Cross-scale evaluation of fuel maps in Colorado ponderosa pine-dominated forests - Johnston, Katelyn J., author; Hoffman, Chad, advisor; Tinkham, Wade, advisor; Hawbaker, Todd, committee member; Vogeler, Jody, committee member (Colorado State University. Libraries, 2025)
Fuel maps are used in every aspect of wildfire management, allowing managers to assess fire risk, predict fire behavior and effects, and guide fuel hazard treatment planning. Despite widespread use of national fuel maps like LANDFIRE, FCCS, and FastFuels, quantitative data on their accuracy and biases across ecosystems and scales remain limited. The few studies evaluating LANDFIRE's canopy fuel maps and FCCS have identified a wide range of errors and conflicting bias trends. Additionally, LANDFIRE's 40 standard fire behavior fuel models have yet to be assessed for their ability to represent fuel component loadings, despite growing use in fuel maps like FastFuels for physics-based fire behavior modeling. The accuracy of FastFuels has not been evaluated due to its recent development. The overall objective of this study was to assess the accuracy and bias of three national fuel mapping products – LANDFIRE, FCCS, and FastFuels – at five different scales. To meet this objective, I sampled surface and canopy fuels from seven sites representing the range of ponderosa pine (Pinus ponderosa Dougl. Ex Laws.) fuel complexes across the Colorado Front Range. Plots at each site were arranged in a 5x5 grid of 0.09 ha pixels to allow for accuracy assessment at 0.09, 0.12, 0.81, 1.44, and 2.25 ha scales. My results indicate that all three national fuel mapping products performed poorly across fuel attributes, with systematic biases and mean absolute errors ranging from 36% to 2590%. Errors and biases associated with LANDFIRE canopy metrics suggest that LANDFIRE is likely to overestimate fuel hazards associated with crown fire initiation, but underestimate crown fire spread hazard, while FastFuels underestimates the hazards associated with both crown fire initiation and spread. Similarly, FBFM40 overestimates key surface fuel components, such as fine fuel loading, which would likely lead to overpredicted surface fire behavior. FCCS metrics crucial for smoke and emissions forecasting, particularly 1000-hour fuels, are also overestimated, potentially inflating emissions projections. I found no significant relationship between mean error and map scale from 0.09 ha to 2.25 ha. The variation observed within and between fuel components and layers of LANDFIRE, FCCS, and FastFuels highlights inherent challenges associated with mapping wildland fuels. The high errors and biases observed in my assessment may have broader implications for fire management and planning, warranting further investigation, as these fuels play an important role in dictating fire behavior and effects. Although advancements in remote sensing and modeling offer opportunities to improve these national fuel mapping products, uncertainties in current products should continue to be quantified and considered when implemented in management activities until these improvements are successfully integrated.
Keywords: FCCS; LANDFIRE; FastFuels; wildland fuels; fire modeling