New allometric models for the USA create a shift in forest carbon estimation, modeling, and mapping

Abstract

The United States national forest inventory (NFI) serves as the foundation for forest aboveground biomass (AGB) and carbon accounting across the nation. These data enable design-based estimates of forest carbon stocks and stock-changes at state and regional levels, but also serve as inputs to model-based approaches for characterizing forest carbon stocks and stock-changes at finer spatial and temporal resolutions. Although NFI tree and plot-level data are often treated as truth in these models, they are in fact estimates based on regional species-group models and parameters known collectively as the Component Ratio Method (CRM). In late 2023 the Forest Inventory and Analysis (FIA) program replaced CRM nationwide with a new National Scale Volume and Biomass Estimators (NSVB) system that offers more precise and accurate representations of forest AGB and carbon. Given the prevalence of model-based AGB studies relying on FIA, there is concern about the transferability of methods from CRM to NSVB models, as well as the comparability of existing CRM AGB products (e.g. maps) to new and forthcoming NSVB AGB products. To begin addressing these concerns we compared previously published CRM AGB maps and estimates to new maps and estimates produced using identical methods with NSVB AGB reference data. Our results suggest that models relying on passive satellite imagery (e.g. Landsat) provide acceptable estimates of point-in-time NSVB AGB and carbon stocks, but fail to accurately estimate growth in mature or closed-canopy forests. We highlight that existing estimates, models, and maps based on FIA reference data are no longer compatible with NSVB, and recommend new methods as well as updated models and maps for accommodating this shift. Our collective ability to adopt NSVB in our modeling and mapping workflows will help us provide the most accurate spatial forest carbon data possible in order to better inform local management and decision making.

Citation

​Johnson, Lucas K.; Mahoney, Michael J.; Domke, Grant M.; Beier, Colin M. 2025. New allometric models for the USA create a shift in forest carbon estimation, modeling, and mapping. Forest Ecology and Management. 589: 122751. 17 p. https://doi.org/10.1016/j.foreco.2025.122751.