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“Over the years we’ve invested significantly in our field data team - focusing on producing trusted ratings. While this ensures the accuracy of our Ratings, it doesn’t allow the scale across the thousands of projects that buyers are considering.”
For more information on carbon credit procurement trends, read our "Key Takeaways for 2025" article. We share five, data-backed tips to improve your procurement strategy.
One more thing: Connect to Supply customers also get access to the rest of Sylvera's tools. That means you can easily see project ratings and evaluate an individual project's strengths, procure quality carbon credits, and even monitor project activity (particularly if you’ve invested at the pre-issuance stage.)
Book a free demo of Sylvera to see our platform's procurement and reporting features in action.
Many companies have embraced climate change initiatives. But do said initiatives actually remove greenhouse gases (GHGs) from the atmosphere and fight global warming?
This question has driven the need for high-integrity carbon credits.
The thing is, you can't guarantee high-integrity carbon credits without accurate forest biomass estimation models. After all, biomass estimations help predict how much carbon a nature-based project can sequester, which determines the number of carbon credits it generates.
Unfortunately, conventional biomass estimation methods, such as allometry and GEDI, fall short. That's why Sylvera developed an innovative approach that uses multi-scale lidar data with deep ground-truthing. This leads to more accurate estimations in terms of aboveground biomass.
What does all this mean to you, the corporate buyer or investor? Accurate biomass estimations produce higher quality carbon credits, which have important market and financial implications.
Why forest aboveground biomass estimation accuracy matters in carbon markets
The term "biomass" refers to the standing dry mass, AKA dead matter, of woody plants.
It's usually expressed as a mass per unit area (e.g. Megagrams per hectare: Mg ha-1) and is used to determine the amount of carbon released and sequestered by forest ecosystems.
Put simply, we can use biomass estimations to determine the physical storage of carbon in wood. We can then use this figure to issue carbon credits for nature-based projects.
Of course, if we can't estimate forest aboveground biomass with reliable accuracy, we can't issue carbon credits for nature-based projects with reliable accuracy either. This could lead to multiple problems, such as over or under-crediting, a poor reputation in the marketplace for companies that purchase low-quality credits, and of course, likely less positive climate impact.
The problem with allometric and satellite-only models
There are multiple ways to estimate forest biomass. Some of the more popular methods use allometric and/or satellite-only models. But these methods are inherently flawed.
For one thing, allometric and satellite-only models rely on tree diameter, height, and species data from a maximum of 4,000 trees. Most models are built on far less. Why is this a problem? Because it doesn't provide an accurate estimation of trees, which leads to poor quality credits.
Just as problematic, these models apply biased assumptions across continents. For example, 35% of GEDI training data comes from the US and Europe, but only 12% comes from Africa and only 8% from Southeast Asia and Australia. These model accuracy issues make estimation of aboveground biomass more difficult.
Furthermore, satellite lidar, like what the entire GEDI program is based on, has very low data point density. For comparison, Sylvera's modeling strategy, which uses terrestrial, drone, and helicopter technology, collects 30-15,000x as many lidar data points per m2 as GEDI.
Sylvera’s solution: multi-scale lidar and real-world ground truthing
Let's dig deeper into the Sylvera solution.
Our team has used terrestrial lidar to measure over 25,000 trees across 220,000+ hectares in specific locations—not random forests. This resulted in 450B+ highly valuable data points that we use to evaluate tropical and subtropical forest and woodland ecosystems.
Since legacy models only measure a maximum of 4,000 trees across 960 hectares, they don't have access to the depth of data that we do. For example, the Sylvera database contains 150,000 datapoints per m2, while allometry databases only have three (height, diameter, and weight.)
But it doesn't stop there. The technology we use to scan trees, like airborne lidar via drones and helicopters, is six times more accurate than the technology used by allometric and satellite-only models.
What’s more, Sylvera has collected data from 80% of all NBS geographies across Africa, Latin America, Southeast Asia, and Australia. This gives us an incredible amount of data to work with, leading to more accurate forest structure and aboveground biomass estimations.
Real-world validation of forest AGB estimation
So, how does Sylvera's optical remotely sensed data translate to real-world scenarios?
When cross-validating data in Peru, Gabon, and Mozambique, our team found that Sylvera's regional remote sensing data model was able to estimate biomass within 1.3% (Peru) and 3% (Gabon) and 2% (Mozambique) of the ground truth value. For these same areas, traditional model estimates were inaccurate by 16% (Peru), 39% (Gabon), and 17% (Mozambique).
Sylvera's AGB estimation model picked up 1.5-2.2x more carbon in the African grasslands of Mozambique than widely used models would suggest - which you can read more about in this Miombo Woodlands study.
Poor biomass estimation models costs the market
The truth is, under-informed model parameters lead to poor tropical forest biomass estimations.
When this happens, carbon credits are issued and priced incorrectly, which damages buyer trust. Even if the project developers who issued the miscredited projects had good intentions.
Over time, poor biomass estimations undermine the entire nature-based project category, which includes REDD+, ARR, IFM , and many others, making them less enticing to potential investors. This shrinks the pool of viable investments and reduces climate mitigation opportunities.
As you can see, model performance is essential. Sadly, most models aren't up to the task.
How to invest with confidence: what better data unlocks
Sylvera estimates aboveground biomass with greater accuracy than other models.
Because of this, Sylvera customers can make more confident procurement decisions. After all, they have access to reliable data, which they can use to secure quality carbon credits on a consistent basis.
This benefits corporate buyers and investors in multiple ways. One, they get quality credits they can use to offset their own emissions or sell at a premium. Two, they maintain a good reputation in the marketplace because the credits they buy come from quality projects that slow the global carbon cycle. And three, they have peace of mind, knowing their actions make financial sense and improve the environment.
Sylvera customers also benefit from post-issuance monitoring tools, so they can keep track of their investments, intervene when necessary, and maintain trust over time.
Request a Sylvera demo today to learn more about our industry-leading remote sensing methods.
In Conclusion
Not all forest aboveground biomass estimations are created equal.
This is important because data quality leads to credit quality. When you consistently buy or invest in quality carbon credits, you avoid fines and earn the public's trust.
Sylvera's lidar remote sensing system eliminates bias, builds confidence, and drives real climate outcomes. This is why it's quickly becoming the most trusted forest biomass estimation approach.
Book a demo of Sylvera now to see how our platform improves biomass accuracy and credit quality.
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