By Bev Betkowski
Combining drone data and machine learning can help cover more ground in monitoring forest soil health, University of Alberta research shows. The findings are published in the journal Forest Ecology and Management. Using both tools to map and monitor soil fungal diversity—a key indicator of a healthy forest ecosystem—proved highly effective and could help reduce the need for boots-on-the-ground soil sampling over huge areas of forest, says Dr. Cameron Carlyle, a professor in the Faculty of Agricultural, Life & Environmental Sciences and a co-author of the study.
"Understanding patterns across the landscape relies heavily on manual soil collection and DNA sequencing at many locations, which means it's labor-intensive and expensive," he notes. "But by integrating remote sensing data—information collected from drones—with soil measurements and machine learning, the research provides a more cost-effective and scalable way to map fungal soil diversity."
Keeping track of that diversity is vital, adds Wanwan Yu, who led the research as a visiting Ph.D. researcher in Carlyle's lab.
"Soil fungi are central to forest function, influencing nutrient cycling, decomposition and tree growth," she notes. "Without that monitoring, we risk losing critical information on biodiversity patterns, ecosystem stability and the ability to manage and conserve forests effectively, particularly in the face of environmental change."
Click here to see more...