Geophysical Research Letters
Declines in eelgrass, an important and widespread coastal habitat, are associated with wasting disease in recent outbreaks on the Pacific coast of North America. This study presents a novel method for mapping and predicting wasting disease using Unoccupied Aerial Vehicle (UAV) with low-altitude autonomous imaging of visible bands. We conducted UAV mapping and sampling in intertidal eelgrass beds across multiple sites in Alaska, British Columbia, and California. We designed and implemented a UAV low-altitude mapping protocol to detect disease prevalence and validated against in situ results. Our analysis revealed that green leaf area index derived from UAV imagery was a strong and significant (inverse) predictor of spatial distribution and severity of wasting disease measured on the ground, especially for regions with extensive disease infection. This study highlights a novel, efficient, and portable method to investigate seagrass disease at landscape scales across geographic regions and conditions.
National Science Foundation
eelgrass wasting disease, GIS, leaf area index, object-oriented image analysis, UAV remote sensing, Zostera
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Urban and Regional Planning
Bo Yang, Timothy L. Hawthorne, Lillian Aoki, Deanna S. Beatty, Tyler Copeland, Lia K. Domke, Ginny L. Eckert, Carla P. Gomes, Olivia J. Graham, C. Drew Harvell, Kevin A. Hovel, Margot Hessing-Lewis, Leah Harper, Ryan S. Mueller, Brendan Rappazzo, Luba Reshitnyk, John J. Stachowicz, Fiona Tomas, and J. Emmett Duffy. "Low-Altitude UAV Imaging Accurately Quantifies Eelgrass Wasting Disease From Alaska to California" Geophysical Research Letters (2023). https://doi.org/10.1029/2022GL101985