Browsing by Author "Smith, Mark Preston"

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    Predicting Fuel Models and Subsequent Fire Behavior from Vegetation Classification Maps
    (2003-09-02) Smith, Mark Preston; Dr. Stacy A. C. Nelson, Committee Member; Dr. E. Carlyle Franklin, Committee Member; Dr. Hugh A. Devine, Committee Chair
    The recent trends in wildland fires have created a level of motivation that requires natural resource managers to predict fires through the use of computer based simulation programs. Using vegetation maps delineated from large-scale aerial photography and fuel loading values collected from fieldwork, I simulated how fire would react to changes in fuel model assignments for Booker T. Washington National Monument (BOWA) and George Washington Birthplace National Monument using FARSITE, a fire simulation program. The environments for these fires were based on weather and fuel conditions found during the summer and fall months for each area. Sample points, stratified by vegetation formation, were selected. Then, field measurements using Brown's transect lines and Burgan and Rothermel ocular procedures were used to calculate the amount of fuel loading in tons/acre for each sample point. These values were then used to assign a fuel load to each vegetation formation class. Then each vegetation polygon on the map was assigned one of the thirteen National Fire Fuel Laboratory fuel models based on fuel load, vegetation type, and overall structure of the surrounding area. The sampling results showed a one to one correspondence of fuel model to vegetation formation. The sensitivity of FARSITE was tested by changing fuel model layers within FARSITE while holding all other variables constant (e.g., weather, moisture, etc.). Rate of spread and fire line intensity were used to compare the differences between the simulations using different fuel models. The results from the simulations showed that there was little sensitivity to changes in the assignment of fuel models for forested vegetation for these sites. The rate of spread and fire line intensity for grass fuel models showed sensitivity to changes in fuel model assignment.