Allometric Models for Biomass Estimation in Forest Ecosystems

Allometric models establish mathematical relationships between easily measured tree attributes and biomass, facilitating non-destructive estimation of forest carbon stocks at scales from individual trees to entire landscapes. Core predictors include diameter at breast height (DBH), total tree height and wood density, combined in power-law functions to estimate aboveground and belowground biomass. These equations may be generic, spanning multiple species and regions, or species- or site-specific, calibrated with destructive sampling data to reduce bias. Advances in remote sensing, such as terrestrial laser scanning and UAV photogrammetry, enable the rapid acquisition of structural variables for large numbers of trees, supporting the refinement of allometric models and the mapping of biomass over extensive areas. Model selection and uncertainty assessment are critical: small sample sizes or unrepresentative calibration datasets can introduce substantial error, while incorporation of wood density, crown structure or height improves accuracy. Allometric models underpin global carbon accounting, guide sustainable forest management and inform climate mitigation strategies by quantifying carbon sequestration and monitoring changes in forest biomass under natural and anthropogenic drivers.

Research from Nature Portfolio

Recent work has highlighted the sensitivity of allometric parameters to sampling design in temperate forests. By integrating airborne LiDAR measurements with field plots, large datasets of tree height and crown dimensions revealed that conventional biomass equations, often derived from limited sample sizes, systematically overestimate tree mass. Analysis demonstrated that increasing the number of calibration trees markedly reduces bias in height–crown-radius and, consequently, biomass estimates, emphasising the need for extensive destructive sampling or remote sensing proxies to ensure robust allometry and reliable carbon stock assessments.

Research from all publishers

Advances in UAV photogrammetry coupled with machine learning have been applied to estimate aboveground biomass in dry dipterocarp forests. Allometric equations calibrated with field-measured DBH and height were enhanced by 3D canopy models, yielding biomass estimates within 10 % of traditional methods and demonstrating the potential of UAV data to improve precision. In a UK temperate woodland, high-resolution terrestrial laser scanning revealed that existing size-to-mass models underestimate biomass by up to 77 % due to calibration bias toward small trees. Detailed laser-derived point clouds enabled the development of novel allometric relationships accounting for large-tree crown mass, substantially reducing landscape-scale error. A study on uncertainty propagation compared locally developed and generic equations across coniferous species, finding that allometric uncertainty can contribute up to 75 % of total biomass error when combined with remote sensing model predictions. This work underlines the importance of independent validation and error quantification in allometric model selection.

Allometric Models for Biomass Estimation in Forest Ecosystems publication trend

The graph below shows the total number of articles in allometric models for biomass estimation in forest ecosystems across all publications each year (not limited to Nature Index journals).

Technical terms

Allometry: The study of the relationship between tree size metrics (e.g. DBH, height) and biomass.

Diameter at breast height (DBH): Trunk diameter measured at 1.3 m above ground, a primary predictor in biomass equations.

Wood density: Dry mass per unit volume of wood, influencing tree biomass and carbon content.

Aboveground biomass (AGB): The total mass of living plant material above soil, typically expressed per hectare.

Terrestrial laser scanning (TLS): Ground-based LiDAR technique that generates detailed three-dimensional point clouds for tree structure analysis.

Uncertainty propagation: A quantitative assessment of how error in allometric equations and remote sensing models combines to affect biomass estimates.

References

1. Small Sample Sizes Yield Biased Allometric Equations in Temperate Forests. Scientific Reports (2015).
2. An Empirical Analysis of Above-Ground Biomass and Carbon Sequestration Using UAV Photogrammetry and Machine Learning Techniques. IEEE Access (2024).
3. Laser scanning reveals potential underestimation of biomass carbon in temperate forest. Ecological Solutions and Evidence (2022).
4. Variability and uncertainty in forest biomass estimates from the tree to landscape scale: the role of allometric equations. Carbon Balance and Management (2020).