arXiv:2606.07949q-bio.PEcs.CV2026-06

用80年影像发现海草骤灭非自然波动,或由高温引发

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

  • 融合多源影像与深度学习,精准追踪海草面积变化
  • 2025年海草面积骤降至0.2公顷,远低于历史最低值3.5公顷
  • 建议用长期基线+季节标准化,避免异常年份误导评估

本研究分析日本濑户内海阿古滩涂近80年海草分布演变,发现大叶藻(Zostera marina)在2025年单年内几乎全部消失。基于1940年代以来的航空照片、高分辨率卫星影像(GRUS,2.5–5米)、月度哨兵-2合成图(10米),重建了海草面积变化。采用基于YOLO的深度学习分割方法,整体精度≥0.9,虽无法区分物种,但准确捕捉植被面积的动态变化。长期平均海草面积为6.8公顷,历史波动范围为1974年3.5公顷至1989年41.3公顷,2025年仅0.2公顷。2019–2026年哨兵-2数据显示明显季节性:初夏增长,秋后下降。2025年夏季后面积急剧下滑,冬季持续处于异常低位。结果表明2025年事件并非正常波动,而是由区域夏季水温升高驱动的快速生态系统转变。研究对海洋生物关键变量(EOVs)和自然状况(SoN)指标在TNFD框架下的披露具有启示意义。相比森林,海草群落需更高时间分辨率,因其显著季节性和突发崩溃均影响面积指标。因此建议:(1)以最长可用记录定义基线并提供生态依据;(2)跨年度比较前进行季节标准化;(3)标记极端异常年份,不作为参考基准。

原文摘要 · Abstract (English)

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.

海草监测生态突变遥感分析气候变化

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