Methods to accurately estimate spatially explicit fuel consumption are needed because consumption relates directly to fire behavior, effects, and smoke emissions. Our objective was to quantify sparkleberry (Vaccinium arboretum Marshall) shrub fuels before and after six experimental prescribed fires at Fort Jackson in South Carolina. We used a novel approach to characterize shrubs non-destructively from three-dimensional (3D) point cloud data collected with a terrestrial laser scanner. The point cloud data were reduced to 0.001 m–3 voxels that were either occupied to indicate fuel presence or empty to indicate fuel absence. The density of occupied voxels was related significantly by a logarithmic function to 3D fuel bulk density samples that were destructively harvested (adjusted R2 = .32, P < .0001). Based on our findings, a survey-grade Global Navigation Satellite System may be necessary to accurately associate 3D point cloud data to 3D fuel bulk density measurements destructively collected in small (submeter) shrub plots. A recommendation for future research is to accurately geolocate and quantify the occupied volume of entire shrubs as 3D objects that can be used to train models to map shrub fuel bulk density from point cloud data binned to occupied 3D voxels.

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Traditional forestry, ecology, and fuels monitoring methods can be costly and error-prone, and are often used beyond their original assumptions due to difficulty or unavailability of more appropriate methods. These traditional methods tend to be rigid and may not be useful for detecting new ecological changes or required data at modern levels of precision [1] . The integration of Terrestrial Laser Scanning (TLS) methods into forest monitoring strategies can cost effectively standardize data collection, improve efficiency, and reduce error, with datasets that can easily be analyzed to better inform management decisions. Affordable (sub-$20K) off-the-shelf TLS units—such as the Leica BLK360— have been used commercially in the built environment but have untapped potential in the natural world for monitoring. Here, we provide a methodology that successfully integrates LiDAR scanning with existing monitoring methods. This new method: Allows for simplified and quick extraction of forestry, fuels and ecological vegetation variables from a single TLS point cloud and quick transect sampling. Streamlines the data collection process, removes sampling bias, and produces data that can be easily processed to provide inputs for models and decision support frameworks. Is adaptable to integrate additional or new environmental measurements.

The impact of a forest canopy on smoke concentration is assessed by applying a numerical weather prediction model coupled with a Lagrangian particle dispersion model to two low-intensity wildland (prescribed) fires in the New Jersey Pine Barrens. A comparison with observations indicates that the coupled numerical model can reproduce some of the observed variations in surface smoke concentrations and plume heights. Model sensitivity analyses highlight the effect of the forest canopy on simulated meteorological conditions, smoke concentrations, and plume heights. The forest canopy decreases near-surface wind speed, increases buoyancy, and increases turbulent mixing. Sensitivities to the time of day, plant area density profiles, and fire heat fluxes are documented. Analyses of temporal variations in smoke concentrations indicate that the effect of the transition from a daytime to a nocturnal planetary boundary layer is weaker when sensible heat fluxes from the fires are stronger. The results illustrate the challenges in simulating meteorological conditions and smoke concentrations at scales where interactions between the fire, fuels, and atmosphere are critically important. The study demonstrates the potential for predictive tools to be developed and implemented that could help fire and air-quality managers assess local air-quality impacts during low-intensity wildland fires in forested environments.