Monitoring Cumulative Long-Term Vegetation Changes Over the Athabasca Oil Sands Region
Bibliographic record
Abstract
This study uses two remotely sensed vegetation indices to investigate cumulative long-term changes of undisturbed vegetation in the Athabasca Oil Sands region of Alberta, Canada, between 1984 and 2012. The Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Wetness Index (NDWI) were derived from both Landsat and MODIS time series, for comparative purposes and to increase confidence in detected trends. Trend analysis of undisturbed forest areas, i.e., area without abrupt changes revealed a consistent decrease in vegetation condition, quantified by an average reduction of 18.6% ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="TeX">${\bf SD} = {\bf 5.02\%} $</tex-math></inline-formula> ) in NDVI and of 31.0% ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="TeX">${\bf SD} = {\bf 10.06\%} $</tex-math></inline-formula> ) in NDWI, over the 28-year period. The study does not conclusively associate the trends with any single stressor, but seeks to quantify the spatial and temporal distribution of cumulative effects resulting from a variety of natural and anthropogenic causes. Examination of the temporal pattern of trends showed an increase in the occurrence of decreasing trends in the last 10 years. The decreasing trends were more frequent closer to mining developments for both the Landsat and MODIS time series. Climate change was not considered a major causal factor as climate normalized trends had little effect on the results. The trend analysis undertaken can be used to enhance <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> monitoring programs for site selection of additional monitoring facilities particularly regarding potential cumulative effects, provide an indication of likely future short-term changes in the region, and to aid in the development of mitigation measures.
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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 | 0.000 |
| 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".