Next‐Generation Biomass Mapping for Regional Emissions and Carbon Inventories: Incorporating Uncertainty in Wildland Fuel Characterization

Biomass mapping is used in variety of applications including carbon assessments, emission inventories, and wildland fire and fuel planning. Single values are often applied to individual pixels to represent biomass of classified vegetation, but each biomass estimate has associated uncertainty that is generally not acknowledged nor quantified. In this study, we developed a geospatial database of wildland fuel biomass values to characterize the inherent variability within and across major vegetation types of the United States and Canada. For vegetation types that had sufficient quantification of biomass by fuel type (e.g., canopy, shrub, herbaceous, fine downed wood, coarse downed wood, and organic soil layers), we developed empirical distribution estimates. Based on available data, fitted distributions will be useful for informing the first‐generation biomass mapping that incorporates variability in loading by vegetation and fuel type and to evaluate potential errors in point estimates given in current map products. Because combustible biomass is a common input in fire and smoke models, variability estimated for fitted distributions can be used to inform data input uncertainty in predictions of wildland fuel consumption and emissions and to provide stochastic inputs of biomass to ensemble simulation models. Plain Language Summary The mass of live and dead vegetation, termed biomass, is used in variety of applications including carbon mapping, wildland smoke emission modeling, and fire management. In many mapping projects, single biomass values are often used to represent classified vegetation types. However, in reality, the biomass of grasslands, shrublands, and forests is extremely variable, and mapped values are associated with a high degree of uncertainty. In this study, we developed a database of wildland fuel biomass by major vegetation type in the United States and Canada. For well‐studied vegetation types we developed mathematical models that statistically represent the distribution of biomass observations. Based on available data, fitted distributions will be useful for informing the first‐generation biomass mapping that incorporates uncertainty estimates by major vegetation type to evaluate potential errors in current map products. Because biomass is a common input in fire and smoke models, fitted distributions can be used to evaluate the uncertainty in predicted wildfire emissions. Key Points This study presents fitted distributions of wildland fue