Regional-scale mapping of native grasslands in Alberta, Canada, using optical and microwave remote sensing
Bibliographic record
Abstract
Canada’s Prairie grasslands are ecologically and agriculturally significant but face threats from climate change, urban expansion, and resource extraction. Accurate mapping of these grasslands is essential for conservation, sustainable management, and understanding land use change. This thesis explores methodological advancements in mapping Prairie grasslands using high-resolution satellite imagery, synthetic aperture radar (SAR), machine learning (ML), and geographic object-based image analysis (GEOBIA). These tools address challenges in distinguishing native grasslands from spectrally similar land cover classes, like seeded forage, which complicates traditional mapping techniques. Focusing on Southern Alberta’s ecoregions, the research investigates the spatial and temporal variability of compact polarimetric C-band SAR backscatter in native and tame grasslands, providing insights into their complex environments. Results highlight the limitations of a "one-size-fits-all" approach due to the variability within the Prairie ecozone, emphasizing the need for spatially robust field monitoring to enhance provincial and federal land cover classification efforts. The findings demonstrate significant improvements in regional-scale grassland mapping accuracy, enabling better identification of at-risk areas and informing targeted conservation initiatives. Additionally, the research underscores the importance of investing in SAR infrastructure for terrestrial vegetation mapping in Canada. By advancing remote sensing methodologies, this study contributes to geography and provides a framework for environmental monitoring and management. Its practical implications extend to policymakers, conservationists, and land managers working to preserve the ecological integrity of Canada’s Prairie grasslands.
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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 | 0.002 |
| 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".