Assessing spatial patterns of burn severity for guiding post-fire salvage logging in boreal forests of Eastern Canada
Bibliographic record
Abstract
Areas affected by forest fires are increasing worldwide, making salvage logging (i.e., harvesting fire-affected trees) an increasingly used practice to reduce the economic impacts of fire on forestry. However, salvage logging can have strong ecological impacts, notably on post-fire forest regeneration and biodiversity. Burn severity (i.e., the degree to which fires impact the vegetation and soil) is also a central element that interacts with pre-fire forest characteristics and salvages logging to control post-fire forest dynamics and biodiversity. In an ecosystem-based forest management context, spatial patterns of burn severity should thus be considered when planning salvage logging operations. This study presents a simple and innovative method to generate burn severity maps with Landsat and Sentinel-2 multispectral imageries to support salvage logging operations rapidly after a fire event. We assembled a unique dataset involving 330 plots from 10 burns (from 2010 to 2020) in which burn severity has been estimated on the field using the composite burn index (CBI) approach in eastern North America. CBI values were modelled as a univariate function of changes in spectral indices using the first cloud-free post-fire satellite images taken after the burns. Our results demonstrate that using free satellite images with straightforward methods can produce reliable and ecologically meaningful burn severity maps within the few weeks following a fire event. The method was then applied to a case study with salvage logging that illustrates how our burn severity maps could be a useful tool for guiding post-fire forestry operations in an ecosystem-based management context. We combined burn severity maps with pre-fire forest composition and age maps to assess immediate post-fire forest. We discuss how such an approach helps to guide salvage planning and maintain residual forests that are representative of the initial post-fire spatial variability in burn severity and pre-fire vegetation. We conclude that rapid mapping of burn severity after a forest fire event may offer many other applications for identifying and managing recently burned forests.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".