Canadian prairie drought assessment through MODIS vegetation indices
Bibliographic record
Abstract
Consecutive droughts that have occurred in the Canadian prairies have resulted in significant economic losses, ecological degradation, and environmental deterioration. The purpose of this study was to investigate the efficiency of remotely sensed data on drought assessment combined with climate data. The study area was the Canadian prairie ecozone in the provinces of Alberta, Saskatchewan, and Manitoba. There objectives were five-fold: 1) comparing Kriging and inverse distance weighting (IDW) interpolation methods, 2) comparing four spectral variables, the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the red and the mid infrared (MIR), 3) comparing three moisture indices (P-PET, P/PET and (P-PET/PET), 4) evaluating the relationships between spectral variables and moisture indices, and 5) assessing drought effects on different ecoregions. Results showed that there is no significant difference between Kriging and IDW, the two interpolation methods. MODIS vegetation indices could effectively assess drought conditions, especially EVI. Among the moisture indices compared, P-PET showed a better result. The impacts of droughts vary from year to year and from ecoregion to ecoregion. Aspen Parkland has higher drought resistance because of tree components.
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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 | 0.001 | 0.001 |
| 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".