Remote Sensing Monitoring of Phragmites Treatment and Fish Habitat Restoration in Long Point, Lake Erie, Canada
Bibliographic record
Abstract
The invasive expansion of Phragmites australis in coastal wetlands, including the Long Point wetland complex in Ontario, has led to significant declines in plant and wildlife diversity, impacting ecosystem functions. Despite ongoing management efforts, the long-term ecological outcomes of Phragmites control remain poorly understood. This study developed a framework to evaluate the long-term efficacy of herbicide treatment by tracking changes in target and non-target plant species and fish habitats in Long Point, Ontario, over an eight-year period (2016–2024). High-resolution satellite imagery from WorldView sensors was classified using a random forest algorithm, achieving over 94% mapping accuracy. Results showed a decrease in Phragmites cover (3–21%) and an increase in fish habitat area (7–58%) within treatment areas. However, some sites also experienced increases in Dead Vegetation (up to 23.6%) and declines in Grass/Herbaceous and Typha (up to 20.5% and 32%, respectively). These findings highlight both the success of Phragmites Best Management Practices and the temporary non-target effects on wetland vegetation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".