Application of large- and medium-scale aerial photographs to forest vegetation management: A case study
Bibliographic record
Abstract
Five experimental conifer release treatments applied to each of four, three- to seven- year-old spruce plantations resulted in a mosaic of woody and herbaceous vegetation complexes after two growing seasons. A combination of 1:5000-scale overview and 1:500-scale sample photographs were evaluated as a means of mapping and quantifying cover in each of eight vegetation and two non-vegetation categories. On 23-cm format, 1:5000-scale photographs, blocks were stereoscopically stratified into areas (> 25 m2) of uniform vegetation. A random selection of eighty 70-mm format, 1:500 photo samples were then used as "training sites" to calibrate strata assessment on the 1:5000 photographs. Remaining sample plots were used to verify the accuracy of the final map product. Verification plots suggested that principle vegetation components such as tall, mid, and low shrub, grass, and herbaceous species were estimated to within 5–10% cover, at least 70% of the time. Errors for lesser components, such as dead shrub, conifer, bare ground and slash were 2–5% cover. Ferns could not be discerned at the 1:5000 scale and there was evidence of occasional confusion between herbaceous species and other life forms, including mid shrub, low shrub, and grass categories. Operational applications of the methodology are discussed. Key words: remote sensing, digitized aerial photographs, vegetation management, forest classification
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".