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

TL;DR

This study evaluates the transition from the legacy CRM to the NSVB system for estimating US forest aboveground biomass (AGB) and carbon, using New York State as a testbed. By replicating Landsat-based NSVB modeling with FIA-derived references and comparing against CRM-based maps, it shows that NSVB yields higher point-in-time AGB estimates and altered geographic patterns due to improved top-and-limb biomass estimation, while Landsat alone struggles to capture growth in mature, closed-canopy forests. The work demonstrates substantial differences in statewide stocks and stock changes between NSVB and CRM, highlights the presence of systematic differences that can be largely described by simple rescaling, and emphasizes the need to update modeling pipelines—potentially incorporating LiDAR—to accurately monitor NSVB-driven carbon dynamics. The findings have direct implications for carbon accounting and forest management decisions, underscoring that the NSVB transition requires holistic retooling of prediction and mapping workflows to ensure accurate, actionable spatial forest carbon data.

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 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 known collectively as the Component Ratio Method (CRM). In late 2023 the Forest Inventory and Analysis (FIA) program introduced a new National Scale Volume and Biomass Estimators (NSVB) system to replace CRM nationwide and offer 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 to new maps 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 quantify growth in mature 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 step-change. 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.

Figures (7)

• Figure 1: NSVB AGB map accuracy results (vs FIA) across multiple scales represented by distances between hexagon centroids. Blue lines are fit to data using cubic splines with three knots.
• Figure 2: Landsat modeled AGB for 2019. a) Predictions made with models trained on NSVB-based FIA reference data. b) Predictions made with models trained on CRM-based FIA reference data.
• Figure 3: Willmott's $\operatorname{d_{r}}$ (measure of agreement, 1 is perfect; Section 2.4) across scales represented by distances between hexagon centroids. Landsat-based NSVB AGB vs FIA-based NSVB AGB (orange) and Landsat-based CRM AGB vs FIA-based CRM AGB (green). Trend lines fit to data using cubic splines with three knots.
• Figure 4: Empirical cumulative distribution function comparisons. Landsat-modeled predictions of AGB vs FIA plot-level estimates of AGB for NSVB and CRM respectively.
• Figure 5: Agreement between NSVB AGB maps and CRM AGB maps across scales represented by distances between hexagon centroids. AC (agreement coefficient) and its systematic (ACs) and unsystematic (ACu) components as defined in Section 2.5.