Carrying out a VM0047 assessment

Modified on Tue, 25 Feb at 12:33 PM

Use Earth Blox to carry out a VM0047 assessment, to quantify carbon removal in afforestation, reforestation, and revegetation (ARR) projects.

The VM0047 methodology, developed by Verra, ensures that an ARR project is ‘additional’, by showing that compared to similar control areas, a greater amount of carbon was removed than would have been without the project.

Use this guide alongside the Carry out a VM0047 assessment package in Earth Blox.

Glossary

Control plots
Uniformly sized areas from within the bounds of the donor pool

Project plots

Uniformly sized areas within the bounds of the project area

Project plots and control plots must be the same size

Donor pool

The area that control plots are selected from. This area has similar characteristics to the project area, and can therefore be used as a comparison.

Stocking index

The dataset, with proven correlation to biomass, used to compare project and control plots.

Step 1: Delineate donor pool

Template workflow: VM0047 Step 1 Delineate donor pool

To calculate the dynamic baseline for a VM0047 project, control plots need to be generated from a suitable donor pool. This template workflow delineates the area of this donor pool by:

Extending the range to 100km of the project site
Excluding areas that are not in the same Ecoregion (Using Resolve Ecoregions 2017)
Excluding existing AFOLU projects (using the Nature-based carbon offset project boundaries dataset from Zenodo)
Excluding areas that are not in the same jurisdictional boundary (Using Overture Maps Foundation data).

Load the Project area to assets

Start by uploading the ARR project area to your Assets, as you may want to use it again later. See Uploading a feature collection to Assets.

Set up your workflow

Once your area is loaded to Assets, add your project area by selecting Add area > Upload new feature.

Select Choose from Assets.

Remove the sample project area by selecting Delete from the area menu

Run the workflow

Look for the Export dataset block, and choose a suitable name for the dataset (the default is “Donor Pool Area”). Adding a version number allows you to keep track if you have to iterate later.
If you want to make any changes to the default options, see “Customise the workflow”, below.
Click Run at the bottom of the workflow
This workflow will run in batch mode (in the background) because it’s processing such large geometries. You can carry on with other work while this happens.
A pop-up in the bottom right corner will show you the progress of your export. Once complete, download the output from your exports.

Customise the workflow (optional)

The first set of blocks in the template use feature datasets to identify the qualifying area:

Within 100km of the project site
Within the same Ecoregion (Using Resolve Ecoregions 2017)
Outside of existing AFOLU projects (from "An open-access database of nature-based carbon offset project boundaries." (2024))
Within the same jurisdictional boundary (Using Overture Maps Foundation data).

This area is called “Initial Donor Pool”

Tip

If the project area is within a subnational jurisdiction that is:

registered under Jurisdictional and Nested REDD+ (JNR), or
delineated by the national or subnational government for reporting REDD+ (e.g., delineated as a discrete Forest Reference Emission Level)

then use the relevant subnational jurisdiction (set adminlevel = 4).

Otherwise, use the country boundary (adminlevel = 2).

A second set of blocks can then be used to further refine the area using image (raster) datasets.

Info

This step is about excluding areas that are likely to produce poor control plots. These datasets are optional, and not required by the VM0047 methodology. In the example template, we filter out water - however if we didn’t, then control plots in areas of water would likely have a stocking index value very different to project plots, and wouldn’t be selected in the plot matching anyway.

Step 2: Generate plots

Now that you have delineated a donor pool area, you can generate candidate control plots within that area as well as project plots from your project area.

Load the Donor Pool Area to Assets

Start by uploading the donor pool area to your Assets. See Uploading a feature collection to Assets.

Load in the Donor Pool Area and Project Area

The workflow uses two sample areas. Swap them out for your project area and donor pool area by selecting “View or change dataset” at the top of the two workflow blocks:

Tip

After selecting your project area from the assets menu, make sure to deselect “Add recommended blocks” in the bottom left corner of the menu before you change the dataset. This will ensure the blocks used in the workflow remain the same.

Customise the plot generation

The Generate sample plots block has the following options:

Option	How to choose
Align to…	Dataset If you know which stocking index you will be using, then you can generate plots using the same grid (coordinate system) - this ensures your plots match up exactly with the grid used in the stocking index. Grid With this option, the block uses a “UTM grid” appropriate for the location.
Plot size	Dataset If you’re aligning the plots to your stocking index dataset, specify the plots in terms of pixels of the stocking index. Grid If you’re using the “UTM grid” to generate plots, then specify the size in metres. Plots must be between 0.09 hectares (30 × 30 m) and 10 hectares for VM0047.
Number of plots	A minimum of 30 Project Plots are required when calculating the performance benchmark. It makes sense to include a contingency - e.g. 5 extra plots - in case a project plot becomes invalid at a monitoring event. We would recommend creating 10-20 times more candidate control plots than you need. A typical example would be: 35 Project Plots 5 Control Plots matched to each Project Plot (i.e. k=5) Therefore: 175 Control Plots required 3500 Candidate Control Plots recommended.
Minimum overlap	For Project Plots, at least 75% of each plot must be within the project area boundary. We recommend the same threshold for Control Plots.

Customise the stocking index

Once the plots have been generated, the stocking index at those locations is calculated using the Calculate zonal statistics block. In the template, an open source ESA biomass dataset is used. However we would recommend using a premium biomass dataset such as Chloris, this:

Will produce the most robust results, anywhere in the world
Ensures you don’t have to worry about spatial gaps in the data (e.g. due to cloud cover)
Ensures you don’t have to worry about temporal gaps in the data (e.g. missing years).

You can save a lot of time in this way. Speak to us if you’d like to explore this option.

Historic data required

VM0047 requires you to calculate the stocking index at three different time points:

At the start of the project (t = 0)
8–10 years before the project start date (i.e. not before t = −10 and no later than t = −8)
At least one other time point between those two.

To change the dates used, use the “Dates” dropdown in the Calculate zonal statistics block.

Important

Ensure that whatever changes you make to the “Project plots” zonal statistics you also make to “Control plots” zonal statistics. The Match sample plots block looks for common attributes, so the names given in “Save as attribute” must be identical between the project plots and control plots.

To use a different stocking index:

1. Add a new Use dataset block, and click “Search datasets” to choose the new stocking index dataset:

2. Make sure it has the right ‘Area of interest’ (e.g. Project Plots)

3. Drag across the Save data for re-use block from the ESA Above Ground Biomass workflow block to the bottom of the new stocking index workflow block.

4. Adjust the zonal statistics blocks (e.g. with new dates) if needed.

5. Delete the original stocking index workflow block

6. Repeat for the Control plots workflow (on the right hand side of the screen).

7. Check that the attribute names are identical for project plots and control plots.

Run the workflow

1. Click Run at the bottom of the workflow

2. This workflow will run in batch (in the background)

3. Once ready, download the output from your exports.

Step 3: Match project and control plots

Load the Project Plots and Candidate Control Plots to assets

Start by uploading the Project Plots and Candidate Control Plots to your assets. See Uploading a feature collection to Assets.

Customise the workflow

Rather than adding the Project Plots and Control Plots to the map, change them directly in the workflow by selecting “View or change dataset” at the top of the two workflow blocks.

Make sure “Add recommended blocks” is unchecked to avoid overwriting the existing blocks.

The Filter by attribute blocks ensure that only control plots with valid SI measurements are included in the matching.

The Match sample plots allows you to match one or more control plots to each project plot. You can customise how many (this is the ‘k’ in ‘k nearest neighbours).

The advantage of a higher k is if there’s noise in the data, then it can even it out. However, if using data like Chloris for your stocking index, there is low noise (unlike if you were using an optical index), so it is fine to have a relatively low k.

The workflow will output a geospatial file of both matched and unmatched control plots using the Export dataset block, and also show the results on the Dashboard using the Create table block.

Matched control plots will include:

The ID of the project plot to which they are matched (matched_uid)
The distance metric describing how close the match is between the control plot and the project plot to which it is matched
A weight metric describing how close the match is between the control plot and the project plot to which it is matched (the sum of the weights of all the control plots matched to a given project plot is 1).