Spatial scaling of wildland fuels for six forest and rangeland ecosystems of the northern Rocky Mountains, USA

Abstract

Wildland fuels are important to fire managers because they can be manipulated to achieve management goals, such as restoring ecosystems, decreasing fire intensity, minimizing plant mortality, and reducing erosion. However, it is difficult to accurately measure, describe, and map wildland fuels because of the great variability of wildland fuelbed properties over space and time. Few have quantified the scale of this variability across space to understand its effect on fire spread, burning intensity, and ecological effects. This study investigated the spatial variability of loading (biomass) across major surface and canopy fuel components in low elevation northern Rocky Mountain forest and rangeland ecosystems to determine the inherent scale of surface fuel and canopy fuel distributions. Biomass loadings (kg m−2) were measured for seven surface fuel components—four downed dead woody fuel size classes (0–6 mm, 6–25 mm, 25–75 mm, and 75 + mm), duff plus litter, shrub, and herb—using a spatially nested plot sampling design within a 1 km2 square sampling grid installed at six sites in the northern US Rocky Mountains. Bulk density, biomass, and cover of the forest canopy were also measured for each plot in the grid. Surface fuel loadings were estimated using a combination of photoload and destructive collection methods at many distances within the grid. We quantified spatial variability of fuel component loading using spatial variograms, and found that each fuel component had its own inherent scale with fine fuels varying at scales of 1–5 m, coarse fuels at 10–150 m, and canopy fuels from 100 to 500 m. Using regression analyses, we computed a scaling factor of 4.6 m for fuel particle diameter (4.6 m increase in scale with each cm increase in particle diameter). Findings from this study can be used to design fuel sampling projects, classify fuelbeds, and map fuel characteristics, such as loading, to account for the inherent scale of fuel distributions to get more accurate fuel loading estimations.