Fires 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.

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The Chihuahua desert is one of the most biologically diverse ecosystems in the world, but suffers serious degradation because of changes in fire regimes resulting in large catastrophic fires. My study was conducted in the Sierra La Mojonera (SLM) natural protected area in Mexico. The purpose of this study was to implement the use of FARSITE fire modeling as a fire management tool to develop an integrated fire management plan at SLM. Firebreaks proved to detain 100% of wildfire outbreaks. The rosetophilous scrub experienced the fastest rate of fire spread and lowland creosote bush scrub experienced the slowest rate of fire spread. March experienced the fastest rate of fire spread, while September experienced the slowest rate of fire spread. The results of my study provide a tool for wildfire management through the use geospatial technologies and, in particular, FARSITE fire modeling in SLM and Mexico.