Estimating stand attributes of complex forest types in subtropical mountain environments by combining the shadow fraction method with analyses of high-dynamic-range photographs
Bibliographic record
Abstract
Shadow fractions can be overestimated because of topographic shadows, which can occupy a significant area on aerial photographs of mountainous terrain. In this study, we first used high-dynamic-range (HDR) image analysis techniques to extract the original canopy shadow from the topographic shadows on aerial photographs. Subsequently, we applied the shadow fraction method to estimate selected forest attributes (stand height, basal area, and stem volume). In this paper, we discuss the effects of tree shadow fraction normalization, auxiliary spectral information, and forest type on forest attribute estimation. HDR image analysis successfully extracted canopy shadow information from topographic shadows. The tree shadow fraction normalization method had no obvious effect. The shadow fraction enhanced spectral information to estimate stand attributes. Using shadow fractions resulted in better estimates of stand height for mixed-hardwood forest ([Formula: see text] = 0.45), basal area for mixed-hardwood forest ([Formula: see text] = 0.50), and stem volume for conifer–hardwood forest ([Formula: see text] = 0.43). This difference in estimated results is related to the shade patterns produced by stand structures in the different forest types.
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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".