Earth Observation based Quantification of Above-ground 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.

This Module provides a transparent, scientifically grounded process by which Isometric quantifies project-scale AGB using Earth Observation based third-party or internally-developed biomass datasets. These datasets are typically derived from satellite-based remote sensing, ground-based calibration data, and predictive statistical models. This Module outlines the procedures by which Isometric selects, validates, applies, and transparently documents Earth Observation based AGB data products, to ensure consistency with the Isometric Standard and governing Protocol.

Earth Observation aboveground biomass (AGB) maps represent the culmination of decades of remote sensing innovation, ecological research, and data integration methods aimed at quantifying vegetative carbon stocks across large spatial extents. These maps typically integrate satellite observations, airborne remote sensing, and in situ forest inventory data to generate spatially continuous predictions of woody aboveground biomass. The goal is to achieve reliable, wall-to-wall AGB estimates across landscapes where field-based measurement alone is economically or technically infeasible.

AGB maps differ from simpler land cover maps or Normalized Difference Vegetation Index (NDVI) in that they attempt to estimate actual tonnes of dry biomass per hectare — and often include uncertainty quantification layers for each pixel. This integration of empirical data, remote sensing, and modeling has enabled a new generation of biomass maps that underpin conservation policy, MRV systems, and nature-based carbon credit issuances.

Common sources of remotely sensed data that underpin Earth Observation based AGB mapping include:

• LiDAR (Light Detection and Ranging): provides high-resolution 3D structural data on canopy height and vegetation density -- includes missions such as GEDI.
• Synthetic Aperture Radar (SAR): sensitive to forest structure and moisture and can penetrate cloud cover, making them useful in the often cloud-covered, humid tropics. Common instruments include the new ESA BIOMASS satellite.
• Optical sensors: offer spectral reflectance data used to derive vegetation indices correlated with canopy vigor and density. Most well studied and utilized, and include well-known missions such as Landsat, MODIS, and Sentinel-2.

These data are calibrated and validated against field measurements using statistical models ranging from linear regressions to complex machine learning algorithms like Random Forests, Support Vector Regression, or ensemble approaches. Model outputs are then spatialized across the landscape using remote sensing inputs, yielding gridded AGB predictions -- such as biomass in tonnes per hectare. Some products also include associated error metrics per pixel.

While these datasets have vastly improved our capacity to monitor biomass, limitations persist. These include, but are not limited to:

• Signal saturation: Optical and radar sensors tend to saturate in high-biomass regions (>200 t/ha), leading to underestimation.
• Coarse resolution: Lower spatial or temporal resolution of Earth Observation products may not capture small-scale heterogeneity or edge effects on forest biomass.
• Geographic bias: Most training data is skewed toward accessible or well-studied sites, reducing generalizability or applicability to more remote sites.
• Uncertainty: Errors in field data, sensor noise, or misregistration may compound and be improperly propagated.

These uncertainties make it essential that Earth Observation based AGB maps used for crediting are transparently benchmarked, adjusted if necessary, and conservatively interpreted.

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 are 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 ($M_{AGB}$ in tonnes) at a given time point, t, through the use of third-party biomass datasets that meet specified eligibility requirements. This Module applies under the following conditions:

• R-AM55-0The project area is ≥ 1 hectare;
• Reliable field measurement data can be consistently collected, reported, and verified;
• Project activities involve direct planting, assisted natural regeneration, improved forest management, or a combination thereof; and
• R-Y90T-0The Project lies within a biome, ecoregion, or forest type for which one or more AGB data products are available that meet the eligibility guidelines as determined by Isometric.

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

Eligibility of proposed third-party AGB maps will be evaluated by Isometric for both overall quality of the product and for suitability of use at the Project location. Any internally developed data products from Isometric will also be upheld to the same eligibility requirements. Eligibility determination will follow criteria outlined by the CEOS validation working group using best practices described in Duncanson et al., (2021)². For overall quality of the data product, forest AGB data products will be assessed for whether they meet the conditions, corresponding to a validation stage of 2 or higher within the CEOS validation hierarchy. These include:

• Product accuracy must be assessed over space and time using reference data that covers at least 30 locations/time points
• Data must be spatially and temporally consistent across representative locations and time periods and must be consistent with similar products
• Results must be published in peer-reviewed scientific literature

To determine whether an AGB map is suitable for use at the project site, the map must have spatial coverage over the full project area and the following conditions must also be met:

• The biome type(s) contained within the project area must have been represented in the reference dataset used for assessing the overall accuracy of the data product
• Temporal coverage of the product is anticipated to continue into the future at a frequency that is in alignment with the Reporting Period interval of the Project (e.g., there is not a planned retirement of remote sensing instruments supporting the data product)

Suitability for the project site will be further evaluated against field data collected at the site via the benchmarking procedure described below.

The Earth Observation based AGB map must be benchmarked against field plot measurements, at least once every 5 years during the Crediting Period unless otherwise specificed in the Protocol or Module the Project is crediting against. Field measurements must follow the requirements set forth in the Area-based Quantification of Above-ground Biomass Module. In addition to the considerations for field sampling included in the referenced Module, field plots for the purpose of validating an AGB map should adhere to the following:

• G-0EDS-0Plot size should be similar to pixel-area of the Earth Observation based AGB map being validated.
• R-HXWF-0The number of plots must be sufficient to establish statistical equivalence with the AGB map using an equivalence test at 90% confidence with +/-10% allowable error.
• G-M39E-0Plot measurements should also be temporally aligned, to the greatest possible extent, with map products.

If the equivalence test is not passed, then the earth observation map cannot be used directly for quantifying $M_{AGB}$ directly. In this case, the earth observation map can be corrected and remapped with field plots to adjust for any local errors. This should be done using a simple linear regression relating field measurements to estimated carbon stock, which requires a sufficient number of field plots to obtain a model with required accuracy (R² ≥ 0.85). This corrective mapping should ideally either be done across many forest ages at once (i.e., a chrono-sequence), or should be remapped periodically.

Models and measurements of aboveground biomass inherently include uncertainty from various data sources which contribute to the quantification. While it is not expected that all uncertainties are exhaustively quantified, Project Proponents must evaluate, report, and conservatively account for identifiable and significant sources following the in the approved approaches outlined in the Isometric Standard. Potential sources of uncertainty to consider include, but are not limited to:

• Pixel-level uncertainty estimates of the AGB map associated with map data sources and development process (e.g., remote sensing instrument errors, model errors)
• Uncertainty associated with any local corrections applied to the AGB product as a result of benchmarking
• Errors associated with field measurements and estimation procedures used for benchmarking (e.g., DBH measurement errors, height estimation bias, wood density variability, plot representativeness)

Project Proponents must report the following information in the Project Design Document (PDD):

• Data products and time periods used
• Description of pre-processing of data for model input
• Description of additional processing of satellite data (e.g., filtering out images with clouds)
• Field measurements for benchmarking:
  • 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
  • Description 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
• Benchmarking results, including discussion and interpretation of results and details of any corrections developed
• Assessment of major sources of uncertainty (e.g., map uncertainty estimates)
  • Quantify uncertainty for key identified sources through the approved approaches outline in the Isometric Standard
  • Report conservative AGB estimates

For each verification, Project Proponents must submit a description and copies of any data used and/or collected for the biomass quantification and benchmarking.

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. Duncanson, L., Armston, J., Disney, M., Avitabile, V., Barbier, N., Calders, K., Carter, S., Chave, J., Herold, M., MacBean, N., McRoberts, R., Minor, D., Paul, K., Réjou-Méchain, M., Roxburgh, S., Williams, M., Albinet, C., Baker, T., Bartholomeus, H., Bastin, J.F., Coomes, D., Crowther, T., Davies, S., de Bruin, S., De Kauwe, M., Domke, G., Dubayah, R., Falkowski, M., Fatoyinbo, L., Goetz, S., Jantz, P., Jonckheere, I., Jucker, T., Kay, H., Kellner, J., Labriere, N., Lucas, R., Mitchard, E., Morsdorf, F., Næsset, E., Park, T., Phillips, O.L., Ploton, P., Puliti, S., Quegan, S., Saatchi, S., Schaaf, C., Schepaschenko, D., Scipal, K., Stovall, A., Thiel, C., Wulder, M.A., Camacho, F., Nickeson, J., Román, M., Margolis, H. (2021). Aboveground Woody Biomass Product Validation Good Practices Protocol. Version 1.0. In L. Duncanson, M. Disney, J. Armston, J. Nickeson, D. Minor, and F. Camacho (Eds.), Good Practices for Satellite- Derived and Product Validation, (p. 236): Land Product Validation Subgroup (WGCV/CEOS), doi:10.5067/doc/ceoswgcv/lpv/agb.001 ↩