Evaluating the potential of Landsat TM/ETM+ imagery for assessing fire severity in Alaskan black spruce forests
Bibliographic record
Abstract
Satellite remotely sensed data of fire disturbance offers important information; however, current methods to study fire severity may need modifications for boreal regions. We assessed the potential of the differenced Normalized Burn Ratio (dNBR) and other spectroscopic indices and image transforms derived from Landsat TM/ETM+ data for mapping fire severity in Alaskan black spruce forests (Picea mariana) using ground measures of severity from 55 plots located in two fire events. The analysis yielded low correlations between the satellite and field measures of severity, with the highest correlation (R2adjusted = 0.52, P < 0.0001) between the dNBR and the composite burn index being lower than those found in similar studies in forests in the conterminous USA. Correlations improved using a ratio of two Landsat shortwave infrared bands (Band 7/Band 5). Overall, the satellite fire severity indices and transformations were more highly correlated with measures of canopy-layer fire severity than ground-layer fire severity. High levels of fire severity present in the fire events, deep organic soils, varied topography of the boreal region, and variations in solar elevation angle may account for the low correlations, and illustrate the challenges faced in developing approaches to map fire and burn severity in high northern latitude regions.
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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 | 0.001 | 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 | 0.001 |
| 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".