A Physics-Based Estimation Model of Forest Aboveground Biomass Integrating Wood Density Classification and GEDI Waveform Retrieval

Forest biomass accumulation significantly alters three-dimensional structural characteristics. This study developed a physics-based allometric growth equation to estimate forest above-ground biomass (AGB) at the footprint level using GEDI L1B full-waveform LiDAR data. The model operates within a well-defined physical framework, systematically integrating canopy structural parameters and species-specific attributes through a three-component architecture. First, it constructs a Waveform Index (WI) characterizing the vertical energy distribution by combining canopy height (H) retrieved from waveform data with the typical crown architecture. The second component incorporates key canopy structural parameters: canopy gap fraction (P), which quantifies vertical openness, and leaf area volume density (LVD), describing the vertical distribution of foliar mass. The third component introduces wood density (ρ). In boreal coniferous forests, the model achieved an R² of 0.66 and an RMSE of 20.22 t/ha, explaining 83% of the observed variance in AGB.The method revealed that biomass accumulation was closely related to canopy height and wood density. While canopy height was directly retrievable from the waveform, wood density data were not readily available at large regional scales. Therefore, this research utilized land cover types as a base map and inferred the distribution of diffuse-porous wood and ring-porous wood forests across China by integrating multiple factors—including climate, topography, and phenology. The species composition was further refined using provincial forest inventory data on dominant tree species, excluding species accounting for less than 5% of a province's forest area. Wood density grades were then classified and incorporated into the footprint-level allometric equation for AGB estimation. This estimation method enables direct parameterization and retrieves AGB directly from satellite observations, while also accounting for the physiological characteristics of trees. This study demonstrates the significant potential for forest AGB estimation by leveraging canopy height and wood density. The proposed approach provides a foundation for forest carbon monitoring in precision forestry.