Mapping surface fuels using LIDAR and multispectral data fusion for fire behavior modeling
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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.