Monitoring Forest Recovery Following Wildfire and Harvest in Boreal Forests Using Satellite Imagery
Bibliographic record
Abstract
In the managed boreal forest, harvesting has become a disturbance as important as fire. To assess whether forest recovery following both types of disturbance is similar, we compared post-disturbance revegetation rates of forests in 22 fire events and 14 harvested agglomerations (harvested areas over 5–10 years in the same vicinity) in the western boreal forest of Quebec. Pre-disturbance conditions were first compared in terms of vegetation cover types and surficial deposit types using an ordination technique. Post-disturbance changes over 30 years in land cover types were characterized by vectors of succession in an ordination. Four post-disturbance stages were identified from the 48 land thematic classes in the Landsat images: “S0” stand initiation phase; “S1” early regeneration phase; “S2” stem exclusion phase; and “S3” the coniferous forest. Analyses suggest that fire occurs in both productive and unproductive forests, which is not the case for harvesting. Revegetation rates (i.e., rapidity with which forest cover is re-established) appeared to be more advanced in harvested agglomerations when compared with entire fire events. However, when considering only the productive forest fraction of each fire, the revegetation rates are comparable between the fire events and the harvested agglomerations. The S0 is practically absent from harvested agglomerations, which is not the case in the fire events. The difference in revegetation rates between the two disturbance types could therefore be attributed mostly to the fact that fire also occurs in unproductive forest, a factor that has to be taken into account in such comparisons.
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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".