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Record W4411137379 · doi:10.1016/j.rsase.2025.101623

Massive increase of intertidal seagrass coverage in a large estuarine system revealed by four decades of Landsat imagery

2025· article· en· W4411137379 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueRemote Sensing Applications Society and Environment · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsUniversité du Québec à ChicoutimiUniversité du Québec à Rimouski
FundersCanadian Space AgencyFonds de recherche du Québec – Nature et technologiesWashington Internships for Students of EngineeringFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of CanadaInstitut Nordique De Recherche En Environnement Et En Santé Au Travail
KeywordsSeagrassIntertidal zoneEstuaryFisheryOceanographyGeographyEnvironmental scienceHabitatRemote sensingEcologyGeologyBiology

Abstract

fetched live from OpenAlex

The ecosystem services and functions of seagrass meadows are indisputable, and knowledge about their coverage is critical for coastal managers worldwide. In this study, the surface area coverage of the foundation species Zostera marina L. (eelgrass) was investigated in four contrasting subregions of the Estuary and Gulf of Saint Lawrence (EGSL), eastern Canada. The meadows in all subregions mainly occupy intertidal zones. Our analysis covered broad spatial (meters to kilometers) and temporal (annual to decadal) scales and revealed unprecedented insights at a local and regional context. We processed surface reflectance products of the Landsat archive through the Google Earth Engine cloud computing platform. The processing scheme only considered emerged areas of intertidal zones from imagery acquired at the lowest tide levels because of inherent limitations imposed by water clarity and the poor radiometric quality for water applications of the early Landsat sensors. The polygons classified as eelgrass encompassed at least 25% coverage of eelgrass for each patch, and the classification scheme showed a very good agreement with coastal ecosystem habitats maps generated by photointerpretation and field validation for the period between 2015 and 2019, with an overall accuracy of approximately 94%. From the 40-year period analyzed (1984–2023), the meadows’ surface area dramatically increased 10- (from approx. 0.3 to 2.5 km 2 ) to 21-fold (from approx. 0.8 to 16.7 km 2 ). The percentage of the intertidal area occupied by eelgrass meadows varied by subregion, ranging between 17% and 82%. In some subregions, meadows expanded landward. Some meadows experienced relatively short-term losses (interannual scale) in three subregions, although these losses differed in their timing. We propose several hypotheses involving hydrodynamic, sedimentological, drift ice and climatic processes that could explain long- and short-term variability of the meadow coverage. However, this complex relationship remains to be investigated. Overall, while showing suitable habitats for eelgrass colonization, this study also revealed the EGSL tidal flats as potentially important areas of biodiversity, carbon storage, and coastal protection against erosion.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.186
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it