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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. [ABSTRACT FROM AUTHOR]

Fire is a major forest degradation component in the Amazon forests. Therefore, it is important to improve our understanding of how the post-fire canopy structure changes cascade through the spectral signals registered by medium-resolution satellite sensors over time. We contrasted accumulated yearly temporal changes in forest aboveground biomass (AGB), measured in permanent plots, and in traditional spectral indices derived from Landsat-8 images. We tested if the spectral indices can improve Random Forest (RF) models of post-fire AGB losses based on pre-fire AGB, proxied by AGB data from immediately after a fire. The delta normalized burned ratio, non-photosynthetic vegetation, and green vegetation (ΔNBR, ΔNPV, and ΔGV, respectively), relative to pre-fire data, were good proxies of canopy damage through tree mortality, even though small and medium trees were the most affected tree size. Among all tested predictors, pre-fire AGB had the highest RF model importance to predicting AGB within one year after fire. However, spectral indices significantly improved AGB loss estimates by 24% and model accuracy by 16% within two years after a fire, with ΔGV as the most important predictor, followed by ΔNBR and ΔNPV. Up to two years after a fire, this study indicates the potential of structural and spectral-based spatial data for integrating complex post-fire ecological processes and improving carbon emission estimates by forest fires in the Amazon. [ABSTRACT FROM AUTHOR]

Secondary forests (SFs) are one of the major carbons sinks in the Neotropics due to the rapid carbon assimilation in their above-ground biomass (AGB). However, the accurate contribution of SFs to the carbon cycle is a great challenge because of the uncertainty in AGB estimates. In this context, the main objective of this study is to explore full polarimetric Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar-2 (ALOS/PALSAR-2) data to model SFs AGB in the Central Amazon. We carried out the forest inventory in 2014, measuring 23 field plots. Supplementary land-use classification history was used to create 120 additional independent sample plots by adjusting growth curves using SFs age and previous land-use intensity from field plots and literature database. Multiple Linear Regression (MLR) analysis was performed to select the best model by corrected weighted Akaike Information Criterion (AICw) and validated by the leave-one-out bootstrapping method. The best-fitted model has six parameters and explained 65% of the above-ground biomass variability. The prediction error was of Root Mean Square Error of the Prediction (RMSEP) = 8.8 ± 3.0 tonnes ha−1 (8.8%). The most explanatory variables for modelling secondary forest AGB were those that result from multiple scattering (Shannon Entropy), volumetric scattering (Bhattacharya decomposition), and double-bounce scattering (ratio VV/HH, vertically transmitted and received polarization/horizontally transmitted and received polarization). Including past-use of SF areas in the model with the Landsat time series classification, as the frequency of clear cuts and the number of years of active land-use before abandonment, the MLR has increased by 10%, achieving 71% of the variability explained by the model. The uncertainty report showed that ground truth AGB estimation (inventory, allometry, and plot expansion factors) might represent 50% of the errors in the modelling estimation. In contrast, Synthetic Aperture Radar (SAR) inversion models (SAR error and regression) have achieved 20%. The results showed that additional information on secondary forest land-use history could improve the performance of AGB recovery models, as well as can be used to expand the sampling units on tropical forests. [ABSTRACT FROM AUTHOR]

Bamboo-dominated forests comprise 1 % of the world’s forests. In southwest Amazon, a prior investigation mapped 16.15 million ha of bamboo-dominated forests by visual interpretation of Landsat and MODIS satellite images. They observed that the near infrared (NIR) wavelength was important to discriminate adult and dead bamboo areas, and estimated an average life cycle of 28 years for the Guadua spp. bamboo that dominate the region. In these bamboo areas, flowering and fruiting occur once in a lifetime before death, which produce massive quantities of necromass. The "bamboo-fire hypothesis" argues that increased dry fuel after die-off enhances fire probability, creating opportunities that favor bamboo. In this study, we developed and validated a method to map the bamboo die-off and its spatial distribution using satellite-derived time series of vegetation reflectance from MODIS (MAIAC) data and explored the fire hypothesis by evaluating the relationship between bamboo die-off and fires detected by MODIS thermal anomalies product in southwest Amazon. Our findings show that the NIR is the most sensitive spectral interval to characterize bamboo growth and cohort age. Automatic detection of historical bamboo die-off achieved an accuracy above 79 %. We mapped and estimated 15.5 million ha of bamboo-dominated forests in the region. The mean patch size during 2001–2017 die-off events was 85 km2, while the largest patch covered up to 6162 km2. The "bamboo-fire hypothesis" was not fully supported, because most bamboo cohorts did not burn after die-off. Nonetheless, fire occurrence was 45 % higher in dead than live bamboo in drought years, associated with ignition sources from land use, suggesting a bamboo-human-fire association. Since fire favors bamboo development, this may contribute to the maintenance of high bamboo density where it is already present, the expansion of bamboo into adjacent bamboo-free forests, or even bring deadly consequences to these adjacent forests under climate change effects. [ABSTRACT FROM AUTHOR]