Area-based Quantification of Aboveground Biomass

Several terrestrial biosphere Carbon Dioxide Removal (CDR) approaches rely on the capture and storage of carbon in living woody biomass -- both aboveground biomass (AGB) and below-ground biomass (BGB). The quantification of BGB is often dependent upon AGB¹, which is more directly observable. Thus, for these pathways, accurately and conservatively quantifying the gross storage of carbon in AGB is crucial for demonstrating net CO₂ removal.

Aboveground biomass (AGB) encompasses all living vegetation above the soil surface, including stems, branches, foliage, and bark. Woody biomass refers to plants whose structure includes lignified stems, such as bamboo, plants, shrubs, and trees.

Field-based measurements underpin biomass estimation, typically identifying species and employing individual plant measurements of diameter at breast height (DBH), vegetation height, and wood density. These measurements feed into allometric equations, which are empirically derived relationships between these empirical parameters and biomass. These equations are species- or biome-specific, and are often developed through extensive field- and laboratory-based research.

This Module was developed based on the current state of the art, publicly available science regarding quantification of woody aboveground biomass and long-term monitoring of terrestrial ecosystems. This Module aims to be scientifically stringent and robust. We recognize that some requirements may exceed the status quo in the market and that there will be opportunities to improve the rigor of this Module.

Additionally, this Module will be reviewed when there is an update to published scientific literature, government policies, or legal requirements which would affect net CO₂e removal quantification or the monitoring guidelines outlined in this Module, or at a minimum of every 2 years.

This Module quantifies aboveground woody biomass across the project area ($M_{AGB}$) at a given time point, t, through direct vegetation sampling and parameter estimation within survey plots and scientifically validated allometric equations.

• The project area is ≥ 1 hectare;
• Reliable field measurement data can be consistently collected, reported, and verified; and
• Project activities involve direct planting, assisted natural regeneration, improved forest management, or a combination thereof.

Throughout this Module, the use of “must” indicates a requirement, whereas “should” indicates a recommendation.

$M_{AGB}$ (tonnes) can be calculated by taking sufficient field plot measurements to obtain an estimate of the mean AGB density, which can then be multiplied by the project area to obtain a total AGB:

$$M_{AGB}(t) = \bar{m}_{AGB}(t) \times A$$

(Equation 1)

Where:

• $\bar{m}_{AGB}(t)$ is average AGB density, in tonnes/hectare.
• $A$ is the total project area, in hectares.

Estimation of $\bar{m}_{AGB}(t)$ is based on direct measurements of tree parameters (e.g., diameter at breast height (DBH) in field plots), and the use of allometric equations to convert tree parameters to biomass.

As field plots may be of unequal area, average AGB density must be calculated as an area-weighted average to ensure the resultant $\bar{m}_{AGB}(t)$ is expressed in tonnes per hectare:

$$\bar{m}_{AGB}(t) = \frac{\sum_{i=1}^{n} AGB_{plot,i}(t) \times a_{plot,i}}{\sum_{i=1}^{n} a_{plot,i}}$$

(Equation 2)

Where:

• $AGB_{plot,i}(t)$ is the AGB density (tonnes/hectare), in plot $i$ at time $t$.
• $a_{plot,i}$ is the area of plot $i$, in hectares.
• $n$ is the number of plots.

Biomass density within each plot at a given time point $AGB_{plot,i}(t)$ is calculated by summing the biomass of all individual woody plants in that plot:

$$AGB_{plot,i}(t) = \frac{\sum_{j=1}^{m_i} AGB_{tree,j}(t)}{a_{plot,i}}$$

(Equation 3)

Where:

• $AGB_{tree,j}(t)$ is the biomass of the $j^th$ tree in plot $i$, derived from an appropriate allometric equation, in tonnes.
• $m_i$ is the number of trees in plot $i$.

If the project area is stratified, this process can be repeated for each distinct project sub-area.

Project Proponents should use independently published allometric equations, from the following sources, in order of preference:

1. Local peer-reviewed equations;
2. Chave, et al. (2014)² for project areas in the Tropics;
3. National forest inventories;
4. IPCC generalized equations.

• Provide documentation of robust validation through peer-reviewed publication or an equally-rigorous scientific review.
• Include validation datasets and clearly report metrics such as root mean square error (RMSE), bias, correlation coefficient, and model skill.
• Provide comparative analysis with established equations to demonstrate equal or improved accuracy.
• Justify suitability explicitly for the Project's specific biome and species composition.

Models and measurements of aboveground biomass inherently include uncertainty from the assumptions and various sources of data which are used in the calculation of biomass.

Field measurements should occur during the leaf-off season, when possible.

Within each plot, the species of individual trees must be recorded, along with the following:

• Tree diameter at 1.3 m above the ground (DBH) must be recorded for all trees in the field plot with a diameter greater than 10 cm and should be recorded for trees with a diameter greater than 5 cm;
• Tree height should be recorded for a subset of trees (e.g., selected using a basal-area factor prism); and
• G-EBVH-0GPS coordinates should be recorded, it is recommended to collect multiple location measurements at each point of interest, using mapping-grade receivers or higher (capable of achieving < 3 m accuracy, e.g., 500 measurements at a rate of 1 point per second).

During the initial years following planting, there may not be many trees with DBH > 5 or 10 cm. However, field plot surveys should still monitor for any potential disease, ecological hazards, and/or mortality as these risks can be higher in young trees.

• Field measurements:
  • Field manual that was followed for sampling
  • Description of field plots, and how they were designed and selected
  • GPS coordinates, shape, size, and orientation of field plots
  • G-20Q6-0Description of measurement approaches and instruments used (e.g., diameter tape or calipers for DBH, clinometer or hypsometer for tree height)

• Allometric equations used and their sources
• Assessment of major sources of uncertainty (e.g., measurement errors, equation parameterization):
  • Quantify uncertainty for key identified sources through the approved approaches outlined in the Isometric Standard.
  • Report conservative AGB estimates

For each Verification, Project Proponents must submit a description of the measurements collected and report the full field inventory data.

Isometric would like to thank Renoster, for their extensive feedback during this Module's development.

1. Pan, Y., Birdsey, R. A., Fang, J., Houghton, R., Kauppi, P. E., Kurz, W. A., ... & Hayes, D. (2011). A large and persistent carbon sink in the world’s forests. Science, 333(6045), 988-993. https://doi.org/10.1126/science.1201609 ↩

2. Chave, J., Réjou‐Méchain, M., Búrquez, A., Chidumayo, E., Colgan, M. S., Delitti, W. B., ... & Vieilledent, G. (2014). Improved allometric models to estimate the aboveground biomass of tropical trees. Global change biology, 20(10), 3177-3190. https://doi.org/10.1111/gcb.12629 ↩

3. Pearson, T. R. (2007). Measurement guidelines for the sequestration of forest carbon (Vol. 18). US Department of Agriculture, Forest Service, Northern Research Station. https://doi.org/10.2737/NRS-GTR-18 ↩

4. Climate Action Reserve (2018). Standardized Inventory Methodology Version 1.0. https://www.climateactionreserve.org/wp-content/uploads/2018/06/Standardized-Inventory-Methodology_v1.0.pdf ↩ ↩²

5. Phillips, O., Baker, T., Feldpausch, T., & Brienen, R., (2001). RAINFOR Field Manual for Plot Establishment and Remeasurement. https://forestplots.net/upload/ManualsEnglish/RAINFOR_field_manual_EN.pdf ↩

6. Chave, J., Condit, R., Lao, S., Caspersen, J. P., Foster, R. B., & Hubbell, S. P. (2003). Spatial and temporal variation of biomass in a tropical forest: results from a large census plot in Panama. Journal of ecology, 91(2), 240-252. https://doi.org/10.1046/j.1365-2745.2003.00757.x ↩

7. White, J. (2013). A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach (INFORMATION REPORT FI-X-010). Natural Resources Canada. https://ostr-backend-prod.azurewebsites.net/server/api/core/bitstreams/3eac62d1-8765-48cb-a48e-a777eb8ae015/content ↩