Accurate reference data to validate burned area (BA) products are crucial to obtaining reliable accuracy metrics for such products. However, the accuracy of reference data can be affected by numerous factors; hence, we can expect some degree of deviation with respect to real ground conditions. Since reference data are usually produced by semi-automatic methods, where human-based image interpretation is an important part of the process, in this study, we analyze the impact of the interpreter on the accuracy of the reference data. Here, we compare the accuracy metrics of the FireCCI51 BA product obtained from reference datasets that were produced by different analysts over 60 sites located in tropical regions of South America. Additionally, fire severity, tree cover percentage, and canopy height were selected as explanatory sources of discrepancies between interpreters’ reference BA classifications. We found significant differences between the FireCCI51 accuracy metrics obtained with the different reference datasets. The highest accuracies (highest Dice coefficient) were obtained with the reference dataset produced by the most experienced interpreter. The results indicated that fire severity is the main source of discrepancy between interpreters. Disagreement between interpreters was more likely to occur in areas with low fire severity. We conclude that the training and experience of the interpreter play a crucial role in guaranteeing the quality of the reference data.

The increasing availability of products generating burned area (BA) maps in recent years necessitates the creation of more accurate reference perimeters to validate these products and provide users with information about their accuracy. For this purpose, reference perimeters were created using Sentinel-2 images in Latin America and the Caribbean (LAC) for the year 2019. The sampling was adapted to the peculiarities of the Sentinel-2 tiling grid system, and statistically representative sample units were selected for biomes and fire activity through stratified random sampling. Fire perimeters were extracted using a Random Forest supervised classification and results were manually supervised and refined. Efforts were made to maximize the temporal length covered by the reference perimeters for each sample, aiming to minimize temporal errors when using the perimeters for validation. The dataset covers 569,214.2 ${\mathrm{k}\mathrm{m}}^2$ (3.5% burned, 88.7% unburned, and 7.8% unobserved). These perimeters were compared with higher spatial resolution PlanetScope-derived perimeters, resulting in 8.4% commission errors and 3.8% omission errors. As a validation exercise, MCD64A1 and FireCCI51 global burned area products were validated using the Sentinel-2 reference dataset created, confirming that the temporal extent of the reference perimeters significantly affects the validation of such products. The reference fire perimeters are publicly available in the Burned Area Reference Database (BARD).

A burned area product was generated from Landsat 8 OLI based three-month composites for the whole territory of Latin America using the processing power of Google earth engine. The product covers the year 2016, and it is guided by thermal anomalies recorded by the, Suomi-NPP satellite Visible Infrared Imaging Radiometer Suite (VIIRS) sensor. The BA algorithm takes into account the spectral contrast between the fire affected areas and the unburned background, using a dedicated thresholding technique. Several spectral indices/bands were used to detect burned pixels by using the spectral signatures extracted at the hotspots. Results showed ~518K km² of burned areas for 2016, in comparison with the ~318K km² of the MODIS MCD64A1 c6 product. The comparison of the automatic method with manually obtained burned perimeters in 40 validation sites showed a very high overall accuracy. Aggregated commission and omission errors for the burned category were found below 20%. The most problematic areas were found related to the low temporal resolution of the sensor and the persistence of the cloud cover, as well as to the confusion with croplands, particularly with regards to commission errors.

Fires crossing international borders challenge nationally organised fire management, yet remain poorly quantified in South America. We combined national MapBiomas Fire burned-area maps for Argentina, Bolivia, Brazil, Colombia, Paraguay and Peru with VIIRS/MODIS active fires and MapBiomas land cover to delineate and characterise transboundary fire events along 14,934 km of borders between 2012 and 2025. We identified 360 events covering 9.23 Mha. The Bolivia–Brazil border concentrated 73.3% of the burned area, followed by Bolivia–Paraguay (15.0%), Brazil–Paraguay (7.4%) and Argentina–Paraguay (4.2%). Three years (2024, 2019, 2020) accounted for 70.8% of the area and the 52 events ≥10,000 ha for 97.6%. Of the 278 events with active fires, 84 showed a directed border crossing; these accounted for 73.7% of the burned area, were balanced between neighbouring countries and crossed a median of 2.5 days after ignition. Natural vegetation made up 94.0% of the burned area, whereas farmland hosted 18.7% of ignition points. Ranking 25-km border segments by recurrence, burned area and crossings identified 18 joint-attention zones, led by the southern Pantanal, that hold 76.1% of the burned area in the border strip on 6% of the border length. These results provide an empirical basis for binational fire management.