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

• URL: http://arxiv.org/abs/2606.07949v1
• Date: Sat, 06 Jun 2026 02:47:31 GMT
• Title: Feasibility to detect rapid change and disappearance of seagrass: Lessons from nearly 80 years of vegetation change in the Ako, Seto Inland Sea, Japan
• Abstract summary: 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.<n>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.
• Score: 0.8971132850029493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Projected climate memory and inherited warm-tail risk in accelerated European summer warming [0.15293427903448018]
  We use ERA5-derived summer indicators for 28 IPCC AR6 European sub-regions over 1950--2024.<n>We find that Mediterranean-state memory improves mean summer-temperature prediction relative to trend-only and ARX baselines.<n>Overall, annual projected memory is not a universal short-horizon predictor, but a physically interpretable inherited risk-loading variable.
  arXiv  Detail & Related papers  (2026-07-28T02:23:06Z)
• Predicting Pseudo-nitzschia harmful algal blooms along the Portuguese Coast using satellite-derived predictors [69.38367341895632]
  Pseudo-nitzschia diatoms pose recurrent risks to coastal ecosystems and harvesting along the Portuguese Atlantic coast.<n>We develop and evaluate a machine-learning framework to predict harmful algal bloom occurrence using exclusively satellite-derived predictors.
  arXiv  Detail & Related papers  (2026-07-08T18:14:24Z)
• From Noisy Historical Maps to Time-Series Oil Palm Mapping Without Annotation in Malaysia and Indonesia (2020-2024) [13.463323389954844]
  We propose a deep learning framework to generate 10-meter resolution oil palm plantation maps for Indonesia and Malaysia from 2020 to 2024.<n>To address the resolution mismatch between 100-meter historical labels and 10-meter imagery, we employ a U-Net architecture optimized with Determinant-based Mutual Information (DMI)<n>Our comprehensive analysis reveals that oil palm coverage in the region peaked in 2022 before experiencing a decline in 2024.
  arXiv  Detail & Related papers  (2026-04-26T15:59:50Z)
• Country-wide, high-resolution monitoring of forest browning with Sentinel-2 [21.30119488990051]
  We present a scalable approach for country-wide mapping of forest greenness anomalies at the 10 m resolution of Sentinel-2 data.<n>We learn a predictive quantile model of the normalised difference vegetation index (NDVI) derived from Sentinel-2 data.<n>The resulting expected seasonal cycles are used to detect NDVI anomalies across Switzerland between April 2017 and August 2025.
  arXiv  Detail & Related papers  (2026-04-02T14:05:22Z)
• Graph Transformer-Based Flood Susceptibility Mapping: Application to the French Riviera and Railway Infrastructure Under Climate Change [0.0]
  This study introduces the first application of a graph transformer (GT) architecture for flood susceptibility mapping to the flood-prone French Riviera.<n>GT incorporates watershed topology using Laplacian positional encoders (PEs) and attention mechanisms.<n> Dimensionality reduction on Laplacian PEs revealed partial clusters, indicating they could capture spatial information.
  arXiv  Detail & Related papers  (2025-03-31T14:04:04Z)
• OXYGENERATOR: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning [50.365198230613956]
  Existing expert-dominated numerical simulations fail to catch up with the dynamic variation caused by global warming and human activities. We propose OxyGenerator, the first deep learning based model, to reconstruct the global ocean deoxygenation from 1920 to 2023.
  arXiv  Detail & Related papers  (2024-05-12T09:32:40Z)
• Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling [58.456404022536425]
  State of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
  High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach. Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
  arXiv  Detail & Related papers  (2023-09-12T13:28:06Z)
• Vision Transformers, a new approach for high-resolution and large-scale mapping of canopy heights [50.52704854147297]
  We present a new vision transformer (ViT) model optimized with a classification (discrete) and a continuous loss function. This model achieves better accuracy than previously used convolutional based approaches (ConvNets) optimized with only a continuous loss function.
  arXiv  Detail & Related papers  (2023-04-22T22:39:03Z)
• Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
  Climate change exacerbates the long-term soil management problem of groundwater contamination. We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO) In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
  arXiv  Detail & Related papers  (2022-11-20T06:46:35Z)
• Soil Erosion in the United States. Present and Future (2020-2050) [8.729045594301041]
  We estimate/predict soil erosion rates by water erosion using three alternative scenarios across the contiguous United States. The baseline model ( 2020) estimates soil erosion rates of 2.32 Mg ha 1 yr 1 with current agricultural conservation practices. The soil erosion forecast for 2050 suggests that all the climate and LULC scenarios indicate either an increase in extreme events or a change in the spatial location of extremes.
  arXiv  Detail & Related papers  (2022-07-14T00:46:37Z)
• Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Bayesian Deep Learning [74.94436509364554]
  We propose a Bayesian deep learning approach to densely estimate forest structure variables at country-scale with 10-meter resolution. Our method jointly transforms Sentinel-2 optical images and Sentinel-1 synthetic aperture radar images into maps of five different forest structure variables. We train and test our model on reference data from 41 airborne laser scanning missions across Norway.
  arXiv  Detail & Related papers  (2021-11-25T16:21:28Z)
• SALT: Sea lice Adaptive Lattice Tracking -- An Unsupervised Approach to Generate an Improved Ocean Model [72.3183990520267]
  We propose SALT: Sea lice Adaptive Lattice Tracking approach for efficient estimation of sea lice dispersion and distribution. Specifically, an adaptive spatial mesh is generated by merging nodes in the lattice graph of the Ocean Model based on local ocean properties. The proposed SALT technique shows promise for enhancing proactive aquaculture management through predictive modelling of sea lice infestation pressure maps in a changing climate.
  arXiv  Detail & Related papers  (2021-06-24T17:29:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.