Interpretation of digital imagery to estimate juvenile stand attributes in managed boreal stands, density, stocking and height
Bibliographic record
Abstract
Forest regeneration monitoring is critical to inform forest management planning, evaluate silvicultural efficacy, and determine achievement of renewal standards in managed forests. We assessed the accuracy of operational monitoring using interpretation (INT) of true colour 7–10 cm digital stereo imagery in juvenile stands across a wide range of species compositions typical of northwestern Ontario’s boreal forest. Using the same grid of 16 m2 circular plots established at a density of 2 ha-1, interpreted stand-level estimates were compared to field survey estimates from summarized plot data. Using 1508 field plots, estimates of density, stocking and height were derived for species and species groups (e.g., poplars) across 46 stands. Species compositions were developed using two approaches (all stems and stocking) and accuracy of INT estimates of density, stocking, and height were analysed using an observed (field data) vs. predicted (INT data) linear modelling approach. The INT approach appears useful for monitoring regeneration and providing stand-level estimates of density and stocking, particularly for conifers as a group and for jack pine. However, INT underestimated deciduous tree density and stocking and failed to distinguish spruce from balsam fir or count white birch saplings. These errors have implications for determination of species composition from INT of leaf-off imagery. An approach to quality control is described, and recommendations for ways to improve operational estimates of height and species composition using INT assessments are provided.
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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".