Identifying leading species using tree crown metrics derived from very high spatial resolution imagery in a boreal forest environment
Bibliographic record
Abstract
Utilizing the spatial information inherent in panchromatic very high spatial resolution (VHSR) imagery, we explored the use of tree crown metrics for identifying leading species over four study sites in the Yukon Territory, Canada. Image segmentation was used to delineate homogeneous forest stands, followed by a tree crown delineation algorithm that identified individual tree crowns within each stand. Leading species in the study area included white spruce, black spruce, lodgepole pine, and trembling aspen. Nonparametric multivariate statistical tests indicated that some tree crown metrics generalized at the stand level have significant utility for discriminating leading species. Based on this result, a classification tree was generated using the crown metrics and independent calibration and validation datasets. The classification tree accurately identified leading species in 72.5% of the stands used for validation (n = 212), with the accuracy for individual species ranging from 43.9% to 100.0%. Most errors resulted from confusion between white spruce and the three other, less common, leading species. This study demonstrates the capacity of the spatial information content of panchromatic VHSR imagery to generate a series of crown metrics for discriminating among four common tree species of the Yukon Territory, a location with spatially and temporally limited forest monitoring practices.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".