As California and the American West head into fire season amid the coronavirus pandemic, scientists are harnessing artificial intelligence and new satellite data to help predict blazes across the region.
Anticipating where a fire is likely to ignite and how it might spread requires information about how much burnable plant material exists on the landscape and its dryness. Yet this information is surprisingly difficult to gather at the scale and speed necessary to aid wildfire management.
Now, a team of experts in hydrology, remote sensing and environmental engineering have developed a deep-learning model that maps fuel moisture levels in fine detail across 12 western states, from Colorado, Montana, Texas and Wyoming to the Pacific Coast.
The researchers describe their technique in the August 2020 issue of Remote Sensing of Environment. According to the senior author of the paper, Stanford University ecohydrologist Alexandra Konings, the new dataset produced by the model could “massively improve fire studies.”
According to the paper’s lead author, Krishna Rao, a PhD student in Earth system science at Stanford, the model needs more testing to figure into fire management decisions that put lives and homes on the line. But it’s already illuminating previously invisible patterns. Just being able to see forest dryness unfold pixel by pixel over time, he said, can help reveal areas at greatest risk and “chart out candidate locations for prescribed burns.”
The work comes at a time of growing urgency for this kind of insight, as climate change extends and intensifies the wildfire season – and as the ongoing COVID-19 pandemic complicates efforts to prevent large fires through controlled burns, prepare for mass evacuations and mobilize first responders.
Read more at Stanford University