Evaluation of spectral indices for estimating burn severity in semiarid grasslands
Bibliographic record
Abstract
Using Landsat imagery, this study was conducted to evaluate a fire disturbance that occurred in Canada’s Grasslands National Park on 27 April 2013. We used spectral indices (e.g. Normalised Burn Ratio (NBR) and Mid-infrared Burn Index (MIRBI)) derived from Landsat images to evaluate burn severity and to analyse the vegetation recovery process. A field survey was conducted to assess burn severity, which we used to evaluate the performance of spectral indices. Responses of the vegetation community to the fire disturbance were also investigated during the field campaign. Results show that the selected spectral indices performed differently for evaluating burn severity, but MIRBI performed best, likely due to its ability to discriminate post-fire residuals. Severely burned areas were distributed along a river where a larger amount of senesced biomass had accumulated before the fire. The semiarid grasslands showed a strong resilience to fire disturbance, and vegetation recovery was likely influenced by burn severity and water availability. Different vegetation types (e.g. grass, trees and shrubs) had distinct recovery rates and, thus, fire influences plant community development. The fire disturbance changed the composition of grass species in the burned area and also promoted invasion by non-native species.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".