Fire researchers and resource managers are dependent upon accurate, spatially-explicit forest structure information to support the application of forest fire behavior models. In particular, reliable estimates of several critical forest canopy structure metrics, including canopy bulk density, canopy height, canopy fuel weight, and canopy base height, are required to accurately map the spatial distribution of canopy fuels and model fire behavior over the landscape. The use of airborne laser scanning (LIDAR), a high-resolution active remote sensing technology, provides for accurate and efficient measurement of three-dimensional forest structure over extensive areas. In this study, regression analysis was used to develop predictive models relating a variety of LIDAR-based metrics to the canopy fuel parameters estimated from inventory data collected at plots established within stands of varying condition within Capitol State Forest, in western Washington State. Strong relationships between LIDAR-derived metrics and field-based fuel estimates were found for all parameters [sqrt(crown fuel weight): R 2 =0.86; ln(crown bulk density): R 2 =0.84; canopy base height: R 2 =0.77; canopy height: R 2 =0.98]. A cross-validation procedure was used to assess the reliability of these models. LIDAR-based fuel prediction models can be used to develop maps of critical canopy fuel parameters over forest areas in the Pacific Northwest.