The Science

Forest degradation through fires and logging is widespread in the Amazon. Though it changes forest structure, forest degradation is difficult to detect from space. A team of researchers used commercial high-resolution satellites and developed a machine learning system to automatically distinguish intact forests from logged or burned forests. They also used aircraft laser sensors to calculate how much carbon degraded forests lose. To get the most precise impact of forest degradation on carbon stocks, the team considered that both their classification and carbon stocks have uncertainties.

The Impact

Researchers found that their machine learning method distinguishes degraded forests from intact forests in 86% of cases. The machine learning approach occasionally confuses logged forests with intact forests but is very good at identifying burnt areas. The team found that logged forests have almost the same amount of carbon as intact forests. However, forest fires can reduce the amount of carbon by 35%.

Summary

Forest degradation from logging and fires impacts large areas of tropical forests. However, the impact of degradation on carbon stocks remains uncertain because degradation is difficult to detect. This research used high-resolution images from PlanetScope and produced a series of metrics that described forest canopy texture. These metrics were then used to train a machine learning classifier to calculate the probability of forests being intact, burned, or logged. The team also used biomass estimates from airborne lidar to calculate the impact of forest degradation on carbon stocks.

The classification approach has an accuracy between 0.69 and 0.93 depending on the site. This study found that changes in carbon stocks due to logging were small but burned forests store 35% less carbon than intact forests. The team expected and found that uncertainty in carbon losses due to degradation increases when they account for uncertainty in classification. However, research showed ignoring classification uncertainty could underestimate the impact of degradation on carbon stocks.

Principal Investigator

Marcos Longo
Lawrence Berkeley National Laboratory
mlongo@lbl.gov

Program Manager

Brian Benscoter
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Environmental System Science
brian.benscoter@science.doe.gov

Funding

This research was funded by the NASA Land Cover and Land Use Change Program and the Next-Generation Ecosystem Experiments Tropics (NGEE Tropics), funded by the U.S. Department of Energy’s (DOE) Office of Science, Biological and Environmental Research (BER) program. The research carried out at the Jet Propulsion Laboratory, California Institute of Technology, was under a contract with NASA.

References