A quick and cost-effective method for monitoring deforestation of oil sands mining activities using Synthetic Aperture Radar and Multispectral real-time satellite data from Sentinel-1 and Sentinel-2.
Bibliographic record
Abstract
Alberta’s oil sands mining operations rank among the largest human-made structures globally. Monitoring through the use of Synthetic Aperture Radar (SAR) and Multispectral satellite imaging is an indispensable strategy in attaining sustainable development and mitigating deforestation in the third-largest verified oil reserves worldwide. This paper introduces a novel approach for cost-effective and reliable monitoring of deforestation caused by oil sands mining, avoiding cumbersome methods. It focuses on observing forest/non-forest areas affected by Suncor Energy Company’s mining assets in Alberta, using a combination of SAR and Multispectral satellite remote sensing. Radar images from Sentinel-1B and Multispectral images from Sentinel-2A were analyzed with SNAP 8.0 and QGIS within a time series from June 2017 to June 2020, providing detailed information to monitor better the potential environmental impact of oil sands mining activities in Canada. The Sentinel satellite system offers several advantages, including near-global coverage, elevated spatial resolution for detecting small-scale deforestation instances, and the ability to track temporal and dynamic changes through time-series analysis. Additionally, the system’s open data policy promotes accessibility, collaboration among researchers, and innovative deforestation monitoring applications. The research results hold potential value for decision-makers, enhancing the efficiency and sustainable development of Suncor,s mining operations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".