Aim: Our aim was to quantify the influence of climate and land use on major fires that occurred during the 2020 drought over the Brazilian Pantanal region.Location: Alto Paraguay Basin, central‐western flank of Brazil.Time period: 2003–2020.Methods: We calculated climatic and burned area anomalies and Spearman's correlation between precipitation and sea surface temperature (SST). We assessed water coverage to identify the impact of the 2020 drought. We produced fire recurrence maps, identified the areas that burned for the first time in 2020, and estimated the carbon loss. Lastly, we assessed the burned areas within land‐use classes and land tenure.Results: The anomalies showed that climatic variables in 2020 had an atypical behaviour, being hotter and drier than the historical average. The water surface area in the Pantanal has been decreasing annually from 2003, and in 2020 the reduction was 34% greater than the annual average. Burned areas in 2020 were 200% greater than the long‐term average, and 35% of the burned areas occurred for the first time in this year. We showed that 84% of these new fires occurred within natural vegetation, with 39% of the burned areas occurring in forests, an increase of 514%. Forest fires were responsible for 47% of the carbon loss in 2020. Finally, 70% of the fires in 2020 occurred within rural properties, 5% in indigenous lands and 10% in protected areas.Main conclusions: The unprecedented fires in 2020 were not only a result of atypical climate, but also a consequence of the intensification of fire‐related human activities. Rural properties play a major role in Pantanal fires, requiring law enforcement and a consistent plan for increasing the protection of the conservation areas. We argue that ineffective fire prevention and the lack of suppression strategies were also key in enhancing the magnitude of these fires.

This work aims to develop a new method to map Land Use and Land Cover (LULC) classes in the São Paulo State, Brazil, using Landsat-8 Operational Land Imager (OLI) data. The novelty of the proposed method consists of selecting the images based on the spectral and temporal characteristics of the LULC classes. First, we defined the six classes to be mapped in the year 2020 as forest, forest plantation, water bodies, urban areas, agriculture, and pasture. Second, we visually analyzed their variability spectral characteristics over the year. Then, we pre-processed these images to highlight each LULC class. For the classification, the Random Forest algorithm available on the Google Earth Engine (GEE) platform was utilized individually for each LULC class. Afterward, we integrated the classified maps to create the final LULC map. The results revealed that forest areas are primarily concentrated in the eastern region of São Paulo, predominantly on steeper slopes, accounting for 19% of the study area. On the other hand, pasture and agriculture dominated 73% of all São Paulo’s landscape, reaching 39% and 34%, respectively. The overall accuracy of the classification achieved 89.10%, while producer and user accuracies were greater than 84.20% and 76.62%, respectively. To validate the results, we compared our findings with the MapBiomas Project classification, obtaining an overall accuracy of 85.47%. Therefore, our method demonstrates its potential to minimize classification errors and offers the advantage of facilitating post-classification editing for individual mapped classes.

Brazil, with more than 8 million km ² , presents six different biomes, ranging from natural grasslands (Pampa biome) to tropical rainfall forests (Amazônia biome), with different land-use types (mostly pasturelands and croplands) and pressures (mainly in the Cerrado biome). The objective of this article is to present a new method to discriminate the most representative land use and land cover (LULC) classes of Brazil, based on the PROBA-V images. The images were converted into vegetation, soil, and shade fraction images by applying the linear spectral mixing model. Then, the pixel-based, highest proportion, annual mosaics of the fraction images, and their corresponding standard deviation images were derived and classified using the random forest algorithm. The following LULC classes were considered: forestlands, shrublands, grasslands, croplands, pasturelands, water bodies, and others. An agreement analysis was conducted with two available LULC maps derived from the Landsat satellite, the MapBiomas, and the Finer Resolution Observation and Monitoring-Global Land Cover (FROM-GLC) projects. Forestlands (412 million ha) and pasturelands (242 million ha) were the two most representative LULC classes; and croplands accounted for 30 million ha. The results presented an overall agreement of 69% and 58% with the MapBiomas and FROM-GLC projects, respectively. The proposed method is a good alternative to support operational projects of LULC map production that are important for planning biodiversity conservation or environmentally sustainable land occupation.

This paper presents a new approach for rapidly assessing the extent of land use and land cover (LULC) areas in Mato Grosso state, Brazil. The novel idea is the use of an annual time series of fraction images derived from the linear spectral mixing model (LSMM) instead of original bands. The LSMM was applied to the Project for On-Board Autonomy-Vegetation (PROBA-V) 100-m data composites from 2015 (~73 scenes/year, cloud-free images, in theory), generating vegetation, soil, and shade fraction images. These fraction images highlight the LULC components inside the pixels. The other new idea is to reduce these time series to only six single bands representing the maximum and standard deviation values of these fraction images in an annual composite, reducing the volume of data to classify the main LULC classes. The whole image classification process was conducted in the Google Earth Engine platform using the pixel-based random forest algorithm. A set of 622 samples of each LULC class was collected by visual inspection of PROBA-V and Landsat-8 Operational Land Imager (OLI) images and divided into training and validation datasets. The performance of the method was evaluated by the overall accuracy and confusion matrix. The overall accuracy was 92.4%, with the lowest misclassification found for cropland and forestland (<9% error). The same validation data set showed 88% agreement with the LULC map made available by the Landsat-based MapBiomas project. This proposed method has the potential to be used operationally to accurately map the main LULC areas and to rapidly use the PROBA-V dataset at regional or national levels.

This paper presents a new method for rapid assessment of the extent of annual croplands in Brazil. The proposed method applies a linear spectral mixing model (LSMM) to PROBA-V time series images to derive vegetation, soil, and shade fraction images for regional analysis. We used S10-TOC (10 days synthesis, 1 km spatial resolution, and top-of-canopy) products for Brazil and S5-TOC (five days synthesis, 100 m spatial resolution, and top-of-canopy) products for Mato Grosso State (Brazilian Legal Amazon). Using the time series of the vegetation fraction images of the whole year (2015 in this case), only one mosaic composed with maximum values of vegetation fraction was generated, allowing detecting and mapping semi-automatically the areas occupied by annual crops during the year. The results (100 m spatial resolution map) for the Mato Grosso State were compared with existing global datasets (Finer Resolution Observation and Monitoring—Global Land Cover (FROM-GLC) and Global Food Security—Support Analyses Data (GFSAD30)). Visually those maps present a good agreement, but the area estimated are not comparable since the agricultural class definition are different for those maps. In addition, we found 11.8 million ha of agricultural areas in the entire Brazilian territory. The area estimation for the Mato Grosso State was 3.4 million ha for 1 km dataset and 5.3 million ha for 100 m dataset. This difference is due to the spatial resolution of the PROBA-V datasets used. A coefficient of determination of 0.82 was found between PROBA-V 100 m and Landsat-8 OLI area estimations for the Mato Grosso State. Therefore, the proposed method is suitable for detecting and mapping annual croplands distribution operationally using PROBA-V datasets for regional analysis.

Deforestation and fire play a critical role in shaping the Brazilian Amazon, yet accurate automated long-term monitoring remains a challenge. This article presents a method for monitoring deforestation (1980–2022) and burned areas (2001–2022) in the state of Rondônia, a deforestation hotspot region in the Brazilian Amazon. We applied the Linear Spectral Mixing Model (LSMM) to Landsat (MSS, TM, and OLI) datasets to derive vegetation, soil, and shade fraction images, effectively reducing data volume while highlighting relevant target features. A threshold-based classification method was then applied to produce annual classification maps, showing forest, non-forest, deforestation, and water bodies. Burned areas were obtained from the MODIS MCD64A1 product. Our deforestation estimates (2008–2022) showed strong agreement with PRODES (Project for Monitoring Deforestation in the Legal Amazon) and GFC (Global Forest Change), achieving an overall accuracy of 89% compared to PRODES. The results reveal a significant decline in forest cover: from 86% in 1986 to 70% in 2000, and just 52% in 2022. Considering the use of fire during the deforestation process, fire activity was also extensive, with approximately 4.5 million hectares burned during the study period—2.9 million hectares within forested areas, of which only 894 thousand hectares remained as forest after burning. These findings provide critical insights into land-use dynamics and fire-related forest loss in the Amazon, offering valuable support for conservation policies and climate change mitigation strategies.