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Reliable predictions of fuel consumption are critical in the eastern United States (US), where prescribed burning is frequently applied to forests and air quality is of increasing concern. CONSUME and the First Order Fire Effects Model (FOFEM), predictive models developed to estimate fuel consumption and emissions from wildland fires, have not been systematically evaluated for application in the eastern US using the same validation data set. In this study, we compiled a fuel consumption data set from 54 operational prescribed fires (43 pine and 11 mixed hardwood sites) to assess each model’s uncertainties and application limits. Regions of indifference between measured and predicted values by fuel category and forest type represent the potential error that modelers could incur in estimating fuel consumption by category. Overall, FOFEM predictions have narrower regions of indifference than CONSUME and suggest better correspondence between measured and predicted consumption. However, both models offer reliable predictions of live fuel (shrubs and herbaceous vegetation) and 1 h fine fuels. Results suggest that CONSUME and FOFEM can be improved in their predictive capability for woody fuel, litter, and duff consumption for eastern US forests. Because of their high biomass and potential smoke management problems, refining estimates of litter and duff consumption is of particular importance.

Fire hazard mitigation planning requires an accurate accounting of fuel complexes to predict potential fire behavior and effects of treatment alternatives. In the southeastern United States, rapid vegetation growth coupled with complex land use history and forest management options requires a dynamic approach to fuel characterization. In this study we assessed potential surface fire behavior with the Fuel Characteristic Classification System (FCCS), a tool which uses inventoried fuelbed inputs to predict fire behavior. Using inventory data from 629 plots established in the upper Atlantic Coastal Plain, South Carolina, we constructed FCCS fuelbeds representing median fuel characteristics by major forest type and age class. With a dry fuel moisture scenario and 6.4 km h −1 midflame wind speed, the FCCS predicted moderate to high potential fire hazard for the majority of the fuelbeds under study. To explore fire hazard under potential future fuel conditions, we developed fuelbeds representing the range of quantitative inventory data for fuelbed components that drive surface fire behavior algorithms and adjusted shrub species composition to represent 30% and 60% relative cover of highly flammable shrub species. Results indicate that the primary drivers of surface fire behavior vary by forest type, age and surface fire behavior rating. Litter tends to be a primary or secondary driver in most forest types. In comparison to other surface fire contributors, reducing shrub loading results in reduced flame lengths most consistently across forest types. FCCS fuelbeds and the results from this project can be used for fire hazard mitigation planning throughout the southern Atlantic Coastal Plain where similar forest types occur. The approach of building simulated fuelbeds across the range of available surface fuel data produces sets of incrementally different fuel characteristics that can be applied to any dynamic forest types in which surface fuel conditions change rapidly.

The Fuel Characteristic Classification System (FCCS) is a software module that records wildland fuel characteristics and calculates potential fire behavior and hazard potentials based on input environmental variables. The FCCS 3.0 is housed within the Integrated Fuels Treatment Decision Support System (Joint Fire Science Program 2012). It can also be run from command line as a stand-alone calculator. The flexible design of FCCS allows users to represent the structural complexity and diversity of fuels created through natural processes (e.g., forest succession and disturbance) and management activities (e.g., forest harvesting and fuels reduction). Each fuelbed is organized into six strata, including canopy, shrubs, herbaceous vegetation, woody fuels, litter-lichen-moss, and ground fuels. Strata are further divided into categories and subcategories. Fuelbeds representing common fuel types throughout much of North America are available in the FCCS reference library. Users may select an FCCS fuelbed to represent their specific project or customize a fuelbed to reflect actual site conditions. The FCCS reports the following results: (1) fuel characteristics by fuelbed, stratum, category, and subcategory; (2) surface fire behavior (i.e., reaction intensity, rate of spread, and flame length); and (3) FCCS fire potential ratings of surface fire behavior, crown fire behavior, and available fuels. With its large fuels data set and ability to represent a wide variety of fuel conditions, the FCCS has numerous applications, from small-scale fuel reduction projects to large-scale emissions and carbon assessments. This report provides technical documentation of the required inputs and computations in the FCCS.

Research activities focused on estimating the direct emissions of carbon from wildland fires across North America are reviewed as part of the North American Carbon Program disturbance synthesis. A comparison of methods to estimate the loss of carbon from the terrestrial biosphere to the atmosphere from wildland fires is presented. Published studies on emissions from recent and historic time periods and five specific cases are summarized, and new emissions estimates are made using contemporary methods for a set of specific fire events. Results from as many as six terrestrial models are compared. We find that methods generally produce similar results within each case, but estimates vary based on site location, vegetation (fuel) type, and fire weather. Area normalized emissions range from 0.23 kg C m−2 for shrubland sites in southern California/NW Mexico to as high as 6.0 kg C m−2 in northern conifer forests. Total emissions range from 0.23 to 1.6 Tg C for a set of 2003 fires in chaparral-dominated landscapes of California to 3.9 to 6.2 Tg C in the dense conifer forests of western Oregon. While the results from models do not always agree, variations can be attributed to differences in model assumptions and methods, including the treatment of canopy consumption and methods to account for changes in fuel moisture, one of the main drivers of variability in fire emissions. From our review and synthesis, we identify key uncertainties and areas of improvement for understanding the magnitude and spatial-temporal patterns of pyrogenic carbon emissions across North America.

Two series of single and stereo photographs display a range of natural conditions and fuel loadings in post-hurricane forests in the southeastern United States. Each group of photos includes inventory information summarizing vegetation composition, structure and loading, woody material loading and density by size class, forest floor loading, and various site characteristics. The natural fuels photo series is designed to help land managers appraise fuel and vegetation conditions in natural settings.

The lack of independent, quality-assured field data prevents scientists from effectively evaluating and advancing wildland fire models. To rectify this, scientists and technicians convened in the southeastern United States in 2008, 2011 and 2012 to collect wildland fire data in six integrated core science disciplines defined by the fire modelling community. These were fuels, meteorology, fire behaviour, energy, smoke emissions and fire effects. The campaign is known as the Prescribed Fire Combustion and Atmospheric Dynamics Research Experiment (RxCADRE) and sampled 14 forest and 14 non-forest sample units associated within 6 small replicate (,10 ha) and 10 large operational (between 10and1000ha)prescribedfires.Precampaignplanningincludedidentifyinghostingagenciesreceptivetoresearchandthe development of study, logistics and safety plans. Data were quality-assured, reduced, analysed and formatted and placed into a globally accessible repository maintained by the US Forest Service Research Data Archive. The success of the RxCADRE project led to the commencement of a follow-on larger multiagency project called the Fire and Smoke Model Evaluation Experiment (FASMEE). This overview summarises the RxCADRE project and nine companion papers that describe the data collection, analysis and important conclusions from the six science disciplines. Additional keywords: fire behaviour, fire effects, fire model evaluation, fire weather, fuel, remote-piloted aircraft system, smoke.

Terrestrial laser scanning (TLS) was used to collect spatially continuous measurements of fuelbed characteristics across the plots and burn blocks of the 2012 RxCADRE experiments in Florida. Fuelbeds were scanned obliquely from plot/block edges at a height of 20 m above ground. Pre-fire blocks were scanned from six perspectives and four perspectives for post-fire at ~2 cm nominal spot spacing. After processing, fuel height models were developed at one meter spatial resolution in burn blocks and compared with field measurements of height. Spatial bias is also examined. The resultant fuel height data correspond closely with field measurements of height and exhibit low spatial bias. They show that field measurements of fuel height from field plots are not representative of the burn blocks as a whole. A translation of fuel height distributions to specific fuel attributes will be necessary to maximise the utility of the data for fire modelling.

A lack of independent, quality-assured data prevents scientists from effectively evaluating predictions and uncertainties in fire models used by land managers. This paper presents a summary of pre-fire and post-fire fuel, fuel moisture and surface cover fraction data that can be used for fire model evaluation and development. The data were collected in the south-eastern United States on 14 forest and 14 non-forest sample units associated with 6 small replicate and 10 large operational prescribed fires conducted during 2008, 2011, and 2012 as part of the Prescribed Fire Combustion and Atmospheric Dynamics Research Experiment (RxCADRE). Fuel loading and fuel consumption averaged 6.8 and 4.1 Mg ha–1 respectively in the forest units and 3.0 and 2.2 Mg ha–1 in the non-forest units. Post-fire white ash cover ranged from 1 to 28%. Data were used to evaluate two fuel consumption models, CONSUME and FOFEM, and to develop regression equations for predicting fuel consumption from ash cover. CONSUME and FOFEM produced similar predictions of total fuel consumption and were comparable with measured values. Simple linear models to predict pre-fire fuel loading and fuel consumption from post-fire white ash cover explained 46 and 59% of variation respectively.

Two series of single and stereo photographs display a range of natural conditions and fuel loadings in sagebrush with grass and ponderosa pinejuniper types in central Montana. Each group of photos includes inventory information summarizing vegetation composition, structure, and loading; woody material loading and density by size class; forest floor depth and loading; and various site characteristics. The natural fuels photo series is designed to help land managers appraise fuel and vegetation conditions in natural settings.

As séries de estereo-fotografias para o Cerrado representam uma variação de formas fisionômicas do Cerrado, incluindo campo limpo, campo sujo, cerrado ralo, cerrado sensu stricto e cerrado denso. As áreas incluem fotografias grande angular e um par de estereo-fotografias complementadas com informações sobre o combustível vivo e morto, a estrutura e composição da vegetação. Estas séries de estereo-fotografias são uma importante ferramenta de manejo, que pode ser usada na avaliação de paisagens através da estimativa do combustível vivo e morto, bem como da estrutura da vegetação. Dados de inventário como os fornecidos nestas séries podem ser usados como variáveis para, por exemplo, a avaliação de “habitats” de animais e insetos, ciclagem de nutrients, microclima e estimativas de seqüestro de carbono . Aqueles que trabalham com pesquisas em fogo encontrarão dados importantes para a predição de consumo de combustível, produção de fumaça e efeitos do fogo durante incêndios florestais e queimadas prescritas.

The characteristics and spatial distribution of fuels are critical for assessing fire hazard, fuel consumption, greenhouse gas emissions and other fire effects. However, fuel maps are difficult to generate and update, because many regions of the world lack fuel descriptions or adequate mapped vegetation attributes to assign these fuelbeds spatially across the landscape. This paper presents a process to generate fuel maps for large areas using remotely sensed information and ancillary fuel characteristic data. The Fuel Characteristic Classification System was used to build fuelbeds for South America and predict potential fire hazard using a set of default environmental variables. A land-cover map was combined with a biome map to define 98 fuelbeds, and their parameters were assigned based on information from global datasets and existing Fuel Characteristic Classification System fuelbeds or photo series. The indices of potential surface fire behaviour ranged from 1.32 to 9, whereas indices of potential crown fire and available fuel for combustion had low to medium values (0–6). This paper presents a geospatial fuels map for South America. This map could be used to assess fire hazard, predict fire behaviour under defined environmental conditions or calculate fuel consumption and greenhouse gas emissions. It could also be easily updated as new remotely sensed information on vegetation becomes available.

This study evaluates the consumption of coarse woody debris in various states of decay. Samples from a northern Idaho mixed-conifer forest were classified using three different classification methods, ignited with two different ignition methods and consumption was recorded. Intrinsic properties that change with decay were measured including carbon to nitrogen ratio, density, heat content, lignin content, moisture content and surface area-to-volume ratio. Consumption for logs in different stages of decay is reported with characterisation of wood properties. Results indicate very decayed coarse woody debris is likely to be consumed to a substantially greater degree than sound coarse woody debris given similar conditions. High consumption occurred in debris with low-density, high-lignin content and high gravimetric heat content; however, lignin content and density showed the highest correlation with consumption. The Maser classification method grouped very rotten logs with high consumption into decay class 4 and the remainder into class 3. Trends in consumption were similar regardless of ignition; however low-intensity long-duration ignition produced higher consumption values. Focus on physical properties is recommended for predictive purposes over any classification method. Logs of other species and in regions with different decomposition and combustion dynamics may display different property ranges and consumption results.

Coarse woody debris serves many functions in forest ecosystem processes and has important implications for fire management as it affects air quality, soil heating and carbon budgets when it combusts. There is relatively little research evaluating the physical properties relating to the combustion of this coarse woody debris with even less specifically addressing decomposition, a condition that eventually affects all debris. We review studies evaluating the combustion and consumption of coarse woody debris in the field and under controlled conditions. The thermal properties affected by decomposition are also reviewed, as are current modelling tools to represent their combustion. Management implications and suggestions for future research are then presented.

Wildfire is a major forest disturbance in interior Alaska that can both directly and indirectly alter ecological processes. We used a combination of pre- and post-fire forest floor depths and post-fire ground cover assessments measured in the field, and high-resolution airborne hyperspectral imagery, to map forest floor conditions after the 2004 Taylor Complex in Alaska’s boreal forest. We applied a linear spectral unmixing model with five endmembers representing green moss, non-photosynthetic moss, charred moss, ash and soil to reflectance data to produce fractional cover maps. Our study sites spanned low to moderately high burn severity, and both black and white spruce forest types; high cover of green or non-photosynthetic moss in the remotely sensed imagery indicated low consumption, whereas high cover of charred moss, ash or soil indicated higher consumption. Strong relationships (R2 = 0.5 to 0.6) between green moss estimated from the imagery and both post-fire depth and percentage consumption suggest that potential burn severity may be predicted by a map of green (live) moss. Given that the depth of the post-fire forest floor is ecologically significant, the method of mapping the condition of the organic forest floor with hyperspectral imagery presented here may be a useful tool to assess the effect of future fires in the boreal region.

We evaluated the utility of the composite burn index (CBI) for estimating fire severity in Alaskan black spruce forests by comparing data from 81 plots located in 2004 and 2005 fire events. We collected data to estimate the CBI and quantify crown damage, percent of trees standing after the fire, depth of the organic layer remaining after the fire, depth of burning in the surface organic layer (absolute and relative), and the substrate layer exposed by the fire. To estimate pre-fire organic layer depth, we collected data in 15 unburned stands to develop relationships between total organic layer depth and measures of the adventitious root depth above mineral soil and below the surface of the organic layer. We validated this algorithm using data collected in 17 burned stands where pre-fire organic layer depth had been measured. The average total CBI value in the black spruce stands was 2.46, with most of the variation a result of differences in the CBI observed for the substrate layer. While a quadratic equation using the substrate component of CBI was a relatively strong predictor of mineral soil exposure as a result of fire (R2 = 0.61, P < 0.0001, F = 60.3), low correlations were found between the other measures of fire severity and the CBI (R2 = 0.00–0.37). These results indicate that the CBI approach has limited potential for quantifying fire severity in these ecosystems, in particular organic layer consumption, which is an important factor to understand how ecosystems will respond to changing climate and fire regimes in northern regions.

To understand the combustion limit of biomass fuels in a longleaf pine (Pinus palustris) forest, an experiment was conducted to monitor the moisture content of potentially flammable forest floor materials (litter and duff) at Eglin Air Force Base in the Florida Panhandle. While longleaf pine forests are fire dependent ecosystems, a long history of fire exclusion has allowed large amounts of pine litter and duff to accumulate. Reintroducing fire to remove excess fuel without killing the longleaf pine trees requires care to burn under litter and duff moisture conditions that alternately allow fire to carry while preventing root exposure or stem girdle. The study site was divided into four blocks that were burned under litter and duff moisture conditions of wet, moist, dry, and very dry. Throughout the 4-month experiment, portable weather stations continuously collected meteorological data, which included continuous measurements of water content in the forest floor material from in situ, time-domain reflectometers. In addition, volumetric moisture samples were collected almost weekly, and pre-burn fuel load and subsequent consumption were measured for each burn. Meteorological variables from the weather stations compared with trends in fuel moisture showed the influence of relative humidity and precipitation on the drying and wetting rates of the litter and duff. Fuel moisture conditions showed significant influence on patterns of fuel consumption and could lead to an understanding of processes that govern longleaf pine mortality.

This paper was presented at the conference ‘Integrating spatial technologies and ecological principles for a new age in fire management’, Boise, Idaho, USA, June 1999 The ongoing development of sophisticated fire behavior and effects models has demonstrated the need for a comprehensive system of fuel classification that more accurately captures the structural complexity and geographic diversity of fuelbeds. The Fire and Environmental Research Applications Team (FERA) of the USDA Forest Service, Pacific Northwest Research Station, is developing a national system of fuel characteristic classification (FCC). The system is designed to accommodate researchers and managers operating at a variety of scales, and who have access to a variety of kinds of input data. Users can generate fuel characteristics by accessing existing fuelbed descriptions (fuelbed prototypes) using generic information such as cover type or vegetation form. Fuelbed prototypes will provide the best available predictions of the kind, quality and abundance of fuels. Users can accept these default settings or modify some or all of them using more detailed information about vegetation structure and fuel biomass. When the user has completed editing the fuelbed data, the FCC system calculates or infers quantitative fuel characteristics (physical, chemical, and structural properties) and probable fire parameters specific to that fuelbed. Each user-described fuelbed is also assigned to one of approximately 192 stylized fuel characteristic classes.