Assessing lean and positional error of individual mature Douglas-fir (<i>Pseudotsuga menziesii</i>) trees using active and passive sensors
Bibliographic record
Abstract
There is a growing demand for point cloud data that can produce reliable single-tree measurements. The most common platforms for obtaining such data are unmanned aircraft systems with passive sensors (UAS), unmanned aircraft equipped with aerial lidar scanners (ALS), and mobile lidar scanners (MLS). Our objectives were to compare the capabilities of the UAS, ALS, and MLS to locate treetops and stems and to estimate tree lean. The platforms were used to produce overlapping point clouds of a mature Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) stand, from which 273 trees were manually identified. Control trees were used to test tree detection accuracy of four algorithms and the number of stems detectable using each platform. Tree lean was calculated in two ways: using the stem location near the canopy and using the treetop. The treetops were detected more accurately from ALS and UAS clouds than from MLS, but the MLS outperformed ALS and UAS in stem detection. The platform influenced treetop detection accuracy, whereas the algorithms did not. The height estimates from the ALS and MLS were correlated (R2 = 0.96), but the MLS height estimates were unreliable, especially as distance from the scanner increased. The lean estimates using the stem locations or treetop locations produced analogous distributions for all three platforms.
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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 | 0.001 | 0.000 |
| 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".