Blog
5 minute read

ARR (Afforestation, Reforestation, and Revegetation) projects are on the rise as a powerful way to restore ecosystems and remove carbon. To ensure these projects are credible and high-impact, several methodologies have adopted dynamic baselines to compare actual gains to what would have happened without intervention on an ongoing basis.
Verra’s VM0047 is one such methodology. In a recently released methodology update, Verra provides further clarity on how developers make best use of remote-sensing technologies for their ARR projects.
In this post, we take a closer look at how Chloris supports the area-based approach of VM0047 to enable high-quality and high-impact ARR projects.
Why Stocking Index Choice is Crucial
A key metric of the area-based approach is the Performance Benchmark (PB) — an estimate of how much more (or less) carbon your project area accumulates compared to similar, non-intervened control areas. To calculate it, you need a Stocking Index, which is a remote sensing metric that serves as a proxy for vegetation growth.
And this is where the stakes get high. Choose a volatile or poorly correlated index, and you risk getting mismatched controls and ultimately fewer credits. While vegetation indices like NDVI, NDFI, and EVI2 are easy-to-access options, they often fail to capture actual biomass changes and are highly sensitive to seasonal noise, as seen in our recent webinar.
Chloris takes a different approach, one rooted in direct above-ground biomass (AGB) estimation using machine learning models trained on LiDAR data. This offers a more accurate, reliable, and verifiable estimate of biomass gains, reducing uncertainty and aligning seamlessly with VM0047 requirements.
Field-Tested and Proven in ARR Projects
Validation is a core principle at Chloris. We rigorously validate our biomass estimates against fully independent, high-quality reference data. And when it comes to VM0047, we took the same approach. We tested our data on real-world ARR projects to compare its performance against commonly used stocking indices.
Reforestation in India (2016-2023)
Compared to NDFI, Chloris data captures 40% more of the project’s additionality for the 2022-2023 monitoring period.
Restoration in Brazil (2014–2024)
In a degraded pasture restoration project, Chloris’ PB values remained consistently between 0.3 and 0.6 throughout the monitoring period. In contrast, NDVI and EVI2 fluctuated starting at a PB of 1, dropping to 0, then rising and falling again due to seasonality and saturation.
A Streamlined Approach to VM0047 Implementation
Beyond data quality, ease of implementation is key to scaling ARR project development efficiently—both in terms of time and cost.
Here’s a quick overview of how Chloris partners with developers to streamline their work and maximize project outcomes:
With Chloris, you don’t just get reliable biomass data—you get a partner who understands the practical realities of ARR project development. Our dedicated support simplifies compliance with VM0047, reduces uncertainty, and helps you unlock the full carbon value of your project.
Ready to take the next step? Reach out to our team at info@chloris.earth to explore how Chloris AGB can support your ARR project with confidence.

The Katingan Mentaya Project protects 157,875 hectares of Indonesian peat-swamp forest, where roughly nine-tenths of the carbon sits below ground. Field plots alone can't map that. Here's how Permian Global built a system that can.

Forest carbon projects are built on a promise of permanence. But the threats to permanence can materialize in days, and the tools most project teams rely on to detect them were never designed to answer the question that matters most: how much carbon is actually at risk?

