Fuel Bed Depth Research Articles

Metaheuristic algorithms for calibration of two-dimensional wildfire spread prediction model

Wildfires are complex phenomena with harmful consequences, ranging from environmental and property destruction to loss of human lives. In this sense, predicting wildfire behaviour is essential to mitigate its impacts and consequences. The Rothermel model is the most used fire rate of spread prediction model. However, input parameter uncertainty is a significant source of prediction error. In this paper, we propose the calibration of the input parameters of the fire propagation model by metaheuristic algorithms under a two-stage framework. The fire spread model consists on the Rothermel model in a two-dimensional approach for surface fires. The proposed calibration is performed in two stages iteratively repeated over time: (i) the calibration of the fire spread model’s input parameters and (ii) the wildfire spread prediction using the calibrated input parameters. The calibration was performed by the genetic algorithm, differential evolution, and simulated annealing, which calibrates the surface-area-to-volume ratio, fuel bed depth, live fuel moisture and dead fuel moisture. The symmetric difference between the real and predicted fire map shapes was defined as the fitness function of all three metaheuristic algorithms. For validation, simulations were done on two prescribed fires. The results for the real and estimated fire behaviour were then compared and revealed that all the tested metaheuristic algorithms produce a better fit to the real fire’s perimeter when compared to the uncalibrated Rothermel model. From the results, differential evolution provided the majority of best results when compared to genetic algorithm and simulated annealing algorithms in each scenario.

Impact of Reference Data Sampling Density for Estimating Plot-Level Average Shrub Heights Using Terrestrial Laser Scanning Data

Terrestrial laser scanning (TLS) data can offer a means to estimate subcanopy fuel characteristics to support site characterization, quantification of treatment or fire effects, and inform fire modeling. Using field and TLS data within the New Jersey Pinelands National Reserve (PNR), this study explores the impact of forest phenology and density of shrub height (i.e., shrub fuel bed depth) measurements on estimating average shrub heights at the plot-level using multiple linear regression and metrics derived from ground-classified and normalized point clouds. The results highlight the importance of shrub height sampling density when these data are used to train empirical models and characterize plot-level characteristics. We document larger prediction intervals (PIs), higher root mean square error (RMSE), and lower R-squared with reduction in the number of randomly selected field reference samples available within each plot. At least 10 random shrub heights collected in situ were needed to produce accurate and precise predictions, while 20 samples were ideal. Additionally, metrics derived from leaf-on TLS data generally provided more accurate and precise predictions than those calculated from leaf-off data within the study plots and landscape. This study highlights the importance of reference data sampling density and design and data characteristics when data will be used to train empirical models for extrapolation to new sites or plots.

A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibration

Wildfires are complex natural events that cause significant environmental and property damage, as well as human losses, every year throughout the world. In order to aid in their management and mitigate their impact, efforts have been directed towards developing decision support systems that can predict wildfire propagation. Most of the available tools for wildfire spread prediction are based on the Rothermel model that, apart from being relatively complex and computing demanding, depends on several input parameters concerning the local fuels, wind or topography, which are difficult to obtain with a minimum resolution and degree of accuracy. These factors are leading causes for the deviations between the predicted fire propagation and the real fire propagation. In this sense, this paper conducts a literature review on optimization methodologies for wildfire spread prediction based on the use of evolutionary algorithms for input parameter set calibration. In the present literature review, it was observed that the current literature on wildfire spread prediction calibration is mostly focused on methodologies based on genetic algorithms (GAs). Inline with this trend, this paper presents an application of genetic algorithms for the calibration of a set of the Rothermel model’s input parameters, namely: surface-area-to-volume ratio, fuel bed depth, fuel moisture, and midflame wind speed. The GA was validated on 37 real datasets obtained through experimental prescribed fires in controlled conditions.

Chronosequence of Fuel Loading and Fuel Depth Following Forest Rehabilitation Frill Treatment of Tanoak to Release Douglas-Fir: A Case Study from Northern California

There is concern that forest management activities such as chemical thinning may increase hazardous fuel loading and therefore increase risk of stand-replacing wildfire. Chemical thinning, often accomplished by frill treatment of unwanted trees, leaves trees standing dead for a time before they fall and become surface fuels. In coastal northern California, frill treatment is used as a forest rehabilitation treatment that removes tanoak (Notholithocarpus densiflorus) to release merchantable conifers from excessive competition. We studied fuel bed depth and fuel loading after frill treatment of tanoak along a 16-year chronosequence that substituted space for time. The total depth of fuel bed was separated into woody fuels, litter, and duff. The height of each layer was variable and greatest on average in post-treatment year 5 after treated tanoak had begun to break apart and fall. Initially, the evergreen tanoak trees retained their foliage for at least a year after treatment. Five years after treatment, many tanoak had fallen and transitioned to become fine- and coarse woody debris. After 11 years, the larger pieces of down wood were mostly classified as rotten. After 16 years, the fuel loading appeared roughly equivalent to pre-treatment levels, however we did not explicitly test for differences due to potential confounding between time and multiple factors such as inter-annual climate variations and site attributes. Nevertheless, our data provide some insight into changes in surface fuel characteristics due to rehabilitation treatments. These data can be used as inputs for fire behavior modeling to generate indicative predictions of fire effects such as fire severity and how these change over time since treatment.

Beyond red crowns: complex changes in surface and crown fuels and their interactions 32 years following mountain pine beetle epidemics in south-central Oregon, USA

BackgroundMountain pine beetle (Dendroctonus ponderosae Hopkins; MPB), a bark beetle native to western North America, has caused vast areas of tree mortality over the last several decades. The majority of this mortality has been in lodgepole pine (Pinus contorta Douglas ex Loudon) forests and has heightened concerns over the potential for extreme fire behavior across large landscapes. Although considerable research has emerged concerning influence of MPB on forest fuels, there has been little work in the climax lodgepole pine forests of south-central Oregon, USA. Specifically, we assessed changes in forest structure and crown and surface fuels across a chronosequence of time since mountain pine beetle (TSB) epidemics in south-central Oregon (1979 to 2008).ResultsWe classified four distinct periods in which significant changes occur in fuels: overstory mortality stage (2 to 4 years TSB), standing snag and snag fall stage (5 to 13 years TSB), regeneration stage (14 to 25 years TSB), and overstory recovery stage (26 to 32 years TSB). Multivariate analyses indicated changes in crown fuels and forest structure following MPB epidemics were driven primarily by basal area of live and standing dead trees, canopy bulk density, canopy base height, and canopy height. Substantial declines in canopy bulk density occurred early (2 to 5 yrs) following beetle activity and slowly recovered over time. The pattern of succession of surface fuels following a MPB epidemic was largely determined by changes over time in 10-h, 100-h, and 1000-h fuel loads, in addition to increasing fuel bed depth. The 100-h fuel load increased over the entire 30-year period, while 1000-h fuel load reached an asymptote 14 to 26 years following epidemic initiation. Live woody fuels increased through the initial overstory mortality stage and began to decrease during the overstory recovery stage.ConclusionsOur key findings concerning changing fuels and forest structure following a MPB epidemic in south-central Oregon lodgepole pine forests include: 1-h fuels and litter changed little over time, surface fuel loads changed dramatically between the standing snag and the regeneration stages, lodgepole pine remained dominant, and canopy bulk density was low throughout the chronosequence. These factors point to the perpetuation of a lodgepole pine dominated system with a mixed-severity fire regime well into the future.

Analysis of the uncertainty of fuel model parameters in wildland fire modelling of a boreal forest in north-east China

Fire propagation is inevitably affected by fuel-model parameters during wildfire simulations and the uncertainty of the fuel-model parameters makes forecasting accurate fire behaviour very difficult. In this study, three different methods (Morris screening, first-order analysis and the Monte Carlo method) were used to analyse the uncertainty of fuel-model parameters with FARSITE model. The results of the uncertainty analysis showed that only a few fuel-model parameters markedly influenced the uncertainty of the model outputs, and many of the fuel-model parameters had little or no effect. The fire-spread rate is the driving force behind the uncertainty of other fire behaviours. Thus, the highly uncertain fuel-model parameters associated with spread rate should be used cautiously in wildfire simulations. Monte Carlo results indicated that the relationship between model input and output was non-linear and neglecting fuel-model parameter uncertainty of the model would magnify fire behaviours. Additionally, fuel-model parameters have high input uncertainty. Therefore, fuel-model parameters must be calibrated against actual fires. The highly uncertain fuel-model parameters with high spatial-temporal variability consisted of fuel-bed depth, live-shrub loading and 1-h time-lag loading are preferentially chosen as parameters to calibrate several wildfires.

Influences of Fuel Bed Depth and Air Supply on Small-Scale Batch-Fed Reverse Downdraft Biomass Conversion

The producer gas composition and the thermochemical conversion process of a small-scale reverse downdraft reactor has been investigated under ten operating conditions with different fuel bed depths and air supply rates. The operating principle of this research reactor is a batch-fed reverse downdraft process, using wood pellets as the solid biomass fuel. The oxygen-limited regime, where the fuel consumption increases nearly linearly with the air supply, has been identified, and four flow rates over the range of this regime have been investigated. The fuel bed depth was varied between one and four reactor diameters (1D (100 mm)–4D (400 mm)). The results demonstrate that increasing the primary air mass flux leads to both greater fuel consumption and higher temperatures as well as heating rates in the reaction front. Greater air supply rates and the resulting higher temperatures lead to a substantial increase in fuel conversion into permanent gases, rather than tars or char, and a rise in the cold gas effici...

Restoration thinning enhances growth and diversity in mixed redwood/Douglas‐fir stands in northern California, U.S.A.

We examined early responses to different forest restoration thinning treatments to evaluate their efficacy in accelerating tree growth rates, changing species composition, and enhancing structural diversity while minimizing stem damage by black bears and reducing slash depth. Treatments were conducted in 15–25‐year‐old mixed redwood/Douglas‐fir stands and consisted of high‐density, low‐density, and localized release thinning, as well as a control. Four years post‐treatment, data collected from 60 large plots in 20 stands spread across the Mill Creek and Rock Creek watersheds in Del Norte County, California, indicated that spatial variability created from thinning treatments was reflected in diameter growth. Trees having no crown competition had on average a 33% greater basal area increment than trees surrounded by neighboring trees. Thinning treatments enhanced redwood representation by preferentially removing other species, but also promoted rapid growth which increased incidences of bear damage. Approximately 25% of redwood trees sustained bear damage in thinned stands. The localized release thinning created a mosaic of circles flanked by unthinned patches within which trees grew slowly. Localized release created more heterogeneity in tree size but did not generate significantly greater fuel bed depths, result in more bear damage, or represent any sacrifice in understory light or plant abundance and species diversity than a more conventional approach to thinning.

Dimensional Analysis on Forest Fuel Bed Fire Spread.

A dimensional analysis was performed to correlate the fuel bed fire rate of spread data previously reported in the literature. Under wind condition, six pertinent dimensionless groups were identified, namely dimensionless fire spread rate, dimensionless fuel particle size, fuel moisture content, dimensionless fuel bed depth or dimensionless fuel loading density, dimensionless wind speed, and angle of inclination of fuel bed. Under no-wind condition, five similar dimensionless groups resulted. Given the uncertainties associated with some of the parameters used to estimate the dimensionless groups, the dimensionless correlations using the resulting dimensionless groups correlate the fire rates of spread reasonably well under wind and no-wind conditions.

MODELOS MATEMÁTICOS EMPÍRICOS PARA DESCREVER O COMPORTAMENTO DO FOGO EM PLANTAÇÕES COMERCIAIS DE EUCALIPTO NO BRASIL

RESUMO A modelagem do comportamento do fogo consiste em uma importante tarefa que pode ser utilizada para atividades de prevenção e combate. Entretanto, com base em estudos anteriores, os modelos comumente utilizados em outros países não o estimam corretamente nos plantios de eucalipto híbrido no Brasil. Sendo assim, este estudo teve por objetivo construir novos modelos empíricos para estimar a velocidade de propagação, comprimento das chamas e consumo do material combustível para o fogo dentro da respectiva vegetação em questão. Para tal, 105 queimas laboratoriais foram realizadas em que as principais características meteorológicas e do material combustível que poderiam interferir no comportamento do fogo foram controladas e/ou medidas. Variáveis dependentes e independentes foram correlacionadas por meio da regressão multivariada. O modelo para a velocidade de propagação proposto baseou-se na velocidade do vento, densidade do leito e no teor de umidade do material combustível de 1h de timelag (r2 = 0,86); o modelo para o comprimento das chamas baseou-se na espessura do leito, no teor de umidade do material combustível de 1h de timelag e na velocidade do vento (r2 = 0,72); o modelo para o consumo do material combustível teve como variáveis independentes o teor de umidade do material combustível de 1h de timelag, a densidade do leito e a carga do material combustível de 1h de timelag (r2 = 0,80). Os modelos construídos serviram de base para o desenvolvimento do software “Eucalyptus Fire Safety System”.

Recovery Status of a Sierra Nevada Forest Plant Community One-Half Century after Wildfire

Comparison of the burned and unburned portions of a Jeffrey pine stand in the eastern Sierra Nevada that was partially destroyed by a wildfire approximately a half century earlier served as the basis of an investigation intended to provide insight into the direction and pace of unaided recovery from such events in this and similar forest cover types. With Jeffrey pine the predominant species regardless of treatment, lodgepole pine was a secondary component of the overstory in both stand portions but a minor representation of white fir in the unburned portion was absent from the burned acreage. Commensurate with the large difference in age, tree dimensions, basal area, and biomass in the overstory of the burned stand portion were greatly exceeded by those of the overstory in the unburned portion. Tree seedlings and saplings were much more abundant in the burned acreage, with those of Jeffrey pine largely accounting for the disparity. Predominant among shrubs in the understory of the burned stand portion was snowbrush ceanothus with that in the unburned portion consisting of prostrate ceanothus, while among herbaceous species a small quantity of Sandberg bluegrass in the former contrasted against none in the latter. Fine and total fuel loading along with fuel bed depth on the burned substrate were greatly exceeded by those on the unburned substrate, while proportionally, less fine fuels but more of the coarsest ones were found on the former compared to the latter.

Evaluation of Mechanized Thinning and Prescribed Fire Effects on Long-Term Fuels Accumulations in Uneven-Aged Jeffrey Pine

Forest thinnings implemented through cut-to-length and whole-tree harvesting followed by underburning were evaluated for their effects on long-term downed and dead fuels accumulations in pure, uneven-aged Jeffrey pine (Pinus jeffreyi Grev. & Balf.) accompanied by isolated California white fir (Abies concolor var. lowiana [Gord.] Lemm.). Based on an initial inventory consisting of dry weight and fuel bed depth measurements conducted posttreatment, accumulations in the cut-to-length treatment were elevated according to most measures. Burned stand portions exhibited smaller quantities of fuels initially in individual timelag categories and in total as well as reduced fuel bed depths compared to their unburned counterparts except for the 1,000-hr fuels in the whole-tree treatment where postburn mortality of small stems nullified this effect. A linkage between initial mortality resulting from prescribed fire and final 1,000-hr fuels, as measured 8 yr later, was established but was probably attributable to combined thinning and burning effects. Over the course of the study, either greater accruals or diminished reductions in loading were apparent within burned stand portions compared to unburned portions, most notably in the whole-tree treatment. Results presented here provide insight into potentially compromised fuels reduction outcomes when implemented in uneven-aged stands on dry forest sites.

Development of Standard Fuel Models in Boreal Forests of Northeast China through Calibration and Validation

Understanding the fire prediction capabilities of fuel models is vital to forest fire management. Various fuel models have been developed in the Great Xing'an Mountains in Northeast China. However, the performances of these fuel models have not been tested for historical occurrences of wildfires. Consequently, the applicability of these models requires further investigation. Thus, this paper aims to develop standard fuel models. Seven vegetation types were combined into three fuel models according to potential fire behaviors which were clustered using Euclidean distance algorithms. Fuel model parameter sensitivity was analyzed by the Morris screening method. Results showed that the fuel model parameters 1-hour time-lag loading, dead heat content, live heat content, 1-hour time-lag SAV(Surface Area-to-Volume), live shrub SAV, and fuel bed depth have high sensitivity. Two main sensitive fuel parameters: 1-hour time-lag loading and fuel bed depth, were determined as adjustment parameters because of their high spatio-temporal variability. The FARSITE model was then used to test the fire prediction capabilities of the combined fuel models (uncalibrated fuel models). FARSITE was shown to yield an unrealistic prediction of the historical fire. However, the calibrated fuel models significantly improved the capabilities of the fuel models to predict the actual fire with an accuracy of 89%. Validation results also showed that the model can estimate the actual fires with an accuracy exceeding 56% by using the calibrated fuel models. Therefore, these fuel models can be efficiently used to calculate fire behaviors, which can be helpful in forest fire management.

Fire spread probabilities for experimental beds composed of mixedwood boreal forest fuels

Although fuel characteristics are assumed to have an important impact on fire regimes through their effects on extinction dynamics, limited capabilities exist for predicting whether a fire will spread in mixedwood boreal forest surface fuels. To improve predictive capabilities, we conducted 347 no-wind, laboratory test burns in surface fuels collected from the mixedwood boreal forest of Saskatchewan. The beds were composed of single fuel types of contrasting characteristics, including feather moss, aspen leaf litter, aspen and alder leaf litter, and twigs. Shredded wood (i.e., excelsior) was included for comparison. An extinction index and logistic model from the literature that balances heat sources and sinks performed well for excelsior, a fuel used to develop the model, but poorly for forest fuels. As a result, we used logistic regression to develop a model for forest fuels finding that fire spread was largely determined by the heat sink, heat of combustion, and fuel bed depth. We found close correspondence between our model and fire spread in an independent sample of beds composed of mixtures of mixedwood fuels (N = 59). Our model can serve as a means of analyzing the relative importance of fuels and weather on extinction dynamics during mixedwood boreal forest fires.

Predicting Surface Fuel Models and Fuel Metrics Using Lidar and CIR Imagery in a Dense, Mountainous Forest

We compared the ability of several classification and regression algorithms to predict forest stand structure metrics and standard surface fuel models. Our study area spans a dense, topographically complex Sierra Nevada mixed-conifer forest. We used clustering, regression trees, and support vector machine algorithms to analyze high density (average 9 pulses/m 2 ), discrete return, small-footprint lidar data, along with multispectral imagery. Stand structure metric predictions generally decreased with increased canopy penetration. For example, from the top of canopy, we predicted canopy height (r 2 0.87), canopy cover (r 2 0.83), basal area (r 2 0.82), shrub cover (r 2 0.62), shrub height (r 2 0.59), combined fuel loads (r 2 0.48), and fuel bed depth (r 2 0.35). While the general fuel types were predicted accurately, specific surface fuel model predictions were poor (76 percent and 50 percent correct classification, respectively) using all algorithms. These fuel components are critical inputs for wildfire behavior modeling, which ultimately support forest management decisions. This comprehensive examination of the relative utility of lidar and optical imagery will be useful for forest science and management.

Patterns of flammability of the California oaks: the role of leaf traits

Fire is one of the most important processes driving plant community composition and structure. Fire regimes are largely governed by climate, vegetation structure, and individual plant traits that influence flammability. We assessed the mechanistic drivers of flammability for a diverse group of 18 California Quercus and allied Chrysolepis and Notholithocarpus species, addressing variation in leaf physical traits, growth form (tree or shrub), phylogeny (Quercus subgenera), and fire regime (low, mixed, or high severity). Differences in flammability were not strongly driven by leaf habit, leaf margin type, or surface area to volume ratio; simple measures of leaf size accounted for most of the observed variation. Further, leaf size was tightly linked to fuelbed depth, a known driver of fire behavior. Litter from trees was generally more flammable than litter from shrubs, primarily a function of differences in leaf size. A hierarchical clustering analysis on the flammability data set divided the oaks into three clusters of low, intermediate, and high flammability, corresponding closely to high-, mixed-, and low-severity fire regimes, respectively. The link between plant flammability traits and fire regime provides further evidence that individual species affect ecosystem processes.

The invasive weed Lantana camara increases fire risk in dry rainforest by altering fuel beds

Berry ZC, Wevill K & Curran TJ (2011). The invasive weed Lantana camara increases fire risk in dry rainforest by altering fuel beds. Weed Research51, 525–533.SummaryThe invasive weed, Lantana camara, dominates numerous tropical and sub‐tropical ecosystems and affects many aspects of ecosystem functioning, one of them being susceptibility to fire intrusion. The process by which L. camara might alter fire regimes in dry rainforest was examined by considering two potential mechanisms: (i) by being more ignitable than native species or (ii) by altering the structure and distribution of biomass to facilitate fire spread. We hypothesised that L. camara might alter fire regimes by both mechanisms. Measurements of leaf dry matter content, twig dry matter content and burn durations in laboratory trials implied that L. camara was less ignitable than native dry rainforest species. Fuel bed depths, leaf litter depths, percentage cover by fuels and amount of medium‐size class fuels were higher in dry rainforest invaded by L. camara than in non‐invaded forests. This suggests that the mechanism by which L. camara alters the fire regime in dry rainforest is by shifting the distribution of available fuels closer to the ground and providing a more continuous fuel layer in the understory. Management should focus on targeting L. camara removal around forest edges adjacent to frequently burned savannas and in areas of high conservation value.

Surface fuel loadings within mulching treatments in Colorado coniferous forests

Recent large-scale, severe wildfires in the western United States have prompted extensive mechanical fuel treatment programs to reduce potential wildfire size and severity. Fuel reduction prescriptions typically target non-merchantable material so approaches to mechanically treat and distribute residue on site are becoming increasingly common. We examined how mulch treatments alter the distribution of woody material by size class by comparing paired mulched and untreated sites in lodgepole pine (Pinus contorta), mixed conifer, ponderosa pine (Pinus ponderosa), and pinyon pine/juniper (Pinus edulis/Juniperus sp.) forests 2–6 years after mechanical treatment. Mulching treatments reduced tree density and canopy bulk density, and increased canopy base height, potentially lowing active crown fire risk. In general, mulching increased total woody surface fuel loadings 2–3-fold, but the magnitude differed among forest types. Average total woody surface fuel loads in the untreated areas ranged between 7 and 12Mgha−1, but increased to 27–63Mgha−1 in treated areas. Large diameter fuels (>7.62cm) represent about 35–69% of the total woody fuel load in the untreated areas, but only about 8–14% of the total fuel load in the treated areas. Woody fuels in treated areas were dominated by material <2.54cm diameter (i.e. 1 and 10h fuels). In general, mulch fuelbed depth was a useful predictor of fuel loading. Mulching created a compact fuelbed (i.e. bulk density 138–150kgm−3) that differs from pretreatment needle-dominated fuelbed and will likely change fire behavior and effects. Quantification of the mulched fuelbed characteristics within these four forest types should aid in modification of current fuel models or creation of new fire behavior fuel models.

Convective heat transfer in fire spread through fine fuel beds

An extensive set of wind-tunnel fires was burned to investigate convective heat transfer ahead of a steadily progressing fire front moving across a porous fuel bed. The effects of fuel and environmental variables on the gas temperature profile and the ‘surface wind speed’ (gas velocity at the fuel bed surface) are reported. In non-zero winds, the temperature of the air near the fuel bed surface decays exponentially with distance from the fire front. In zero winds, the temperature decreases rapidly within a very short distance of the flame front, then decays slowly thereafter. The maximum air temperature decreases as the free stream wind speed, packing ratio and fuel moisture content increase. The characteristic distance of the exponential decay increases strongly with the free stream wind speed and decreases with the packing ratio and surface area-to-volume ratio of the fuel. The surface wind speed depends strongly on the free stream wind speed, and to a lesser extent on packing ratio, fuel bed depth and fuel moisture content. There are three general regimes for the surface flow: (1) a constant velocity flow of approximately half the free stream flow, far from the flame front; (2) an intermediate zone of minimum flow characterised by low or reversed flow; and (3) a region near the flame front where the velocity rises rapidly almost to the free stream velocity. The boundaries between the three regions move further from the flame front with increasing wind speed, in a way which is only slightly affected by fuel geometry.

Mastication and prescribed fire impacts on fuels in a 25-year old ponderosa pine plantation, southern Sierra Nevada

Due to increases in tree density and hazardous fuel loading in Sierra Nevadan forests, land management is focusing on fuel reduction treatments to moderate the risk of catastrophic fires. Fuel treatments involving mechanical and prescribed fire methods can reduce surface as well as canopy fuel loads. Mastication is a mechanical method which shreds smaller trees and brush onto the surface fuel layer. Little data exist quantifying masticated fuel beds. Despite the paucity of data on masticated fuels, land managers desire fuel loading, potential fire behavior and fire effects such as tree mortality information for masticated areas. In this study we measured fuel characteristics before and after mastication and mastication plus prescribed burn treatments in a 25-year old ponderosa pine ( Pinus ponderosa C. Lawson) plantation. In addition to surface fuel characteristics and tree data collection, bulk density samples were gathered for masticated material. Regressions were created predicting masticated fuel loading from masticated fuel bed depth. Total masticated fuel load prior to fire treatment ranged from 25.9 to 42.9 Mg ha −1, and the bulk density of masticated fuel was 125 kg m −3. Mastication treatment alone showed increases in most surface fuel loadings and decreases in canopy fuel loads. Masticated treatment in conjunction with prescribed burning reduced both surface and canopy fuel loads. Detailed information on fuel structure in masticated areas will allow for better predictions of fire behavior and fire effects for fire in masticated fuel types. Understanding potential fire behavior and fire effects associated with masticated fuels will allow managers to make decisions on the possibility of mastication to create fuel breaks or enhance forest health.