USE OF LANDSAT-8 OLI IMAGERY AND LOCAL INDIGENOUS KNOWLEDGE FOR EELGRASS MAPPING IN EEYOU ISTCHEE
Bibliographic record
Abstract
Abstract. The eastern coastline of James Bay (Eeyou Istchee) is known to be home to beds of subarctic eelgrass (Zostera marina L.). These eelgrass beds provide valuable habitat and food source for coastal and marine animals and contribute valuable ecosystem services such as stabilization of the shoreline all along the coast. Despite reports from Cree communities that eelgrass bed health has declined, limited research has been performed to assess and map the spatial distribution of eelgrass within the bay. This study aims to address that issue by evaluating the capability of Landsat-8 Operational Land Imager (OLI) imagery to establish a baseline map of eelgrass distribution in 2019 in the relatively turbid waters of Eeyou Istchee. Three images acquired in September 2019 were merged and classified using Random Forests into the following classes: Eelgrass, Turbid Water, Highly Turbid Water, and Optically Deep Water. The resulting classified image was validated against 108 ground truth data that were obtained from both the eelgrass health and Hydro-Quebec research team. The resulting overall accuracy was 78.7%, indicating the potential of the Random Forests classifier to estimate baseline eelgrass coverage in James Bay using Landsat-8 imagery. This project is part of a Cree driven project, the Coastal Habitat Comprehensive Research Program (CHCRP). The CHCRP aims to combine Cree's traditional knowledge with Western science to better understand environmental changes in the coastal ecosystems and ecosystem services of eastern James Bay. The study is funded by a MITACS grant sponsored by Niskamoon Corporation, an indigenous non-profit organization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".