Using lidar-Derived fuel maps with farsite for fire behavoir modeling
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abstract
Fires have become intense and more frequent in the United States. Fuel distribution is very important for predicting fire behavior. The overall aim of this project is to model fire behavior using FARSITE and investigate differences in modeling outputs using fuel model maps, which differ in accuracy, in east Texas. This software requires as input spatial data themes such as elevation, slope, aspect, surface fuel model, and canopy cover along with separate weather and wind data. Seven fuel models, including grass, brush and timber models, are identified in the study area. To perform modeling sensitivity analysis, two different fuel model maps were used, one obtained by classifying a QuickBird image and the other obtained by classifying a LIDAR and QuickBird fused data set. Our previous investigations showed that LIDAR improves the accuracy of fuel mapping by at least 10%. According to our new results, LIDAR derived data also provides more detailed information about characteristics of fire. This study will show the importance of using accurate maps of fuel models derived using new LIDAR remote sensing techniques.