Detection of post-fire residuals using high- and medium-resolution satellite imagery
Bibliographic record
Abstract
Forest fires can burn across large forested areas over short time periods, but they rarely consume all the trees in their path. Fires leave live irregularly shaped patches or rows of mature trees known as "residuals" within the fire perimeter. IKONOS and Landsat Enhanced Thematic Mapper Plus satellite imagery were acquired over two forested areas affected by fire in the northern boreal forest of Alberta. Each image was classified and residuals were detected with greater than 88% accuracy. Residual patches were grouped into nine minimum mapping unit (MMU) classes and area, patch, and shape level metrics were calculated for each group. Analysis of metric results highlighted how the choice of satellite imagery used to characterize and quantify residuals, the size of the MMU used to define the residuals, and human induced land use cover change (LUCC) processes occurring within fire perimeters were interrelated factors that impacted estimates of residual numbers and sizes. Residual metrics calculated in one fire perimeter should therefore be carefully assessed according to local land use and land cover change dynamics before suggesting that residual information captured in any fire perimeter can typify residual patterns elsewhere. Key words: remote sensing, high resolution, medium resolution, satellite imagery, forest fires, wildfire, residual forest islands, geographic information systems (GIS), minimum mapping unit
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".