The value of local allometries from airborne laser scanning for tropical forest biomass estimates

To accurately assess forest carbon stocks for climate change projections, information on tree height and stem diameter is essential. However, a persistent lack of reliable plot-level inventory data, particularly in carbon-rich tropical forests, leads to local biases in biomass estimates. Pantropical allometries for tree dimensions and biomass have reduced bias at the regional level, but there continue to be inconsistencies and biases at the local level due to reference data quality. For example, classical instruments for measuring tree height such as clinometers and rangefinders have limited accuracy in dense, closed-canopy forests and can only be applied over small scales. The present study seeks to establish the effectiveness of airborne laser scanning data for determining site-specific allometric relationships between stem diameter and tree height, thereby improving the accuracy of above-ground biomass estimations (AGB) at the plot level. We used 118.75 ha of ground inventory data from a vast network of permanent sample plots in a tropical moist forest in French Guiana. The plots covered a range of forest structure and heights as they included undisturbed forests as well as previously logged over  plots. Ground data was combined with data derived from airborne laser scanning (ALS) to establish allometric height-diameter (H-DBH) models, both via a Bayesian multilevel modeling approach and an individual-based forest model (Canopy Constructor approach). Our results show that replacing a universal pantropical allometry with a locally derived species specific ALS-based H-DBH relationship nearly halved the mean height prediction error. Incorporating species identity into Bayesian models contributed to more than 50% of the total error reduction, a pattern reliably inferred by Canopy Constructor even without direct crown measurements. Both approaches yielded consistent AGB predictions, which were 11 to 13% higher (40 to 54 t ha -1 ) than those obtained using pantropical allometries. These findings underscore the potential of ALS data to enhance biomass estimations by reducing biases at local scales, providing a more accurate foundation for carbon stock assessments in tropical forests.