Assessing Surface Water Dynamics of Wetlands in Reclaimed Mining Areas in the Athabasca Oil Sands Region, Alberta, Canada, with Time-Varying Sentinel-1 SAR and Sentinel-2 Multi-Spectral Imagery
Bibliographic record
Abstract
Wetlands provide critical ecological and socio-economic benefits, covering approximately 45% of the Athabasca Oil Sands Region in Alberta, Canada. However, open-pit oil sand mining has led to widespread wetland loss. While reclamation efforts are ongoing, the development of effective wetland monitoring methods remain essential. This paper presents a novel approach to tracking wetland dynamics in reclaimed and reference landscapes using Sentinel-1 SAR and Sentinel-2 multispectral imagery. We assess surface water extent and emergent vegetation, validating our satellite-based measurements against high-resolution UAV-derived wetland area data (R2=0.902). Our results reveal minor differences in intra-annual variability in wetland area between wetlands in reclaimed versus those in reference landscapes. Wetlands exhibit a positive log-linear relationship between maximum depth and variability in open-water area, a pattern that was consistent between landscape types. Intra- and interannual variability in spatial extent were both positively associated with wetland area. This paper introduces the first ground-truthed automated wetland monitoring approach for the region. These findings document the similarities in range of variation between wetlands developing in reclaimed and reference landscapes and provide a simple tool to support long-term monitoring to document the persistence of wetlands forming in reclaimed landscapes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".