral separability and high temporal resolution. Thus, characterizing the effects of temporal lag in image acquisitions in relation to fire occurrence is of fundamental importance to improve our understanding of how fire-related environmental and ecological processes are connected to multitemporal spectral information. We used a harmonized series of Sentinel-2A/2B and Landsat-7/8 images paired with a fire experiment specifically designed for remote sensing assessment to evaluate short-term spectral responses of fire in tropical savannas. Our experimental design included strategic geolocation of experimental plots and separate prescribed burnings during the early- and mid-dry seasons. The M-statistic was used to assess spectral separability between burned and control treatments, and the temporal stability was assessed using fire scar latency, a custom statistic adapted to capture scar spectral responses over time. Finally, linear regressions were fitted to explore the association between consumed fuel load and spectral indices. The variation of normalized burn ratio and mid-infrared bispectral index indices stood out with the highest separability (M-statistic > 3) in both treatments and moderate-high association with consumed fuel load (R2 > 0.54). We observed that burned areas spectral sensitivity depends not only on timing of image acquisition but also on fire season and their interactions with land surface phenology. Our characterization provides a solid benchmark for future experimental assessments of fire spectral responses and provides new insights for improving fire monitoring algorithms.

Brazil is among the global leaders of native vegetation conversion and agricultural expansion rates. The rates of native vegetation suppression and recovery, as well as the drivers underlying land conversion dynamics, vary among the six Brazilian biomes. Here we analyze the trajectories of four native vegetation types, three anthropic classes and water in Brazil and its biomes between 1985 and 2024. Using trajectory analysis, we also investigate land change history in areas of cropland expansion. The proportion of trajectories varied between native vegetation types and biomes, with forests showing a larger proportion of stable areas, savannas a higher proportion of loss and grasslands and wetlands a greater proportion of trajectories involving alternation, mostly in the Pantanal. Pasture gain was proportionally higher in Pantanal and the Amazon while in the Atlantic Forest its loss was proportionally higher than in other biomes. Gain of croplands was proportionally higher in the Amazon, Cerrado and Caatinga. The proportion of native vegetation and pasture trajectories in areas of croplands expansion also varied markedly between and within biomes. Our analysis advances the original trajectories method by examining per-pixel trajectories across different classes, revealing crucial regional differences in land use history.