Uncertainty [MGD Sections]

UNFCCC decisions and requirements
IPCC good practice guidance
Relationship to UNFCCC
GHGI coverage, approaches, methods and tiers
Design decisions relevant to national forest monitoring systems
Land cover, land use and stratification
Forest reference emission levels and forest reference levels
Quality assurance and quality control
Guiding principles – Requirements and design decisions
Estimation methods for REDD+ activities
Integration frameworks for estimating emission and removals
Selecting an integration framework
Activity data x emission/removal factor tools
Fully integrated tools
Practical considerations in choosing an integration tool
Guiding principles – Methods and approaches
Remote sensing observations
Coarse resolution optical data
Medium resolution optical data
High resolution optical data
L-band Synthetic aperture radar
C-band and X-band SAR
LIDAR
Global forest cover change datasets
Ground-based observations
National forest inventories
Auxiliary data
Guiding principles – Remote sensing and ground-based observations
Activity data
Methods for estimating activity data
Maps of forest/non-forest, land use, or forest stratification
Detecting areas of change
Additional map products from remote sensing
Estimating uncertainty of area and change in area
Estimating total emissions/removals and its uncertainty
REDD+ requirements and procedures
Reporting forest reference emission levels and forest reference levels
Technical assessment of forest reference emission levels and forest reference levels
Reporting results of REDD+ activities
Technical analysis of the REDD+ annex to the BUR
Additional advice on REDD+ reporting and verification
Guiding Principles – Reporting and verification of emissions and removals
Financial considerations
Country examples – Tier 3 integration
Use of global forest change map data
Relative efficiencies
Developing and using allometric models to estimate biomass

Record Keeping [MGD Sections]

Integration + Estimation [MGD Sections]

Ground Based Observations [MGD Sections]

5.2.1.1   Use of forest inventory plot data and allometric models for biomass estimation Previous topic Parent topic Child topic Next topic

Biomass carbon density is generally estimated from forest inventory (plot measurements) (Chapter 4, Section 4.2.1) by using allometric models which relate biomass to surrogate measurements such as trunk diameter and height. Allometric models are established using destructive sampling which can be expensive. Some appropriate allometric models may already exist, and supplementary studies can fill gaps for other major species or forest types and environmental zones identified. Growth and yield trials, forest experiments and other quality data sources held by universities or other research agencies may be useful for the development or verification of models. The spatial, environmental or other limits of such models will need to be determined to ensure they are not applied outside their domain of relevance, as this may introduce bias. Any gaps, especially in the root-to-shoot or below-ground allometrics could be filled through targeted new studies.
Allometric models should preferably estimate above- and below-ground biomass, and be developed for relevant tree species and circumstances. The application of species specific allometric models requires knowledge of species composition within the identified forest strata. Different allometric models may be needed for each stratum, therefore the availability of appropriate allometric models can be a practical constraint on the number of forest strata used, and new allometric models may need to be developed. Some useful biomass information may already be available through an NFI, and the agency responsible for the NFI should be consulted via the NFMS about the relationship between NFI data, the proposed stratification for estimating REDD+ activities, and the availability of suitable allometric models. This should be done before new field work is done or further stratification is decided. Advice on the design of NFIs and the challenges of measuring plots in tropical rainforests is provided in Chapter 4, Section 4.2.1.
FAO and CIRAD have published a manual Opens in new window on how to develop allometric models and a database of existing models with information on the circumstances under which they apply Opens in new window and information on associated uncertainties. For native forests, which may contain many different species, application of species-specific allometric models may be impractical, in which case non-species-specific, regionally relevant allometric models can be used (Chave et al., 2004). Generic equations are based on large numbers of trees sampled across landscapes, and tend to be more reliable than locally developed equations if these are based on only a small number of trees (Chave et al., 2005; Chave et al., 2014; Paul et al., 2016).
The suitability of existing allometric models should be evaluated before they are applied in new circumstances. This requires consideration of how many trees were destructively sampled to develop the model, how well the sampled trees used to develop the model match the diameter distribution in the population of trees to which it is applied, and how well the model can estimate the biomass of an independent set of sample trees (Roxburgh et al., 2015; Perez-Cruzado et al., 2015). For model validation, a minimum of 10 trees need to be sampled in homogenous monoculture forest and many more (perhaps 50) in diverse natural forests. In all cases, allometric models should not be applied to trees outside the diameter range used to develop the model because this can introduce serious errors and bias (see Appendix F).Where allometric models that only estimate above-ground biomass are available below-ground biomass can be estimated using root-to-shoot ratios, default values are available from IPCC(1), although this approach will increase uncertainties.
Biomass densities should be multiplied by mass of carbon per mass of biomass to convert to carbon densities. The default ratio in the GPG2003 is 0.5(2). More specific figures for tree components and forest domains are given by IPCC(3). Box 26 provides some further considerations relating to the development and use of allometric models.
Estimating annual net change in biomass carbon stocks can be achieved by repeated plot measurements or from estimates of annual growth and carbon loss due to wood removals (including firewood) and /or other disturbances such as fire. Transfer of biomass to dead organic matter is based on estimates of annual biomass carbon lost due to mortality and carbon transfer to slash if the forest has been harvested.
Tier 2 and 3 approaches for biomass carbon stock change estimation allows for a variety of methods, and uses country-specific data to calculate the above-ground biomass growth and losses generally from repeated NFI data and/or empirical growth models. Implementation may differ from one country to another, due to differences in inventory methods, forest conditions and available activity data.

Box 26: Considerations when establishing and applying allometric models

When establishing allometric models, the range of tree sizes sampled should cover that encountered in the forest of interest. Failure to sample adequately large trees (many trees may exceed 100 cm in diameter in tropical forests; Henry et al., 2010) will result in very uncertain biomass estimates(4). Generally tree diameters should be measured at least 130 cm above the ground and below the first branching point.
In the application of allometric models countries should select models that have been developed consistently with established and validated practice and that best represent their forest types, measured stem diameters and heights. In both cases (establishment and application) all methods and choices should be well documented and justified.

 (1)
 (2)
The 2006 guidelines use 0.47. Countries should be consistent in the value they apply.
 (3)
 (4)
Chave, et al. (2004) found that for tropical rainforest the coefficient of variation associated with the allometric model was ~ 20% when 20 trees were sampled to construct it, but that this declined to 10% when the sample size was ~50 trees.