Land change attribution based on Landsat time series and integration of ancillary disturbance data in the Athabasca oil sands region of Canada
Bibliographic record
Abstract
The Alberta Oil Sands (AOS) is a unique area in Canada undergoing significant disturbance and recovery due to a variety of anthropogenic and natural factors. Accurately quantifying these changes in space and time is important for assessing ecosystem status and trends. In this research, we implemented an approach to combine Landsat time series for the period 1984–2012 with ancillary change datasets to derive detailed change attribution in the AOS. Detected changes were attributed to causes including fire, forest harvest, surface mining, insect damage, flooding, regeneration, and several generic change classes (abrupt/gradual, with/without regeneration) with accuracies ranging from 74% to 100% for classes that occurred frequently. Lower accuracies were found for the generic gradual change classes which accounted for less than 3% of the affected area. Timing of abrupt change events were generally well captured to within ±1 year. For gradual changes timing was less accurate and variable by change type. A land-cover time series was also created to provide information on “from-to” change. A basic accuracy assessment of the land cover showed it to be of moderate accuracy, approximately 69%. Results show that fire was the major cause of change in the region. As expected, surface mine development and related activities have increased since 2000. Insect damage has become a more significant agent of change in the region. Further investigation is required to determine if insect damage is greater than past historical events and to determine if industrial development is linked to the increasing trend observed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".