A remote sensing methodology for sub-stand growth and yield projection of post-harvest forest regeneration
Bibliographic record
Abstract
Abstract Sustainable forest management is driven by accurate characterization of current forest conditions and predictions of future stand development. The spatial scale of these data affects the level of detail with which decisions can be made. We demonstrate a methodology to forecast forest regeneration attributes derived from fine spatial resolution image data at a sub-stand resolution. A previously trained deep learning model was applied to 10 m × 10 m image tiles to delineate crowns by species in 13-year-old previously harvested stands in Alberta, Canada. Delineated crowns were used to produce stem counts and derive top height measurements from photogrammetrically derived canopy height models. These data were combined with site index values from ecosite phase classification to forecast future forest attributes with the Growth and Yield Projection System. We compared initial and forecasted image-derived forest attributes with conventional methods utilizing field data inputs. The image-derived and field data projections had similar trajectories and endpoints, affirming the utility of this methodology for conventional use. In addition, by mapping growth and yield projections to the 10 m × 10 m grid, our approach provides a spatially detailed resource that can be used to advance understanding of silviculture dynamics and better inform decision-making throughout stand development.
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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 | 0.000 |
| 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 | 0.000 |
| 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".