Rebecca C. Scholten

The Global Fire Emissions Database (GFED) is widely used to quantify spatiotemporal variability and long-term trends in burned area and fire emissions, supporting assessments of fire impacts on ecosystems and atmospheric composition. GFED has historically relied on observations from the Moderate Resolution Imaging Spectroradiometer (MODIS), but orbital drift and planned sensor decommissioning pose challenges for maintaining record continuity and near-real-time (NRT) monitoring. Here we present the GFED5 near-real-time extension (GFED5NRT), a global fire emissions dataset that enables daily NRT analyses using active fire observations from the Visible Infrared Imaging Radiometer Suite (VIIRS). GFED5NRT uses biome- and region-specific lookup tables of effective fire area and fuel consumption, derived from VIIRS observations and standard GFED5 datasets, to estimate burned area and emissions from VIIRS active fire counts in a manner consistent with the GFED5 time series. Comparisons with GFED5 and independent datasets show strong agreement in spatial patterns, seasonal cycles, and interannual variability of fire activity. GFED5NRT captures recent major fire extremes and provides daily global NRT estimates of burned area and emissions for multiple trace gases and aerosols. Together, GFED5 and GFED5NRT provide a coherent framework for long-term analyses and NRT monitoring of evolving fire regimes in a changing climate. The GFED5NRT dataset is publicly available at https://doi.org/10.5281/zenodo.18702700 (Chen et al., 2026).

Understanding contemporary patterns of wildland fire activity is critical to support management of new extremes. Satellite data now enable sub-daily fire spread tracking, allowing observation of the full range of fire growth patterns in a region. Here, we applied the Fire Events Data Suite (FEDS) algorithm to Suomi-NPP VIIRS active fire detections to derive 12-hourly perimeters for every fire detected across the conterminous United States (CONUS) from 2012 to 2025. We validated FEDS final perimeters against Monitoring Trends in Burn Severity (MTBS) data. FEDS and MTBS yield consistent estimates of final fire extent, with the strongest agreement in ecoregions with heavier fuels. Analysis of the 14-year FEDS record revealed that fire activity is highly episodic, with over half of all CONUS fire spread concentrated during just 14% of 12-hour time intervals. The heavy tail of the observed size distribution of large fire spread events (>1.179 km2/12hr) is well described by a truncated power law, which can serve as an empirical baseline for benchmarking fire spread models and projecting risks of extreme spread events under shifting fire regimes. Sub-daily fire tracking information from FEDS captures important diurnal, seasonal, and interannual variability in ecoregion-scale fire spread patterns, helping characterize contemporary pyrogeography, including the influence of small fires not captured by MTBS. The FEDS dataset offers a pathway to link concurrent weather, fuels, and suppression activity to observed fire spread to further explore drivers of episodic spread and extreme fire growth, and project how they may change under future climate conditions.

While climate‐driven increases in wildfires are altering the composition and function of Arctic ecosystems, the impact of burn severity on longer‐term changes in community composition and ecosystem fluxes remains less understood, creating uncertainty in estimates of the biome's carbon storage capacity. Here, we used field and remote sensing observations from the 2007 Anaktuvuk River Fire on Alaska's North Slope to explore post‐fire changes in gross primary production (GPP) and solar‐induced fluorescence (SIF). A key goal was to develop a mechanistic understanding of the relationships between burn severity, active layer thickness, post‐fire plant functional type composition, and GPP, using SIF to explore these linkages at a landscape scale. We measured GPP using eddy covariance flux towers at sites with high and moderate burn severity and at a nearby unburned control site. In high burn severity areas, GPP increased by 38 ± 19% and SIF increased by 48 ± 34% 11–15 years after the fire compared to unburned controls. A positive correlation across space between SIF and post‐fire shrub cover within the fire perimeter ( r = 0.38, p < 0.01) suggests that shrub expansion (43 ± 22%) was a key driver of the observed pattern of SIF enhancement. Post‐fire shrub cover, in turn, was positively correlated to active layer thickness ( r = 0.33, p < 0.01) and burn severity ( r = 0.76, p < 0.01). Our findings suggest that more frequent and severe Arctic wildfires could accelerate shrub dominance and increases in photosynthesis, trends that are already being driven by climate warming.

Boreal North America experienced unprecedented consecutive extreme fire seasons during 2023–2025, marked by record-breaking burned area and emissions. We identify a series of interconnected mechanisms that enabled these back-to-back extremes. A multi-year drought—the most severe in at least 45 years—lowered the water table and desiccated organic soils, allowing more fires to persist over winter under the snowpack. In 2024 and 2025, 14–22% of the burned area in boreal North America originated from overwintering fires. Spring terrestrial water storage anomalies predicted subsequent fire activity, indicating that hydrological monitoring can provide early warning months in advance. These multi-year carryover effects challenge current fire danger systems that treat seasons independently and demonstrate that multi-year climate variability can override long-term wetting trends predicted under climate change.

The Global Fire Emissions Database (GFED) is widely used to quantify spatiotemporal variability and long-term trends in burned area and fire emissions, supporting assessments of fire impacts on ecosystems and atmospheric composition. GFED has historically relied on observations from the Moderate Resolution Imaging Spectroradiometer (MODIS), but orbital drift and planned sensor decommissioning pose challenges for maintaining record continuity and near-real-time (NRT) monitoring. Here we present the GFED5 near-real-time extension (GFED5NRT), a global fire emissions dataset that enables daily NRT analyses using active fire observations from the Visible Infrared Imaging Radiometer Suite (VIIRS). GFED5NRT uses biome- and region-specific lookup tables of effective fire area and fuel consumption, derived from VIIRS observations and standard GFED5 datasets, to estimate burned area and emissions from VIIRS active fire counts in a manner consistent with the GFED5 time series. Comparisons with GFED5 and independent datasets show strong agreement in spatial patterns, seasonal cycles, and interannual variability of fire activity. GFED5NRT captures recent major fire extremes and provides daily global NRT estimates of burned area and emissions for multiple trace gases and aerosols. Together, GFED5 and GFED5NRT provides a coherent framework for long-term analyses and NRT monitoring of evolving fire regimes in a changing climate. The GFED5NRT dataset is publicly available at https://doi.org/10.5281/zenodo.18702700 (Chen et al., 2026).

Recent record-breaking wildfire seasons, have predominantly been studied from the perspective of fire weather, without distinguishing between fire dynamics in upland forests and peatlands. From 2016 through 2023, peatlands represented on average 6.9% of the burned area in Arctic-boreal Canada, underscoring their critical role in wildfire prediction and carbon cycle impact at the landscape scale. Using a machine learning model trained on meteorological, hydrological, land cover, and ignition datasets, we reveal a fundamental divergence in the factors controlling burned area across peatlands and upland forests. We demonstrate that although seasonal drought is the primary control factor for both, the hydrological controls are more important in peatlands than in upland forests, where population density, open water fraction and forest fraction play a major role in determining spatiotemporal burned area patterns. Our findings highlight the necessity of developing fuel type-specific models that explicitly account for peatlands, given their disproportionate importance for carbon storage and their distinct fire dynamics.

Wildfires are an emerging peril in traditional natural hazard risk assessment. Remote sensing data comprises the most comprehensive data source for their assessment. However, scientists and practitioners in Disaster Risk Reduction are faced with several fire products from different satellite missions, whose differences, advantages and limitations can be difficult to access and understand, especially for users outside the remote sensing domain. This complicates the process of identifying the most appropriate dataset, making it a challenging and time-consuming endeavor, and in some cases can result in suboptimal or even erroneous results. We address this issue by offering a concise overview of remote sensing fire products and clarifying terms that are interpreted differently across scientific communities, with a focus on their application in risk assessment. Moreover, we provide risk estimates based on different historic wildfire hazard sets. These are derived from MODIS satellite products for the years 2002–2024, leveraging burned area, fire radiative power and land use information. We join these hazard sets with exposure datasets (representing physical assets, population and forested area) and damage records to calibrate their vulnerabilities to wildfires. These form the basis for estimating wildfire impacts and risks, while quantifying uncertainties related to the chosen hazard representation. Such risk analyses find application in prioritising adaptation options and in designing insurance products.

Major advances have been made in understanding carbon emissions from boreal forest fires, yet substantial knowledge gaps remain for tundra fires. Tundra ecosystems are increasingly susceptible to fire activity due to climate change, posing a threat to their large organic soil carbon stocks and the permafrost they insulate. Additionally, studies assessing remote sensing fire severity metrics in tundra landscapes are limited. Here, we report carbon (C) combustion estimates from two large tussock tundra fires (1048 km²) that occurred in Southwest Alaska, USA, in 2022. We quantified above-and belowground carbon stocks and combustion (kg C m⁻²) at 45 field plots (36 burned, 9 unburned) one year post-fire. Soil burn depth was determined using paired moss patches, and aboveground carbon stocks were calculated using allometric equations for shrubs and tussocks. Fire severity was estimated using the field-based geometrically structured Composite Burn Index (GeoCBI) at these 45 plots and at 41 additional plots where only GeoCBI was measured. We upscaled our field-based findings using the differenced normalized burn ratio (dNBR) derived from Sentinel-2 imagery. Tussock tundra landscapes emitted an average of (± standard deviation) 1.59 ± 0.55 kg C m⁻², consuming 11% of the total pre-fire carbon stock. The majority of the loss (75%) came from belowground stocks, with an average burn depth of 6.9 ± 2.1 cm. By scaling carbon combustion with the dNBR ( R² = 0.42, p < 0.001), we estimated total carbon emissions from tussock tundra across both fires at 0.81 ± 0.22 Tg. GeoCBI estimates were moderately correlated with the dNBR ( R² = 0.63, p < 0.001) across vegetation types. This work provides important information on the impacts of fires on the carbon balance of tundra ecosystems and demonstrates that dNBR, a well-established remote sensing proxy for forest fires, is also effective for mapping fire severity in tundra landscapes.

Climate change is intensifying fire behavior, with the largest and fastest-spreading fires causing the greatest impacts on people and ecosystems. Yet the mechanisms driving variability and trends in large fires remain poorly understood. Using 12-hour satellite-derived fire tracking data from 2012 to 2023, we show that the merging of separate ignitions into multi-ignition complexes is a key process amplifying fire size and destructive potential across temperate and boreal ecoregions. Multi-ignition fires account for 31% of the burned area in California and 59% in the Arctic-boreal domain, spread faster and persist longer than single-ignition fires, and disproportionately contribute to extreme fire years in California, Canada, and Siberia. They also generate stronger atmospheric feedbacks, produce more pyrocumulonimbus events, and strain firefighting capacity by dispersing suppression resources. Recognizing and accounting for fire-merging dynamics are critical for improving wildfire prediction, risk assessment, and management.