Stability of boreal forest stands during recent climate change: evidence from Landsat satellite imagery
Bibliographic record
Abstract
Aim To detect possible expansion of boreal forest stands in response to recent warming. Previous modelling studies have concluded that major shifts in vegetation patterns, including changes in boreal forest extent, could arise during the next two centuries under global warming scenarios. However, field investigations of tree stands at ecotones have so far revealed little indication of stand response to warming during the last 100 years. This study uses a c. 25‐year record of Landsat satellite observations to quantify changes in forest stand extent in two areas of northern Canada. Location Two regions of northern Canada, near Richmond Gulf, Quebec, and Great Slave Lake, north‐west Territories. Methods Normalized‐difference vegetation index (NDVI) plots across forest‐tundra boundaries were obtained from radiometrically corrected Landsat imagery acquired during the 1970s and 1990s. These curves were evaluated to look for changes over the c. 25‐year period related to forest stand expansion. Results Although forest‐tundra boundaries could be clearly mapped from the satellite data, no obvious change in forest boundaries was apparent during the duration of the image time series, constraining recent geographical expansion rates to <200–300 m per century. Also, no evidence for local expansion of forest stands (e.g. within sheltered valleys) was found. Main conclusions The results are consistent with field observations, and suggest that, at the moment, boreal forest extents remain basically stable. This may reflect inherent lags between forest response and climate change, or competitive pressures between tree stands and surrounding tundra and herbaceous vegetation.
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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 | 0.002 |
| 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 | 0.000 |
| 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".