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Reliable estimates of pre-burn biomass and fuel consumption are important to estimate wildland fire emissions and assist in prescribed burn planning. We present empirical models for predicting fuel consumption in natural fuels from 60 prescribed fires in ponderosa pine-dominated forests in the western US and 60 prescribed fires in long-needle pine forests in the southeastern US. There was high variability across sites, but total surface fuel biomass was generally much lower on southern sites (23.0 ± 11.6 Mg ha −1 ) than western sites (61.5 ± 35.8 Mg ha −1 ). Differences in surface fuel composition, pre-burn loading and fuel consumption between the southern and western pine consumption datasets justify the development of regional models for predicting fuel consumption. Southern pine models of herb, shrub and 1-h consumption have close model fit with narrow prediction intervals across the range of sampled values. Relationships between 10-h and 100-h pre-burn loading and consumption produced models with reasonable fit but with no significant correlation with fuel moisture. Model fit of litter and duff consumption models was relatively poor compared to the other southern fuel categories. Western models were developed for 1-h, 10-h and 100-h fine wood, sound coarse wood, rotten coarse wood, litter and duff. All western models had high coefficients of variability, and model residuals indicate higher uncertainty with increasing pre-burn biomass. Although empirical models are widely used, they have limitations in that they are constrained by burning conditions and ranges of predictor variables.

There is limited research on the exposure of wildland firefighters to smoke because of the operational obstacles when monitoring air pollutants in the field. In this work, a grid of portable sensors was used to measure PM2.5 and CO concentrations in the near-source region during the burn of two shrubland research blocks in Central Portugal. Strong spatial variability of smoke levels was observed in the analysis of the ratios between mean concentrations of neighbouring sensors, with values as high as 4.4 for PM2.5 and 7.4 for CO. These large gradients were registered at a distance of only 5 m suggesting that considerable differences on individual exposure can occur depending on the location of that individual in relation to the smoke plume trajectory. Also, peak events of 2–3 times the mean were observed in periods exceeding 6 min. In the two experiments, the average concentrations of both PM2.5 and CO were higher during smouldering, which represents a risk of acute exposure due to the closer proximity of firefighters to the emission source during mop-up, stressing the importance of wearing portable gas detectors for managing critical exposure. The collected data constitutes a step forward in the effort to understand the mechanisms controlling the exposure during firefighting operations, by providing a source of information on near-ground concentration fluctuations within a biomass-burning smoke plume at a fine spatial-temporal resolution.

Fuel consumption specifies the amount of vegetative biomass consumed during wildland fire. It is a two-stage process of pyrolysis and combustion that occurs simultaneously and at different rates depending on the characteristics and condition of the fuel, weather, topography, and in the case of prescribed fire, ignition rate and pattern. Fuel consumption is the basic process that leads to heat absorbing emissions called greenhouse gas and other aerosol emissions that can impact atmospheric and ecosystem processes, carbon stocks, and land surface reflectance. It is a critical requirement for greenhouse gas emission inventories. There are several fuel consumption models widely used by scientists and land managers including the First Order Fire Effects Model, Consume, and CanFIRE. However, these models have not been thoroughly evaluated with an independent, quality assured, fuel consumption data set. Furthermore, anecdotal evidence indicates the models have limited ability to predict consumption of specific fuel bed categories such as tree crowns, deep organic layers, and rotten logs that can contribute significantly to greenhouse gases. If we are to move forward in our ability to assess the contribution of wildland fire to greenhouse gas to the atmosphere, our current fuel consumption models must be evaluated and modified to improve their predictive capabilities. Finally, information is lacking on how much black and brown carbon from wildland fire is generated during the combustion process and how much remains on site becoming sequestered in soils, partially offsetting greenhouse gas emissions. This synthesis focuses on the process and modeling of fuel consumption and knowledge gaps that will improve our ability to predict fuel consumption and the resulting greenhouse gas emissions.

Mastication is an increasingly common fuels treatment that redistributes ‘‘ladder’’ fuels to the forest floor to reduce vertical fuel continuity, crown fire potential, and fireline intensity, but fuel models do not exist for predicting fire behavior in these fuel types. Recent fires burning in masticated fuels have behaved in unexpected and contradictory ways, likely because the shredded, compact fuel created when trees and shrubs are masticated contains irregularly shaped pieces in mixtures quite different from other woody fuels. We review fuels characteristics and fire behavior in masticated fuels across the United States. With insights from the few laboratory and field burning experiments conducted, we highlight the variation likely to occur across different ecosystems in which these treatments are being widely implemented. Masticated debris has a propensity to flame and smolder for long durations. Fuel variability and vegetation response will likely influence whether or not treatments reduce long-term fire hazard. We identify key science needs that will better elucidate fire behavior and effects in these treatments. With mastication widely applied in an expanding wildland–urban interface it is crucial to understand how such fuels burn. What we learn about combustion in these fuels will inform effective fuels management in these and other mixed fuels.

Ground, airborne and spaceborne data were collected for a 450 ha prescribed fire implemented on 18 October 2011 at the Henry W. Coe State Park in California. The integration of various data elements allowed near coincident active fire retrievals to be estimated. The Autonomous Modular Sensor-Wildfire (AMS) airborne multispectral imaging system was used as a bridge between ground and spaceborne data sets providing high quality reference information to support satellite fire retrieval error analyses and fire emissions estimates. We found excellent agreement between peak fire radiant heat flux data (less than 1% error) derived from near-coincident ground radiometers and AMS. Both MODIS and GOES imager active fire products were negatively influenced by the presence of thick smoke, which was misclassified as cloud by their algorithms, leading to the omission of fire pixels beneath the smoke, and resulting in the underestimation of their retrieved fire radiative power (FRP) values for the burn plot, compared to the reference airborne data. Agreement between airborne and spaceborne FRP data improved significantly after correction for omission errors and atmospheric attenuation, resulting in as low as 5 difference between AquaMODIS and AMS. Use of in situ fuel and fire energy estimates in combination with a collection of AMS, MODIS, and GOES FRP retrievals provided a fuel consumption factor of 0.261 kg per MJ, total energy release of 14.5 x 10(exp 6) MJ, and total fuel consumption of 3.8 x 10(exp 6) kg. Fire emissions were calculated using two separate techniques, resulting in as low as 15 difference for various species

Fuel Loading Models (FLMs) and Fuel Characteristic Classification System (FCCSs) fuelbeds are used throughout wildland fire science and management to simplify fuel inputs into fire behavior and effects models, but they have yet to be thoroughly evaluated with field data. In this study, we used a large dataset of Forest Inventory and Analysis (FIA) surface fuel estimates (n = 13,138) to create a new fuel classification called Fuel Type Groups (FTGs) from FIA forest type groups, and then keyed an FLM, FCCS, and FTG class to each FIA plot based on fuel loadings and stand conditions. We then compared FIA sampled loadings to the keyed class loading values for four surface fuel components (duff, litter, fine woody debris, coarse woody debris) and to mapped FLM, FCCS, and FTG class loading values from spatial fuel products. We found poor performances (R2 < 0.30) for most fuel component loadings in all three classifications that, in turn, contributed to poor mapping accuracies. The main reason for the poor performances is the high variability of the four fuel component loadings within classification categories and the inherent scale of this variability does not seem to match the FIA measurement scale or LANDFIRE mapping scale.

Background/objective A study was undertaken at the United States Department of Energy's Savannah River Site (SRS), Aiken, South Carolina to investigate radionuclide activity concentrations in litter and duff from select areas at SRS. Litter (i.e. vegetative debris) and duff (i.e. highly decomposed vegetative debris) can often be the major fuels consumed during prescribed burns and have potential to release radiological contaminants into the environment. Methods Repeated samples from 97 locations were collected systematically across SRS and analyzed for radionuclide activity. Radionuclide activity concentrations found in litter and duff were compared. As spatial trends were of interest, spatial distributions of radionuclide activity concentrations found in litter and duff and spatial dependency amongst the data were explored. Results 7 Be, 40 K, and 137 Cs showed statistically significant proportional differences between litter and duff samples. Duff sample concentrations for 137 Cs ( p 40 K ( p = 0.0015) were statistically higher compared to litter samples. 7 Be activity concentrations were statistically higher in litter as compared to duff ( p 40 K litter and duff samples, spatial correlation tests were not significant at p = 0.05 and the maps did not indicate any apparent high concentrations centered near possible radionuclide sources (i.e. SRS facilities). For 7 Be litter samples, significant spatial correlation was calculated ( p = 0.0085). No spatial correlation was evident in the 7 Be duff samples ( p = 1.0000) probably due to small sample size ( n = 7). 137 Cs litter and duff samples showed significant spatial correlations ( p p Conclusions To date, few studies characterize radionuclide activity concentrations in litter and duff, and to our knowledge none present spatial analysis. Key findings show that across SRS, 137 Cs is the primary radionuclide of concern, with the highest number of samples reported above MDC in litter (51.4%) and duff samples (83.2%). However, 137 Cs litter and duff spatial trends in the maps generated from the kriging parameters do not appear to directly link the areas with higher activity concentrations with SRS facilities. The results found herein provide valuable baseline monitoring data for future studies of forest surface fuels and can be used to evaluate changes in radioactivity in surface fuels in the southeast region of the U.S.

Fuel treatment effectiveness in Southern forests has been demonstrated using fire behavior modeling and observations of reduced wildfire area and tree damage. However, assessments of treatment effectiveness may be improved with a more rigorous accounting of the fuel characteristics. We present two case studies to introduce a relatively new approach to characterizing fuels and predicting potential fire behavior, fuel consumption, and emissions in Southern forests using the Fuel Characteristic Classification System (FCCS) and Consume. The first case study provides fine-scale ( Results from the fine-scale assessment indicate that fuel treatments reduce reaction intensity, rate of spread, and flamelength by up to 58%, 57%, and 63%, respectively. Fuel loading of strata that control surface fire behavior (i.e., shrubs, grasses, fine woody fuels, and litter) range from 32.0 Mg ha−1 in the thinned, untreated unit to 8.5 Mg ha−1 in the unthinned unit treated with herbicides and prescribed fire. Based on model predictions, up to 80% less fuel would be consumed with concomitant reduction in emissions during a wildfire occurring in the treated units compared to the untreated unit. Assessments of potential fire behavior across the study area indicate that overall hazard is low to moderate for this forested landscape. However, localized areas of high surface fire and crown fire potential were identified. Plot- and stand-based modeling both suggest that the potential for high to extreme fire behavior exists for this landscape. Combined, the two case studies highlight the ability of the FCCS to represent measured fuel characteristics and predict differences in potential fire behavior resulting from fuel treatments. Even small differences in fuel characteristics resulting from fuel treatments or site variation could be detected, allowing the effects of both ecological processes and management actions to be quantified.

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.

Landscape-scale fire behavior analyses are important to inform decisions on resource management projects that meet land management objectives and protect values from adverse consequences of fire. Deterministic and probabilistic geospatial fire behavior analyses are conducted with various modeling systems including FARSITE, FlamMap, FSPro, and Large Fire Simulation System. The fundamental fire intensity algorithms in these systems require surface fire behavior fuel models and canopy cover to model surface fire behavior. Canopy base height, stand height, and canopy bulk density are required in addition to surface fire behavior fuel models and canopy cover to model crown fire activity. Several surface fuel and canopy classification efforts have used various remote sensing and ecological relationships as core methods to develop the spatial layers. All of these methods depend upon consistent and temporally constant interpretations of crown attributes and their ecological conditions to estimate surface fuel conditions. This study evaluates modeled fire behavior for an 80,000 ha tract of land in the Atlantic Coastal Plain of the southeastern US using three different data sources. The Fuel Characteristic Classification System (FCCS) was used to build fuelbeds from intensive field sampling of 629 plots. Custom fire behavior fuel models were derived from these fuelbeds. LANDFIRE developedmore » surface fire behavior fuel models and canopy attributes for the US using satellite imagery informed by field data. The Southern Wildfire Risk Assessment (SWRA) developed surface fire behavior fuel models and canopy cover for the southeastern US using satellite imagery. Differences in modeled fire behavior, data development, and data utility are summarized to assist in determining which data source may be most applicable for various land management activities and required analyses. Characterizing fire behavior under different fuel relationships provides insights for natural ecological processes, management strategies for fire mitigation, and positive and negative features of different modeling systems. A comparison of flame length, rate of spread, crown fire activity, and burn probabilities modeled with FlamMap shows some similar patterns across the landscape from all three data sources, but there are potentially important differences. All data sources showed an expected range of fire behavior. Average flame lengths ranged between 1 and 1.4 m. Rate of spread varied the greatest with a range of 2.4-5.7 m min{sup -1}. Passive crown fire was predicted for 5% of the study area using FCCS and LANDFIRE while passive crown fire was not predicted using SWRA data. No active crown fire was predicted regardless of the data source. Burn probability patterns across the landscape were similar but probability was highest using SWRA and lowest using FCCS.« less

In many fire-prone forests in the United States, changes occurring in the last century have resulted in overstory structures, conifer densities, down woody structure and fuel loads that deviate from those described historically. With these changes, forests are presumed to be unsustainable. Broad-scale treatments are proposed to reduce fuels and promote stand development on trajectories toward more sustainable structures. Yet little research to date has identified the effects of these treatments, especially in low elevation dry ponderosa pine (Pinus ponderosa) and Douglas-fir (Pseudotsuga menziesii) forests. We report initial fuelbed conditions and changes immediately and 4–6 years after fuel reduction treatments in an operational-scale, replicated (N = 4), completely randomized experiment in northeastern Oregon. Treatments included a single entry thin from below conducted in 1998, a late season burn conducted in 2000, a thin followed by burning (thin + burn), and a no action treatment which served as a control. Between 1998 and 2004, litter mass declined about 4.4 Mg ha−1 in thin units, about 4.1 Mg ha−1 in thin + burn units, and about 1.5 Mg ha−1 in burn units. Duff mass did not change with treatment. Mass of woody fuels in the 1 and 10 h timelag classes increased in thin units immediately after treatment, but fell to nearly pre-treatment levels by 2004. Mass of woody fuels in the 100 h timelag class increased about 1.6 Mg ha−1 in thin units, and decreased about 0.7 Mg ha−1 in burn units and about 0.1 Mg ha−1 in thin + burn units. There was no difference in the change in total woody fuel mass among all treatments. About 62% of the residual trees in the burn and the thin + burn units were charred; charring extended up the bole about 1.1 m. About 50% of the residual trees in the burn and thin + burn units had scorched lower crowns. Mean scorch height was 4.3 m. Log density was greater in control units compared to actively treated units and greater in the thin units compared to burn or thin + burn units. We used the Fuel Characteristic Classification System (FCCS) to construct a representative fuelbed for each unit from inventoried data and to calculate three indices of fire potential as measures of the change in fire hazard resulting from our treatments: surface fire behavior, crown fire behavior, and fuels available for consumption. Projected flame length, rate of spread, and reaction intensity are derived as metrics of future surface fire behavior. These results are discussed in the context of management options for restoration of ecosystem health in similar low elevation dry ponderosa pine and Douglas-fir forests.

The inherent spatial and temporal heterogeneity of fuelbeds in forests of the southeastern United States may require fine scale fuel measurements for providing reliable fire hazard and fuel treatment effectiveness estimates. In a series of five papers, an intensive, fine scale fuel inventory from the Savanna River Site in the southeastern United States is used for building fuelbeds and mapping fire behavior potential, evaluating fuel treatment options for effectiveness, and providing a comparative analysis of landscape modeled fire behavior using three different data sources including the Fuel Characteristic Classification System, LANDFIRE, and the Southern Wildfire Risk Assessment. The research demonstrates that fine scale fuel measurements associated with fuel inventories repeated over time can be used to assess broad scale wildland fire potential and hazard mitigation treatment effectiveness in the southeastern USA and similar fire prone regions. Additional investigations will be needed to modify and improve these processes and capture the true potential of these fine scale data sets for fire and fuel management planning.

We characterized recent historical and current vegetation composition and structure of a representative sample of subwatersheds on all ownerships within the interior Columbia River basin and portions of the Klamath and Great Basins. For each selected subwatershed, we constructed historical and current vegetation maps from 1932 to 1966 and 1981 to 1993 aerial photos, respectively. Using the raw vegetation attributes, we classified and attributed cover types, structural classes, and potential vegetation types to individual patches within subwatersheds. We characterized change in vegetation spatial patterns using a suite of class and landscape metrics, and a spatial pattern analysis program. We then translated change in vegetation patterns to change in patterns of vulnerability to wildfires, smoke production, and 21 major forest pathogen and insect disturbances. Results of change analyses were reported for province-scale ecological reporting units (ERUs). Here, we highlight significant findings and discuss management implications. Twentieth century management activities significantly altered spatial patterns of physiognomies, cover types and structural conditions, and vulnerabilities to fire, insect, and pathogen disturbances. Forest land cover expanded in several ERUs, and woodland area expanded in most. Of all physiognomic conditions, shrubland area declined most due to cropland expansion, conversion to semi- and non-native herblands, and expansion of forests and woodlands. Shifts from early to late seral conifer species were evident in forests of most ERUs; patch sizes of forest cover types are now smaller, and current land cover is more fragmented. Landscape area in old multistory, old single story, and stand initiation forest structures declined with compensating increases in area and connectivity of dense, multilayered, intermediate forest structures. Patches with medium and large trees, regardless of their structural affiliation are currently less abundant on the landscape. Finally, basin forests are now dominated by shade-tolerant conifers, and exhibit elevated fuel loads and severe fire behavior attributes indicating expanded future roles of certain defoliators, bark beetles, root diseases, and stand replacement fires. Although well intentioned, 20th-century management practices did not account for landscape-scale patterns of living and dead vegetation that enable forest ecosystems to maintain their structure and organization through time, or for the disturbances that create and maintain them. Improved understanding of change in vegetation spatial patterns, causative factors, and links with disturbance processes will assist managers and policymakers in making informed decisions about how to address important ecosystem health issues.

Timber harvesting residues have typically been burned within coniferous forest areas of the eastern Cascade Mountains of Washington State. Concerns about air pollution and quantities of coarse woody debris have generated interest in alternative residue treatments that will clear areas for planting and still reduce fire hazard. A total of six residue treatments were compared (three slash treatments where residues were burned (spring broadcast burning, fall broadcast burning, piling and burning) and three treatments where residues were not burned (clearing, chopping, pulling unmerchantable material)), along with no slash treatment to determine the effects of alternative approaches on soils and seedlings. Slash-treatment effects on soil bulk density and temperature, air temperature, and seedling growth and survival were examined at four different sites in the eastern Cascade Mountains. Results show that seedling performance was best with spring broadcast burning across all sites over five years of growth. Average height growth of Douglas-fir (Pseudotsuga menziesii) and lodgepole pine (Pinus contorta) over five years was 90 cm in the spring broadcast burn, with 75, 72, 71, 71, 66, and 61 cm for no slash treatment, piling and burning, pulling unmerchantable material, chopping, fall broadcast burn, and clearing, respectively. Seedling height growth may be related to soil temperatures that are closer to optimal for root and shoot growth when large quantities of slash were not insulating the soil. Soil bulk density did not appear to affect seedling growth (bulk density ranged from 0.7 to 1.15 g cm−3). None of the slash treatments that avoid burning increased growth relative to no slash treatment, and some may adversely affect survival. If slash burning can not be used to reduce quantities of timber harvesting residues, then leaving slash untreated appears to be the best alternative for seedling growth.