WVU researchers Steve Kannenberg and Jie Hu are using artificial intelligence and edge computing to process environmental data in near real time, helping scientists better understand how ecosystems respond to rapidly unfolding events like droughts and wildfires.
(Submitted Photo)
With help from
artificial intelligence, West Virginia
University researchers are
speeding up environmental scientists’ access to time-sensitive data about
ecosystem health, allowing experts to respond rapidly to
events like droughts or wildfires.
Supported by an Early-Concept Grant for Exploratory Research from the National Science
Foundation,Steve Kannenberg,
assistant professor ofbiologyin theWVU Eberly College of Arts and
Sciences, is integrating AI directly into local
monitoring sensors to help scientists track ecosystem health in near real time.
Every day,
forests, grasslands, and ecosystems around the world “breathe,” constantly
trading carbon, water, and energy with the atmosphere, Kannenberg explained.
These
exchanges shape everything from local water supplies to how ecosystems respond
to a warming climate.
But Kannenberg added that it can take scientists months, sometimes years, for
environmental data to become something they can use — a gap he and WVU
postdoctoral researcher Jie
Hu are working to close.
Scientists
studying ecosystem fluxes rely on field structures known as eddy
covariance towers, or flux towers. These are specialized weather stations
equipped with ultra-fast sensors to collect data. But historically, researchers
have faced a major bottleneck: By the time usable reports reach them, critical
ecological events have long passed.
“We have amazing tools to measure how these ecosystems
breathe, but because measurements are taken 20 times per second, processing
this huge, tremendous amount of data into something that’s
quality controlled takes immense effort,” Kannenberg
said. “In these networks of flux towers, there
can be a latency of months to years before data is ready to share with the
community. It’s that issue this grant is trying to tackle.”
Currently,
researchers must manually sift through massive datasets to quality control the
results after specialized <a href="https://bitcomme.com/quickbooks-accounting-software-options/” title=”QuickBooks: Accounting Software Options”>software runs initial complex algorithms. They must
filter out sensor glitches caused by rain or wind, fill gaps in missing records,
and tailor calculations to each specific site. These tedious processes slow the
sharing of vital information.
“The traditional way to process this data relies on very
established mathematical and physics-based equations that take a long time to
calculate,” Kannenberg said. “We’re using AI tools to circumvent those complex
equations and process the data much more quickly.”
As co-principal investigator, Hu is
leading the development of a “processing pipeline” that rapidly turns
environmental observations into ready-to-use data products.
“The main goal is to make these products
easy to access by cutting the long delays in data release and providing
interactive and visual diagnostics,” she said.
To
enable researchers to watch ecosystems respond to events as they unfold, the
project pairs AI with a national network of monitoring stations called the
National Ecological Observatory Network, or NEON. Kannenberg
also relies on “edge computing,” which uses very small, on-site computers to
crunch numbers where and when they’re collected, instead of shipping data off
to cloud servers.
“In
the waiting period, any extreme environmental events can be missed. If there
was a severe drought, we wouldn’t know how that’s impacting the ecosystem until
a year down the line. If there was a wildfire, we wouldn’t know how much carbon
was being burned off that ecosystem until years down the line,”
Kannenberg said.
The
instant data processing will give users a
chance to catch transient “hot spots” and “hot moments” of biological activity,
such as sudden bursts of plant growth after rain, localized drought stress, or
nonbiological emissions from nearby vehicles, which are usually lost in
traditional, delayed averages.
“Faster release of
accessible flux data doesn’t just help scientists understand how ecosystems are
responding to change. It also gives land managers and decision-makers a
near-real-time view of environmental conditions,” Hu said.
Real-time
processing also solves operational delays.
“NEON
is a government-funded effort to have a standardized set of towers in major
biomes across North America,” Kannenberg
said. “A ton of money and time goes toward this,
but they don’t know if there’s a problem with their instrumentation until
someone physically collects the data and processes it.”
To remove the technical barriers, the team is building an interactive,
plain-language AI chatbot interface. The tool allows anyone, from students to
policymakers, to ask simple questions about the data, such as: “Was there a
drought last year in West Virginia? How did that impact the
forests?” The goal is to deliver scientifically grounded answers. Kannenberg also
plans to integrate the interactive tool into his Ecosystem Ecology course at
WVU.
They plan to test
their approach at two very different sites: a grassland known for unpredictable
bursts of activity at the Central Plains Experimental Range in Colorado and the
Harvard Forest in Massachusetts, a temperate forest with a steadier seasonal
rhythm.
“I’m excited to
streamline the process and see how new technologies in artificial intelligence
and edge computing can accelerate discoveries we haven’t even imagined yet,” Hu
said.
Kannenberg
said the value lies less in any single scientific discovery than in what faster
access to data makes possible.
“When
I first moved to Morgantown in 2023, there was a severe drought. Cheat Lake was
visibly drying up, leaving boats sitting in mud,” Kannenberg said. “It’s
difficult for scientists to study the impacts of sudden events like that
because we must scramble to assemble
teams and equipment after the fact. By having an automatic system that rapidly
alerts us to ongoing environmental extremes, we can more
easily have targeted field campaigns or measurement campaigns to better
understand these events.”
The
project builds on Kannenberg’s ongoing research into
how forests and drylands store carbon and respond to a changing climate,
including his recent work onthe western United States’
23-year megadrought.
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