Feasibility to detect rapid change and disappearance of seagrass: Lessons from nearly 80 years of vegetation change in the Ako, Seto Inland Sea, Japan
Researchers have developed a deep learning model, utilizing YOLO-based segmentation, to accurately track seagrass distribution over nearly 80 years using various aerial and satellite imagery. The study focused on the Ako tidal flat in Japan, where a significant disappearance of seagrass occurred in 2025, reducing the area from a historical mean of 6.8 ha to just 0.2 ha. This rapid ecosystem shift, likely caused by elevated water temperatures, highlights the need for finer temporal resolution in monitoring seagrass, especially for nature-related disclosures. AI
IMPACT Demonstrates deep learning's utility in ecological monitoring, potentially improving environmental reporting and conservation efforts.