Reducing damaging “ultra-emission” methane leaks could soon become much easier–thanks to a new, open-source tool that combines machine learning and orbital data from multiple satellites, including one attached to the International Space Station.
Methane emissions originate anywhere food and plant matter decompose without oxygen, such as marshes, landfills, fossil fuel plants—and yes, cow farms. They are also infamous for their dramatic effect on air quality. Although capable of lingering in the atmosphere for just 7 to 12 years compared to CO2’s centuries-long lifespan, the gas is still an estimated 80 times more effective at retaining heat. Immediately reducing its production is integral to stave off climate collapse’s most dire short-term consequences—cutting emissions by 45 percent by 2030, for example, could shave off around 0.3 degrees Celsius from the planet’s rising temperature average over the next twenty years.
[Related: Turkmenistan’s gas fields emit loads of methane.]
Unfortunately, it’s often difficult for aerial imaging to precisely map real time concentrations of methane emissions. For one thing, plumes from so-called “ultra-emission” events like oil rig and natural gas pipeline malfunctions (see: Turkmenistan) are invisible to human eyes, as well as most satellites’ multispectral near-infrared wavelength sensors. And what aerial data is collected is often thrown off by spectral noise, requiring manual parsing to accurately locate the methane leaks.
A University of Oxford team working alongside Trillium Technologies’ NIO.space has developed a new, open-source tool powered by machine learning that can identify methane clouds using much narrower hyperspectral bands of satellite imaging data. These bands, while more specific, produce much more vast quantities of data—which is where artificial intelligence training comes in handy.
The project is detailed in new research published in Nature…
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