Enhanced forest inventories in Canada: implementation, status, and research needs
Bibliographic record
Abstract
Forest inventory practices in Canada have evolved over time with changes in forest management priorities, advances in technology, fluctuations in the marketplace, societal expectations, and generational shifts in the workforce. Provincial and territorial governments in Canada are vested with forest management responsibilities and each jurisdiction has adopted forest inventory approaches that reflect jurisdictional information needs and contexts. Typically, these inventories are strategic in nature and spatially explicit, providing stand-level forest attribute information derived from a two-phase approach involving manual air photo interpretation and stratified ground plot sampling. Airborne laser scanning (ALS; also known as light detection and ranging or lidar) has emerged as a transformative data source for forest inventories and is now considered operational, with the resulting outputs commonly referred to as enhanced forest inventories (EFI). Herein we review and synthesize how EFIs are influencing forest inventory practice in Canada. We characterize the spatial coverage and characteristics of ALS data acquired for forest inventory purposes, summarize the current status of EFI implementation within Canada’s provinces and territories, identify emerging trends associated with these EFIs, and consider these EFIs in the broader global context. We also highlight common research gaps towards the development of a nationally and globally relevant research agenda to support the greater integration of remotely sensed data into forest inventory programs in Canada and beyond.
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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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.016 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".