Canopy bulk density (CBD) is an important crown characteristic needed to predict crown fire spread, yet it is difficult to measure in the field. Presented here is a comprehensive research effort to evaluate six indirect sampling techniques for estimating CBD. As reference data, detailed crown fuel biomass measurements were taken on each tree within fixed-area plots located in five important conifers types in the western United States, using destructive sampling following a series of four sampling stages to measure the vertical and horizontal distribution of canopy biomass. The six ground-based indirect measurement techniques used these instruments: LI-COR LAI-2000, AccuPAR ceptometer, CID digital plant canopy imager, hemispherical photography, spherical densiometer, and point sampling. These techniques were compared with four aggregations of crown biomass to compute CBD: foliage only, foliage and small branchwood, foliage and all branchwood (no stems), and all canopy biomass components. Most techniques had the best performance when all canopy biomass components except stems were used. Performance dropped only slightly when the foliage and small branchwood canopy biomass aggregation (best approximates fuels available for crown fires) was employed. The LAI-2000, hemispherical photography, and CID plant canopy imager performed best. Regression equations that predict CBD from gap fraction are presented for all six techniques.Résumé : La densité apparente de la canopée (DAC) est une caractéristique importante de la cime qui est nécessaire pour prédire la propagation d'un feu de cime mais qui est cependant très difficile à mesurer sur le terrain. Les auteurs présentent ici un travail de recherche exhaustif dont le but était d'évaluer six techniques indirectes d'échantillonnage pour estimer la DAC. Les données de référence proviennent de mesures détaillées de la biomasse des combustibles dans la cime prises sur chaque arbre dans des placettes à superficie fixe situées dans cinq types importants de forêt résineuse de l'ouest des États-Unis en utilisant une approche destructrice après avoir procédé à une série d'échantillonnages en quatre étapes pour mesurer la distribution verticale et horizontale de la biomasse dans la canopée. Les six techniques indirectes de mesure sur le terrain comprenaient les instruments suivants : le LAI-2000 de LI-COR, le ceptomètre d'AccuPAR, l'imageur digital du couvert végétal CID, la photographie hémisphérique, le densiomètre sphérique et l'échantillonnage par point. Ces techniques ont été comparées à quatre regroupements de la biomasse de la cime pour calculer la DAC : feuillage seulement, feuillage et petites branches, feuillage et toutes les branches (sans la tige) et toutes les composantes de la biomasse de la cime. La plupart des techniques offraient la meilleure performance lorsque toutes les composantes de la biomasse de la canopée, à l'exception de la tige, étaient utilisées. La performance diminuait juste un peu avec l'utilisation du regroupement de la biomasse de la cano...

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.

We investigated the spatial variability of a number of wildland fuel characteristics for the major fuel components found in six common northern Rocky Mountain ecosystems. Surface fuel characteristics of loading, particle density, bulk density, and mineral content were measured for eight fuel components-four downed dead woody fuel size classes (1, 10, 100, 1000 hr), duff, litter, shrub, and herb-on nested plots located within sampling grids to describe their variability across spatial scales. We also sampled canopy bulk density, biomass, and cover for each plot in the grid. The spatial distribution and variability of surface and canopy fuel characteristics are described using the variance, spatial autocorrelation, semi-variograms, and Moran's I. We found that all fuels had high variability in loading (two to three times the mean), and this variability increased with the size of fuel particle. We also found that fuel components varied at different scales, with fine fuels varying at scales of 1 to 5 m, coarse fuels at 10 to 150 m, and canopy fuels at 100 to 500 m. Findings and data from this study can be used to sample, describe, and map fuel characteristics, such as loading, at the appropriate spatial scales to accommodate the next generation of fire behavior prediction models.Keywords: wildland fire, biomass, landscape ecology, woody debris, spatial scale, mineral content, particle density, bulk density, loading AuthorsRobert E. Keane is a Research Ecologist with the U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station at the Missoula Fire Sciences Laboratory in Montana. Since 1985, Keane has developed various ecological computer models for research and management applications. His recent research includes (1) developing ecological computer models for exploring landscape, fire, and climate dynamics; (2) describing, classifying, and mapping fuel characteristics; (3) investigating the ecology and restoration of whitebark pine; and (4) conducting fundamental wildland fuel science. He

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Quantifying the historical range and variability of landscape composition and structure using simulation modeling is becoming an important means of assessing current landscape condition and prioritizing landscapes for ecosystem restoration. However, most simulated time series are generated using static climate conditions which fail to account for the predicted major changes in future climate. This paper presents a simulation study that generates reference landscape compositions for all combinations of three climate scenarios (warm-wet, hot-dry, and current) and three fire regime scenarios (half historical, historical, and double historical fire frequencies) to determine if future climate change has an effect on landscape dynamics. We applied the spatially explicit, state-and-transition, landscape fire succession model LANDSUM to two large landscapes in west-central Montana, USA. LANDSUM was parameterized and initialized using spatial data generated from the LANDFIRE prototype project. Biophysical settings, critical spatial inputs to LANDSUM, were empirically modeled across the landscape using environmental gradients created from historical and modeled future climate daily weather data summaries. Successional pathways and disturbance probabilities were assigned to these biophysical settings based on existing field data and extensive literature reviews. To assess the impact of changes in climate and fire regime, we compared simulated area burned and landscape composition over time among the different simulation scenario combinations using response variables of Sorenson's index (a global measure of similarity) and area occupied by the dominant vegetation class (simple indicator of change in landscape composition). Results show that simulated time series using future predicted climate scenarios are significantly different from the simulated historical time series and any changes in the fire regime tend to create more dissimilar and more variable simulated time series. Our study results indicate that historical time series should be used in conjunction with simulated future time series as references for managing landscapes. Published by Elsevier B.V.