Mapping nearshore and intertidal marine habitats with remote sensing and GPS: the importance of spatial and temporal scales
Bibliographic record
Abstract
We present results and experience of mapping and assessment of algae and seagrass beds in British Columbia using air photo interpretation, compact airborne spectrographic imager, ground surveys using a high precision GPS, and conventional survey techniques. Low-level colour air photography was found to be an effective and accurate methodology, but only when accompanied by thorough ground-truthing. Ground-truthing was conducted by foot surveys at low tide or by observations from a boat or hovercraft at high water. Results from CASI were more problematic, especially to determine the boundaries of similar plant or algae habitats, but could be improved with more spectral signature data of specific plant species as well as consideration of the seasonal change of those spectra. Mapping of algae beds using a GPS at 1:500 scale (est.) was tested by foot surveys to delineate the boundary of the habitat or by a grid system where the positions of vertical and horizontal transects were georeferenced using the instrument. The latter method was accurate and relatively efficient and enabled quick mapping of the habitat units into a GIS. There are very few data on the temporal change of the extent and position of specific habitats in our region. However by reviewing data in a land tenure data base obtained with conventional survey techniques (e.g. transits) we determined that the number of hectares of nearshore habitat used for industrial purposes (log storage) has declined over the past decade in the Strait of Georgia. As an indicator of trends in habitat use over the entire shoreline of the Strait, these data were sufficient. In an effort to increase the role of the public in habitat monitoring over time, we developed and tested methods for mapping by citizens. The methods in the "Shorekeeper's Guide" give citizens options for using conventional survey and/or GPS methods for mapping beaches in coastal communities. Scales for this work are about 1:500.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".