Accurately Measuring Carbon Content in Forests

Treeconomy uses Encord to collect reliable, granular data on trees. This is used to more accurately measure the carbon content in forests. More reliable carbon credit data bolsters Treeconomy’s reputation, allows the company to charge 50-150% more for carbon credits, and enables them to redirect more funds to reforestation efforts. 

Customer

Treeconomy, an Earth Tech company, was created to better incentivize the planting of trees. It does so by accurately quantifying carbon captured and stored by trees (using sophisticated technology), packaging them as carbon offsets, and selling these high-quality carbon offsets to industries. Profits generated benefit landowners and incentivize them to preserve forests and plant trees. 

What sets Treeconomy apart from competitors is its use of world-class remote sensing, machine learning, and monitoring tools. This enables Treeconomy to more accurately measure carbon offsets and detect changes to a project location that could alter carbon levels. Doing so helps to assure investors that their nature-based carbon credits are real, that trees are really growing, and that the project is delivering on its impact claims. 

Problem

Accurately measuring the amount of carbon in a forest is extremely important for carbon offsetting. Errors can lead to greenwashing and a loss of trust in the carbon market. As Treeconomy Co-Founder Robert Godfrey notes “Right now there are a number of companies being called out and caught with bad credits on their books, where projects have maybe grossly overestimated their climate impact.” 

Yet measuring carbon in forests is difficult. Traditionally, this was done by meticulously going from tree to tree with a tape measure and making broad extrapolations based on these measurements. The process was time-consuming and error-prone. Monitoring changes in the forest - like growth or deforestation - was similarly laborious. 

To address these challenges, Treeconomy explored open-source data and satellite imagery but found they had limitations. For example, satellite imagery still required them to manually count trees, a time-consuming and inefficient process. They created a computer vision algorithm to detect tree crowns from high-resolution drone and satellite data imagery but determined that larger and more relevant data sets were required to produce more accurate results. 

Solution

Treeconomy turned to Encord as an intuitive and flexible data annotation tool capable of labeling hundreds of images at once. The tool has enabled Treeconomy to train its computer vision algorithm to accurately count the number of trees in a given location.

One of the big advantages of Encord for Treeconomy is its compatibility with Microsoft Azure and ease of use. This enabled the team to seamlessly integrate their existing data sets, thereby saving time and effort. 

Using Encord resulted in a positive discovery for Treeconomy: The tool could facilitate their future plans of labeling specific tree species - not just counting trees - and remove the need to hire staff for the task. 

Results

Encord has helped Treeconomy gather more accurate data and save time. As Godfrey explains, “Encord helps us to offer a ‘best in class’ capability for counting trees. It has helped us to improve the computer vision algorithm that allows us to delineate individual tree tops. [We were able] to count the entire forest in two minutes as opposed to two weeks.” 

By accurately quantifying trees in a forest, Treeconomy has been able to create more reliable carbon offsets. According to Godfrey, this has allowed the company to package and sell these offsets at premiums ranging from 50% to 150% above those of alternative options. 

This increased revenue is not only good for the company’s bottom line but is channeled back into the communities, benefiting landowners and supporting reforestation efforts. Ultimately, these actions reinforce Treeconomy's dedication to transforming sustainable land use into an economically rewarding and competitive venture.

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