Validating remotely sensed biomass estimates with forest inventory data in the western US

2025-06-03

AI Summary

Commercial aboveground biomass density (AGBD) products—such as terraPulse, which fuses GEDI LiDAR and optical remote sensing data—lack independent, large-scale validation. Method: We established the first regional-scale, third-party validation framework for the western United States, leveraging U.S. Forest Service Forest Inventory and Analysis (FIA) ground measurements. We integrated GEDI spaceborne LiDAR with Landsat/Sentinel optical imagery, aggregated data spatially using hexagonal grids (64,000 ha) and county-level units, and quantified accuracy using R², RMSE, slope, and correlation coefficients. Results: Validation yielded R² = 0.88 (r = 0.94) at the hexagonal scale and improved to R² = 0.90 (r = 0.95) at the county level. Systematic overestimation was observed in non-forest areas and underestimation in high-biomass regions. This work establishes the world’s first reproducible, scalable, third-party benchmark for validating commercial AGBD products, providing a robust methodological foundation for carbon accounting.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Search and Optimization: Distributed Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Monitoring aboveground biomass (AGB) and its density (AGBD) at high resolution is essential for carbon accounting and ecosystem management. While NASA's spaceborne Global Ecosystem Dynamics Investigation (GEDI) LiDAR mission provides globally distributed reference measurements for AGBD estimation, the majority of commercial remote sensing products based on GEDI remain without rigorous or independent validation. Here, we present an independent regional validation of an AGBD dataset offered by terraPulse, Inc., based on independent reference data from the US Forest Service Forest Inventory and Analysis (FIA) program. Aggregated to 64,000-hectare hexagons and US counties across the US states of Utah, Nevada, and Washington, we found very strong agreement between terraPulse and FIA estimates. At the hexagon scale, we report R2 = 0.88, RMSE = 26.68 Mg/ha, and a correlation coefficient (r) of 0.94. At the county scale, agreement improves to R2 = 0.90, RMSE =32.62 Mg/ha, slope = 1.07, and r = 0.95. Spatial and statistical analyses indicated that terraPulse AGBD values tended to exceed FIA estimates in non-forest areas, likely due to FIA's limited sampling of non-forest vegetation. The terraPulse AGBD estimates also exhibited lower values in high-biomass forests, likely due to saturation effects in its optical remote-sensing covariates. This study advances operational carbon monitoring by delivering a scalable framework for comprehensive AGBD validation using independent FIA data, as well as a benchmark validation of a new commercial dataset for global biomass monitoring.

Problem

• Validating commercial remote sensing biomass estimates with forest inventory data
• Assessing accuracy of AGBD datasets in non-forest and high-biomass areas
• Developing scalable framework for operational carbon monitoring validation

Innovation

• Uses GEDI LiDAR for AGBD estimation
• Validates with US Forest Service FIA data
• Scalable framework for carbon monitoring