AN ANALYSIS OF DIGITAL WETLAND VEGETATION MAP COVERAGES. PRODUCED BASED ON AERIAL PHOTOGRAPHY AND SATELLITE IMAGERY NETLEY-LIBAU MARSH, 2001
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. Landsat imagery, though of a coarser spatial resolution, has the advantage of high temporal and spectral resolution. In this study, a classified digital vegetation map was created for Netley-Libau Marsh in 2001 using GIS software, Landsat 7 imagery, and a visual classification methodology, for the purposes of comparison with an existing digital vegetation map produced by Grosshans et al (2004) from aerial photography obtained during the same year. Visual delineation and classification of Landsat multispectral imagery was a method suitable for producing wetland maps which distinguish vegetated from non-vegetated areas with a high degree of accuracy, as compared to the truthed Grosshans et al map. Whereas that photography-based mapping exercise distinguished 23 vegetation classes grouped under six marsh zones; this study was able to successfully distinguish five marsh zones – water, not vegetated, emergent wetland vegetation, wet meadow, and upland. Further distinction and categorization of three marsh zones into seven vegetation classes was also possible, but with a lower degree of accuracy. This report describes the methods used to evaluate differences in surface area of equivalent classes between the two mapping exercises. It also provides recommendation for the future analysis of Landsat images to produce a time series of classified digital vegetation maps that may be used to explore relationships between lake and river hydrology and wetland plant cover. This knowledge will be fundamental to guide management and remediation efforts for the benefit of Netley-Libau Marsh and Lake Winnipeg.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| 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) | 0.002 | 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".