Forest-fire-scar aging using SPOT-VEGETATION for Canadian ecoregions
Bibliographic record
Abstract
The mapping of Canadian fires is a large effort supported by provincial, territorial, and federal agencies. Remote sensing techniques can aid in mapping, especially in remote areas and during busy fire seasons. The SPOT-VEGETATION (SPOT-VGT) sensor has previously shown promise at distinguishing fire scars on the landscape. The usefulness of SPOT-VGT to age fires in 18 Canadian ecoregions was evaluated for a period up to 50 years since fire, analysing more than 250 000 pixels (nominal resolution about 1 km2). The SPOT-VGT reflectances were evaluated using the ratio of the short-wave infrared band (1.581.75 µm) to near-infrared band (0.780.89 µm), compared with the Canadian large-fire database (fires greater than 200 ha in size). Nonlinear regressions were significant for all ecoregions with r2 values being greater than 0.57 for 16 of them. Five ecoregions groupings had similar relationships, consistent with their contiguous pattern on the landscape. The prediction of fire-scar age depends on ecoregion and can be successful over periods as short as 6 years to as long as 30 years. The root mean square error for all ecoregions ranged from 5 years for recent burns to about 12 years for three decades following fire. This tool is useful to get approximate fire-scar ages, but the accuracy is limited because of the variation in forest succession on the landscape, and it cannot replace more detailed mapping done currently by fire agencies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".