Written by: Emily Boragine
Division of Communications and Marketing
MANHATTAN— Hikers on the Konza Prairie may look up this summer and see something unusual in the air. It's K-State research in action. K-State researchers are employing aerial data in their mission to understand and manage the rapid spread of woody plants across the Great Plains. Known as woody encroachment, the transformation of open grasslands into shrub and tree-dominated landscapes is impacting biodiversity, livestock forage, water resources and even wildfire risk.
"Woody encroachment is a pattern that is happening in grasslands all around the world, where areas that used to be grasses, wildflowers and other herbaceous species are seeing a rapid and large increase in shrubs and trees," said Zak Ratajczak, assistant professor of biology at K-State. "That's not to say that all of these changes are negative, but in a lot of these grassy ecosystems, shrubs and trees move in and change things drastically."
Remote sensing meets machine learningPublished in the open-access journal Remote Sensing, a recent study led by K-State master's student Brynn Noble and Ratajczak offers a cost-effective, open-access approach to detecting woody encroachment across landscapes as small as six-by-six feet. The system combines aerial imagery from federal programs with ground-based data collected by K-State researchers."We're at the point where we can say if that area is a grassland, if it's a shrub, if it's a tree or if it's an evergreen tree with around 97% accuracy," Ratajczak said. "Meaning that 97% of the time we say that pixel is this type of vegetation, we're getting it right.
A training ground for technology — and students
The researchers developed the classification models using data from the Konza Prairie, a long-term ecological research site jointly managed by K-State and The Nature Conservancy."In this relatively small area of about 10 square miles, we have a lot of the major vegetation types that you find across eastern Kansas," Ratajczak said. "And when we're trying to train a computer to find a diverse array of vegetation types, that's an invaluable resource."That means researchers — including students — can quickly validate or correct model predictions based on firsthand knowledge of the landscape. The students gather training data for the models and get experience working with a Geographic Information System, or GIS, and computer coding along the way.One of those students is Brynn Noble, who co-led the research study. Her work alongside Ratajczak has made a major contribution to what the team hopes will become a regional, if not statewide, mapping tool.
An eye toward the future
Ratajczak said he hopes to share the resources through a website or an app for regional early-warning systems.Eventually, the team envisions partnerships with landowners to gather additional training data and refine their models.