Using high spatial resolution hyperspectral imagery to map intertidal habitat structure in Hood Canal, Washington, U.S.A.
Bibliographic record
Abstract
An understanding of the distribution and structure of discontinuous nearshore habitats is critical to effectively manage estuarine resources, especially migratory and mobile nekton that may depend on the integrity of shallow-water environments. We used 19-band compact airborne spectrographic imager (CASI) imagery, collected at 1.5 m spatial resolution, to map two cover classes of eelgrass (Zostera marina) and six other estuarine habitat classes along 64 km of the intertidal shoreline of Hood Canal in the Pacific Northwest, United States. We used control points derived from digital orthoquads (DOQ) and a differential global positioning system (GPS) to geometrically correct CASI flight lines to within 4.3–23.5 m root mean square error (RMSE). After unsupervised and supervised classification, we found good correspondence between classified eelgrass polygons and field training and ground-truthing data. Although this was not the focus of our habitat mapping, the same was true for most of the other habitat classes, except for oyster beds, which were difficult to separate from the wet sand–gravel–cobble class. We are encouraged by the ability of CASI to produce spatially accurate, high-resolution descriptions of intertidal habitat structure. Results from this study will become the foundation of a broader study to develop a quantitative index of essential habitat quality for migrating juvenile summer chum salmon (Oncorhynchus keta) in Hood Canal. When CASI image processing is complete, fisheries scientists–managers will be able to effectively evaluate estuarine landscape patterns at a spatial scale appropriate for migrating juvenile summer chum salmon.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".