Large-scale detection of vegetation dynamics using MODIS images and BFAST: A case study in Quebec, Canada
Bibliographic record
Abstract
BFAST (Breaks For Additive Seasonal and Trend) method and MODIS NDVI data were used to detect vegetation dynamics in Quebec during the period of 2000-2012. The Permanent Sample Plots (PSP) data were used to assess the detection method. The results demonstrated that 25.68% of the study area experienced NDVI trend changes during the research period. The detected timing of the biggest changes showed obviously that the areas with the biggest change in 2009 and 2002 were the top two with area percentages of 29.12% and 17.41%, respectively. The results suggested that abrupt vegetation greening occurred especially in 2009 with 58.33% of the overall abrupt greening. The abrupt vegetation browning occurred especially in 2002 with 28.22% of the overall abrupt browning. “Total cut” and “total burning” could be monitored easily using BFAST approach while “insect outbreak” and “plantation” could not be detected satisfyingly.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".