Browsing by Subject "FARSITE"
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Item Mapping surface fuels using LIDAR and multispectral data fusion for fire behavior modeling(2009-05-15) Mutlu, MugeFires have become intense and more frequent in the United States. Improving the accuracy of mapping fuel models is essential for fuel management decisions and explicit fire behavior prediction for real-time support of suppression tactics and logistics decisions. This study has two main objectives. The first objective is to develop the use of LIght Detection and Ranging (LIDAR) remote sensing to assess fuel models in East Texas accurately and effectively. More specific goals include: (1) developing LIDAR derived products and the methodology to use them for assessing fuel models; (2) investigating the use of several techniques for data fusion of LIDAR and multispectral imagery for assessing fuel models; (3) investigating the gain in fuels mapping accuracy with LIDAR as opposed to QuickBird imagery alone; and, (4) producing spatially explicit digital fuel maps. The second objective is to model fire behavior using FARSITE (Fire Area Simulator) and to investigate differences in modeling outputs using fuel model maps, which differ in accuracy, in east Texas. Estimates of fuel models were compared with in situ data collected over 62 plots. Supervised image classification methods provided better accuracy (90.10%) with the fusion of airborne LIDAR data and QuickBird data than with QuickBird imagery alone (76.52%). These two fuel model maps obtained from the first objective were used to see the differences in fire growth with fuel model maps of different accuracies. According to our results, LIDAR derived data provides accurate estimates of surface fuel parameters efficiently and accurately over extensive areas of forests. This study demonstrates the importance of using accurate maps of fuel models derived using new LIDAR remote sensing techniques.Item Simulating Historic Landscape Patterns of Fire in the Southern Appalachian Mountains: Implications for Fire History and Management(2014-05-21) Gass, Ellen RFire suppression policies implemented in the early 20th century led to a decrease in fire-associated species and ecosystems in the southern Appalachian Mountains. As managers work towards restoration, a greater understanding of the pre-suppression fire regime is needed. Fire frequency and seasonality can be determined from physical fire records, such as fire scars, but fire size, fire cycle, ignition density, and ignition source are more difficult to ascertain. Using FARSITE, a spatially explicit fire model, I predicted past fire spread in the western Great Smoky Mountains National Park (GSMNP). Results showed a mean pre-suppression fire size of over an order of magnitude larger than fires on current landscape conditions (567 ha vs. 45 ha). Large fire sizes would have encouraged fire-associated vegetation and continuous flammable fuelbeds. In addition, the current lightning ignition rate within the study area resulted in a 120-135 year pre-suppression lightning fire cycle, which indicates that natural fires were influential on the landscape. This fire cycle is shorter than the lightning fire cycle experienced today (approx. 25-30,000 years). Using the mean fire return interval from previous research, I determined the potential contribution of lightning and anthropogenic ignitions to the fire cycle. This contributes to the debate on the importance of lightning versus anthropogenic ignitions to the pre-suppression fire regime. Most importantly, the estimation of mean fire size, fire cycle, and ignition density for lightning and anthropogenically ignited fires may aid federal resource managers as they use lightning ignitions and prescribed burns to restore fire-associated ecosystems in the GSMNP and other areas of the southern Appalachians.