The Amazon rainforest, historically fire-resistant, is experiencing an alarming increase in wildfires due to climate extremes and human activity. The 2023-2024 drought, surpassing previous records, combined with forest fragmentation, has dramatically heightened fire vulnerability. Analysing the Tropical Moist Forest (TMF) and Global Wildfire Information System (GWIS) datasets, we found a 152 % surge in forest disturbances from deforestation and degradation in 2024, reaching a 2-decade peak of 6.64 Mha (million hectares). Forest degradation, particularly large-scale degradation linked to fires, increased by over 400 %, largely exceeding deforestation. Brazil and Bolivia experienced the most severe impacts, with Bolivia seeing 9 % of its intact forest burned in 2024. Fire-driven forest degradation in the Pan-Amazon released 791 +/- 86 Mt CO2 (million tonnes of carbon dioxide equivalent, +/- 1 standard deviation) in 2024, a 7-fold increase compared to the previous 2 years, surpassing emissions from deforestation. The escalating fire occurrence, driven by climate change and unsustainable land use, threatens to push the Amazon towards a catastrophic tipping point. Urgent, coordinated efforts are crucial to mitigate these drivers and to prevent irreversible ecosystem damage.

Background In African savannas, the most intense fires occur during the late dry season, when fuel availability is high and atmospheric relative humidity low. The Fire Weather Index (FWI) system has been used to measure the effort required for fire suppression and explore the impact of climate on fires.Aims This study assesses trends in FWI system indices from 1979 to 2022 and their influence on burned area (BA).Methods We employed the Theil-Sen slope estimator and contextual Mann-Kendall test to evaluate the presence of significant trends in FWI system indices during the early dry season (EDS) and late dry season (LDS), and assessed how trends in BA depend on fire weather.Key results We found distinct patterns in fire weather trends between the EDS and LDS, the LDS showing more widespread areas of increasing trends. However, only 28% of the regression analyses showed significant relationships with BA, suggesting a moderate influence of the FWI system on BA interannual variance.Conclusions Fire weather severity is increasing faster and more extensively during the LDS than the EDS. Additional factors play a significant role in shaping BA trends. Proactively managing anthropogenic fires during the moister EDS can help mitigate fire intensity, reduce emissions and support biodiversity conservation efforts.

This paper presents a review of concepts related to wildfire risk assessment, including the determination of fire ignition and propagation (fire danger), the extent to which fire may spatially overlap with valued assets (exposure), and the potential losses and resilience to those losses (vulnerability). This is followed by a brief discussion of how these concepts can be integrated and connected to mitigation and adaptation efforts. We then review operational fire risk systems in place in various parts of the world. Finally, we propose an integrated fire risk system being developed under the FirEUrisk European project, as an example of how the different risk components (including danger, exposure and vulnerability) can be generated and combined into synthetic risk indices to provide a more comprehensive wildfire risk assessment, but also to consider where and on what variables reduction efforts should be stressed and to envisage policies to be better adapted to future fire regimes. Climate and socio-economic changes entail that wildfires are becoming even more a critical environmental hazard; extreme fires are observed in many areas of the world that regularly experience fire, yet fire activity is also increasing in areas where wildfires were previously rare. To mitigate the negative impacts of fire, those responsible for managing risk must leverage the information available through the risk assessment process, along with an improved understanding on how the various components of risk can be targeted to improve and optimize the many strategies for mitigation and adaptation to an increasing fire risk.

Climate and natural vegetation dynamics are key drivers of global vegetation fire, but anthropogenic burning now prevails over vast areas of the planet. Fire regime classification and mapping may contribute towards improved understanding of relationships between those fire drivers. We used 15 years of daily active fire data from the MODIS fire product (MCD14ML, collection 6) to create global maps of six fire descriptors (incidence, size inequality, season length, interannual variability, intensity, and fire season modality). Using multiple correspondence analysis (MCA) and hierarchical agglomerative clustering, we identified three fire macroregimes - Wild, Tamed, and Domesticated,each of which splitting into prototypical and transitional regimes. Interpretation of the six fire regimes in terms of their main drivers relied on the global maps of anthromes and Köppen climate types. The analysis yielded a two-dimensional space where the principal dimension of variability is primarily defined by interannual variability in fire activity and fire season length, and the secondary axis is based mainly on fire incidence. The Wild fire macroregime occurs mostly in cold wildlands, where burning is sporadic and fire seasons are short. Tamed fires predominate in seasonally dry tropical rangelands and croplands with high fire incidence. Domesticated fires are characteristic of humid, warm temperate and tropical croplands and villages with low fire incidence. The Tamed and Domesticated fire macroregimes, representing managed burning, account for 86% of all active fires in our dataset and for 70% of the global burnable area. Fourteen percent of active fires were found in the cold wildlands, and in the rangelands and forests of steppe and desert climates of the Wild macroregime. These results highlight the extent of human control over global pyrogeography in the Anthropocene.

The present study is focused on investigating the capabilities of the combined use of synthetic aperture radar (SAR) Sentinel-1 (S1) and optical Sentinel-2 (S2) for burn severity mapping. For this purpose, a fire that occurred in August 2018 in southern Portugal was analyzed. The composite burn index (CBI) was used to visually classify geo-referenced photographs in the field and create the training data for image classification. A supervised classification was carried out using the machine learning random forests (RF) algorithm, on which the optimization of the parameters setting was carried out through an exhaustive grid search approach. In order to assess the advantages of combining optical and SAR data, and the importance of each band, the approach was tested separately on three data combinations (S1, S2 and S1 + S2) and feature importance was computed to evaluate the contribution of each input layer. The multi-class F-score, used to assess the accuracy of the map, reached a value of 0.844 when both the datasets were combined (S1 + S2), compared with the values 0.514 and 0.805 achieved by only SAR (S1) and only optical (S2), respectively.

Increasing efforts are being devoted to understanding fire patterns and changes highlighting the need for a consistent database about the location and extension of burned areas (BA). Satellite-derived BA mapping accuracy in the Brazilian savannas is limited by the underestimation of burn scars from small, fragmented fires and high cloudiness. Moreover, systematic mapping of BA is challenged by the need for human intervention in training sample acquisition, which precludes the development of automatic-generated products over large areas and long periods. Here, we developed a multi-sensor, active fire-supervised, one-class BA mapping algorithm to address several of these limitations. Our main objective is to generate a long-term, detailed BA atlas suitable to improve fire regime characterization and validation of coarse resolution products. We use composite images derived from the Landsat satellite to generate end-of-season maps of fire-affected areas for the entire Cerrado. Validation exercises and intercomparison with BA maps from a semi-automatic algorithm and visual photo interpretation were conducted for the year 2015. Our results improve the BA mapping by reducing omission errors, especially where there is high cloud frequency, few active fires are detected, and burned areas are small and fragmented. Finally, our approach represents at least a 45% increase in BA mapped in the Cerrado, in comparison to the annual extent detected by the current coarse global product from MODIS satellite (MCD64), and thus, it is capable of supporting improved regional emissions estimates.

Biomass burning in the Brazilian Amazon is modulated by climate factors, such as droughts, and by human factors, such as deforestation, and land management activities. The increase in forest fires during drought years has led to the hypothesis that fire activity decoupled from deforestation during the twenty-first century. However, assessment of the hypothesis relied on an incorrect active fire dataset, which led to an underestimation of the decreasing trend in fire activity and to an inflated rank for year 2015 in terms of active fire counts. The recent correction of that database warrants a reassessment of the relationships between deforestation and fire. Contrasting with earlier findings, we show that the exacerbating effect of drought on fire season severity did not increase from 2003 to 2015 and that the record-breaking dry conditions of 2015 had the least impact on fire season of all twenty-first century severe droughts. Overall, our results for the same period used in the study that originated the fire-deforestation decoupling hypothesis (2003–2015) show that decoupling was clearly weaker than initially proposed. Extension of the study period up to 2019, and novel analysis of trends in fire types and fire intensity strengthened this conclusion. Therefore, the role of deforestation as a driver of fire activity in the region should not be underestimated and must be taken into account when implementing measures to protect the Amazon forest.

Changes in climate, land use, and land management impact the occurrence and severity of wildland fires in many parts of the world. This is particularly evident in Europe, where ongoing changes in land use have strongly modified fire patterns over the last decades. Although satellite data by the European Forest Fire Information System provide large-scale wildland fire statistics across European countries, there is still a crucial need to collect and summarize in-depth local analysis and understanding of the wildland fire condition and associated challenges across Europe. This article aims to provide a general overview of the current wildland fire patterns and challenges as perceived by national representatives, supplemented by national fire statistics (2009–2018) across Europe. For each of the 31 countries included, we present a perspective authored by scientists or practitioners from each respective country, representing a wide range of disciplines and cultural backgrounds. The authors were selected from members of the COST Action “Fire and the Earth System: Science & Society” funded by the European Commission with the aim to share knowledge and improve communication about wildland fire. Where relevant, a brief overview of key studies, particular wildland fire challenges a country is facing, and an overview of notable recent fire events are also presented. Key perceived challenges included (1) the lack of consistent and detailed records for wildland fire events, within and across countries, (2) an increase in wildland fires that pose a risk to properties and human life due to high population densities and sprawl into forested regions, and (3) the view that, irrespective of changes in management, climate change is likely to increase the frequency and impact of wildland fires in the coming decades. Addressing challenge (1) will not only be valuable in advancing national and pan-European wildland fire management strategies, but also in evaluating perceptions (2) and (3) against more robust quantitative evidence.

Coarse spatial resolution of remote sensing imagery still hampers a comprehensive representation of long-term fire patterns at the regional level, in particular in areas characterized by small and sparse fire scars. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor launched in 2011 upgrades the spatial resolution (375 m) and gives continuity to the Earth long-term monitoring initiated by Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Therefore, aiming to assess VIIRS 375 m imagery capabilities to improve the accuracy and reliability of fire scars mapping over the Brazilian Cerrado, we developed a burned area detection algorithm (VIIRS-SVM) based on machine learning techniques. For this purpose, the (V, W) burnt index adjusted to VIIRS near-infrared and middle-infrared channels and the One-Class Support Vector Machine algorithm were used for burned area identification. The VIIRS-SVM algorithm was applied over the Brazilian Cerrado and evaluated against reference scars from 15 Landsat-8 scenes during the fire season of 2015, covering a large area with substantial variability in terms of fire scars characteristics. We also performed a comparison with the MCD64A1 collection-6 product over the validation sites. Relying on VIIRS 375 m imagery, the VIIRS-SVM algorithm allows an enhancement of 25% in discrimination of small and medium fire scars (25 to 1000 ha), when compared to the MODIS-derived product. Results have demonstrated that the enhancement of medium and small fire scars mapping over the Cerrado is possible using VIIRS sensor capabilities.

Fire is a natural disturbance in the Brazilian savannas, Cerrado, with substantial ecological and economic impacts. Most studies have characterized the fire regime in this biome using climate drivers but neglected the geographical variation of anthropogenic activities. These factors can trigger inappropriate fire-fighting decisions and biodiversity conservation policies. This takes special relevance in fire-prone biomes with recent fire management policies as Cerrado, which have been highly modified over the last decades due to changes in land use and climate. Here, we aim to identify how variations in climate and anthropogenic drivers influence burned area (BA) trends at the regional level (microregions) in Cerrado. We evaluated satellite-derived BA (MCD64, collection 6) for 172 microregions from 2001 to 2018 across the entire biome. The Canadian Forest Fire Weather Index (FWI) was used as a proxy of climate using meteorological variables from ECMWF’s ERA5 reanalysis product. The human leverage, considered here as population density (PD) and land use (LU), were derived, respectively, from the annual census of the Brazillian Institute of Geography and Statistics (IBGE) and from a Brazilian platform of annual land use/cover mapping (MapBiomas). Recent BA trends considering the drivers FWI, LU and PD, were estimated using the non-parametric Theil-Sen regression and the modified Mann-Kendall test. Results showed BA trends over the last 18 years were significant and spatially contrasted along Cerrado: positive trends were found in the north-eastern region (in particular, the most recent agricultural frontier in Brazil: MATOPIBA) whereas the south-western region showed negative trends. PD showed positive trends in all microregions and, similarly, LU obtained positive trends over most of Cerrado. Positive FWI trends were also found over the central and north-eastern regions and FWI was the driver that explained most of BA variance in Cerrado. LU and PD were found to have much more complex relations with BA. Moreover, regarding the seasonal variability of microregions with positive and negative trends, the former were found to begin earlier in June and last longer, indicating that the overall fire season in Cerrado may be extending. The approach presented here allows the exploration of recent trends affecting fires, crucial to inform and support better allocation of resources in fire management under current and future conditions. The study was funded by Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil (CNPQ) through grants 305159/2018-6 and 441971/2018-0. P. Silva is funded by Fundação para a Ciência e a Tecnologia (FCT), grant number SFRH/BD/146646/2019.

Ever since colonial times, the rural inhabitants of Guinea-Bissau have been blamed for lighting uncontrolled fires all over the country. Based on in-depth ethnographic research in two regions, a country-level rapid rural appraisal, and analysis of satellite active fire data, this essay shows how burning practices are, however, diversified according to cultural, socio-economic, demographic and agro-ecological conditions, and how they have been changing recently as a locally-developed adaptation to new farming systems. Many new bush-fire uses correspond to current best practices (e.g., use of firebreaks, backfiring, burning at cooler hours of the day), but under a scenario of changing climate and land uses there is room for improving fire management to reduce negative environmental impacts, while preserving cultural, economic and biodiversity benefits generated by some traditional burning practices. The design of public policies for reducing material damages, biodiversity loss, and carbon emissions from fires requires an understanding of their local drivers. Following from that, the development of ecological management practices must engage with farmers’ knowledge and networks/institutions and meet their priorities and needs.

Global biomass burning impacts millions of hectares annually resulting in high social, environmental and economic costs. Satellite-based active fire detection products provide key information in support of land management and science applications and are available routinely from a variety of sources. Data validation is an important aspect guiding product development and characterization and is addressed in this study with the use of miniaturized sensors paired to unmanned airborne vehicles, or drones. Specifically, we deployed a custommade broadband spectral radiometer along with a commercial off-the-shelf infrared camera (FLIR Zenmuse XT) mounted to small consumer drones (DJI's Phantom3 and Inspire) flown over small prescribed burns implemented so as to coincide with the overpass times of different earth observing satellites (e.g., NASA Terra & Aqua, NOAA/NASA S-NPP, USGS Landsat-8, and ESA Sentinel-2). Near-simultaneous fire radiative power retrievals were obtained using the airborne and spaceborne data acquired during prescribed fires conducted in grasslands and savannas plots in the Brazilian states of Rio de Janeiro, Tocantins and Mato Grosso do Sul between July 2017 and September 2018. A set of standard operating procedures were defined with attention to satellite active fire data validation requirements (e.g., reference data calibration) and subsequently adopted for each of the fires sampled. Airborne and spaceborne observations were co-located and temporally paired to within 2sec, and path transmittances calculated in order to account for atmospheric attenuation of fire retrievals. Our results showed good agreement (differences as low as 5%) between drone and satellite-based fire retrievals while also serving to demonstrate the potential for fully reproducible satellite data validation protocols using small sensor and drone technologies.

Increasing availability of dense time series of moderate spatial resolution satellite data for mapping global burned areas calls for mapping algorithms designed to easily integrate data at different spatial and temporal resolutions, irrespective of particular grid constraints. In this paper, we describe a novel hybrid approach for global burned area mapping that combines active fire data and time series of surface reflectance using graphs, which provide a flexible and efficient way of extracting spatiotemporal consistent patches. Our approach has three main steps. Firstly, we analyze burn-sensitive vegetation index time series to determine for each location a set of events, which are the dates for which the spectral-temporal signal indicates the possibility of a burn. Secondly, we explore the spatiotemporal distribution of all events and active fires to determine a subset of events with strong evidence of corresponding to burned areas. Those events are used as positive occurrences for training a one-class maximum entropy classifier and obtain, for each candidate event, a likelihood of it actually corresponding to a burn. Finally, we build a graph that combines all previous information, from which we extract spatiotemporal patches of densely connected events. Patches with strong evidence of burning determine the burned area map at any given time period. This research is part of the European Space Agency's Climate Change Initiative (ESA-CCI) and aims ultimately at generating Sentinel-3 daily global burned area products at 500 m spatial resolution. Towards that end, we test our approach with spatially and spectrally similar MODIS gridded surface reflectance (MOD/MYDO9GA), as well as non-gridded active fire (MCD14ML) 2008 data and CCI global land cover maps. Using 105 independent Landsat fire reference perimeters to validate global results, we show that our algorithm applied to MOD/MYDO9GA data (PT-M09) has very similar accuracy (44%) measured by the Dice coefficient compared with MCD64A1 v006 (45%). Moreover, PT-M09 exhibits a higher commission error but a lower omission error than MCD64A1. Due to their coarse resolution, this kind of product cannot capture very small burn areas. The bias relative to the reference burned area indicates that the algorithm presented in the current study underestimates burned area by 14% of the area actually burned according to reference data, which is lower than the underestimation in MCD64A1 (28%). We also analyzed the temporal accuracy of the patch based algorithm and concluded that the average time difference to active fires detection is 3.43 days, which is similar to MCD64A1 (3.75 days). Finally, we performed a sensitivity analysis which shows that total mapped burned areas varies only 3% when the main algorithm threshold that separates "burned" and "unburned" patches varies from quantile 45% to 55%.

Across the globe, human activities have been gaining importance relatively to climate and ecology as the main controls on fire regimes and consequently human activity became an important driver of the frequency, extent and intensity of vegetation burning worldwide. Our objective in the present study is to look for weekly cycles in vegetation fire activity at global scale as evidence of human agency, relying on the original MODIS active fire detections at 1 km spatial resolution (MCD14ML) and using novel statistical methodologies to detect significant periodicities in time series data. We tested the hypotheses that global fire activity displays weekly cycles and that the weekday with the fewest fires is Sunday. We also assessed the effect of land use and land cover on weekly fire cycle significance by testing those hypotheses separately for the Villages, Settlements, Croplands, Rangelands, Seminatural, and Wildlands anthromes. Based on a preliminary data analysis of the daily global active fire counts periodogram, we developed an harmonic regression model for the mean function of daily fire activity and assumed a linear model for the de-seasonalized time series. For inference purposes, we used a Bayesian methodology and constructed a simultaneous 95% credible band for the mean function. The hypothesis of a Sunday weekly minimum was directly investigated by computing the probabilities that the mean functions of every weekday (Monday to Saturday) are inside the credible band corresponding to mean Sunday fire activity. Since these probabilities are small, there is statistical evidence of significantly fewer fires on Sunday than on the other days of the week. Cropland, rangeland, and seminatural anthromes, which cover 70% of the global land area and account for 94% of the active fires analysed, display weekly cycles in fire activity. Due to lower land management intensity and less strict control over fire size and duration, weekly cycles in Rangelands and Seminatural anthromes, which jointly account for 53.46% of all fires, although statistically significant are weaker than those detected in Croplands.

Global fire monitoring systems are crucial to study fire behaviour, fire regimes and their impact at the global scale. Although global fire products based on the use of Earth Observation satellites exist, most remote sensing products only partially cover the requirements for these analyses. These data do not provide information like fire size, fire spread speed, how fires may evolve and joint into single event, or the number of fire events for a given area. This high level of abstraction is very valuable; it makes it possible to characterize fires by types (either size, spread, behaviour, etc.). Here, we present and test a data mining work flow to create a global database of single fires that allows for the characterization of fire types and fire regimes worldwide. This work describes the data produced by a data mining process using MODIS burnt area product Collection 6 (MCD64A1). The entire product has been computed until the present and is available under the umbrella of the Global Wildfire Information System (GWIS).

The Brazilian Cerrado vegetation has been increasingly threatened over the last decades and the significant loss of ~50% in its native cover is also related in part to the absence of a consistent fire policy in the region. Although the Cerrado is adapted to natural fires the significant changes in fire regimes might alter this biome irreparably. With future projections of a drier and warmer climate, it is thus crucial to understand how fire regimes are evolving to allow a better management and allocation of resources. Satellite technology currently provides a tool to effectively study fire regimes through the identification of active fires and burned areas (BA). Here we use the MODIS MCD64A1 Collection 6 product to evaluate BA patterns in Cerrado over the last 18 years (2001-2018). Intra and inter-annual variabilities are assessed along with spatial analysis, with special focus on the existing Conservation Units; the existence of significant trends and shifts are also investigated. Results show that the dry period from August to October accounts for around 60% of the total BA. Fire extent varies considerably from year to year showing a strong dependence on climatic conditions, which are responsible for more than 2/3 of the interannual variability of burned area for the last two decades. Very distinct patterns for its South-western and North-eastern and regions are seen, with negative and positive trends, respectively. The latter region is the new agricultural frontier with extensive and mechanized farms, whereas the former has mostly been cleared of native vegetation and is now dominated by grasslands and smallscale croplands. Conservations units near by the Arc of Deforestation and the new agriculture frontiers of MATOPIBA present the higher amount of burned area.