Fuel Model Selection for BEHAVE in Midwestern Oak Savannas
Author(s) -
Keith W. Grabner,
John Dwyer,
Bruce E. Cutter
Publication year - 2001
Publication title -
northern journal of applied forestry
Language(s) - English
Resource type - Journals
eISSN - 1938-3762
pISSN - 0742-6348
DOI - 10.1093/njaf/18.3.74
Subject(s) - environmental science , litter , selection (genetic algorithm) , prescribed burn , hardwood , fuel efficiency , forestry , ecology , agroforestry , computer science , geography , engineering , biology , automotive engineering , machine learning
BEHAVE, a fire behavior prediction system, can be a useful tool for managing areas with prescribed fire. However, the proper choice of fuel models can be critical in developing management scenarios. BEHAVE predictions were evaluated using four standardized fuel models that partially described oak savanna fuel conditions: Fuel Model 1 (Short Grass), 2 (Timber and Grass), 3 (Tall Grass), and 9 (Hardwood Litter). Although all four models yielded regressions with R2 in excess of 0.8, Fuel Model 2 produced the most reliable fire behavior predictions. North. J. Appl. For. 18(3):74–80.
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