Journal
METHODSX
Volume 5, Issue -, Pages 1597-1604Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.mex.2018.11.006
Keywords
Surface fire; Low-Intensity fire; Forest fire; Longleaf pine; Pinus palustris; Fuel load; Bulk density; Volume; Fine scale; Fuel heterogeneity; Fuel sampling
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Funding
- US Department of Defense Strategic Environmental Research and Development Program [RC-2243]
- US Department of Agriculture Forest Service National Fire Plan
- University of Montana National Center for Landscape Fire Analysis
- US Department of Agriculture Forest Service, Southern Research Station, Athens, GA
- Center for Forest Disturbance Science, Athens, GA
- Tall Timbers Research Station, Tallahassee, FL
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Surface fuels are the critical link between structure and function in frequently burned pine ecosystems, which are found globally (Williamson and Black, 1981; Rebertus et al.,1989; Glitzenstein et al.,1995) [1-3]. We bring fuels to the forefront of fire ecology through the concept of the Ecology of Fuels (Hiers et al. 2009) [4]. This concept describes a cyclic process between fuels, fire behavior, and fire effects, which ultimately affect future fuel distribution (Mitchell et al. 2009) [5]. Low-intensity surface fires are driven by the variability in fine-scale (sub-m level) fuels (Loudermilk et al. 2012) [6]. Traditional fuel measurement approaches do not capture this variability because they are over-generalized, and do not consider the fine-scale architecture of interwoven fuel types. Here, we introduce a new approach, the 3D fuels sampling protocol that measures fuel biomass at the scale and dimensions useful for characterizing heterogeneous fuels found in low-intensity surface fire regimes. Traditional fuel measurements are oversimplified, prone to sampling bias, and unrealistic for relating to fire behavior (Van Wagner, 1968; Hardy et al., 2008) [7,8]. We developed a novel field sampling approach to measuring 3D fuels using an adjustable rectangular prism sampling frame. This voxel sampling protocol records fuel biomass, occupied volume, and fuel types at multiple scales. This method is scalable and versatile across ecosystems, and reduces sampling bias by eliminating the need for ocular estimations. Published by Elsevier B.V.
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