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Article << Previous     |     Next >>   Contents Vol 18(3)

The wildland fuel cell concept: an approach to characterize fine-scale variation in fuels and fire in frequently burned longleaf pine forests

J. Kevin Hiers A, Joseph J. O’Brien B E, R. J. Mitchell A, John M. Grego C, E. Louise Loudermilk D

A Joseph W. Jones Ecological Research Center at Ichauway, Route 2, Box 2324, Newton, GA 39870, USA.
B USDA Forest Service, Center for Forest Disturbance Science, 320 Green Street, Athens, GA 30602, USA.
C Department of Statistics, University of South Carolina, Columbia, SC 29208, USA.
D School of Natural Resources and Environment, University of Florida, Gainesville, FL, USA.
E Corresponding author. Email: jjobrien@fs.fed.us
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In ecosystems with frequent surface fire regimes, fire and fuel heterogeneity has been largely overlooked owing to the lack of unburned patches and the difficulty in measuring fire behavior at fine scales (0.1–10 m). The diverse vegetation in these ecosystems varies at these fine scales. This diversity could be driven by the influences of local interactions among patches of understorey vegetation and canopy-supplied fine fuels on fire behavior, yet no method we know of can capture fine-scale fuel and fire measurements such that these relationships could be rigorously tested. We present here an original method for inventorying of fine-scale fuels and in situ measures of fire intensity within longleaf pine forests of the south-eastern USA. Using ground-based LIDAR (Light Detection and Ranging) with traditional fuel inventory approaches, we characterized within-fuel bed variation into discrete patches, termed wildland fuel cells, which had distinct fuel composition, characteristics, and architecture that became spatially independent beyond 0.5 m2. Spatially explicit fire behavior was measured in situ through digital infrared thermography. We found that fire temperatures and residence times varied at similar scales to those observed for wildland fuel cells. The wildland fuels cell concept could seamlessly connect empirical studies with numerical models or cellular automata models of fire behavior, representing a promising means to better predict within-burn heterogeneity and fire effects.

Keywords: fire behavior, fire effects, fuel heterogeneity, Pinus palustris, prescribed fire.

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