Understanding this carbon cycle is key to climate change research, according to Hamdi Zurqani, an assistant professor of geospatial science for the Arkansas Forest Resources Center and the College of Forestry, Agriculture and Natural Resources at the University of Arkansas at Monticello. The center is headquartered at UAM and conducts research and extension activities through the Arkansas Agricultural Experiment Station and the Cooperative Extension Service, the University of Arkansas System Division of Agriculture's research and outreach arms.

"Forests are often called the lungs of our planet, and for good reason," Zurqani said. "They store roughly 80 percent of the world's terrestrial carbon and play a critical role in regulating Earth's climate."

To measure a forest's carbon cycle, a calculation of forest aboveground biomass is needed. Though effective, traditional ground-based methods for estimating forest aboveground biomass are labor-intensive, time-consuming and limited in spatial coverage abilities, Zurqani said.

In a study recently published in Ecological Informatics, Zurqani shows how information from open-access satellites can be integrated on Google Earth Engine with artificial intelligence algorithms to quickly and accurately map large-scale forest aboveground biomass, even in remote areas where accessibility is often an issue.

Zurqani's novel approach uses data from NASA's Global Ecosystem Dynamics Investigation LiDAR, also known as GEDI LiDAR, which includes three lasers installed on the International Space Station. The system can precisely measure three-dimensional forest canopy height, canopy vertical structure and surface elevation. LiDAR stands for "light detection and ranging" and uses light pulses to measure distance and create 3D models.

Zurqani also used imagery data from the European Space Agency's collection of Earth observation Copernicus Sentinel satellites -- Sentinel-1 and Sentinel-2. Combining the 3D imagery from GEDI and the optical imagery from the Sentinels, Zurqani improved the accuracy of biomass estimations.

The study tested four machine learning algorithms to analyze the data: Gradient tree boosting, random forest, classification and regression trees, or CART, and support vector machine. Gradient tree boosting achieved the highest accuracy score and the lowest error rates. Random forest came in second, proving reliable but slightly less precise. CART provided reasonable estimates but tended to focus on a smaller subset. The support vector machine algorithm struggled, Zurqani said, highlighting that not all AI models are equally suited for estimating aboveground forest biomass in this study.

The most accurate predictions, Zurqani said, came from combining Sentinel-2 optical data, vegetation indices, topographic features, and canopy height with the GEDI LiDAR dataset serving as the reference input for both training and testing the machine learning models, showing that multi-source data integration is critical for reliable biomass mapping.

Why it matters

Zurqani said that accurate forest biomass mapping has real-world implications for better accounting of carbon and improved forest management on a global scale. With more accurate assessments, governments and organizations can more precisely track carbon sequestration and emissions from deforestation to inform policy decisions.

The road ahead

While the study marks a leap forward in measuring aboveground forest biomass, Zurqani said the challenges remaining include the impact weather can have on satellite data. Some regions still lack high-resolution LiDAR coverage. He added that future research may explore deeper AI models, such as neural networks, to refine predictions further.

"One thing is clear," Zurqani said. "As climate change intensifies, technology like this will be indispensable in safeguarding our forests and the planet."

Together, the papers in Nature Metabolism and Nature Communications trace the first complementary wiring diagram of hunger and satiety in ways that could refine today's blockbuster weight-loss drugs and blunt their side effects.

One study, led by Zhiping Pang of Robert Wood Johnson Medical School's Center for NeuroMetabolism, pinpointed a slender bundle of neurons that runs from the hypothalamus to the brainstem.

The cells bristle with GLP-1 receptors, the proteins mimicked by weight-loss drugs such as Ozempic. When Pang's team hit the pathway with pulses of light, well-fed mice quit eating; when they silenced the circuit or deleted the receptor, the animals packed on weight. Fasting weakened the connection until a burst of natural or synthetic GLP-1 restored it.

"The synapse is a volume knob that only turns up when energy stores are low," Pang said, warning that drugs that keep the signal high around the clock could disrupt the brain's normal rhythm and create some of the side effects of GLP-1 drugs such as nausea, vomiting, constipation or diarrhea and muscle wasting.

For the other paper, Mark Rossi, who co-leads the Center for NeuroMetabolism with Pang, charted the circuit that triggers hunger. His group traced inhibitory neurons in the stria terminalis to similar cells in the lateral hypothalamus.

When researchers triggered the connection, a suddenly hungry mouse would sprint for sugar water; when they blocked it, the animals lounged even after a long fast.

Hormones modulated the effect. An injection of ghrelin, the gut's hunger messenger, revved food seeking, while leptin, the satiety signal, slammed it shut. Overfed mice gradually lost the response, but it returned after diets made them thin again.

"Pang's pathway shuts things down," Rossi said. "Ours steps on the accelerator."

Although the circuits sit in different corners of the brain, members of both teams saw the same principle: Energy state rewires synapses quickly. During a fast, the hunger circuit gains sensitivity while the satiety circuit loosens; after a meal, the relationship flips.

It is the first time researchers have watched the push-pull mechanism operate in parallel pathways, a yin-yang arrangement that may explain why diets and drugs that treat only one side of the equation often lose power over time and may help in making drugs that work even better than today's generation of GLP-1 medications.

GLP-1 mimics such as Wegovy and Zepbound can trigger double-digit weight loss but also nausea, diarrhea and, in some cases, muscle wasting. Pang's data suggest a therapy targeting only the brainstem circuit and sparing peripheral organs might curb eating without the side effects. Conversely, Rossi's work hints that restoring the body's response to the hunger-regulating hormone ghrelin could help dieters who plateau after months of calorie cutting.

Both projects relied on the modern toolkit of neural biology - optogenetics to fire axons with laser light, chemogenetics to silence them, fiber-optic photometry to watch calcium pulses and old-fashioned patch-clamp recordings to monitor single synapses. Those techniques allowed the researchers to tune individual pathways with a precision that has only recently become possible.

Follow-up work from both teams will explore more questions that could improve drug design. Pang wants to measure GLP-1 release in real time to see whether short bursts, rather than constant exposure, are enough to calm appetite. Rossi is cataloging the molecular identity of his hunger-trigger cells in hopes of finding drug targets that steer craving without crushing the joy of eating.

"You want to keep the system's flexibility," Rossi said. "It's the difference between dimming the lights and flicking them off."

Allowing the brain to correctly rebalance the desire to eat or stop eating throughout the day, rather than using drugs to keep desire constantly low, may be an important ingredient in tomorrow's weight-loss prescriptions.