WATER AND VEGETATION COVER IN NETLEY-LIBAU MARSH 1990 – 2013. PHASE II REPORT: A TIME SERIES ANALYSIS BASED ON LANDSAT IMAGERY
Bibliographic record
Abstract
Netley-Libau Marsh, the largest coastal wetland adjoining Lake Winnipeg, has been mapped by aerial photography in the past (Grosshans et al 2004; Verbiwski 1986), indicating a trend of vegetation loss, but a lack of historic aerial photography has limited mapping efforts to sporadic intervals. Satellite imagery, though of a coarser spatial resolution, has the advantage of high temporal and spectral resolution. Using Landsat images and a methodology developed in an earlier phase of this study (Watchorn 2014), a time series of classified vegetation cover maps was produced for twelve years between 1990 and 2013. Water cover maps were produced for another eight years within this interval, resulting in a time series representing 20 years of this 23-year period. This time series allowed for an investigation into relationships between the extent and distribution of Netley’s vegetation community to underlying hydrological factors on adjoining Lake Winnipeg and the Red River, which can be used to guide future marsh remediation measures. The analysis of this time series indicates that the long-term trend of vegetation loss in Netley-Libau Marsh has not been steady, nor has it been unidirectional. Observed vegetation change – both loss and gain – was characterised by sudden dramatic changes disrupting periods of relative stasis. Lake Winnipeg water level was identified as the major factor responsible for shifting the balance between emergent wetland vegetation and open water. Periods of low water as short as one year had dramatic and persistent effects on emergent vegetation cover, particularly in smaller lakes. Regenerated emergent vegetation was less persistent in the large Netley Lake, suggesting that marsh bathymetry is dynamic. This study also identified that Lake Winnipeg water level and Red River flow are both contributing factors which influence the extent of wet meadows around Netley-Libau Marsh. Decreased river discharges and lake levels were correlated with increasing use of these regions as hayed or cultivated land. Finally, the interpretation of the cover map time series indicates the connectivity between the marsh lakes and Lake Winnipeg has varied and is presently increasing.
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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".