Capturing accurate kelp canopy extent: integrating tides, currents, and species-level morphology in kelp remote sensing
Bibliographic record
Abstract
Surface-canopy forming kelps (Macrocystis pyriferaandNereocystis luetkeana) can be monitored along the Northeast Pacific coast using remote sensing. These kelp canopies can be submerged by tides and currents, making it difficult to accurately determine their extent with remote sensing techniques. Further, both species have morphologically distinct canopies, each made up of structures with differing buoyancies, and it is not well understood whether the differing buoyancies between these species’ canopies affects their detectability with remote sensing technologies. Here, we collectedin situabove-water spectral signatures for the surface-canopies ofNereocystisandMacrocystis, providing the first direct hyperspectral comparison between the structures that make up the canopies of these species. Additionally, we compare the strength of their red-edge and near-infrared band signals, as well as the normalized difference red-edge (NDRE) and normalized difference vegetation index (NDVI) values. At the bed level, we compare detection of kelp canopy extent using both NDRE and NDVI classified unoccupied aerial vehicle imagery. We also characterized how changing tides and currents submerge the canopies of both species, providing insights that will allow remote sensors to more accurately determine the extent of kelp canopy in remote sensing imagery. Observations of canopy structures paired within situhyperspectral data and simulated multispectral data showed that more buoyant kelp structures had higher reflectance in the near-infrared wavelengths, but even slightly submerged canopy structures had a higher reflectance in the red-edge rather than the near-infrared. The higher red-edge signal was also evident at the bed level in the UAV imagery, resulting in 18.0% more canopy classified with NDRE than with NDVI. The area of detected canopy extent decreased by an average of 22.5% per meter of tidal increase at low current speeds (<10 cm/s), regardless of the species present. However, at higher current speeds (up to 19 cm/s),Nereocystiscanopy decreased at nearly twice the average rate of kelp beds in low-current conditions. Apart from the strong differences in high-current regions, a robust linear relationship exists between kelp canopy extent and tidal height, which can aid in understanding the errors associated with remote sensing imagery collected at different tidal heights.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".