Incorporating biophysical gradients and uncertainty into burn severity maps in a temperate fire‐prone forested region
Bibliographic record
Abstract
Abstract As forest fire activity increases worldwide, it is important to track changing patterns of burn severity (i.e., degree of fire‐caused ecological change). Satellite data provide critical information across space and time, yet how satellite indices relate to individual measures of burn severity on the ground (e.g., tree mortality or surface charring) and how these relationships change across biophysical gradients remain unclear. To address these knowledge gaps, we used Bayesian hierarchical zero‐one‐inflated beta (ZOIB) regression models with nearly 600 plots of individual field measures of burn severity distributed across the U.S. Rocky Mountains. We asked the following: How do three commonly used satellite indices of burn severity relate to individual field measures of canopy burn severity and forest‐floor burn severity (Q1)? Then, using the highest ranked satellite index, how is reliability affected by biophysical gradients that can be captured in accessible geospatial data (e.g., latitude, slope) (Q2) and stand‐structure data typically available only with field data (Q3)? The Relative differenced Normalized Burn Ratio (Rd NBR ) outperformed the differenced Normalized Burn Ratio (dNBR) and the Relative Burn Ratio (RBR) across canopy and forest‐floor measures of burn severity, but differences among index performances were minor. Overall, indices performed better for field measures of canopy burn severity than for forest‐floor measures. The relationship between Rd NBR and individual field measures of burn severity changed across several biophysical gradients. For example, the same value of Rd NBR corresponded to different field levels of burn severity depending on latitude, pre‐fire forest structure, and pre‐fire beetle outbreaks—and effects of biophysical gradients were often different for canopy vs. forest‐floor measures of burn severity. We show that estimating field measures of burn severity using satellite indices can be improved by including biophysical information, but if variables that are difficult to obtain without field data (e.g., pre‐fire beetle outbreak severity) are lacking, we suggest caution in interpreting satellite indices of burn severity across gradients of pre‐fire biophysical conditions. Finally, using an example fire, we illustrate contrasting maps of burn severity that arise from differences in the relationship between individual field measures of burn severity and Rd NBR after accounting for error in those relationships.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".