Classification of wetland habitat and vegetation communities using multi-temporal Ikonos imagery in southern Saskatchewan
Bibliographic record
Abstract
The Prairie Habitat Monitoring Program, led by Environment Canada, is tasked with assessing and monitoring landscapes for waterfowl and other migratory birds in Manitoba, Saskatchewan, and Alberta. Prairie habitat assessments have been conducted using transects to sample land cover and land use changes and have shown that wildlife habitat, both wetland and upland, is declining in areal extent. An investigation into the use of high-resolution imagery to assist in these assessments was performed in the summer of 2000. Spring and summer Ikonos-2 images, including both panchromatic and multi-spectral bands, were classified according to a Stewart and Kantrud (S&K) wetland habitat class system used for monitoring Canadian prairie wetlands. Two significant issues were noted in the classification process: the S&K wetland habitat classes contained similar vegetation assemblages or communities, and field crews identified areas as homogeneous on the ground that contained mixtures of vegetation communities due to the nature of the S&K classes. Following a traditional training data collection exercise based on the discrimination results in the available field sites, S&K wetland habitat classes could be classified with approximately 47% overall accuracy using multi-temporal imagery, a normalized difference vegetation index (NDVI), and texture measures. Based on these results, individual vegetation communities within these habitat classes were segregated based on sketch maps prepared for each field plot, and an assessment of these communities showed they could be distinguished much more readily, resulting in greater than 84% overall accuracy.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".