Effects of pulse density on predicting characteristics of individual trees of Scandinavian commercial species using alpha shape metrics based on airborne laser scanning data
Bibliographic record
Abstract
Operational pulse density affects the measurements based on airborne laser scanning (ALS) data, especially at the individual tree level. The minimum density required depends on the interpretation methodology used, i.e., knowing the requirements is a prerequisite for a successful ALS data acquisition. We evaluate these requirements for a recently introduced alpha shape metrics approach in which computational volume and complexity metrics derived from ALS point clouds are utilized to produce actual tree-level characteristics. We simulated thinnings to the ALS return data using a test dataset of a total of 92 dominant or codominant trees detected and delineated manually from very high density (approximately 40 returns/m2) initial ALS data and produced species and diameter at breast height estimates with the thinned datasets. We compared the alpha shape metrics approach with alternative methods, making additional use of tree-level ALS data, and examined the sensitivity of the different methods to pulse density. The results show that in addition to the species classification possibilities recognized earlier, alpha shape metrics computed from very high density ALS data are also useful for predicting tree dimensions. Upon analysing the thinned data, the alpha shape metrics were generally discovered to suffer most from a lower pulse density. On the other hand, tree level canopy height distribution variables appeared to be more neutral for the pulse density and could be used at low density levels to complement and stabilize the alpha shape based methods for predicting both species and diameter. The results indicate that, provided individual trees can be accurately delineated, the species and diameter of mature coniferous trees in particular can be predicted using ALS data, even with a very low pulse density. As the alpha shape metrics performed well at densities that were only moderate for the individual tree delineation approach, more research is suggested to determine their full potential. Additionally, identifying trees automatically using more representative data needs to be examined to generalize the obtained result.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.001 |
| 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".