We developed user-friendly tools and techniques to estimate foliage biomass and monitor live fuel using a ground-based 3D scanner. We targeted canopy fuel metrics, surface fuel metrics, and terrain metrics commonly used in fire behavior modeling. Our methods minimize model training requirements and improve model transferability by leveraging a physics based model and prior estimates of leaf properties. We tested these methods across a range of vegetation densities and structures spanning shrublands, woodlands, and forests in Central Arizona and found strong agreement between lidar estimates and conventional estimates of total canopy fuel load (R² = 0.75, RMSE = 0.1kg/m²) and moderate agreement in maximum canopy bulk density (R²=0.39, RMSE=0.04 kg/m³). We demonstrated how these methods can provide precise, rapid estimates of pre- and post-treatment fuel conditions to better understand wildfire risk and treatment effectiveness.