Mapping cladophora and other submerged aquatic vegetation in the Great Lakes using satellite imagery
Bibliographic record
Abstract
Under EPA GLRI funding, the Michigan Tech team has developed and verified a remote sensing algorithm to map the extent of Cladophora and other submerged aquatic vegetation (SAV) in the nearshore zone of the Great Lakes using an index that corrects for the effect of water depth. With this algorithm, maps of SAV were generated from recent Landsat satellite imagery for all areas of the lower four Great Lakes that are shallow enough to detect the lake bottom. The area mapped varies depending on water clarity, with maximum mapping depth ranging from >20 m in Lake Michigan to 7 m in Lake Erie. The maps show that 28%, 15%, 30%, and 40% of the visible bottom of Lakes Michigan, Huron, Erie and Ontario, respectively, are colonized by SAV. The total mapped area of SAV is estimated to represent between 130,000 and 260,000 metric tonnes dry weight based on published biomass density measurements. This new mapping approach was validated using field data for an overall map accuracy of 83%. The archive of Landsat imagery dating back to 1973 was also utilized to document historic changes in SAV extent and water clarity, showing increases in SAV extent in most areas following the introduction of invasive mussels. The time series analyses also captured the observed increases in water clarity in all four lakes. Overall, the effects of invasive zebra and quagga mussels on water clarity and phosphorus availability in the Lakes are enabling benthic vegetation to grow more densely and in deeper water than was previously possible, resulting in nuisance blooms even in areas without strong point sources of nutrients. These new maps will support Cladophora management efforts and help to prioritize areas for nutrient abatement programs.
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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) | 0.000 | 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".