Effect of minimum diameter at breast height and standing dead wood field measurements on the accuracy of ALS-based forest inventory
Bibliographic record
Abstract
Where airborne laser scanning (ALS) measures the entire aboveground vegetation, the target of a stand-level forest inventory is usually the living tree stock above a given diameter but excluding standing dead trees. The aim here was to investigate the effects of varying field-measured minimum diameters (3–10 cm) and standing dead wood on ALS-based forest inventories. The characteristics considered in this case were volume, basal area, number of stems, mean diameter, and mean height for each species, as well as the total growing stock and the total aboveground biomass. The field data comprised measurements of all trees that were ≥3 cm at breast height (1.3 m) on 601 sample plots located in pine-dominated managed forests in eastern Finland. The results showed that the minimum diameter had a significant effect on the estimates obtained in young forests, for which the three smallest minimum diameter datasets (3, 4, and 5 cm) gave the most accurate estimates. Minimum diameter had no marked influence in the case of middle-aged or mature forests. The inclusion of standing dead trees did not have any effect on the estimates of living tree characteristics. The effect of minimum diameter is minor where large-area inventory applications are concerned; however, especially from a silvicultural point of a view, a minimum diameter of 3 cm should be employed in young forests, for which a large proportion of the tree stock usually consists of small trees, i.e., with diameters of <5 cm.
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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 | 0.021 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".