Application of Landsat satellite imagery to monitor land‐cover changes at the Athabasca Oil Sands, Alberta, Canada
Bibliographic record
Abstract
A major advantage of satellite remote sensing is that the imagery acquired provides a synoptic view of the landscape. Thus, repeat coverage by the satellite on a regular basis permits the detection of changes in land‐cover over time. This study demonstrates the application of remote sensing technology to the monitoring of mining activities at the Athabasca Oil Sands region of Alberta, Canada. First, we describe the techniques used to match a time sequence of Landsat imagery, both spatially and spectrally, to ensure that the spectral changes through time are due to land‐cover variations. A series of spectral trajectories were then extracted to assess changes in land‐cover through time. Secondly, a land‐cover classification was produced from the baseline 1984 imagery and, using historic and future mine extents, the classification was analyzed to determine the proportion of each land‐cover type affected through development. Results of the analysis indicate that since 1984 there has been a larger reduction in mixedwood dense and broadleaf vegetation classes than mixedwood sparse or dense conifer stands in the area. Based on the delineations of mine‐site activity, the area of woodland and wetland habitat subject to development has increased from approximately 2,520 hectare (ha) in 1984 to 32,930 ha in 2005.
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".