Mapping Canada's Rangeland and Forage Resources using Earth Observation
Bibliographic record
Abstract
This thesis implements a pixel-based random forest classifier for differentiating rangeland, pastureland, and forage crops using multi-temporal optical and radar Earth observation (EO) data.Previous efforts to create an inventory of rangeland and forage resources across the Canadian Prairies using EO have not achieved desired accuracies due to spectral reflectance similarities amongst the classes and partly as a result of the regional variability of climate, soil type and management practices.Field data related to land cover type and dominant species composition were collected during the 2015 growing season at study areas west of Brandon, MB and near Lethbridge, AB.Landsat-8 (LS8) multispectral and derived phenological variables, as well as mid-summer RADARSAT-2 (RS2) backscatter data were integrated into a Random Forest (RF) classification.The results of this study highlight the importance of spring optical image acquisitions to differentiate between rangeland and seeded forage in multi-date optical land cover classifications, as well as low performance of mid-summer RS2 backscatter for differentiating rangeland and seeded forage alone and in combination with LS8.
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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 | 0.002 |
| 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) | 0.002 | 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".