We are investigating the use of large footprint, full-return, lidar remote sensor data to depict crown fuel amount and configuration. The project focuses on two landscapes in the Sierra Nevada Mountains where bioregional planning and monitoring are underway and there is a lack of consistent and reliable crown fuel mapping. This project installed a sufficiently large network of sample sites across two landscapes to provide robust statistical analysis of the relationships between ground-based measurements of crown dimensions and remotely sensed predictions. This will provide a consistent basis for mapping and monitoring crown fuels. A total of 233 vegetation sampling plots were established in the northern Sierra Nevada and 285 plots were established in the southern Sierra Nevada. Analysis was conducted on collected crown dimension data (height to partial crown and height to full crown) to determine crown bulk density (CBD) and height to live crown (HLC) measurements for identified vegetation types. Analysis has and is being conducted to compare the results of the field-collected measurements with the remotely sensed forest structure predictions. Remotely sensed LVIS data has been processed and is currently being used to map canopy height, biomass, habitat suitability and other forest structural traits (Dubaya and Hyde). Results to date show that lidar can map canopy heights well in the Sierra Nevada (R2=0.75, SE 8.2 m), with increasing accuracy away from plot edges (R2=0.93, SE 4.8 m). Canopy cover was estimated within 8% of measured values (R2=0.81). Biomass was also estimated successfully, with a RMSD of 251 Mg/ha (R2=0.83). Various models for crown base height and crown bulk density have been developed and compared in terms of expected fire behavior using FARSITE. This includes models based on lidar outputs only (e.g. 10% cumulative canopy energy) as well as empirical regression models based on combinations of lidar outputs (maximum amplitude, canopy energy) and derived characteristics (canopy cover). Results to date show that lidar provides greater range of estimated crown bulk densities and height to live crowns and captures spatial heterogeneity well. FARSITE model predictions show greater fire spread and size with lidar-based crown fuel data than other Landsat TM-inventory plot extrapolated fuels data.