Measuring individual tree height using a combination of stereophotogrammetry and lidar
Bibliographic record
Abstract
Photogrammetric methods using parallaxes can be employed to measure tree heights on aerial photographs. Because it is often impossible to measure ground elevation near trees growing in dense forests, such height measurements remain prone to error. Our objective was to solve this problem by combining a stereomodel and a digital terrain model (DTM) produced by an airborne-scanning system that uses light detection and ranging (lidar). A stereopair of scanned aerial photographs was first registered to a lidar DTM. The elevation of the apex of 202 Thuja occidentalis (L.) individuals was measured by an observer on a digital photogrammetric workstation. The tree base elevations were read from the lidar DTM and subtracted from the corresponding apex elevations to calculate individual tree heights. These were then compared with the heights measured in the field. The average photo-lidar bias was 0.59 m, and the average deviation of 1.01 m decreased to 0.88 m when the bias was removed. It was demonstrated that the photographic clearness of the tree apices influences the height error, while the density of the lidar echoes under the forest canopy does not. Using this method, retrospective studies of changes in tree height become feasible by using archived aerial photographs and recent lidar DTMs.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".