To meet the data requirements of physics-based fire models and FASMEE objectives, traditional fuel and consumption measures need to be integrated with spatially explicit, three-dimensional data. One of the challenges of traditional fuel measurement techniques is that they must either remove or alter the fuels that are a primary determinant of fire behavior and smoke production. Remote measurement methods can non-destructively provide three-dimensional, point-cloud representations of fuels but still rely on traditional measures to quantify fuel loads, surface-area-to-volume ratios, fuel moistures and other intrinsic properties of fuels. Coupling traditional measurements with remotely sensed datasets can allow for scaling up observations from fine-scale inputs to physics-based models to coarse scale fuels characterization required by smoke models such as WRF-SFIRE-CHEM and DaySmoke. Hierarchical sampling across a range of spatial scales can also provide an important sensitivity analysis of what scale of observations is needed for models of interest.

In the Phase I planning phase of the Fire and Smoke Model Evaluation Experiment, the Fuel and Consumption discipline team specified a multi-scale fuel measurement and modeling framework to characterize pre-burn and post-burn fuels in proposed large-scale prescribed burn units in the southwestern (SW) and southeastern (SE) United States. As proposed by the Fuels Discipline team, traditional measures of fuels will be integrated with remotely-sensed point cloud data to provide estimates of pre- and post-fire fuel mass, volume, or density in physical measurement units and in 3D within the same domain as physics-based fire models. The density and extent of the point cloud and ground-based measurements will be contingent on fuel type and structure, but in general, sites with fine surface fuel beds that vary at sub-meter scales, typical of the SE sites, will be characterized at higher resolution (≤ 1 m), whereas sites with fuel elements that vary at the scale of individual trees, which is more typical in the candidate SW sites, will be characterized at coarser resolution (≥ 1 m). Across all burn units, pre- and post-burn overstory tree crown structure will be spatially characterized using airborne laser scanning (ALS), otherwise known as LiDAR. Where finer-scale surface fuels are the focus, particularly in the SE, Terrestrial Laser Scanner (TLS) and Unmanned Aerial Systems (UAS) derived point clouds will sample fuels at higher resolution within limited extents. Sampling will be conducted in Highly Instrumented Plots (HIPs) or along transects within each operational prescribed burn.