Canopy Fuels Research Articles

The effects of sudden oak death on foliar moisture content and crown fire potential in tanoak

The introduction of non-native pathogens can have profound effects on forest ecosystems resulting in loss of species, changes in species composition, and altered fuel structure. The introduction of Phytophthora ramorum, the pathogen recognized as causing Sudden Oak Death (SOD), leads to rapid decline and mortality of tanoak ( Lithocarpus densiflorus) in forests of coastal California, USA. We tracked foliar moisture content (FMC) of uninfected tanoaks, SOD-infected tanoaks, SOD-killed (dead) tanoaks, and surface litter for 12 months. We found that FMC values differed significantly among the three categories of infection. FMC of uninfected tanoaks averaged 82.3% for the year whereas FMC of infected tanoaks had a lower average of 77.8% (ANOVA, P = 0.04). Dead trees had a significantly lower FMC, averaging 12.3% (ANOVA, P < 0.01) for the year. During fire season (June–September), dead tanoak FMC reached a low of 5.8%, with no significant difference between dead canopy fuels and surface litter (ANOVA, P = 0.44). Application of low FMC values to a crown ignition model results in extremely high canopy base height values to escape crown ignition. Remote estimation of dead FMC using 10-h timelag fuel moisture shows a strong correlation between remote automated weather station (RAWS) 10-h timelag fuel moisture data and the FMC of dead leaves ( R 2 = 0.78, P < 0.01). Results from this study will help refine the decision support tools for fire managers in SOD-affected areas as well as conditions in other forests where diseases and insect epidemics have altered forest canopy fuels.

Fusion of LiDAR and imagery for estimating forest canopy fuels

Due to increased fuel loading as a result of fire suppression, land managers in the American west are in need of precise information about the fuels they manage, including canopy fuels. Canopy fuel metrics such as canopy height (CH), canopy base height (CBH), canopy bulk density (CBD) and available canopy fuel (ACF) are specific inputs for wildfire behavior models such as FARSITE and emission models such as FOFEM. With finer spatial resolution data, accurate quantification of these metrics with detailed spatial heterogeneity can be accomplished. Light Detection and Ranging (LiDAR) and color near-infrared imagery are active and passive systems, respectively, that have been utilized for measuring a range of forest structure characteristics at high resolution. The objective of this research was to determine which remote sensing dataset can estimate canopy fuels more accurately and whether a fusion of these datasets produces more accurate estimates. Regression models were developed for ponderosa pine ( Pinus ponderosa) stand representative of eastern Washington State using field data collected in the Ahtanum State Forest and metrics derived from LiDAR and imagery. Strong relationships were found with LiDAR alone and LiDAR was found to increase canopy fuel accuracy compared to imagery. Fusing LiDAR with imagery and/or LiDAR intensity led to small increases in estimation accuracy over LiDAR alone. By improving the ability to estimate canopy fuels at higher spatial resolutions, spatially explicit fuel layers can be created and used in wildfire behavior and smoke emission models leading to more accurate estimations of crown fire risk and smoke related emissions.

Spatial fuel data products of the LANDFIRE Project

The Landscape Fire and Resource Management Planning Tools (LANDFIRE) Project is mapping wildland fuels, vegetation, and fire regime characteristics across the United States. The LANDFIRE project is unique because of its national scope, creating an integrated product suite at 30-m spatial resolution and complete spatial coverage of all lands within the 50 states. Here we describe development of the LANDFIRE wildland fuels data layers for the conterminous 48 states: surface fire behavior fuel models, canopy bulk density, canopy base height, canopy cover, and canopy height. Surface fire behavior fuel models are mapped by developing crosswalks to vegetation structure and composition created by LANDFIRE. Canopy fuels are mapped using regression trees relating field-referenced estimates of canopy base height and canopy bulk density to satellite imagery, biophysical gradients and vegetation structure and composition data. Here we focus on the methods and data used to create the fuel data products, discuss problems encountered with the data, provide an accuracy assessment, demonstrate recent use of the data during the 2007 fire season, and discuss ideas for updating, maintaining and improving LANDFIRE fuel data products.

Landscape-scale changes in canopy fuels and potential fire behaviour following ponderosa pine restoration treatments

We evaluated canopy fuels and potential fire behaviour changes following landscape-scale restoration treatments in a ponderosa pine forest at Mt Trumbull, Arizona, USA. The goal of the project was to restore historical forest structure by thinning and burning, thereby reducing canopy fuels and minimising active crown fire potential. We measured 117 permanent plots before (1996–97) and after (2003) treatments. The plots were evenly distributed across the landscape and represented an area of ~1200 ha, about half of which was an untreated control. We compared canopy fuel estimates using three different methods to assess whether fire behaviour modelling outputs were sensitive to the choice of canopy fuel equation. Treatments decreased canopy fuel load by 43–50% from 0.77–1.83 kg m–2 to 0.44–0.91 kg m–2 (the range of values reflects the different canopy fuel equations) and decreased canopy bulk density by 42–61% from 0.038–0.172 kg m–3 to 0.022–0.067 kg m–3 in the treated area, while slight increases occurred in the control. We applied two fire models to estimate potential fire behaviour: FlamMap and NEXUS. These models differ in several important features but predicted outcomes were consistent: under extreme drought and wind conditions, the proportion of the landscape susceptible to active crown fire decreased in the treated area while little change occurred in the control.

Mechanical fuel treatment effects on fuel loads and indices of crown fire potential in a south central New Mexico dry mixed conifer forest

The ability of mechanical fuel reduction treatments to mitigate severe fire behavior in dry mixed conifer forests is of interest to land managers as well as the public. We compared fuel loads and indices of crown fire potential to test treatment effectiveness following commercial and non-commercial treatments with differing slash prescriptions in mixed conifer stands at three sites on the Lincoln National Forest in south central New Mexico. Surface and canopy fuels were measured and used to develop custom fuel models in NEXUS 2.0 to estimate torching and crowning indices. Results indicated herbaceous fuel loads were unchanged compared to controls 2 years post-non-commercial and 1 year post-commercial harvest treatment. Sound 1000-h fuels were greater in the scatter and commercial treatments compared to control treatments. The commercial treatment resulted in stand structure closer to historical conditions. Canopy base height increased in all treatments except in stands previously treated 20–30 years ago. Commercial harvest was the only treatment to reduce canopy bulk density and the potential for active crown fire, with the exception of one pile treatment. Non-commercial pile treatments increased the TI. However, due to the danger of crown fire initiation from adjacent stands, further overstory removal is needed in non-commercial treatments to lower crowning potential. Prescribed fire may be used to increase treatment effectiveness in all overstory removal treatments by decreasing surface fuels.

Characterizing Canopy Fuels to Predict Fire Behavior in Pitch Pine Stands

Abstract Destructive sampling of 31 pitch pine (Pinus rigida P. Mill) trees ranging in dbh from 2.7 to 42.5 cm and in height from 4.1 to 23.8 m provided a complete inventory of needles and small-diameter branch weights used to characterize canopy fuels to predict fire behavior in pitch pine stands. Regression equations using dbh as an independent variable predict canopy bulk density with an r2 &gt; 0.93. The results provide managers with a method of evaluating the effectiveness of thinning operations in reducing crown fire potential in well-stocked stands. To demonstrate the application of the method, we calculated the wind speed (Crowning Index [CI]) needed to sustain an active crown fire in thinned and unthinned pitch pine stands in Montague, Massachusetts. Thinning to 2.8 m2/ha basal area increased the CI from 34 to 98 km/hour.

Canopy fuel characteristics and potential crown fire behavior in Aleppo pine (Pinus halepensis Mill.) forests

Canopy fuel characteristics that influence the initiation and spread of crown fires were measured in representative Aleppo pine (Pinus halepensis Mill.) stands in Greece. Vertical distribution profiles of canopy fuel load, canopy base height and canopy bulk density are presented. Aleppo pine canopy fuels are characterized by low canopy base height (3.0–6.5 m), while available canopy fuel load (0.96–1.80 kg/m2) and canopy bulk density (0.09–0.22 kg/m3) values are similar to other conifers worldwide. Crown fire behavior (probability of crown fire initiation, crown fire type, rate of spread, fireline intensity and flame length) in Aleppo pine stands with various understory fuel types was simulated with the most updated crown fire models. The probability of crown fire initiation was high even under moderate burning conditions, mainly due to the low canopy base height and the heavy surface fuel load. Passive crown fires resulted mostly in uneven aged stands, while even aged stands gave high intensity active crown fires. Assessment of canopy fuel characteristics and potential crown fire behavior can be useful in fuel management and fire suppression planning.

Canopy Fuel Characteristics and Fuel Load in Young Black Pine Trees

ABSTRACTCrown structure and fuel loading are important factors affecting crown fire occurrence and behavior. This paper reports on the canopy fuel characteristics and regression models to determine Canopy Fuel Loading (CFL) of young black pine (Pinus nigra Arnold) trees. Equations were based on the data from 11 destructively sampled black pine plantation trees. This data was used to develop best equations fit with ordinary least squares procedures that estimate available and total canopy fuel load for young black pine plantation trees. Model fits for available and total canopy fuel load were generally good. Results indicated that available and total canopy fuel load could be easily predicted using diameter at breast height (D), variable. The resulting equations were able to account for 94% of the observed variation in the total canopy fuel load. Average tree canopy fuel load was 8.9 kg. On average 41% of canopy fuel load was allocated to the needle, 13% to the fine branch, 20% to the medium branch, 18% to thick branch and 8% to the very thick branch. Vertical distribution of canopy fuels was variable. Most foliage was found on the main branches of the medium portion of the canopy. The lower and upper portions of the crown had relatively less foliage. The results of the study allowed also for the comparisons of linear and nonlinear equations of biomass estimation commonly used in literature.

Estimating canopy fuel characteristics in five conifer stands in the western United States using tree and stand measurements

Assessment of crown fire potential requires quantification of canopy fuels. In this study, canopy fuels were measured destructively on plots in five Interior West conifer stands. Observed canopy bulk density, canopy fuel load, and vertical profiles of canopy fuels are compared with those estimated from stand data using several computational techniques. An allometric approach to estimating these canopy fuel characteristics was useful, but, for accuracy, estimates of vertical biomass distribution and site-adjustment factors were required. Available crown fuel was estimated separately for each tree according to species, diameter, and crown class. The vertical distribution of this fuel was then modeled within each tree crown on the basis of tree height and crown base height. Summing across trees within the stand at every height yielded an estimated vertical profile of canopy fuel that approximated the observed distribution.

Role of Foliar Moisture Content in the Silvicultural Management of Forest Fuels

Abstract In combination with measured stand data and assumed environmental conditions, reasonable estimates of foliar moisture content (FMC) are necessary to determine and justify silvicultural targets for canopy fuels management strategies. FMC often is overlooked in fuels planning, with default values substituting for more precise estimates, but its role is operationally significant in the selection of silvicultural targets even at modest surface fire intensities. This article discusses the role of FMC in canopy ignition and summarizes the results of 11 studies on the FMC values and trends for 16 North American conifers. FMC values ranged from 73 to 480% but varied by species, foliage age, and season. The information presented here will be helpful to managers engaging in canopy fuels planning with the use of popular fire behavior and fuels management software (e.g., NEXUS, Fuels Management Analyst, and the Forest Vegetation Simulator's Fire and Fuels Extension). West. J. Appl. For. 21(4):228–231.

Fuel treatments alter the effects of wildfire in a mixed-evergreen forest, Oregon, USA

We had the rare opportunity to quantify the relationship between fuels and fire severity using prefire surface and canopy fuel data and fire severity data after a wildfire. The study area is a mixed-evergreen forest of southwestern Oregon with a mixed-severity fire regime. Modeled fire behavior showed that thinning reduced canopy fuels, thereby decreasing the potential for crown fire spread. The potential for crown fire initiation remained fairly constant despite reductions in ladder fuels, because thinning increased surface fuels, which contributed to greater surface fire intensity. Thinning followed by underburning reduced canopy, ladder, and surface fuels, thereby decreasing surface fire intensity and crown fire potential. However, crown fire is not a prerequisite for high fire severity; damage to and mortality of overstory trees in the wildfire were extensive despite the absence of crown fire. Mortality was most severe in thinned treatments (80%–100%), moderate in untreated stands (53%–54%), and least severe in the thinned and underburned treatment (5%). Thinned treatments had higher fine-fuel loading and more extensive crown scorch, suggesting that greater consumption of fine fuels contributed to higher tree mortality. Fuel treatments intended to minimize tree mortality will be most effective if both ladder and surface fuels are treated.

Estimating forest canopy fuel parameters using LIDAR data

Fire researchers and resource managers are dependent upon accurate, spatially-explicit forest structure information to support the application of forest fire behavior models. In particular, reliable estimates of several critical forest canopy structure metrics, including canopy bulk density, canopy height, canopy fuel weight, and canopy base height, are required to accurately map the spatial distribution of canopy fuels and model fire behavior over the landscape. The use of airborne laser scanning (LIDAR), a high-resolution active remote sensing technology, provides for accurate and efficient measurement of three-dimensional forest structure over extensive areas. In this study, regression analysis was used to develop predictive models relating a variety of LIDAR-based metrics to the canopy fuel parameters estimated from inventory data collected at plots established within stands of varying condition within Capitol State Forest, in western Washington State. Strong relationships between LIDAR-derived metrics and field-based fuel estimates were found for all parameters [sqrt(crown fuel weight): R 2=0.86; ln(crown bulk density): R 2=0.84; canopy base height: R 2=0.77; canopy height: R 2=0.98]. A cross-validation procedure was used to assess the reliability of these models. LIDAR-based fuel prediction models can be used to develop maps of critical canopy fuel parameters over forest areas in the Pacific Northwest.

Simulating forest fuel and fire risk dynamics across landscapes—LANDIS fuel module design

Understanding fuel dynamics over large spatial (10 3–10 6 ha) and temporal scales (10 1–10 3 years) is important in comprehensive wildfire management. We present a modeling approach to simulate fuel and fire risk dynamics as well as impacts of alternative fuel treatments. The approach is implemented using the fuel module of an existing spatially explicit forest landscape model, LANDIS. The LANDIS fuel module tracks fine fuel, coarse fuel and live fuel for each cell on a landscape. Fine fuel is derived from vegetation types (species composition) and species age, and coarse fuel is derived from stand age (the oldest age cohorts) in combination with disturbance history. Live fuels, also called canopy fuels, are live trees that may be ignited in high intensity fire situations (such as crown fires). The amount of coarse fuel at a given time is the result of accumulation and decomposition processes, which have rates defined by ecological land types. Potential fire intensity is determined by the combination of fine fuel and coarse fuel. Potential fire risk is determined by the potential fire intensity and fire probability, which are derived from fire cycle (fire return interval) and the time since last fire. The LANDIS fuel module simulates common fuel management practices including prescribed burning, coarse fuel load reduction (mechanical thinning), or both. To test the design of the module, we applied it to a large landscape in the Missouri Ozarks. We demonstrated two simulation scenarios: fire suppression with and without fuel treatment for 200 years. At each decade of a simulation, we analyzed fine fuel, coarse fuel, and fire risk maps. The results show that the fuel module correctly implements the assumptions made to create it, and is able to simulate basic cause–effect relationships between fuel treatment and fire risk. The design of the fuel module makes it amendable to calibration and verification for other regions.

Changes in canopy fuels and potential fire behavior 1880?2040: Grand Canyon, Arizona

We applied detailed forest reconstructions measured on broad-scale plot grids to initialize forest simulation modeling in 1880 and modeled spatially explicit changes in canopy fuels (canopy biomass, canopy bulk density, species composition) and potential fire behavior (crowning index) through 2040, a 160-year period. The study sites spanned a 500-m elevational gradient from ponderosa pine forest through higher-elevation mixed conifer, aspen, and spruce-fir forests on the North Rim of Grand Canyon National Park in northern Arizona. The simulations were relatively accurate, as assessed by comparing the simulation output in the year 2000 with field data collected in 1997–2001, because a regionally calibrated simulator was used (Central Rockies variant of the Forest Vegetation Simulator) and because we added regeneration by species and density in the correct historical sequence. Canopy biomass increased at all sites, rising an average of 122% at the low-elevation sites and 279% at the high-elevation sites. The intermediate-elevation site, where mixed conifer vegetation predominated, began with the highest canopy biomass in 1880 but had the lowest increase through 2040 (39%). Canopy bulk density increased roughly in parallel with canopy biomass; however, density values were considered less accurate in non-contemporary dates because they were based on assumptions about canopy volume. Species composition of canopy fuels was consistent at low elevation (ponderosa pine) but shifted strongly toward mesic species at higher elevations, where ponderosa pine declined in absolute as well as relative terms. Potential crown fire behavior was assessed with the Nexus model in terms of crowning index (CI), the windspeed required to sustain active canopy burning. CI values decreased 23–80% over the modeled period. Canopy fuel and CI values were mapped across the entire North Rim landscape. At a threshold windspeed of 45 km/h, only 6% of the landscape was susceptible to active crown fire in 1880 but 33% was susceptible by 2000. Implications of the changes over time and space include altered contemporary habitats and the high likelihood of rapid, broad-scale disturbance by fire. If managers choose to intervene to reduce canopy fuel mass and continuity, actions should be guided by the distinct ecological attributes of the different forest types.

Changes in canopy fuels and potential fire behavior 1880–2040: Grand Canyon, Arizona

We applied detailed forest reconstructions measured on broad-scale plot grids to initialize forest simulation modeling in 1880 and modeled spatially explicit changes in canopy fuels (canopy biomass, canopy bulk density, species composition) and potential fire behavior (crowning index) through 2040, a 160-year period. The study sites spanned a 500-m elevational gradient from ponderosa pine forest through higher-elevation mixed conifer, aspen, and spruce-fir forests on the North Rim of Grand Canyon National Park in northern Arizona. The simulations were relatively accurate, as assessed by comparing the simulation output in the year 2000 with field data collected in 1997–2001, because a regionally calibrated simulator was used (Central Rockies variant of the Forest Vegetation Simulator) and because we added regeneration by species and density in the correct historical sequence. Canopy biomass increased at all sites, rising an average of 122% at the low-elevation sites and 279% at the high-elevation sites. The intermediate-elevation site, where mixed conifer vegetation predominated, began with the highest canopy biomass in 1880 but had the lowest increase through 2040 (39%). Canopy bulk density increased roughly in parallel with canopy biomass; however, density values were considered less accurate in non-contemporary dates because they were based on assumptions about canopy volume. Species composition of canopy fuels was consistent at low elevation (ponderosa pine) but shifted strongly toward mesic species at higher elevations, where ponderosa pine declined in absolute as well as relative terms. Potential crown fire behavior was assessed with the Nexus model in terms of crowning index (CI), the windspeed required to sustain active canopy burning. CI values decreased 23–80% over the modeled period. Canopy fuel and CI values were mapped across the entire North Rim landscape. At a threshold windspeed of 45 km/h, only 6% of the landscape was susceptible to active crown fire in 1880 but 33% was susceptible by 2000. Implications of the changes over time and space include altered contemporary habitats and the high likelihood of rapid, broad-scale disturbance by fire. If managers choose to intervene to reduce canopy fuel mass and continuity, actions should be guided by the distinct ecological attributes of the different forest types.

Site environment characterization of downed woody fuels in the Rincon Mountains, Arizona: regression tree approach

Characterization of forest fuels is key to successful implementation of any fire management system. Great strides have been made in the characterization of forest canopy fuels by the use of remote sensing technology. Remote sensing of surface fuels is, however, limited by the physical intervention of the overlaying canopy. This limitation underscores the importance of exploring alternative approaches that relate site environment characteristics to the production and accumulation of understory fuels. This study predicts downed woody fuel loadings based on variables such as topography, fire history, and vegetation type in the forested area of the Rincon Mountains in southern Arizona. We used classification and regression trees (CART) to make these predictions. Results show that fine woody fuel loadings are predicted best by vegetation type and slope. Coarse woody fuels are predicted best by differences in elevation.