Close-range photogrammetry with spherical panoramas for mapping spatial location and measuring diameters of trees under forest canopies
Bibliographic record
Abstract
Measurement of tree attributes is important to collect information for forest management. Close-range photogrammetry with spherical panoramas has seen very little development and applications compared with aerial photography. This study develops methods to extract azimuth, horizontal distance, diameter at breast height, and upper stem diameters of individual trees from spherical panoramas based on (i) the trigonometry principle (TRIGO), (ii) the TRIGO corrected for terrain slope (TRIGOSLP), and (iii) the pinhole camera model (PINHOLE). Twenty-three horizontal point sample plots were randomly established in plantations in Taiwan, for a sample size of 486 trees. Results showed that tree azimuth was accurately and precisely estimated. TRIGO performed the worst in accuracy and precision for all other tree attributes. TRIGOSLP improved the results of TRIGO but had large estimation errors. PINHOLE achieved the best overall precision for all other tree attributes but was slightly inaccurate for estimating upper stem diameters. PINHOLE requires approaching a tree to attach a target of known size but has the ability to extract an almost continuous set of upper stem diameters from the tree, which could improve estimation of tree volume. Thus, PINHOLE could potentially be an alternative measurement system for hard-to-measure tree attributes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".