University of Oxford researchers, in partnership with Trillium Technologies’ NIO.space, have developed a tool to automatically detect methane plumes on Earth from orbit using machine learning with hyperspectral data. This could help identify excessive ‘super emitters’ of methane and enable more effective action to reduce greenhouse gas emissions. The findings have been published in the journal Nature Scientific Reports.
Although Net Zero targets focus on CO2 emissions, combating methane emissions is also a critical activity to slow rising temperatures. Methane is 80 times as effective in trapping heat as CO2, but has a much shorter atmospheric lifetime (around 7 to 12 years compared to centuries). Acting quickly to reduce methane emissions from anthropogenic sources would therefore have an immediate impact on slowing global heating and improving air quality. It has been estimated that readily achievable methane emission reductions could deliver nearly 0.3° C of avoided warming over the next two decades.
Until now, however, there have been only very few methods to readily map methane plumes from aerial imagery and the processing step is highly time-consuming. This is because methane gas is transparent to both the human eye and the spectral ranges used in most satellite sensors. Even when satellite sensors operate in the correct spectral range to detect methane, the data is often obscured by noise, requiring laborious manual approaches to effectively identify the plumes.
Read More: University of Oxford
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