Expansion of treed area over Canada’s forested ecosystems: spatial and temporal trends
Bibliographic record
Abstract
Abstract Monitoring changes in forest cover is critical to understanding forest dynamics and informing sustainable forest management practices. Both Canada’s National Forest Inventory and satellite-based monitoring programs have consistently reported an increasing trend in treed area over recent decades, despite differences in survey design, spatial resolution, and temporal representation. This study presents a spatially explicit mapping approach that integrates annual satellite-derived land cover data (1984–2022) with historical disturbance records to analyze treed area dynamics at regional and national scales. Using this approach, we assess the spatial and temporal trends in treed areas in Canada’s forested ecosystems over nearly four decades, distinguishing between treed area gains resulting from recent disturbances and those related to older, pre-1984 baseline events. Our analysis revealed an average annual increase in treed area of 0.19% nationally (632 655 ha per year), resulting in a total increase of 24.04 Mha (7.2%) over the 39-year period. This increase is mainly due to post-disturbance tree regrowth and natural expansion of trees into previously non-treed areas, especially along forest edges, in gaps, and in wetlands transitioning to treed vegetation. Tree dynamics varied by ecozone, with northern regions (e.g. Hudson Plains, Taiga Cordillera) experiencing the greatest relative gains in treed area, while southern regions (e.g. Montane Cordillera) showed localized declines due to wildfire and other disturbances. Comparisons with National Forest Inventory data revealed similar trends in treed area increase, with notable differences in the direction of change for some ecozones, such as the Hudson Plains and Montane Cordillera. By providing spatially and temporally detailed insights, this study complements sample-based national statistics and provides annualized, spatially explicit mapping that enhances our understanding of forest dynamics and informs similar applications in other regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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".