On average, wildfires destroy over 6 million acres of land each year in the US. Last year, the U.S. Forest Service spent over $1 billion fighting those fires, mostly out West. Blazes start easily where there’s plenty of dry fuel, such as dead grass, sticks and other dried plants. Knowing how much fuel is available can help fire management teams know where and when to be on high alert. And a teen has figured out a way to home in on such areas quickly.
Nadine Han, 13, is a seventh grader at Boston Latin School. When she built a robot three years ago with FIRST LEGO League, hers was designed to prevent wildfires. When the girl’s family visited Yellowstone National Park, later that year, what did they see? Evidence of wildfires. So Nadine followed up on that theme for her science fair project, and decided to find out how to predict wildfires.
The teen learned about a satellite called SMAP. Launched by the National Aeronautics and Space Administration in 2015, it detects how wet or dry the soil is. It also can tell whether that moisture is frozen or liquid. SMAP’s data on soil moisture can be combined with other data to determine the vegetation(植被) water content, meaning the amount of water present in the plants. SMAP’s data and the vegetation water content data are both freely available from the National Snow and Ice Data Center. For her project, the teen downloaded a year’s worth of data.
Having compared plant water content and fuel moisture data for 1,413 different locations, she found that an increase in plant water content measured by the satellite was linked with an increase in fuel moisture as measured on the ground. For her study, 87% of the sites showed such a link.
“This suggests it’s feasible to use satellite data to estimate fuel moisture on the ground,” Nadine concludes. The teen now hopes her technique will help land managers determine fire risks more quickly. Next, Nadine hopes to make a computer program that will estimate fire risk using satellite data.
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