Effects of imaging conditions on crown diameter measurements from high-resolution aerial images
Bibliographic record
Abstract
Imaging geometry, the structure of the forest, and certain tree properties can cause inaccuracy in image measurements of the crown dimensions of individual trees. Measurement error of the crown diameter was studied in relation to various factors to explain this error. A secondary aim was to generate calibration models for improving the accuracy of crown diameter image measurements. The crown diameters of a total of 715 sample trees in southern Finland were measured in the field and from aerial photographs at scales 1:6000, 1 : 12 000, and 1 : 16 000. The photo grammetric image measurement seemed to systematically underestimate the true crown diameter, and the major factor affecting the bias was tree species. The mean underestimation varied from 0.30 to 0.80 m, with root mean square errors of 0.951.10 m depending on the tree species. Linear regression analysis was employed to define the factors that had an effect on the image measurements, and calibration models in the form of linear regression models were generated. The calibration models worked reasonably well, and the root mean square error for the calibrated observations decreased by 22% for Scots pine (Pinus sylvestris L.), 53% for Norway spruce (Picea abies (L.) Karst), and 47% for silver birch (Betula pendula Roth).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".