Mapping surface fuel models using lidar and multispectral data fusion for fire behavior

Wild-land fires have become intense and more frequent all over the world. 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. The overall aim of this paper is to develop the use of lidar (LIght Detection and Ranging) remote sensing to accurately and effectively assess fuel models in East Texas. 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 when using lidar as opposed to QuickBird imagery alone; and (4) producing spatially explicit digital fuel maps. Estimates of fuel models were compared with in-situ data collected over 62 plots. We employ a unique approach to classify fuel models using a combination of lidar height bins and multispectral image data. Different image processing approaches for fusing lidar and multispectral data, such as the Minimum Noise Fraction (MNF) and Principle Component Analysis (PCA), were used to improve the overall accuracy of image classification. Supervised image classification methods provided better accuracy (90.10%) with the fusion of airborne lidar data with QuickBird data than with QuickBird imagery alone (76.52%).According to our results, lidar derived data provide 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.