Using Landsat Imagery to Assess Burn Severity of National Forest Inventory Plots
Bibliographic record
Abstract
As the frequency and size of wildfires increase, accurate assessment of burn severity is essential for understanding fire effects and evaluating post-fire vegetation impacts. Remotely-sensed imagery allows for rapid assessment of burn severity, but it also needs to be field validated. Permanent forest inventory plots can provide burn severity information for the field validation of remotely-sensed burn severity metrics, although there is often a mismatch between the size and shape of the inventory plot and the resolution of the rasterized images. For this study, we used two distinct datasets: (1) ground-based inventory data from the United States national forest inventory to calculate ground-based burn severity; and (2) remotely-sensed data from the Monitoring Trends in Burn Severity (MTBS) database to calculate different remotely-sensed burn severity metrics based on six weighting scenarios. Our goals were to test which MTBS metric would best align with the burn severity of national inventory plots observed on the ground, and to identify the superior weighting scenarios to extract pixel values from a raster image in order to match burn severity of the national inventory plots. We fitted logistic and ordinal regression models to predict the ground-based burn severity from the remotely-sensed burn severity averaged from six weighting scenarios. Among the weighting scenarios, two scenarios assigned weights to pixels based on the area of a pixel that intersected any parts of a national inventory plot. Based on our analysis, 9-pixel weighted averages of the Relative differenced Normalized Burn Ratio (RdNBR) values best predicted the ground-based burn severity of national inventory plots. Finally, the pixel specific weights that we present can be used to link other Landsat-derived remote sensing metrics with United States forest inventory plots.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".