LiDAR Based Quantification of Woody Biomass

Several terrestrial biosphere Carbon Dioxide Removal (CDR) approaches rely on the capture and storage of carbon in living woody biomass, including both aboveground biomass (AGB) and belowground 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, shrubs, and trees. Belowground woody biomass (BGB) encompasses the subsurface woody biomass in root systems.

This Module outlines requirements for quantification of woody biomass over a project area using Airborne Laser Scanning (ALS) measurements. This Module includes requirements for ALS data acquisition, ALS data processing, modeling, and verification.

ALS data can also be used to develop other types of models for forest carbon, such as through Individual Tree Crown modeling. Individual Tree Crown modeling is not in scope for this current Protocol version, but guidelines may be included in future iterations.

This approach involves estimating woody biomass by linking field measurements of forest inventory with ALS data to enable area-wide mapping of forest attributes.

Generally, the following steps are used:

1. Collection of ALS data, such as LiDAR from the air using drones, helicopters, or airplanes.
2. Processing of ALS data, including the transformation of point clouds to structured data.
3. Developing predictive models that link ALS data to co-located ground measurements.
4. Using the predictive model to map forest attributes, including AGB, across a project area.

A number of decisions are made throughout the process. The requirements and recommendations for each step are detailed in subsequent sections.

The following resources are suggested for guidance on best practices:

• White, 2013: A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach (Version 2.0)²
• Næsset, 2013: Area-Based Inventory in Norway–From Innovation to an Operational Reality ³
• White et al., 2017: A model development and application guide for generating an enhanced forest inventory using airborne laser scanning data and an area-based approach ⁴

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 that 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 the total CO₂ equivalent stored as aboveground woody biomass across the project area ($CO_2e_{AGB,t}$) at a given time point, t, measured using airborne laser scanning. Where included as a pool, it also quantifies the total CO₂ equivalent stored as belowground woody biomass across the project area ($CO_2e_{BGB,t}$) at a given time point, t, which is derived from the measured aboveground biomass.

This Module is applicable to Projects where reliable field measurement data can be consistently collected, reported, and verified.

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

This Module allows for two different area-based approaches for calculating $M_{AGB}(t)$ (tonnes), the total aboveground biomass at a given time point, using ALS data. The recommended approach is to use wall-to-wall ALS measurements that have full coverage over the project area. In the other approach, ALS data collection can occur over representative subplots over a portion of the project area.

In the wall-to-wall approach, $M_{AGB}$ of the project area is calculated by summing all the values of biomass density derived from the ALS observations and modeling across the entire project area:

$$M_{AGB}(t) = {\sum_{i=1}^{n} AGB_{pixel,i}(t) \times a_{pixel}}$$

(Equation 1)

Where:

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

In the alternative approach, ALS data is collected over representative subplots, and then used to estimate the mean biomass density ($\bar{m}_{AGB}$) over the project area.

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

(Equation 2)

Where:

• $\bar{m}_{AGB}(t)$ is average AGB density, in tonnes/hectare at time $t$.
• $AGB_{pixel,i}(t)$ is the AGB density (tonnes/hectare) in pixel $i$ at time $t$.
• $a_{pixel}$ is the pixel area, in hectares.
• $j$ is the number of pixels measured across the subplots.

$M_{AGB}$ can then be calculated using the estimate of the mean AGB density, which can then be multiplied by the project area to obtain a total AGB:

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

(Equation 3)

Where:

• $A_{project}$ is the total project area, in hectares.

If the project area is stratified, Equations 2 and 3 can be repeated for each project sub-area.

For both of the approaches above, the total aboveground biomass carbon stock in tonnes CO₂ equivalent at time, $t$, is further calculated as:

$$CO_2e_{AGB}(t) = \frac{44}{12} \times CF \times M_{AGB}(t)$$

(Equation 4)

Where:

• $\frac{44}{12}$ is the ratio of mass of CO₂ to mass C, used to convert to tonnes of CO₂
• $CF$ is the carbon fraction for the tree species in the project area, in tonne C per tonne dry biomass

The carbon fraction, $CF$, must be chosen from the following hierarchy:

1. A regional and species-specific factor that is justified based on scientific literature (e.g., Doraisami et al., 2022⁵). This is the preferred approach to have the most accurate estimate and avoid overestimation;
2. If the above is not available, then a genus-specific or national average factor that is justified based on scientific literature can be used;
3. As a last resort, if it is proven that the above two factors are not available, then a default factor of 47%, which is a mean across species, can be used^6,7.

Where belowground biomass (BGB) is an included as a carbon pool, the belowground biomass is calculated as:

$$CO_2e_{BGB}(t) = CO_2e_{AGB}(t) \times RS$$

(Equation 5)

Where:

• $RS$ is the dimensionless project-specific root-to-shoot ratio

Appropriate root-to-shoot ratios should be selected by regional and species-specific factors that are justified based on scientific literature (e.g., USFS's Component Ratio Method or similar national-level species-specific ratios). This is the preferred approach to have the most accurate estimate and avoid overestimation. If sufficient evidence is provided to demonstrate that no suitable project-specific factor can be obtained, matching to the ecological zone and continent of the project area, based on the IPCC 2019 Table 4.4⁶, must be used. In this case, sufficient evidence documenting the unsuccessful search for project specific factors must also be supplied. Acceptable evidence must show (1) a list of search terms used within a research database (e.g., Web of Science, Google Scholar) that encapsulate the region and species relevant to the Project, and (2) the relevant species are not included in the list of species for which root-to-shoot ratio data are available on the TRY Plant Trait Database⁸.

Where the root:shoot ratios are species or genus specific, the project-area root:shoot ratio must be calculated as a weighted value to represent the species mix within the project area.

The uncertainty in selected $RS$ factors must be reported from the same source dataset. For example, the IPCC 2019 Chapter on Forest Land⁶ provides an uncertainty in the root-to-shoot ratio.

Some considerations around the data acquisition parameters are detailed below and are based on guidance from White (2013)² and Duncanson et al. (2021)⁹. These are recommendations rather than strict requirements. It is expected that the acquisition parameters will be tailored to the setting and conditions to yield the required DTM resolution, and that these may need to change over time as canopy closure occurs and forest density increases.

Projects must provide information on the acquisition parameters that are used, and the guidance for each provided below should be considered in how the acquisition parameters are tailored to the project area, but do not represent strict requirements:

Alternative airborne methods for generation of canopy height maps may be eligible in consultation with Isometric.

For each flight, the following must be reported:

Project Proponents are strongly encouraged to use standardized tools and published QA/QC protocols to maximize ease of replicability and transparency, several such protocols are referenced in Duncanson et al. (2021)⁹.

As the DTM is used in the calculation of vegetation height and terrain is assumed to remain stable over the Crediting Period, Projects must demonstrate terrain consistency across all acquisitions via one of the following:

• Using a single reference DTM (e.g., one derived prior to canopy closure) across all acquisitions and demonstrating vertical co-registration of subsequent point clouds to the reference DTM in stable, non-vegetated areas; or
• Demonstrating that the mean difference between DTMs from successive acquisitions is not significantly different from 0 (p $\leq$ 0.05) and reporting the RMSE and spatial distribution of differences.

Any areas where identified terrain disturbances have occurred (e.g., landslides) must be excluded from this assessment.

This section describes the requirements for developing and applying ALS-based predictive models which link ALS structure data to AGB. Throughout this section, training is used to refer to this process of model fitting and calibration. Section 8 further details the requirements for the assessment of model performance (validation) and uncertainty. This is done via a benchmarking process which requires periodic collection of field plots from the project area.

Predictive ALS-based AGB maps have been developed with a variety of statistical models. The selection of the best modeling approach will be site and project-specific. The following types of models are acceptable for use:

• Parametric modeling techniques, e.g., linear regression or variants such as linear mixed-effects models and non-linear mixed effects models. These models have many desirable properties when combined with a design-based sample of forest inventory plots. However, these models are restricted by assumptions (e.g., normality) and usually work best in homogeneous forest ecosystems (e.g., pine plantations).
• Regression trees, e.g., Random Forests or Gradient Boosting. These are the most common techniques used to construct AGB maps. Algorithms like Random Forests are robust to features displaying multicollinearity (most of the features computed using packages like lidrmetrics or FUSION are highly correlated) and can identify high-dimensional interactions among the predictor features. However, Random Forests are not capable of extrapolating beyond the bounds of the dataset, as the algorithms work via a recursive partitioning of the observed feature space during training. As such, when using tree-based models, it is important to ensure the plot data captures the full variability of the landscape. This again highlights the benefit of using a stratified or structurally guided sampling approach¹¹.
• Deep learning, e.g., Neural Networks or Convolutional Neural Networks¹². Deep learning methods are highly data dependent and require a very large number of plot acquisitions. As such, it is common to pre-train these models on large datasets and then “tune” them on a local plot dataset.

It is recommended to use the simplest possible statistical model that yields high-performance. White et al. (2017)⁴, in particular Tables 4, 5 and 6 therein can be used to support key decisions and considerations in selecting a modeling approach.

In general, it is expected that the model will be trained on measurements of biomass collected via field plots from the project area collected alongside ALS data. Where field plots are specifically collected for model training purposes the field measurements should follow the requirements in Section 8.1. Training data may also include other supplementary data sources and types (e.g., national or regional inventory plots; terrestrial laser scanning data).

Where the model is updated over the course of the Crediting Period (e.g., new training data are incorporated), the updated model should be applied retroactively to any prior ALS data collections used in the overall removals calculation (e.g., the initial biomass stock calculations) to ensure consistency in the biomass change calculation.

The benchmarking process serves two purposes. These data are used both to validate the model and assess its skill in the project context (see Section 8.2), and are further used to support the broader uncertainty assessment.

Benchmarking must use field plot measurements of woody biomass collected within the Project Area following the requirements within the Area-based Quantification of Woody Biomass Module, with the exceptions and additions below.

• Number of Plots: As under the Area-based Module, there is not a strict requirement for the number of plots that must be collected for benchmarking. However, it is noted that additional plots can further reduce uncertainty in the model and are thus encouraged.

Models and measurements of above-ground biomass inherently include uncertainty from various data sources which contribute to the quantification.

Potential sources of uncertainty to consider include, but are not limited to:

• Spatially explicit uncertainty estimates of the AGB estimates associated with ALS data collection and model estimation (e.g., ALS measurement 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)

The uncertainty associated with the ALS data collection and woody biomass quantification are used to support the overall uncertainty assessment within the Protocol under which the project is crediting.

Project Proponents are responsible for reporting the required information in this module in the PDD and at each verification. Project Proponents are also responsible for maintaining relevant data records following the requirements in the governing Protocol. Here a brief summary of reporting requirements are provided for convenience, but the referenced sections should be consulted for further details and specific requirements.

ALS Acquisition (see Section 5)

• Frequency of ALS acquisition
• Acquisition parameters
• Flight metadata
• Details on instruments and calibrations
• Spatial coverage of ALS data
• DTM resolution

ALS Processing (see Section 6)

• Details on processing workflow and software
• DTM accuracy
• Height threshold for subcanopy vegetation

Model Development and Training Data (see Section 7.1)

• Model type and basis
• Model applicability to project area
• Model documentation and code
• Training data and supporting details

Model Application (see Section 7.2)

• Reporting on project range relative to the training range of ALS metrics
• Procedure for AGB map generation and project area biomass estimation

Benchmarking Plots (see Section 8.0)

• Details on sampling design and justification
• Other details and data-sharing as required by the Area-based Quantification Module

Validation (see Section 8.2)

• Details on out-of-sample approach and spatial independence
• Required validation and skill assessments for the model
• Details on cross-validation procedures and alignment with requirements, where applicable

Uncertainty (see Section 9)

• Identification of key sources of uncertainty and characterization of their uncertainty

In addition to a primary certified Validation and Verification Body (VVB), independent third-party consultants may be required for the evaluation of ALS-based quantification. Consultants must have relevant experience with quantification of AGB using an area-based approach via LiDAR. This must be demonstrated through work experience, post-graduate degrees, research projects, peer-reviewed papers, or equivalent experience.

Isometric would like to thank the following external contributors to this Module:

• Melissa Fedrigo PhD (GeoRubix Solutions)
• Renoster

1. Pan, Y., Birdsey, R. A., Fang, J., Houghton, R., Kauppi, P. E., Kurz, W. A., Phillips, O.L., Shvidenko, A., Lewis, S.L., Canadell, J.G., Ciais, P., Jackson, R.B., Pacala, S.W., McGuire, A.D., Piao, S., Rautiainen, A., Sitch, S., & 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. 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 ↩ ↩²

3. Næsset, E. (2013). Area-based inventory in Norway–from innovation to an operational reality. In Forestry applications of airborne laser scanning: concepts and case studies (pp. 215-240). Dordrecht: Springer Netherlands. ↩

4. White, J. C., Tompalski, P., Vastaranta, M. A., Wulder, M. A., Saarinen, N. P., Stepper, C., & Coops, N. C. (2017). A model development and application guide for generating an enhanced forest inventory using airborne laser scanning data and an area-based approach. Natural Resources Canada. ↩ ↩²

5. Doraisami, M., Kish, R., Paroshy, N. J., Domke, G. M., Thomas, S. C., & Martin, A. R. (2022). A global database of woody tissue carbon concentrations. Scientific Data, 9(1), 284. https://doi.org/10.1038/s41597-022-01396-1 ↩

6. Aalde, Harald. vol. 4, Institute for Global Environmental Strategies (IGES), 2006. IPCC Guidelines for National Greenhouse Gas Inventories: Agriculture, Forestry and Other Land Use. https://www.osti.gov/etdeweb/biblio/20880391 ↩ ↩² ↩³

7. McGroddy, M. E., Daufresne, T., & Hedin, L. O. (2004). Scaling of C: N: P stoichiometry in forests worldwide: Implications of terrestrial redfield‐type ratios. Ecology, 85(9), 2390-2401. https://doi.org/10.1890/03-0351 ↩

8. Kattge, J., Bönisch, G., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., ... & Cuntz, M. (2020). TRY plant trait database–enhanced coverage and open access. Global change biology, 26(1), 119-188. ↩

9. 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 ↩ ↩²

10. American Society for Photogrammetry and Remote Sensing (ASPRS). (2024). ASPRS Positional Accuracy Standards for Digital Geospatial Data (Edition 2, Version 2). ASPRS. https://doi.org/10.14358/ASPRS.PAS.2024 ↩

11. Queinnec, M., Coops, N. C., White, J. C., McCartney, G., & Sinclair, I. (2022). Developing a forest inventory approach using airborne single photon lidar data: from ground plot selection to forest attribute prediction. Forestry: An International Journal of Forest Research, 95(3), 347-362. https://doi.org/10.1093/forestry/cpab051 ↩

12. Ayrey, E., Hayes, D. J., Kilbride, J. B., Fraver, S., Kershaw, J. A., Cook, B. D., & Weiskittel, A. R. (2021). Synthesizing disparate LiDAR and satellite datasets through deep learning to generate wall-to-wall regional inventories for the complex, mixed-species forests of the eastern United States. Remote Sensing, 13(24), 5113. https://doi.org/10.3390/rs13245113 ↩

13. Meyer, H., & Pebesma, E. (2021). Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods in Ecology and Evolution, 12(9), 1620-1633. https://doi.org/10.1111/2041-210X.13650 ↩

14. Johnson, L. K., Mahoney, M. J., Bevilacqua, E., Stehman, S. V., Domke, G. M., & Beier, C. M. (2022). Fine-resolution landscape-scale biomass mapping using a spatiotemporal patchwork of LiDAR coverages. International Journal of Applied Earth Observation and Geoinformation, 114, 103059. https://doi.org/10.1016/j.jag.2022.103059 ↩

15. Goodbody, T. R., Coops, N. C., Queinnec, M., White, J. C., Tompalski, P., Hudak, A. T., Auty, D., Valbuena, R., LeBoeuf, A., Sinclair, I., McCartney, G., Prieur, J., & Woods, M. E. (2023). sgsR: a structurally guided sampling toolbox for LiDAR-based forest inventories. Forestry, 96(4), 411-424. https://doi.org/10.1093/forestry/cpac055 ↩