Property owners typically raise revenue from forestland through development or timber sales. The carbon credit market offers an alternative, allowing landowners to get paid for managing their forests sustainably.
Many landowners, though, have been locked out of this market by high costs. Selling carbon credits involves estimating how much carbon is stored in the forest, and making those estimates requires expensive field surveys. As a result, only owners with large landholdings can afford to enter the market.
Remote sensing technologies—satellite optical imagery, radar, lidar, and more—promise to change the game. If costs are reduced, more landowners may be able to enter carbon markets, helping fight climate change and preserve forest ecosystems.
Use of remote sensing, however, requires careful study.
“The natural response is to just run with these new technologies,” says David Saah, Principal Investigator with Spatial Informatics Group (SIG). “Our approach is to take a step back and see whether it really works.”
Can remote sensing replace or supplement current inventory methods? Can it integrate with the rigorous protocols of carbon registries? SIG is leading the way in assessing the use of remote sensing in estimating forest carbon.
“Before we put these new technologies to work—before we use them operationally—we do the basic scientific research,” Saah says. “That’s what makes SIG unique.”
As part of this assessment effort, scientists with SIG Carbon recently completed a major report for the American Carbon Registry (ACR) assessing potential uses of remote sensing in forest carbon projects.
“The report examines how remote sensing is being used to innovate in carbon projects,” says Tim Kramer, Director of Operations for SIG Carbon. “It shows the great potential of remote sensing—but also the challenges that remain.”
Forest Inventories and Carbon Markets
In entering the carbon market, landowners make a long-term commitment to manage their forests sustainably and are issued credits based on the amount of carbon stored on the land.
The landowners can sell the credits to companies on the carbon offset market. These companies buy credits either to satisfy a government mandate (known as the compliance market) or because they have voluntarily committed to reducing their carbon footprint (the voluntary market).
On the voluntary market, credits are issued by registries such as Climate Action Reserve and American Carbon Registry. These registries require that carbon projects follow strict protocols to ensure that the credits issued accurately represent the carbon stored in the forest, both at the start and throughout the life of the project.
A third-party verification body vets the project before the credits are issued, and projects must also undergo regular monitoring, reporting, and verification to ensure protocols continue to be followed.
Traditionally, carbon has been estimated using on-site field sampling. Plots are chosen to represent the various forest cover types on the property, and workers take measurements of the trees in those plots. Those measurements are then used to estimate carbon storage in the property as a whole.
Tree-growth models are used to estimate yearly growth in biomass, and typically these estimates are supplemented with repeated on-site field measurements every five to ten years.
“The cost of on-site field sampling and the subsequent verification process is the largest project development cost and the most time-consuming aspect of establishing a forest carbon project,” Kramer says. “That’s why there’s so much interest in remote sensing.”
Remote Sensing Approaches
Remote sensing has the potential to inventory forest biomass at lower cost than that required by in-person field visits. There are a number of technologies at play, including the following:
- Optical imagery sensors, often mounted on satellites, collect data as electromagnetic radiation from the sun reflects off Earth’s surface. These sensors detect visible light as well as infrared (including heat) and multispectral (multiple areas of the spectrum, including infrared) radiation.
- Radar—including satellite-mounted synthetic aperture radar (SAR)—sends out pulses of microwave energy towards a target and detects the energy that is reflected back.
- Lidar, usually carried on drones or airplanes, sends out laser light to Earth’s surface and collects data on the light that is reflected back. A key strength of lidar data is the ability to see under trees, because some of the laser pulses penetrate vegetation and reflect off Earth’s surface.
These sensors can collect imagery at a variety of resolutions. Higher-resolution data can provide information at the scale of individual trees, a level of detail that can enable accurate carbon accounting. Medium-resolution can be used to characterize stands of trees and some types of forest structure, but not individual trees; this type of data can be used to estimate biomass at the regional scale. Low-resolution can detect only very coarse patterns of forest, but it is good at assessing biomass at very large geographic scales.
The different types of data help answer questions at different spatial scales. Medium- and low-resolution data are inexpensive—or even free, from government sources—and can answer broad regional questions, such as how much carbon is in New England forests.
For a very small area at a single point in time, lidar can provide highly detailed data, such as accurately assessing the amount of biomass. For the carbon markets, only such small-scale, high-resolution data is useful. But because lidar data is so expensive to collect and process, it’s not a practical choice if data needs to be collected repeatedly or over large areas—a requirement for forest carbon projects.
Understanding how these different types of data might be combined to estimate biomass for a proposed project is the key to making Earth observable data useful to the carbon markets.
“In order to get really good forest inventory data, you need to have certain spectral resolutions and spatial resolutions and temporal resolutions, and you need to capture data on forest structure and tree height,” Saah says. “Up until four or five years ago, the technology was not at the point where you had the type of resolution you needed.”
Charles Kerchner, Managing Director of SIG Carbon, agrees. “Remote sensing just hasn’t been a cost-effective tool to apply to carbon project development,” he says. “But that’s changing—the technology is moving so fast.”
The Promise and Challenge of Remote Sensing
Remote sensing can be used to estimate the amount of aboveground woody biomass in a given area, the proportion of trees belonging to particular species, changes in biomass caused by harvest or fire, and a number of other forest characteristics.
This information, however, is useful only if it can be integrated into the rigorous protocols established by the carbon registries. The registries demand high accuracy in carbon estimates and the clear quantification of uncertainties. If a remote sensing technology cannot offer the necessary degree of certainty, then the registries cannot use it.
The new report SIG produced is designed to help the American Carbon Registry better understand these technologies, as well as the limitations associated with integrating them into carbon protocols and carbon markets.
One key limitation is a lack of transparency. In traditional forest carbon quantification methods, foresters measure all of the trees (height, species, diameter at breast height, etc.) within a random selection of plots. The calculations used to determine the amount of carbon and the uncertainty associated with those estimates are well understood and easily replicated.
By contrast, the model-based estimates produced by remote sensing are a black box: the models are proprietary, and there’s no cost-effective way to check the results. Because they can’t be independently replicated, these estimates lack the required transparency.
In addition, the accuracy of these estimates as applied to different forests is unclear. A model could be very precise for Forest A but wildly inaccurate for Forest B. Another model, based on different data, could perform in the opposite way, accurate for Forest B and inaccurate for Forest A.
Currently, remote sensing shows the most promise in monitoring existing forest carbon projects over time. It is relatively simple to identify an existing project area and then use remote sensing imagery to determine whether trees have been cut down. Remote sensing also can be used in the initial identification of areas that store a great deal of carbon and therefore show potential for new forest carbon projects.
“These are easy questions for remote sensing, but they’re not game-changers,” Kramer says, “They don’t help democratize forest carbon by lowering costs.”
That’s because much of the cost lies in the initial sampling and inventory process, which must still be done on-site. Because of the need for independent verification and a transparent means to assess uncertainty, remote sensing cannot yet help with those tasks in ways that satisfy the requirements of carbon registries and offset markets.
“The costs continue to drop, and the fidelity of the data is getting to the point where we can actually use it,” Saah says. “But getting the technology and the science to align with what the policymakers and registries are comfortable with—that's the challenge.”
SIG researchers are rising to meet that challenge. Thanks to a major grant from NASA’s Research Opportunities in Space and Earth Science (ROSES) program, SIG and its partners are examining ways to quantify uncertainty in estimates of forest biomass, and open forest carbon opportunities to far more people.
You can read about SIG’s NASA ROSES research in our next blog post.