LiDAR 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 above-ground 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.

Above-ground 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 outlines requirements for quantification of AGB over a project area using Airborne Laser Scanning (ALS) measurements, also known as an area-based approach. 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.

An area-based approach for estimating $M_{AGB}$ involves 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 transformation of point-clouds to structure data.
3. Developing predictive models which 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 above-ground 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 the mass of above-ground 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. This Module is applicable to Projects which meet the following requirements:

• 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.

For the purposes of this Module, any allometric equations employed by the Project must have widespread acceptance in scientific literature or rigorous evidence supporting its applicability. Newly developed allometric equations must undergo validation against peer-reviewed standards and reference datasets.

This Module allows for two different area-based approaches for calculating $M_{AGB}$ (tonnes) 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, $M_{AGB}$ for 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 $i$.
• $a_{pixel}$ 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.
• $AGB_{pixel,i}(t)$ is the AGB density (tonnes/hectare), in pixel $i$.
• $a_{pixel}$ 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.

ALS data collection must occur throughout the Crediting Period, at the end of each Reporting Period for all Projects crediting under the Reforestation Protocol; all Projects crediting under Improved Forest Management, Agroforestry, or Mangrove Restoration must follow the respective collection requirements. Wall-to-wall laser scanning measurements are highly recommended. Laser scanning of representative subplots throughout the project area is also permissible with rigorous statistical analysis to constrain the uncertainty associated with subplot selection and upscaling.

The ALS data acquisition must yield sufficient ground sampling to obtain a digital elevation model of 1 m resolution or greater over the observed area. Minimum requirements for data acquisition parameter values are detailed below and are based on guidance from White (2013)² and Duncanson et al. (2021)⁵. In some settings (e.g., areas with dense canopies), these parameter values may need to be more stringent than these minimum requirements in order to meet the digital elevation model resolution requirement. Minimum parameter requirements include:

• Scan angle must be ≤ 12 degrees. Dense canopies should be observed using a smaller scan angle to ensure some pulses can reach the ground.
• Pulse density must be ≥ 4 points per squared meter. Denser canopies may need a greater greater pulse density to reach sufficient ground sampling. Project Proponents are encouraged to consult prior studies done in comparable ecosystems for best practices.
• For wall-to-wall sampling, along-track and cross-track overlap must be greater than 50% and ideally up to 80% and 60%, respectively.
• For subplot sampling, a minimum of 25% coverage of the project area (after removing areas with scan-angles >12%) is required, and a simple systematic sampling design (i.e., flight lines) should be sufficient in most cases. In some instances flight lines may need to be drawn in such a way as to ensure the intersection with all strata. Overlap is not expected here.

The recommended quality control process will differ between instruments. Techniques must be specific to high-resolution LiDAR data and be sufficiently documented to permit replication, including for quality control, filtering, and statistical analysis.

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)⁵. A typical LiDAR processing workflow consists of the following steps, the specifics of which must be documented:

1. Ground point classification
2. Derivation of a digital terrain model
3. Height normalization (i.e., flatten the point cloud)
4. Noise removal (i.e., data errors, power lines, etc.)
5. Derivation of forest structure metrics
   • Typical metrics include:
     • The percent of points above a given threshold
     • The percentage of points in different brackets
     • Kernel Density Estimation of point cloud heights
     • Statistical indices that characterize vertical complexity

Software tools including lidR, FUSION, and PyLidar implement a large number of these procedures using transparent approaches.

Returns below 2 meters above ground must be excluded from analysis as these typically represent understory vegetation and can weaken the relationship between ALS-derived metrics and biomass densities. Project Proponents may set a lower threshold if they are able to provide field survey data demonstrating that the altered threshold still excludes at least 90% of understory vegetation or evidence from a peer-reviewed study in the same type of ecosystem using the altered threshold.

Digital Elevation Model (DEM) data must adhere to fundamental, supplemental, and consolidated vertical accuracy requirements⁶.

This section describes the requirements for developing ALS-based predictive models to link ALS structure data to AGB. Previously developed models will be considered acceptable for use when documentation is provided on the development of the model that demonstrates these procedures were followed.

Overlapping ground plots with ALS plots are required to develop ALS-based predictive models. These plots by default should be within the project area, but may be outside the project area conditional on consultation with and approval by Isometric. Specific requirements for ground plot measurements include:

• Plot Selection:

  • Ground plots must include representation of the full range of forest characteristics and structural variability in the project area.
  • Plot selection should be based on stratification into stands using meaningful forest characteristics, such as forest age, species, site index, slope, etc. Plots should then be randomly placed within those stands.
  • It is recommended to use Structurally Guided Sampling to ensure that plots representative of the study area are included for training. In Structurally Guided Sampling, ALS metrics (e.g., height) are used as the source of data for stratification. See Goodbody et al., 2023⁷ for more details on Structurally Guided Sampling.
  • Representativeness of ground plots must be reported, such as through Principle Component Analysis graphs.
  • The placement of sample plots should account for spatial auto-correlation, e.g., by setting a minimum distance between plots of 100 m, or based on the decorrelation length.

• Plot Size:

  • Plot radii of ≥8m is recommended. Smaller plot sizes must be justified considering in-plot variability, georeferencing accuracy and spatial heterogeneity of the project area.
  • Plot size must be on the order of the intended grid cell used for mapping, i.e. validation plot size should be equivalent to output pixel size.

• Plot Measurements:

  • Ground plot position measurements must use mapping-grade receivers with a position error ≤5 m⁸.
  • At minimum, the following parameters must be collected: Species, status, height, DBH, stem number according to standard practices (See Module on Area-based Quantification of Above-ground Biomass).
  • Derived values must be reported (e.g., basal area, volume, AGB).
  • Extra care must be taken when conducting ground plot measurements in steep and/or complex terrain, thick canopies or areas with a dense understory.
  • Plot measurements must be temporally aligned, to the greatest possible extent, with ALS data collection.

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 approach⁹.
• Deep learning, e.g., Neural Networks or Convolutional Neural Networks¹⁰. 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.

Regardless of the particular type of model selected from the above options, models used to estimate AGB must:

• Be based on peer-reviewed methodologies, with evidence provided of prior usage in peer-reviewed studies or in applications in industry or government;
• Be applicable to the project area, including species and ecoregion. See Meyer and Pebesma (2021)¹¹ and Johnson et al. (2022)¹² for guidance on how to assess this; and
• Have a detailed workflow that justifies:
  • Model selection (e.g., rationale for modeling approach)
  • Feature selection (e.g., input features must be selected based on scientific rationale, or methods such as principal component analysis)
  • Model architecture (e.g., for deep learning, the number of hidden layers must be described)
  • Dataset collation (e.g., where data originated, how was data resampled or split for training/validation/testing)
  • Validity of assumptions (e.g., demonstrate adherence to fundamental assumptions in modeling approach)

Any models used under this Module must be well-validated and skillful for the purpose that they are used for. Proof of model validation can be achieved through either:

• A track record of use in science, industry, or government applications, which is demonstrated through multiple peer-reviewed papers, or proof of usage in a number of previous applications. Furthermore, the model must be relevant to the project area and tree species (e.g., covers similar ecoregions); or
• Newly developed models without a track record of usage must be validated against reputable data sources, which include quality-controlled in situ measurements and public datasets adhering to FAIR (Findable, Accessible, Interoperable and Reusable) principles.

All models must undergo a skill assessment based on a test dataset not used for model development or training. The test data must include data that is representative of the project area.

Model skill assessment must report r², RMSE, bias, and a 1:1 plot of model performance. The model skill assessment must be used to inform a conservative uncertainty discount.

• Acceptable models must achieve RSE and RME within (or less than) the interquartile ranges reported in Zolkos et al. (2013)¹³ for similar forest types.
• Distributions of the input parameter range for which the model was developed and validated must be reported, as the skill assessment is only applicable within this range.

Once the model has been developed, it can be applied to ALS data to generate an AGB map. The following requirements ensure appropriate model use:

• There must be a check in each verification that the distribution of training data encompasses the feature ranges observed during the Reporting Period. Models cannot be applied outside the range of conditions contained in model development.
  • Non-forest areas or areas beyond the range of training data (e.g., different age, different forest type) must be masked out.
• If the model is developed using leaf-on data, it must not be applied to leaf-off data, and vice versa.

Non-parametric models commonly exhibit a phenomena called “regression-to-the-mean”. Machine learning models work by optimizing a loss function with the goal of obtaining the lowest possible error rate. Often, the lowest possible error will be obtained by over-estimating areas with low AGB densities and under-estimating areas with large AGB densities. This can be observed in the one-to-one regression plots of many papers (e.g., see Figure 3, Panel 1 in Pflugmacher et al., 2014)¹⁴.

When a model exhibits regression to the mean, it is not appropriate to simply sum the pixel values within a given area as the systematic error of the model has not been accounted for. This is a considerable problem for regrowth modeling. Non-parametric models that use satellite imagery will almost always dramatically over-estimate low-biomass densities. Similarly, there is good evidence that the “green-up” of stands to pre-disturbance levels in moderate resolution vegetation indices will predate the actual structural regeneration of the stand by many years¹⁵.

Model-assisted estimation is a framework that allows ancillary information to be incorporated into the estimation procedure. This can allow for more precise estimates of AGB density. The typical setup for a model-assisted estimation procedure is as follows:

1. A design-based sample of the landscape has been acquired.
2. A model (parametric or non-parametric) is then fit using ancillary datasets (e.g., multispectral features, LiDAR features, land cover information, etc.) and spatially applied to all pixels within the sampling domain.
3. A model assisted estimator is then used that integrates the mapped predictions and the plots to produce a final estimate.
   • It is worth noting that a model-assisted estimate of AGB will not be worse than a non-model assisted estimator. If the model that links the ancillary variables with the plot-level data is not performant, the model-assisted estimator will produce the same result as a non-model assisted estimator.

Details for model assisted estimation, and an associated R package, can be found in McConville et al. (2020)¹⁶.

There are many geostatistical considerations that come into play when developing statistical models. Spatial autocorrelation can violate the assumption of independence. This is why clustered plot designs are less efficient than other sampling schemes; the clustered plots are effectively pseudo-replicates. Under a design-based model-assisted framework, this is not a concern. However, it is a concern if the pixels in a map are being summed as all pixels within the same forest patch are pseudo replicates. Therefore, the sampling size is falsely inflated, given an optimistic estimate of the variance of the estimated mean.

The ALS data 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 external map must adhere to the following:

• Plot measurements must occur within two years of the ALS campaign and should be temporally aligned to the greatest possible extent with the ALS data collection.
• The number of plots should be sufficient to establish statistical equivalence with the ALS data using an equivalence test at 90% confidence with +/-10% allowable error.
  • If the equivalence test is not passed, then the ALS data cannot be used directly for quantifying $M_{AGB}$ directly. In this case, the AGB 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 reasonable accuracy (such as an R$^2$ ≥ 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. Alternative approaches must be approved by Isometric and VVB.

Models and measurements of above-ground 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 Section 2.5.7 of the Isometric Standard. 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)

Project Proponents must report the following information in the PDD, to ensure transparency and with enough detail to allow for repeatability:

• Data acquisition:
  • ALS plots, including justification for the stratification method used and how spatial autocorrelation is accounted for, when the subplot method is selected
  • Instruments and calibrations used, which must adhere to manufacturer's specifications
  • Spatial coverage of laser scanning
  • Flight altitude and velocity of drone/plane
  • Acquisition parameters, e.g., beam divergence, scan angle, scan rate, pulse density, swath overlap, environmental conditions during acquisition, dates, altitude
• Data processing:
  • Processing methods and documentation, including software and specific procedures followed
• Ground plots used for model development:
  • Justification of plot selection
  • Raw data and derived values
  • Representativeness of plots
• Model development and performance:
  • Modeling approach and justification
  • Relevant model skill assessment, such as r$^2$, RMSE (absolute and as a percent of mean), bias, and/or a 1:1 plot of model performance at the resolution of the output AGB map.
    • Project Proponents must produce the relevant model skill assessment based on the common practice of the literature in consultation with Isometric
  • Distributions of the input parameter range for which the model was built must be reported
  • Data and code used to train the model, including metrics extracted from ALS measurements
• AGB map generation procedure
• Quantification of $M_{AGB}$ from generated AGB map
• 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., model uncertainty estimates):
  • Quantify uncertainty for key identified sources through the approved approaches outlined in Section 2.5.7 of 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.

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 Renoster, for their extensive feedback during this Module's development.

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