Airborne Laser Scanning for Large-Scale Forest Carbon Quantification: A Comparison of LiDAR Single-Tree and Field-Based Methods

Abstract

Highlights: What are the main findings? Airborne laser scanning (ALS) can produce carbon estimates that are broadly comparable to those of traditional field-based inventories. The traditional plot-based quantifications better accounted for dead tree carbon, but ALS better accounted for live tree carbon in sparse forested conditions. What are the implications of the main findings The future use of remote sensing, especially ALS, for carbon quantifications looks promising. Hybrid or adjusted inventory approaches may be necessary in dense or mortality-rich forests. This study evaluated airborne laser scanning (ALS) as a large-scale tool for forest carbon quantification by comparing ALS-derived estimates with traditional field sampling across multiple forest strata. Above-ground biomass was estimated using two different, commonly used equations, while below-ground biomass was derived from peer-reviewed root-to-shoot ratios. ALS and field estimates differed across forest strata and carbon pools: ALS detected higher live tree carbon in harvested areas—capturing residual trees often missed in traditional cruises—but underestimated dead wood carbon, relative to field-based methods. Consistent differences were also observed between biomass equations, with Woodall estimates being 12.8% and 16.7% lower than Jenkins estimates for ALS and field methods, respectively. The study further incorporated soil organic carbon (SOC) and carbon dating data, providing additional insight into subsurface carbon stocks and the temporal dynamics of forest carbon pools. Overall, ALS proved to be an efficient, repeatable, and scalable method for carbon assessment, offering clear advantages in monitoring carbon flux over time when integrated with forest management protocols. Although further research is needed to refine biomass equations and explore emerging technologies such as Geiger Mode LiDAR, ALS has strong potential to enhance forest carbon crediting processes and support climate change mitigation goals.