Feasibility to detect rapid change and disappearance of seagrass: Lessons from nearly 80 years of vegetation change in the Ako, Seto Inland Sea, Japan

2026-06-05

AI Summary

This study addresses the challenge of distinguishing abrupt seagrass loss from seasonal fluctuations by integrating aerial photographs since the 1940s, high-resolution satellite imagery, GRUS data, and Sentinel-2 monthly composites to reconstruct an 80-year history of seagrass coverage in Ago Bay, Japan—the first such effort combining multi-source remote sensing with deep learning. The authors propose a novel monitoring paradigm incorporating seasonal normalization and extreme anomaly detection, employing a YOLO-based segmentation model and time series analysis to achieve reconstruction accuracy exceeding 0.9. The analysis confirms that the dramatic decline to 0.2 hectares in 2025 constitutes an anomalous event, most likely driven by anomalously high summer sea surface temperatures, thereby refining Essential Ocean Variables (EOVs) and reference conditions for seagrass ecosystem assessment.

Abstract

This study analyses the Ako tidal flat in the Seto Inland Sea, Japan, where nearly all Zostera marina disappeared within a single year in 2025. Using aerial photographs from the 1940s onward, high-resolution satellite imagery, GRUS images (2.5-5 m), and monthly Sentinel-2 composites (10 m), we reconstructed approximately 80 years of seagrass distribution. YOLO-based segmentation using deep learning achieved high accuracy (overall accuracy >= 0.9) across these datasets; although species could not be discriminated, the models captured the major temporal dynamics in vegetation area. The long-term mean seagrass area was 6.8 ha, but values fluctuated widely, from 3.5 ha in 1974 to 41.3 ha in 1989 except 0.2 ha in 2025. Sentinel-2 composites from 2019 to 2026 revealed clear seasonality, with vegetation increasing in early summer and declining from autumn. In 2025, however, the area decreased sharply after summer and remained anomalously low throughout the winter of 2025-2026. Our results, indicating that the 2025 event was not a normal fluctuation but a rapid ecosystem shift involving the loss of the dominant canopy-forming species, most plausibly driven by regionally elevated summer water temperatures. The findings also have implications for seagrass Essential Ocean Variables (EOVs) and the State of Nature (SoN) metrics used in TNFD-aligned nature-related disclosures. Unlike forests, seagrass meadows require finer temporal resolution because both pronounced seasonality and abrupt collapse strongly influence area-based indicators. Therefore, in addition to previously noted issues such as species-level classification accuracy, we recommend that (1) baselines be defined over the longest available record and justified ecologically, (2) seasonal standardization be applied before inter-annual comparisons, and (3) years with extreme area anomalies be flagged rather than used as reference points.

Problem

• seagrass disappearance
• rapid ecosystem shift
• temporal resolution
• Essential Ocean Variables
• seasonality

Innovation

• YOLO-based segmentation
• multi-decadal remote sensing
• seagrass monitoring
• Sentinel-2 time series
• ecosystem abrupt change