Biomass Estimation Techniques in Forest Ecosystems
Summary
Estimation of forest biomass underpins assessments of carbon storage, ecosystem productivity and sustainable management. Traditional field‐based approaches rely on allometric models that relate easily measured tree attributes—typically diameter at breast height and height—to biomass of individual components (stem, branches, foliage and roots). Ensuring additivity among components has led to the development of simultaneous‐equation systems or ratio estimators that maintain consistency between parts and total biomass. Advances in statistical methods, such as seemingly unrelated regression and Bayesian frameworks, have improved parameter estimation by addressing measurement error, heteroscedasticity and prior knowledge integration. Remote sensing techniques—most notably airborne LiDAR—provide spatially extensive canopy structure data that can be linked to biomass through calibrated inversion models, enabling stand‐level mapping with reduced field effort. Emerging methods combine multispectral imagery, UAV surveys and machine‐learning algorithms to refine estimates across diverse forest types. Recent work emphasises belowground biomass and stump‐root systems, recognising their significant carbon pools. Harmonising field and remote‐sensing data supports global carbon accounting, informs REDD+ initiatives and guides adaptive forest management under climate change.
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Biomass Estimation Techniques in Forest Ecosystems publication trend
The graph below shows the total number of articles in biomass estimation techniques in forest ecosystems across all publications each year (not limited to Nature Index journals).
Technical terms
Allometric model: A statistical relation linking tree dimensions (e.g. diameter, height) to biomass of plant components.
Additivity: A property of component models whereby the sum of individual biomass estimates equals total biomass.
Airborne LiDAR: A remote‐sensing technology using laser pulses from aircraft to generate high‐resolution three-dimensional measurements of canopy structure.
Seemingly unrelated regression (SUR): A multivariate estimation technique that fits multiple equations simultaneously, accounting for cross‐equation error correlations.
Diameter at breast height (dbh): Tree trunk diameter measured at 1.30 m above ground, a standard predictor in biomass equations.
References
- Ratio estimators for aboveground biomass and its parts in subtropical forests of Brazil. Ecological Indicators (2023).
- Estimation and modeling of the biomass and carbon storage in the stump and root of Populus deltoides. Environmental Challenges (2024).
- Compatible Biomass Model with Measurement Error Using Airborne LiDAR Data. Remote Sensing (2023).
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