Understanding fine-scale variability in understory fuels is increasingly important as physics-based fire behavior models drive needs for higher-resolution data. Describing fuelbeds 3Dly is critical in determining vertical and horizontal distributions of fuel elements and the mass, especially in frequently burned pine ecosystems where fine-scale fuels arrangement drives fire intensity and resulting fire effects. Here, we describe research involving the use of highly resolved 3D models. We create fuelbeds using individual grass, litter, and pinecone models designed from field measurements. These fuel models are distributed throughout the fuelbed to replicate fuel distribution in rectified nadir photography taken for each plot. The simulated fuelbeds are converted into voxel arrays and biomass is estimated from calculated surface area between mesh vertices for each voxel. We compare field-based fuel depth and biomass with simulated estimates to demonstrate similarities and differences. Biomass distributions between simulated fuel beds and terrestrial laser scan data correlated well using Weibull shape parameters (r = 0.86). Our findings indicate that integration of field, simulated, and terrestrial laser scanner data will improve characterization of fuel mass, type, and spatial allocations that are important inputs to physics-based fire behavior models.

Requirements for describing coniferous forests are changing in response to wildfire concerns, bio-energy needs, and climate change interests. At the same time, technology advancements are transforming how forest properties can be measured. Terrestrial Laser Scanning (TLS) is yielding promising results for measuring tree biomass parameters that, historically, have required costly destructive sampling and resulted in small sample sizes. Here we investigate whether TLS intensity data can be used to distinguish foliage and small branches (≤0.635 cm diameter; coincident with the one-hour timelag fuel size class) from larger branchwood (>0.635 cm) in Douglas-fir (Pseudotsuga menziesii) branch specimens. We also consider the use of laser density for predicting biomass by size class. Measurements are addressed across multiple ranges and scan angles. Results show TLS capable of distinguishing fine fuels from branches at a threshold of one standard deviation above mean intensity. Additionally, the relationship between return density and biomass is linear by fuel type for fine fuels (r² = 0.898; SE 22.7%) and branchwood (r² = 0.937; SE 28.9%), as well as for total mass (r² = 0.940; SE 25.5%). Intensity decays predictably as scan distances increase; however, the range-intensity relationship is best described by an exponential model rather than 1/d². Scan angle appears to have no systematic effect on fine fuel discrimination, while some differences are observed in density-mass relationships with changing angles due to shadowing.

Terrestrial laser scanning (TLS) was used to collect spatially continuous measurements of fuelbed characteristics across the plots and burn blocks of the 2012 RxCADRE experiments in Florida. Fuelbeds were scanned obliquely from plot/block edges at a height of 20 m above ground. Pre-fire blocks were scanned from six perspectives and four perspectives for post-fire at ~2 cm nominal spot spacing. After processing, fuel height models were developed at one meter spatial resolution in burn blocks and compared with field measurements of height. Spatial bias is also examined. The resultant fuel height data correspond closely with field measurements of height and exhibit low spatial bias. They show that field measurements of fuel height from field plots are not representative of the burn blocks as a whole. A translation of fuel height distributions to specific fuel attributes will be necessary to maximise the utility of the data for fire modelling.