Wetland Change Analysis in Alberta, Canada Using Four Decades of Landsat Imagery
Bibliographic record
Abstract
In this study, wetland trends in Alberta were investigated in the past four decades using Landsat satellite imagery to produce updated information about wetland changes and to prevent further degradation of these valuable natural resources. All the processing steps and analyses were conducted in Google Earth Engine (GEE) to produce 16 wetland maps from 1984 to 2020. A comprehensive change analysis showed (1) approximately 18% of the province was subjected to change; (2) in terms of wetland classes, there was a decreasing trend for the Shallow Water and Swamp classes and an increasing trend for the Fen and Marsh classes; (3) in terms of non-wetland classes, there was a considerable decreasing trend for the Forest class and increasing trend for the Grassland/Shrubland class; (4) wetland loss was approximately 22,000 km2, which was mainly due to the conversion of wetlands to Forest and Grassland/Shrubland; (5) wetland gain was approximately 24,000 km2, which was mainly due to the conversion from the Forest class to wetlands, especially the Swamp and Fen classes; (6) The highest class transition was from Cropland to Grassland/Shrubland and vice versa (29,000 km2), from Forest to different wetland classes (18,000 km2), and from Fen to Forest (6,000 km2). In summary, the results of this study provided the first comprehensive information on wetland trends in Alberta over the past 37 years and will assist policymakers to adjust the required/established policies to mitigate the potential wetland changes due to anthropogenic activities and climate-related events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".