Asian Scientist (September 11, 2024) – Researchers at Kyoto University in Japan have developed an advanced method to automatically detect global methane emissions. Driven by a deep learning model called a vision converter, the technology uses novel multispectral satellite imagery to detect methane emissions. The technology has significantly improved the accuracy and frequency of methane detection worldwide. The study was published in nature communications
Methane is a powerful greenhouse gas. Its short-term warming effect is stronger than that of carbon dioxide. Therefore, reducing global methane levels is crucial to combating climate change. However, accurate monitoring of methane emissions has been a difficult challenge for decades due to limitations of existing methods. Current detection methods require trade-offs between resolution, coverage, and detection accuracy. This research leverages artificial intelligence (AI) to address these challenges.
“Our method has the potential to provide high-frequency and high-resolution methane detection from point sources, which paves the way for systematic quantification methods,” said Bertrand Rouet-Leduc, lead author of the study and a researcher at Kyoto University’s Institute for Disaster Prevention. , Japan and Geolabe Los Alamos, USA. He emphasized that there is an urgent need to control methane levels because of its stronger short-term warming effect. “Curbing methane emissions is the fastest way we can slow global warming. It may be the only way we can slow global warming in our lifetime,” he said.
A team of researchers has developed a unique artificial intelligence model that can detect methane emissions in Sentinel-2 satellite data. It identifies plumes as small as 0.01 square kilometers, corresponding to methane sources emitting 200-300 kilograms of methane per hour. The frequency and resolution of methane detection is higher than previous methods, marking an important step toward automated, high-resolution methane detection on a global scale. To check the model’s accuracy and reliability, the researchers validated their findings by comparing them to measurements of methane in the air.
Rouet-Leduc explains the team’s innovative approach: “Previous methods of detecting methane in satellite data relied on processing the data to enhance the methane signal and then having humans look at the data. Instead, training artificial intelligence to do this job achieves two things: It’s cheap and automated, allows us to analyze large amounts of data, and it can significantly improve detection accuracy by about an order of magnitude.
Researchers are making up for the scarcity of real-world examples by using synthetic methane plumes to train artificial intelligence models. The training involves embedding simulated methane signatures in satellite imagery and then teaching the artificial intelligence to locate these hidden signals. This process can be repeated millions of times to ensure robust training of AI models. “It’s like finding a needle in a haystack,” Rouet-Leduc said. “It’s easy to lose a needle in a haystack, but it’s a challenge to find it again. Likewise, we can simulate methane leaks in satellite data and train artificial intelligence to locate them.
At the same time, the research results can also be applied to the work done by governments and other environmental agencies. This innovation can support atmospheric emissions reduction efforts by providing frequent and high-resolution methane emissions data. This will help facilitate rapid remediation of leaking assets and aid investment decisions to reduce methane emissions. “Our approach opens up the possibility of detecting the precise sources of methane emissions at scale, down to the specific emitting assets,” Rouet-Leduc said. “We can now detect more than 80% of the sources of methane in areas like the Permian Basin, one of the major sources of anthropogenic methane in the United States. This allows for comprehensive monitoring over an extended period of time at just the cost of A fraction of the work previously required.
Going forward, the research team plans to use cloud computing to enhance analysis and compile a global emissions inventory. This will make detection better and reduce any possible errors, aiding global efforts to reduce greenhouse gas emissions and combat climate change.
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Source: Kyoto University; Photo: Shelly Liew/Asian Scientist Magazine
The article can be found at: Automated detection of methane emissions in multispectral satellite imagery using visual converters
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