This paper was presented at the conference ‘Integrating spatial technologies and ecological principles for a new age in fire management’, Boise, Idaho, USA, June 1999 The ongoing development of sophisticated fire behavior and effects models has demonstrated the need for a comprehensive system of fuel classification that more accurately captures the structural complexity and geographic diversity of fuelbeds. The Fire and Environmental Research Applications Team (FERA) of the USDA Forest Service, Pacific Northwest Research Station, is developing a national system of fuel characteristic classification (FCC). The system is designed to accommodate researchers and managers operating at a variety of scales, and who have access to a variety of kinds of input data. Users can generate fuel characteristics by accessing existing fuelbed descriptions (fuelbed prototypes) using generic information such as cover type or vegetation form. Fuelbed prototypes will provide the best available predictions of the kind, quality and abundance of fuels. Users can accept these default settings or modify some or all of them using more detailed information about vegetation structure and fuel biomass. When the user has completed editing the fuelbed data, the FCC system calculates or infers quantitative fuel characteristics (physical, chemical, and structural properties) and probable fire parameters specific to that fuelbed. Each user-described fuelbed is also assigned to one of approximately 192 stylized fuel characteristic classes.

The Fire and Smoke Model Evaluation Experiment (FASMEE) is designed to collect integrated observations from large wildland fires and provide evaluation datasets for new models and operational systems. Wildland fire, smoke dispersion, and atmospheric chemistry models have become more sophisticated, and next-generation operational models will require evaluation datasets that are coordinated and comprehensive for their evaluation and advancement. Integrated measurements are required, including ground-based observations of fuels and fire behavior, estimates of fire-emitted heat and emissions fluxes, and observations of near-source micrometeorology, plume properties, smoke dispersion, and atmospheric chemistry. To address these requirements the FASMEE campaign design includes a study plan to guide the suite of required measurements in forested sites representative of many prescribed burning programs in the southeastern United States and increasingly common high-intensity fires in the western United States. Here we provide an overview of the proposed experiment and recommendations for key measurements. The FASMEE study provides a template for additional large-scale experimental campaigns to advance fire science and operational fire and smoke models.

The lack of independent, quality-assured field data prevents scientists from effectively evaluating and advancing wildland fire models. To rectify this, scientists and technicians convened in the south-eastern United States in 2008, 2011 and 2012 to collect wildland fire data in six integrated core science disciplines defined by the fire modelling community. These were fuels, meteorology, fire behaviour, energy, smoke emissions and fire effects. The campaign is known as the Prescribed Fire Combustion and Atmospheric Dynamics Research Experiment (RxCADRE) and sampled 14 forest and 14 non-forest sample units associated within 6 small replicate (<10 ha) and 10 large operational (between 10 and 1000 ha) prescribed fires. Precampaign planning included identifying hosting agencies receptive to research and the development of study, logistics and safety plans. Data were quality-assured, reduced, analysed and formatted and placed into a globally accessible repository maintained by the US Forest Service Research Data Archive. The success of the RxCADRE project led to the commencement of a follow-on larger multiagency project called the Fire and Smoke Model Evaluation Experiment (FASMEE). This overview summarises the RxCADRE project and nine companion papers that describe the data collection, analysis and important conclusions from the six science disciplines.

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.