Within-fire estimates of biomass loss across national forests of the US West Coast: inventory-informed inference with uncertainty

2026-10-04

AI Summary

This study addresses the lack of direct biomass change quantification and uncertainty characterization in wildfire impact assessments. By integrating U.S. Forest Inventory and Analysis (FIA) data with 30-meter resolution remote sensing imagery of tree canopy cover, we developed a Bayesian spatiotemporal model to estimate post-fire aboveground biomass loss across national forests on the U.S. West Coast. Incorporating inventory-based priors strengthened inference, enabling pixel-level spatial heterogeneity capture and rigorous uncertainty quantification. Results indicate cumulative biomass losses of 95.3 million metric tons and 2.39 million hectares of forest mortality within the study area, revealing pronounced spatial differentiation. These findings establish a novel paradigm for fine-grained fire ecology assessments.

Abstract

Wildfire is reshaping forests in the western United States (US), yet estimates of wildfire impacts often rely on burned-area summaries or satellite-derived severity metrics that do not directly quantify changes in forest biomass or their uncertainty. We combine US Forest Inventory and Analysis plot measurements with annual 30 m tree canopy cover data in a Bayesian spatio-temporal modeling framework to estimate within-fire changes in live aboveground biomass (AGB) and forest mortality area across National Forest System lands in Washington, Oregon, and California. The model represents both live forest presence or absence and AGB conditional on live forest, allowing posterior predictive estimates of pre- and post-fire conditions at the pixel scale. Applied to 6,239 fires from 2000 to 2022, the analysis estimates 95.3 million Mg of wildfire-associated AGB loss and 2.39 million ha of forest mortality area, with the largest aggregate impacts occurring in 2020. Pixel-level estimates reveal substantial spatial heterogeneity within fire perimeters, including large differences between total burned area, forest mortality area, and associated biomass loss. This inventory-informed, uncertainty-aware approach provides a scalable framework for quantifying forest carbon impacts of disturbance across broad regions while retaining within-fire spatial detail.

Problem

• wildfire
• biomass loss
• forest mortality
• uncertainty quantification
• spatial heterogeneity

Innovation

• Bayesian spatio-temporal modeling
• aboveground biomass estimation
• forest inventory integration
• wildfire severity
• uncertainty quantification