How Accurate Is Forest Carbon Measurement in 2025?

Forest carbon credits are a major pillar of the voluntary carbon market—even though forest-based carbon sequestration projects have been subject to intense criticism in recent years.

Most people agree: forest management projects aren't the problem. Said forest management projects simply need to be measured in reliable ways to ensure climate change mitigation. Unfortunately, most measurement models are based on outdated and/or underpowered methods.

In this article, we look at why measurement accuracy matters, how measurement impacts carbon accounting, and how Sylvera is transforming measurement processes in 2025 and beyond.

Why forest carbon measurement matters more than ever

Governments are increasingly focused on carbon credit quality, as evidenced by the EU's Carbon Removals & Carbon Farming (EU CRCF) regulation and Article 6 of the Paris Agreement.

At the same time, studies show that current protocols governing forest carbon credits are flawed. As such, projects that follow these protocols do not sequester as much carbon dioxide as previously assumed. This has led to scrutiny of forest carbon credits.

What's the solution? That's simple: accurate forest carbon stock measurement. 

When investors have accurate information regarding tree carbon pools, they can invest in high-quality credits that mitigate climate change and slow global warming. This is good for the planet and for business, as investors can avoid public backlash that results from low-integrity projects.

At the end of the day, inaccurate forest carbon data leads to over crediting, missed co-benefits, and reputational risk. This is why spot-on forest carbon measurement is vital.

Traditional forest carbon methods: Are they good enough?

Traditional forest carbon methods rely on allometry and satellite-based models. The question is, are these methods accurate enough to ensure quality forest carbon projects? Let's see...

What are allometric models and why are they still used?

Allometry is the study of body size as it relates to the other biological attributes of living organisms.

Allometric models are commonly used to estimate tree biomass and carbon stocks by applying complex mathematical functions to tree measurables such as diameter, height, and weight. Said models can be developed for individual tree species or entire forests in different regions.

Unfortunately, allometric models are typically based on data from just 100 to 4,000 trees. Worse, many of these trees are disproportionately small and native to select biomes, which makes it difficult to estimate the biomass of larger trees in various tropical forests around the world.

Still, allometric modeling persists because it's so engrained in the industry. At Sylvera, we're working to change this by using advanced technology to collect up to 180x more ground-truth data.

How accurate are satellite-based models?

Satellite-based models, such as the Global Ecosystem Dynamics Investigation (GEDI), use airborne lidar technology to measure deforestation's impact on climate change.

Put simply, NASA installed a full-wave lidar system on the International Space Station, which fires lasers at the earth to illuminate its surface and measure forest canopy height, vertical structure, and elevation.

GEDI is a great project that produced a necessary public resource. But it isn't known for perfect accuracy. In 2022 the GEDI team stated, "While these allometric models are known to have high uncertainties, the set of allometric models adopted for [GEDI] was the most generalized available."

The main limitations with GEDI have to do with low resolution, coverage biased towards the United States and Europe, and a lack of ground-truth calibration.

Sylvera’s approach: From ground-truth to global accuracy

Sylvera is on a mission to improve forest carbon measurement and ensure investors procure quality credits that mitigate climate change. Here's how we're making it happen:

Terrestrial lidar and 3D forest modeling

First, our team of scientists use terrestrial lidar and 3D forest modeling technology. To date, we've scanned 25,000 individual trees across 220,000 hectares around the globe.

Our use of advanced technology allows us to train our models on real biomass, not generic tree shapes. And our commitment to mapping large swaths of land gives us access to more data.

All in all, the Sylvera database contains 450B+ data points, which makes it six times more accurate than other models. If you want spot-on information, you want Sylvera.

Regional ground-truth across 80% of tropical NBS geographies

Sylvera's database isn't just large, it's diverse.

For example, over 35% of the data used in other biomass estimates comes from the United States and Europe, while only 12% comes from Africa and 8% comes from Southeast Asia and Australia.

In contrast, Sylvera has collected regional ground-truth data from 80% of tropical NBS geographies. Broken down, that's 30% from Africa, 32% from Latin America, and 38% from Southeast Asia and Australia. This makes measuring carbon in forests with Sylvera more reliable.

Sylvera in action: Achieving results in Mozambique

In 2022, we partnered with The World Bank and the government of Mozambique to produce a 50,000 hectare map of the above-ground carbon stocks across Miombo woodlands.

Thanks to our use of modern technologies like multi-scale lidar and machine learning, plus a measurement system that's up to 13x more accurate than conventional methodologies, we realized that previous projects had significantly underestimated forest carbon stocks in the area.

Because of our research, the carbon industry will be able to calibrate a state-of-the-art monitoring, reporting, and verification (MRV) system that produces more accurate data in the future.

What defines high-quality forest carbon measurement?

So, how do you measure forest carbon? At Sylvera, we consider above-ground biomass, below ground biomass, and soil carbon. We also monitor forest degradation on a regular basis.

Above-ground biomass (AGB)

The term above-ground biomass, often abbreviated to ABG, refers to living vegetation above the soil. This includes a tree's stem, stump, branches, bark, seeds, and foliage.

AGB estimates are made to predict tree diameters, heights, and canopy volumes, which are then used to predict a forest's carbon storage capabilities and issue the correct amount of carbon credits.

If AGB estimates are off, which can happen when poor quality data is used, project developers issue credits incorrectly, which hurts buyers and erodes public trust in the carbon credits system.

Below-ground biomass and soil carbon

The term below-ground biomass refers to living vegetation beneath the soil, such as roots. The term soil carbon, on the other hand, refers to solid carbon stored in soil around the world.

Both below-ground biomass and soil carbon are often excluded from carbon sequestration estimates. In the rare case they're used, they're poorly modeled.

Because of this, traditional forest carbon measurement techniques make it impossible to know how much carbon dioxide proposed and/or existing forest ecosystems store. Thankfully, Sylvera is changing this by developing the industry's first scalable soil carbon modeling approach. 

Time series and forest degradation monitoring

Finally, high-quality forest carbon measurement requires consistent forest degradation monitoring, i.e. tracking natural forests to ensure they continue storing carbon at a similar rate.

Notice, we used the word "consistent". Static snapshots of forest degradation don't give us the full picture. Regular updates are needed to understand how droughts, fires, and encroachment compromise forest biomass, lead to dead trees, and prevent proper carbon capture.

How does measurement accuracy affect carbon markets?

Trees remove carbon dioxide from the atmosphere via photosynthesis.

Of course, the greenhouse gas doesn't just disappear. Trees store carbon in their trunks, branches, leaves, and roots. In fact, roughly 50% of a tree's dry organic material is carbon.

Because of this, organizations develop specific projects—planting trees, restoring previously planted forests, etc.—to sequester carbon dioxide and slow global warming. Doing so produces carbon credits, each of which represents one tonne of CO2 that's been removed from the atmosphere.

As you can see, measurement accuracy has a huge effect on carbon markets. Estimate the impact of a carbon forest project incorrectly and you could under or over issue carbon credits.

Let's dig a bit deeper into this issue:

Buyer confidence and pricing

Poor measurement accuracy destroys buyer confidence. When this happens, buyers allocate less budget to the voluntary carbon market and important climate projects go unfunded.

Also, less demand for carbon credits lowers prices, disincentivizing project developers. Said developers ask, "Why should I work hard to bring a project to life when I won't benefit financially?"

On the flip side, accurate measurements lead to better project ratings, which help developers command higher price premiums and stimulate support for climate-based initiatives.

Project design and developer outcomes

Poor measurement begets poor modeling. The result? More non-permanence buffers, which shrinks the pool of reserves and harms other carbon projects in the future.

Conversely, good measurement begets good modeling. This leads to fewer non-permanence buffers, greater confidence in the MRV process, and more access to financial backing for carbon projects. In other words, good measurement supports the entire carbon credits industry.

How Sylvera is shaping the future of forest carbon data

As mentioned, Sylvera partnered with The World Bank and the government of Mozambique to map the Miombo Woodlands. This was phase one in our Global Carbon Mapping Project.

The Global Carbon Mapping Project is Sylvera's initiative to create accurate, large-scale carbon data for forests around the world, using the most advanced multi-scale lidar technology.

To complete this project, we've developed research partnerships with numerous governments and academic bodies. Together, we're committed to producing transparent, scalable, and regionally representative data that can be used by all to improve the environment and slow global warming.

But human effort isn't enough. We've also invested in artificial intelligence (AI) and machine learning (ML) tools to help us process and analyze the vast amount of data we collect. The tools help us identify patterns, train models, refine biomass estimates, and ultimately, improve the accuracy of carbon data over time. Thanks to Sylvera's investment in AI and ML tech, our solution is ultra precise and scalable.

Use accurate data to ensure climate change mitigation

Accurate forest carbon measurement is foundational to credible, investible climate action.

Without proper data, it's impossible to issue the correct number of carbon credits. And poor credit issuance leads to distrust, less investment, and fewer projects throughout the world.

Sylvera is committed to accurate forest carbon measurement. Thanks to our science-first, regionally grounded, and transparently measured carbon data—not to mention our advanced technology—we're changing the way the industry thinks about, collects, and uses forest carbon data.

Want to see how Sylvera can unlock better outcomes for your team? Request a free demo now.