Multi-temporal Burned area Mapping Using Logistic Regression Analysis and Change Metrics
Bibliographic record
Abstract
This paper describes a procedure developed for continental-scale mapping of burned boreal forest at 10-day intervals. The basis of the technique is a multiple logistic regression model applied to 1 km SPOT VEGETATION (VGT) clear-sky composites. Independent variables consist of multi-temporal change metrics representing 10-day and surrounding 30-day changes in reflectance and in two vegetation indices. The metrics account for seasonal phenological variation by normalizing them to the reflectance trajectory of background vegetation. Three spatial-contextual tests are applied to the per-pixel logistic model output to remove false burned pixels and increase the sensitivity of detection. The procedure was tested over Canada using conventional fire surveys and burned area statistics from the 1998-2000 fire seasons. The area of falsely mapped burns was small (2% commission error over two provinces), and most burns larger than 10 km2 were accurately detected and mapped (R2 = 0.90, P<0.005, n = 91). National-level VGT burned areas for 1998-2000 were within 3-17% of fire management agency burned area compiled by the Canadian Interagency Forest Fire Centre (CIFFC).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".