The Coming Data Renaissance for Global Forests

An Interview with the Biome Team by Fred Bahnson

The Coming Data Renaissance for Global Forests

Remote sensing technology may help us measure forests accurately over large regions, but such a method lacks granularity. The map is not the territory. If you’re seeking a highly accurate forest inventory, you have to set foot in the forest itself and measure individual trees. That process is called ground truthing.

Ground truthing, however, as any forester will tell you, is both expensive and difficult. It takes a highly skilled team. That team has to travel to the project site, measure trees, catalog the data, then type that data into an Excel spreadsheet the old-fashioned way, by hand. It’s tedious, error prone, and antiquated. 

As a technology and science company catalyzing a global supply of nature-based carbon projects, Earthshot required a better method. We needed a way to collect data for our science team, perform MRV, and assist our operations teams. The tech didn’t exist. So we created it. It’s an app called Biome, and though it’s not ready for the public, it’s currently undergoing a series of tests in beta mode.

In September, the creators of Biome composed of Margaux Masson-Forsythe, Isak Diaz, Mary Huang, and Anthony Marefat, along with forest ecologist Joe Hughes, traveled to the Azuero peninsula of Panama for a week of field tests. Following their trip, Fred Bahnson, perhaps the least tech-savvy member of Earthshot, sat down with the Biome team to find answers to some fairly basic questions. As the responses below indicate, the ingenuity and technological acumen of this team is anything but basic. 

Panama became the site for the app’s first major stress test. Measuring multiple species on the Azuero Peninsula’s often-challenging terrain, Biome’s creators witnessed its potential to create a “data renaissance,” one in which land stewards will be empowered to measure the world’s great forests.


Fred: What problem does the Biome app solve?

Anthony: Biome is an app that collects tree data. The problem that it solves is that in any context involving a forest, scientists need the data to answer questions like: what grows here? What trees were lost? What new trees should be planted? There’s just so little data on the most important forests in the world. Biome observes and generates that data in a way that’s immediately useful.

Fred: What’s wrong with the current methodology?

Isak: You have to sub-sample a plot that represents a much larger area, then extrapolate based on the sample plot. Maybe you have a team of four or five people. One person with a tape measure. Another with a laser. Another with a clinometer to measure slope. Someone to collect the data.

When that team goes out to measure trees in the field, they’re concerned with three things: tree height, species classification, and diameter. But these environments can be remote, and traveling through dense forests or mountains is difficult. Then there are bugs. And the weather. The team has to put a marker in the ground, then log everything, and if they make a transcription error? All their data is lost. It’s a slow process.

FB: And that’s what Biome solves?

Isak: Yeah, Biome streamlines all that. You can take a single picture. You don’t have to measure a tree by hand anymore. Biome’s tree height measuring method is more accurate. One person with a smartphone captures all the data in digital format. 

Mary: We’re also using AR, or augmented reality, for tree height and plot boundary. It works a lot better than a range finder, or a laser, or using a measuring rope while walking on the side of the hill. On our trip to Panama we found that AR is surprisingly effective, especially on hills. 

Isak: Also, you have to consider that forest inventories are done by different people, from different nationalities, which means they may not be doing it the same way. Biome standardizes the process.  

Fred: So what happens to the data Biome collects? 

Margaux: The data collected in Biome can be exported as a CSV file for spreadsheets. It’s also linked to pictures of the tree. We have photos of the trunk. We are working on species classification to be able to verify the genus and species of the tree onsite.  Right now the most state of the art science still relies on scientific papers to get tree measurement data, and sometimes that data is incorrect. 

Mary: The CSV is one format. We can also put it on the web. We will be able to share the data in a variety of ways that will be valuable to scientists, as well as valuable to nature-based carbon projects. It increases a project’s transparency. 

Fred: Can a farmer or land steward anywhere use this?

Isak: Eventually we want to be Android-compatible, but for now it’s just for iPhones with Lidar (12 & 13). One thing I’ll mention: the rest of the carbon credit industry is moving toward satellite imagery. The problem with that is they still need to ground truth their data to get Verra certified. There’s still a need for field inventories to calibrate the models. Biome solves that. Even if everything goes to satellites, Biome could be the ground truth tool for all remote sensing technology.  

Margaux: For remote sensing, there are some images of biomass taken from around the world that we use for carbon projections. But a lot of those are imprecise, or incorrect. When you have ground measurements, you can refine your models and make them better. For example, some of the data from our Panama trip was already used to invalidate one of the remote sensing models, as well as to refining carbon projections for our Azuero project.

Mary: There are different workflows that Biome is used for. Biomass projections is one area of work. Another is MRV (Monitoring, Reporting, and Verification). The verification happens at a certain cadence to get carbon credits issued.

Fred: what is the business model for Biome?

Mary: Right now it’s to reduce the costs of projects like ARR and REDD+. It takes 5X less time to collect data. Which makes the project affordable enough to be financed. Another future use case is scoping for high quality carbon projects. Our hypothesis is that more accurate data can inform a stronger financial case.

Isak: Both investors and land stewards want the best possible return, and what they need is data. That’s the bottleneck. If we knew exactly how much data we can generate in every part of the planet, we’d be doing much better. Biome is a way to scale Earthshot’s operations. We are using tech to actually scale reforestation. If we’re going to save the planet, everything has to be scaled. The end goal is to make everything automatic. Right now, it’s all opaque. The whole point of the tech is to make it automated. 

Fred: How does Biome sync with LandOS?

Margaux: LandOS is a place to see all of Earthshot’s projects. Whether it’s ARR or REDD+, you can see carbon projections for a plot of land. You have a visual map. If it’s REDD+ you can see deforestation risk, for example. Biome can add ground measurement data. Field data. We can show that this person went and took measurements of 10-meter plots, and here’s the data to prove it.   

Isak: One day, we want Biome to send everything to the cloud and LandOS will have access to that data. 

Mary: LandOS answers the question: is carbon credit financing going to work out for my forest project? Not every conservation project works out for carbon credits. LandOS is working to automate some of the financial calculations and projections. 

Isak: It’s like car insurance. If you have a plot of land, you’ll be able to plug in your coordinates and get a quote on how much carbon your forest will produce.

Fred: How did the app work in Panama? Any troubleshooting?

Anthony: There were a few bugs, but overall it worked better than we imagined. The big take away was validation, both internally and externally. We’re onto something here. It’s already working far better than classical tools. We proved the demand. It’s already a thing.

Fred: What was the response from people you met in Panama?

Isak: Wherever we go, the response is always the same: excitement. The people who take forest inventories for a living, like scientists, were thrilled. Everywhere you go the scientists don’t have enough funding or enough tools, so if you’re making a tool for them, they get really excited.  

In Peru we talked to a scientist who does species classification. When you’re doing that in the Amazon, it’s really difficult to classify species. So this scientist was really excited to use Biome to measure trees more effectively. Biome saves him a lot of time.   

Fred: What are the next steps for Biome?

Isak: The future vision is eventually for everyone to have the app.

Margaux: Eventually we want to measure not only tree data, but other species. We want people to have the ability to identify birds by sound or photo, then quantify that as part of the project to measure biodiversity. 

Isak: The need to measure biodiversity is really important, but that requires data. If you can prove biodiversity for a carbon project, you can charge 50% more for your carbon credits. People don’t want eucalyptus monocultures. They want polycultures.

Mary: In Panama we visited several different sites: a monoculture, then a secondary forest over 20 years old, then another forest 50 years old. It was very obvious which forest was more diverse—the last one. You could feel it in the change in humidity. You could smell it.

Isak: Yeah, that monoculture forest was completely different. It was like a corn field. Nothing there.

Fred: When you think about Biome’s potential, what most excites you? 

Mary: We’re trying to save the world by planting more trees and saving rainforests. This is really difficult! Carbon credits have the potential to direct a lot of funding to save rainforests and grow new forests. One bottleneck has been collecting data. Traditionally, it’s a pain in the ass [laughs]. Having a mobile app makes this easier. Using computer vision makes measuring way more awesome. Trees are different shapes, so doing it well makes a difference. 

Anthony: What excites me the most is to see Biome catalyze the widespread, decentralized generation of high-quality forestry data. Full stop. If we’re successful and get it distributed? There's no telling what new types of interventions could arise. Think of all the perks we just talked about: five pieces of equipment removed from your pack; barrier of entry reduced; and for your troubles you get higher quality data, faster. We have so precious little data. If that data were abundant and rich and coming from all the different forest regions—it’s exciting to think what data renaissance could come of this. 

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