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Fire impacts many vegetated ecosystems across the world. The severity of a fire is major component in determining post-fire effects, including soil erosion, trace gas emissions, and the trajectory of recovery. In this study, we used imaging spectroscopy data combined with Multiple Endmember Spectral Mixture Analysis (MESMA), a form of spectral mixture analysis that accounts for endmember variability, to map fire severity of the 2013 Rim Fire. We evaluated four endmember selection approaches: Iterative Endmember Selection (IES), count-based within endmember class (In-CoB), Endmember Average Root Mean Squared Error (EAR), and Minimum Average Spectral Angle (MASA). To reduce the dimensionality of the imaging spectroscopy data we used uncorrelated Stable Zone Unmixing (uSZU). Fractional cover maps derived from MESMA were validated using two approaches: (1) manual interpretation of fine spatial resolutionWorldView-2 imagery; and (2) ground plots measuring the Geo Composite Burn Index (GeoCBI) and the percentage of co-dominant and dominant trees with green, brown, and black needles. Comparison to reference data demonstrated fairly high correlation for green vegetation and char fractions (r2 values as high as 0.741 for the MESMA ash fractions compared to classifiedWorldView-2 imagery and as high as 0.841 for green vegetation fractions). The combination of uSZU band selection and In-CoB endmember selection had the best trade-offbetween accuracy and computational efficiency. This study demonstrated that detailed fire severity retrievals based on imaging spectroscopy can be optimized using techniques that would be viable also in a satellite-based imaging spectrometer.

Relationships between boreal wildfire emissions and day-to-day variations in meteorological variables are complex and have important implications for the sensitivity of high-latitude ecosystems to climate change. We examined the influence of environmental conditions on boreal fire emissions and fire contributions to regional trace gas variability in interior Alaska during the summer of 2013 using two types of analysis. First, we quantified the degree to which meteorological and fire weather indices explained regional variability in fire activity using four different products, including active fires, fire radiative power, burned area, and carbon emissions. Second, we combined daily emissions from the Alaskan Fire Emissions Database (AKFED) with the coupled Polar Weather Research and Forecasting/Stochastic Time-Inverted Lagrangian Transport model to estimate fire contributions to trace gas concentration measurements at the Carbon in Arctic Reservoirs Vulnerability Experiment-NOAA Global Monitoring Division (CRV) tower in interior Alaska. Tower observations during two high fire periods were used to estimate CO and CH4 emission factors. We found that vapor pressure deficit and temperature had a level of performance similar to more complex fire weather indices. Emission factors derived from CRV tower measurements were 134 ± 25 g CO per kg of combusted biomass and 7.74 ± 1.06 g CH4 per kg of combusted biomass. Predicted daily CO mole fractions from AKFED emissions were moderately correlated with CRV observations (r = 0.68) and had a high bias. The modeling system developed here allows for attribution of emission factors to individual fires and has the potential to improve our understanding of regional CO, CH4, and CO2 budgets.

The area burned by Southern California wildfires has increased in recent decades, with implications for human health, infrastructure, and ecosystem management. Meteorology and fuel structure are universally recognized controllers of wildfire, but their relative importance, and hence the efficacy of abatement and suppression efforts, remains controversial. Southern California's wildfires can be partitioned by meteorology: fires typically occur either during Santa Ana winds (SA fires) in October through April, or warm and dry periods in June through September (non-SA fires). Previous work has not quantitatively distinguished between these fire regimes when assessing economic impacts or climate change influence. Here we separate five decades of fire perimeters into those coinciding with and without SA winds. The two fire types contributed almost equally to burned area, yet SA fires were responsible for 80% of cumulative 1990-2009 economic losses ($3.1 Billion). The damage disparity was driven by fire characteristics: SA fires spread three times faster, occurred closer to urban areas, and burned into areas with greater housing values. Non-SA fires were comparatively more sensitive to age-dependent fuels, often occurred in higher elevation forests, lasted for extended periods, and accounted for 70% of total suppression costs. An improved distinction of fire type has implications for future projections and management. The area burned in non-SA fires is projected to increase 77% (±43%) by the mid-21st century with warmer and drier summers, and the SA area burned is projected to increase 64% (±76%), underscoring the need to evaluate the allocation and effectiveness of suppression investments.

Boreal fires burn into carbon-rich organic soils, thereby releasing large quantities of trace gases and aerosols that influence atmospheric composition and climate. To better understand the factors regulating boreal fire emissions, we developed a statistical model of carbon consumption by fire for Alaska with a spatial resolution of 450 m and a temporal resolution of 1 day. We used the model to estimate variability in carbon emissions between 2001 and 2012. Daily burned area was mapped using imagery from the Moderate Resolution Imaging Spectroradiometer combined with perimeters from the Alaska Large Fire Database. Carbon consumption was calibrated using available field measurements from black spruce forests in Alaska. We built two nonlinear multiplicative models to separately predict above- and belowground carbon consumption by fire in response to environmental variables including elevation, day of burning within the fire season, pre-fire tree cover and the differenced normalized burn ratio (dNBR). Higher belowground carbon consumption occurred later in the season and for mid-elevation forests. Topographic slope and aspect did not improve performance of the belowground carbon consumption model. Aboveground and belowground carbon consumption also increased as a function of tree cover and the dNBR, suggesting a causal link between the processes regulating these two components of carbon consumption. Between 2001 and 2012, the median carbon consumption was 2.54 kg C m-2. Burning in land-cover types other than black spruce was considerable and was associated with lower levels of carbon consumption than for pure black spruce stands. Carbon consumption originated primarily from the belowground fraction (median Combining double low line 2.32 kg C m-2 for all cover types and 2.67 kg C m-2 for pure black spruce stands). Total carbon emissions varied considerably from year to year, with the highest emissions occurring during 2004 (69 Tg C), 2005 (46 Tg C), 2009 (26 Tg C), and 2002 (17 Tg C) and a mean of 15 Tg C year-1 between 2001 and 2012. Mean uncertainty of carbon consumption for the domain, expressed as 1 standard deviation (SD), was 0.50 kg C m-2. Uncertainties in the multiplicative regression model used to estimate belowground consumption in black spruce stands and the land-cover classification were primary contributors to uncertainty estimates. Our analysis highlights the importance of accounting for the spatial heterogeneity of fuels and combustion when extrapolating emissions in space and time, and the need for of additional field campaigns to increase the density of observations as a function of tree cover and other environmental variables influencing consumption. The daily emissions time series from the Alaskan Fire Emissions Database (AKFED) presented here creates new opportunities to study environmental controls on daily fire dynamics, optimize boreal fire emissions in biogeochemical models, and quantify potential feedbacks from changing fire regimes.

Carbon emissions from boreal forest fires are projected to increase with continued warming and constitute a potentially significant positive feedback to climate change. The highest consistent combustion levels are reported in interior Alaska and can be highly variable depending on the consumption of soil organic matter. Here we present an approach for quantifying emissions within a fire perimeter using remote sensing of fire severity. Combustion from belowground and aboveground pools was quantified at 22 sites (17 black spruce and five white spruce-aspen) within the 2010 Gilles Creek burn in interior Alaska, constrained by data from eight unburned sites. We applied allometric equations and estimates of consumption to calculate carbon losses from aboveground vegetation. The position of adventitious spruce roots within the soil column, together with estimated prefire bulk density and carbon concentrations, was used to quantify belowground combustion. The differenced Normalized Burn Ratio (dNBR) exhibited a clear but nonlinear relationship with combustion that differed by forest type. We used a multiple regression model based on transformed dNBR and deciduous fraction to scale carbon emissions to the fire perimeter, and a Monte Carlo framework to assess uncertainty. Because of low-severity and unburned patches, mean combustion across the fire perimeter (1.98 ± 0.34 kg C m-2) was considerably less than within a defined core burn area (2.67 ± 0.40 kg C m-2) and the mean at field sites (2.88 ± 0.23 kg C m-2). These areas constitute a significant fraction of burn perimeters in Alaska but are generally not accounted for in regional-scale estimates. Although total combustion in black spruce was slightly lower than in white spruce-aspen forests, black spruce covered most of the fire perimeter (62%) and contributed the majority (67 ± 16%) of total emissions. Increases in spring albedo were found to be a viable alternative to dNBR for modeling emissions.

High temporal resolution information on burnt area is needed to improve fire behaviour and emissions models. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) thermal anomaly and active fire product (MO(Y)D14) as input to a kriging interpolation to derive continuous maps of the timing of burnt area for 16 large wildland fires. For each fire, parameters for the kriging model were defined using variogram analysis. The optimal number of observations used to estimate a pixel's time of burning varied between four and six among the fires studied. The median standard error from kriging ranged between 0.80 and 3.56 days and the median standard error from geolocation uncertainty was between 0.34 and 2.72 days. For nine fires in the south-western US, the accuracy of the kriging model was assessed using high spatial resolution daily fire perimeter data available from the US Forest Service. For these nine fires, we also assessed the temporal reporting accuracy of the MODIS burnt area products (MCD45A1 and MCD64A1). Averaged over the nine fires, the kriging method correctly mapped 73% of the pixels within the accuracy of a single day, compared with 33% for MCD45A1 and 53% for MCD64A1. Systematic application of this algorithm to wildland fires in the future may lead to new information about vegetation, climate and topographic controls on fire behaviour. © IAWF 2014.

Boreal fires burn carbon-rich organic soils, thereby releasing large quantities of trace gases and aerosols that influence atmospheric composition and climate. To better understand the factors regulating boreal fire emissions, we developed a statistical model of carbon consumption by fire for Alaska with a spatial resolution of 500 m and a temporal resolution of one day. We used the model to estimate variability in carbon emissions between 2001 and 2012. Daily burned area was mapped using imagery from the Moderate Resolution Imaging Spectroradiometer combined with perimeters from the Alaska Large Fire Database. Carbon consumption was calibrated using available field measurements from black spruce forests in Alaska. We built two nonlinear multiplicative models to separately predict above- and belowground carbon consumption by fire in response to environmental variables including elevation, day of burning within the fire season, pre-fire tree cover and the differenced normalized burn ratio (dNBR). Higher belowground consumption occurred later in the season and for mid-elevation regions. Aboveground and belowground consumption also increased as a function of tree cover and the dNBR, suggesting a causal link between the processes regulating these two components of consumption. Between 2001 and 2012, the median fuel consumption was 2.48 kgCm-2 and the median pixel-based uncertainty (SD of prediction error) was 0.38kgCm . There were considerable amounts of burning in other cover types than black spruce and consumption in pure black spruce stands was generally higher. Fuel consumption originated primarily from the belowground fraction (median = 2.30kgCm-2 for all cover types and 2.63kgCm-2 for pure black spruce stands). Total carbon emissions varied considerably from year to year, with the highest emissions occurring during 2004 (67TgC), 2005 (44TgC), 2009 (25TgC), and 2002 (16TgC) and a mean of 14TgC per year between 2001 and 2012. Our analysis highlights the importance of accounting for the spatial heterogeneity within fuels and consumption when extrapolating emissions in space and time. This data on daily burned area and emissions may be useful for in understanding controls and limits on fire growth, and predicting potential feedbacks of changing fire regimes. [ABSTRACT FROM AUTHOR]

ABSTRACT The combined effects of erosive rains, steep slopes and human land use have caused severe land degradation in the Ethiopian Highlands for several thousand years, but since the 1970s, however, land rehabilitation programmes have been established to try to reverse deterioration. In order to characterize and quantify the transformations in the north Ethiopian Highlands, a study was carried out over 8884 km2 of the Tigray Highlands of northern Ethiopia. Using Landsat Multispectral Scanner and later Thematic Mapper imagery (1972, 1984/1986 and 2000), historical terrestrial photographs (1974-1975) and fieldwork (2008), we prepared land use and cover maps. For assessing the use of the historical terrestrial photographs, Landsat images from 1972 were classified using two different methods, namely conventional change detection (image differencing) and ground truthing (using the historical photographs of 1974-1975). Results show that the use of terrestrial photographs is promising, as the classification accuracy based on this method (Kappa coefficient 0·54) is better than the classification accuracy of the method based on image differencing (Kappa coefficient 0·46). Major land use and cover changes indicate the following: (1) a gradual but significant decline in bare ground (32 per cent in 1972 to 8 per cent in 2000); (2) a significant increase of bushland (25 to 43 per cent) and total forest area (including eucalypt plantations, 2·6 to 6·3 per cent); and (3) creation of numerous lakes and ponds. The dominant change trajectory (27 per cent of the study area) indicates a gradual or recent vegetation increase. These changes can be linked to the population growth and the introduction of land rehabilitation initiatives, complemented by growing awareness of land holders. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Burn severity is an important parameter in post-fire management. It incorporates both the direct fire impact (vegetation depletion) and ecosystem responses (vegetation regeneration). From a remote sensing perspective, burn severity is traditionally estimated using Landsat''s differenced normalized burn ratio (dNBR). In this case study of the large 2007 Peloponnese (Greece) wildfires, Landsat dNBR estimates correlated reasonably well with Geo composite burn index (GeoCBI) field data of severity (R 2 =0.56). The usage of Landsat imagery is, however, restricted by cloud cover and image-to-image normalization constraints. Therefore a multi-temporal burn severity approach based on coarse spatial, high temporal resolution moderate resolution imaging spectroradiometer (MODIS) imagery is presented in this study. The multi-temporal dNBR (dNBRMT) is defined as the 1-year integrated difference between burned pixels and their unique control pixels. These control pixels were selected based on time series similarity and spatial context and reflect how burned pixels would have behaved in the case no fire had occurred. Linear regression between downsampled Landsat dNBR and dNBRMT estimates resulted in a moderate-high coefficient of determination R 2 =0.54. dNBRMT estimates are indicative for the change in vegetation productivity due to the fire. This change is considerably higher for forests than for more sparsely vegetated areas like shrub lands. Although Landsat dNBR is superior for spatial detail, MODIS-derived dNBRMT estimates present a valuable alternative for burn severity mapping at continental to global scale without image availability constraints. This is beneficial to compare trends in burn severity across regions and time. Moreover, thanks to MODIS''s repeated temporal sampling, the dNBRMT accounts for both first- and second-order fire effects. [Copyright &y& Elsevier]

Climate change increases fire-favorable weather in forests, but fire trends are also affected by multiple other controlling factors that are difficult to untangle. We use machine learning to systematically group forest ecoregions into 12 global forest pyromes, with each showing distinct sensitivities to climatic, human, and vegetation controls. This delineation revealed that rapidly increasing forest fire emissions in extratropical pyromes, linked to climate change, offset declining emissions in tropical pyromes during 2001 to 2023. Annual emissions tripled in one extratropical pyrome due to increases in fire-favorable weather, compounded by increased forest cover and productivity. This contributed to a 60% increase in forest fire carbon emissions from forest ecoregions globally. Our results highlight the increasing vulnerability of forests and their carbon stocks to fire disturbance under climate change.

Climate change has disproportional effects on Arctic-boreal ecosystems, as the increase of air temperatures in these northern regions is several times higher than the global average. Ongoing warming and drying have resulted in recent record-breaking fire years in Arctic-boreal ecosystems, resulting in substantial carbon emissions that might accelerate climate change. While recent trends in Arctic-boreal burned area have been well documented, it is still unclear how fire intensity has changed. Fire intensity relates to the energy release from combustion and to a large extent drives the impact of a fire on the vegetation and soils, the emission of various gasses and the combustion completeness of different fuels. Here, we used the active fire product from the Moderate Resolution Imaging Spectroradiometer (MODIS) to examine trends in fire radiative power (FRP) over the entire Arctic-boreal region. We found a significant increase in annual median fire intensity between 2003 and 2022 in Eurasian boreal forests, for both daytime (increase of 0.392 MW/km 2 per year, R 2  = 0.56, p < 0.001) and nighttime fires (increase of 0.175 MW/km 2 per year, R 2  = 0.47, p < 0.001), while no general trend in FRP was observed in boreal North America. This increase in FRP in Eurasian boreal forests was strongly associated with simultaneous increases in air temperature, vapour pressure deficit, fire weather and fuel availability. We estimated that for Eurasia with each degree increase in air temperature, annual median daytime FRP increases with 1.58 MW/km 2 in the tundra and 0.94 MW/km 2 in the taiga. Climate change has thus resulted in a widespread and clear increase in fire intensity in central and eastern Eurasia while we could not discern clear trends in Arctic-boreal North America. Arctic-boreal fire intensity may further increase with climate change, with potentially major consequences for fire regimes, carbon emissions and society., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Wildfire activity in Arctic and boreal regions is rapidly increasing, with severe consequences for climate and human health. Regional long-term variations in fire frequency and intensity characterize fire regimes. The spatial variability in Arctic-boreal fire regimes and their environmental and anthropogenic drivers, however, remain poorly understood. Here we present a fire tracking system to map the sub-daily evolution of all circumpolar Arctic-boreal fires between 2012 and 2023 using 375 m Visible Infrared Imaging Radiometer Suite active fire detections and the resulting dataset of the ignition time, location, size, duration, spread and intensity of individual fires. We use this dataset to classify the Arctic-boreal biomes into seven distinct 'pyroregions' with unique climatic and geographic environments. We find that these pyroregions exhibit varying responses to environmental drivers, with boreal North America, eastern Siberia and northern tundra regions showing the highest sensitivity to climate and lightning density. In addition, anthropogenic factors play an important role in influencing fire number and size, interacting with other factors. Understanding the spatial variability of fire regimes and its interconnected drivers in the Arctic-boreal domain is important for improving future predictions of fire activity and identifying areas at risk for extreme events., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2024.)

In most of the world, conditions conducive to wildfires are becoming more prevalent. Net carbon emissions from wildfires contribute to a positive climate feedback that needs to be monitored, quantified, and predicted. Here we use a causal inference approach to evaluate the influence of top-down weather and bottom-up fuel precursors on wildfires. The top-down dominance on wildfires is more widespread than bottom-up dominance, accounting for 73.3% and 26.7% of regions, respectively. The top-down precursors dominate in the tropical rainforests, mid-latitudes, and eastern Siberian boreal forests. The bottom-up precursors dominate in North American and European boreal forests, and African and Australian savannahs. Our study identifies areas where wildfires are governed by fuel conditions and hence where fuel management practices may be more effective. Moreover, our study also highlights that top-down and bottom-up precursors show complementary wildfire predictability across timescales. Seasonal or interannual predictions are feasible in regions where bottom-up precursors dominate., (© 2023. The Author(s).)

The summers of 2019, 2020, and 2021 experienced unprecedented fire activity in northeastern Siberia, driven by record high spring and summer temperatures. Many of these fires burned in permafrost peatlands within the Arctic Circle. We show that early snowmelt together with an anomalous Arctic front jet over northeastern Siberia promoted unusually warm and dry surface conditions, followed by anomalously high lightning and fire activity. Since 1966, spring snowmelt has started 1.7 days earlier each decade. Moreover, Arctic front jet occurrences in summer have more than tripled in frequency over the last 40 years. These interconnected climatological drivers promote extreme fire activity in eastern Siberia, including a northward shift of fires, which may accelerate the degradation of carbon-rich permafrost peatlands.

Intensifying wildfire activity and climate change can drive rapid forest compositional shifts. In boreal North America, black spruce shapes forest flammability and depends on fire for regeneration. This relationship has helped black spruce maintain its dominance through much of the Holocene. However, with climate change and more frequent and severe fires, shifts away from black spruce dominance to broadleaf or pine species are emerging, with implications for ecosystem functions including carbon sequestration, water and energy fluxes, and wildlife habitat. Here, we predict that such reductions in black spruce after fire may already be widespread given current trends in climate and fire. To test this, we synthesize data from 1,538 field sites across boreal North America to evaluate compositional changes in tree species following 58 recent fires (1989 to 2014). While black spruce was resilient following most fires (62%), loss of resilience was common, and spruce regeneration failed completely in 18% of 1,140 black spruce sites. In contrast, postfire regeneration never failed in forests dominated by jack pine, which also possesses an aerial seed bank, or broad-leaved trees. More complete combustion of the soil organic layer, which often occurs in better-drained landscape positions and in dryer duff, promoted compositional changes throughout boreal North America. Forests in western North America, however, were more vulnerable to change due to greater long-term climate moisture deficits. While we find considerable remaining resilience in black spruce forests, predicted increases in climate moisture deficits and fire activity will erode this resilience, pushing the system toward a tipping point that has not been crossed in several thousand years., Competing Interests: The authors declare no competing interest., (Copyright © 2021 the Author(s). Published by PNAS.)

Forest fires are usually viewed within the context of a single fire season, in which weather conditions and fuel supply can combine to create conditions favourable for fire ignition-usually by lightning or human activity-and spread 1-3 . But some fires exhibit 'overwintering' behaviour, in which they smoulder through the non-fire season and flare up in the subsequent spring 4,5 . In boreal (northern) forests, deep organic soils favourable for smouldering 6 , along with accelerated climate warming 7 , may present unusually favourable conditions for overwintering. However, the extent of overwintering in boreal forests and the underlying factors influencing this behaviour remain unclear. Here we show that overwintering fires in boreal forests are associated with hot summers generating large fire years and deep burning into organic soils, conditions that have become more frequent in our study areas in recent decades. Our results are based on an algorithm with which we detect overwintering fires in Alaska, USA, and the Northwest Territories, Canada, using field and remote sensing datasets. Between 2002 and 2018, overwintering fires were responsible for 0.8 per cent of the total burned area; however, in one year this amounted to 38 per cent. The spatiotemporal predictability of overwintering fires could be used by fire management agencies to facilitate early detection, which may result in reduced carbon emissions and firefighting costs.

Boreal wildfires are increasing in intensity, extent, and frequency, potentially intensifying carbon emissions and transitioning the region from a globally significant carbon sink to a source. The productive southern boreal forests of central Canada already experience relatively high frequencies of fire, and as such may serve as an analog of future carbon dynamics for more northern forests. Fire-carbon dynamics in southern boreal systems are relatively understudied, with limited investigation into the drivers of pre-fire carbon stocks or subsequent combustion. As part of NASA's Arctic-Boreal Vulnerability Experiment, we sampled 79 stands (47 burned, 32 unburned) throughout central Saskatchewan to characterize above- and belowground carbon stocks and combustion rates in relation to historical land use, vegetation characteristics, and geophysical attributes. We found southern boreal forests emitted an average of 3.3 ± 1.1 kg C/m 2 from field sites. The emissions from southern boreal stands varied as a function of stand age, fire weather conditions, ecozone, and soil moisture class. Sites affected by historical timber harvesting had greater combustion rates due to faster carbon stock recovery rates than sites recovering from wildfire events, indicating that different boreal forest land use practices can generate divergent carbon legacy effects. We estimate the 2015 fire season in Saskatchewan emitted a total of 36.3 ± 15.0 Tg C, emphasizing the importance of southern boreal fires for regional carbon budgets. Using the southern boreal as an analog, the northern boreal may undergo fundamental shifts in forest structure and carbon dynamics, becoming dominated by stands <70 years old that hold 2-7 kg C/m 2 less than current mature northern boreal stands. Our latitudinal approach reinforces previous studies showing that northern boreal stands are at a high risk of holding less carbon under changing disturbance conditions., (© 2020 John Wiley & Sons Ltd.)

Fire is a primary disturbance in boreal forests and generates both positive and negative climate forcings. The influence of fire on surface albedo is a predominantly negative forcing in boreal forests, and one of the strongest overall, due to increased snow exposure in the winter and spring months. Albedo forcings are spatially and temporally heterogeneous and depend on a variety of factors related to soils, topography, climate, land cover/vegetation type, successional dynamics, time since fire, season, and fire severity. However, how these variables interact to influence albedo is not well understood, and quantifying these relationships and predicting postfire albedo becomes increasingly important as the climate changes and management frameworks evolve to consider climate impacts. Here we developed a MODIS-derived 'blue sky' albedo product and a novel machine learning modeling framework to predict fire-driven changes in albedo under historical and future climate scenarios across boreal North America. Converted to radiative forcing (RF), we estimated that fires generate an annual mean cooling of -1.77 ± 1.35 W/m 2 from albedo under historical climate conditions (1971-2000) integrated over 70 years postfire. Increasing postfire albedo along a south-north climatic gradient was offset by a nearly opposite gradient in solar insolation, such that large-scale spatial patterns in RF were minimal. Our models suggest that climate change will lead to decreases in mean annual postfire albedo, and hence a decreasing strength of the negative RF, a trend dominated by decreased snow cover in spring months. Considering the range of future climate scenarios and model uncertainties, we estimate that for fires burning in the current era (2016) the cooling effect from long-term postfire albedo will be reduced by 15%-28% due to climate change., (© 2019 John Wiley & Sons Ltd.)

High-latitude ecosystems have the capacity to release large amounts of carbon dioxide (CO 2 ) to the atmosphere in response to increasing temperatures, representing a potentially significant positive feedback within the climate system. Here, we combine aircraft and tower observations of atmospheric CO 2 with remote sensing data and meteorological products to derive temporally and spatially resolved year-round CO 2 fluxes across Alaska during 2012-2014. We find that tundra ecosystems were a net source of CO 2 to the atmosphere annually, with especially high rates of respiration during early winter (October through December). Long-term records at Barrow, AK, suggest that CO 2 emission rates from North Slope tundra have increased during the October through December period by 73% ± 11% since 1975, and are correlated with rising summer temperatures. Together, these results imply increasing early winter respiration and net annual emission of CO 2 in Alaska, in response to climate warming. Our results provide evidence that the decadal-scale increase in the amplitude of the CO 2 seasonal cycle may be linked with increasing biogenic emissions in the Arctic, following the growing season. Early winter respiration was not well simulated by the Earth System Models used to forecast future carbon fluxes in recent climate assessments. Therefore, these assessments may underestimate the carbon release from Arctic soils in response to a warming climate., Competing Interests: The authors declare no conflict of interest.

Megafires have lasting social, ecological, and economic impacts and are increasing in the western contiguous United States. Because of their infrequent nature, there is a limited sample of megafires to investigate their unique behavior, drivers, and relationship to forest management practices. One approach is to characterize critical information pre-, during, and post-fire using remote sensing. In August 2013, the Rim Fire burned 104,131 ha and in September 2014, the King Fire burned 39,545 ha. Both fires occurred in California's Sierra Nevada. The areas burned by these fires were fortuitously surveyed by airborne campaigns, which provided the most recent remote sensing technologies not currently available from satellite. Technologies include an imaging spectrometer spanning the visible to shortwave infrared (0.38-2.5 μm), a multispectral, high-spatial resolution thermal infrared (3.5-13 μm) spectroradiometer, and Light Detection and Ranging that provide spatial resolutions of pixels from 1 × 1 m to 35 × 35 m. Because of the unique information inherently derived from these technologies before the fires, the areas were subsequently surveyed after the fires. We processed and provide free dissemination of these airborne datasets as products of surface reflectance, spectral metrics and forest structural metrics ( http://dx.doi.org/10.3334/ORNLDAAC/1288). These data products provide a unique opportunity to study relationships among and between remote sensing observations and fuel and fire characteristics (e.g., fuel type, condition, structure, and fire severity). The novelty of these data is not only in the unprecedented types of information available from them before, during, and after two megafires, but also in the synergistic use of multiple state of the art technologies for characterizing the environment. The synergy of these data can provide novel information that can improve maps of fuel type, structure, abundance, and condition that may improve predictions of megafire behavior and effects, thus aiding management before, during, and after such events. Key questions that these data could address include: What drives, extinguishes, and results from megafires? How does megafire behavior relate to fire and fuel management? How does the size and severity of a megafire affect the ecological recovery of the system?, (© 2016 by the Ecological Society of America.)