Individual tree-based species classification for uneven-aged, mixed-deciduous forests using multi-seasonal WorldView-3 images
Bibliographic record
Abstract
Individual tree-based species maps are valuable for sustainable forest practices from both economic and ecological perspectives. Recent advances in high spatial resolution remote sensing provide the opportunity to map trees species with greater resolution and accuracy. This study aims to classify tree species at the individual tree level by using multi-seasonal WorldView-3 images. Our study site is in Haliburton Forest and Wildlife Reserve, located in the Great Lakes-St. Lawrence region of Central Ontario, Canada. The multi-seasonal WorldView-3 images collected in 2015 include late spring, mid-summer, early-fall, and late-fall scenes. Our analysis indicates that the overall accuracy of individual tree-based species classification is the highest when using mid-summer, followed by the late-spring image, and the early-fall image. The synergistic use of above three-seasonal images improves the classification accuracy substantially. Adding late-fall image fails to increase the classification accuracy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".