Stand density estimators based on individual tree detection and stochastic geometry
Bibliographic record
Abstract
Individual tree detection methods leave smaller trees hiding below larger trees undetected. This is a problem for remote sensing forest inventories, leading, for example, to severe underestimation of stand density. We develop new methods of formulating the probability of detecting individual trees — the detectability — based on stochastic geometry and use them to derive estimators of stand density. We assume that a tree remains undetected if the centre point of the crown falls within an erosion set based on the larger tree crowns. These estimators allow the tree to be undetected even if a portion of its crown would be visible, taking into account possible differences in the accuracy of remote sensing data and detection algorithms. The behaviour of these estimators is quantified using 36 field plots and compared with a previously proposed estimator. The best estimator according to the data used, allowing trees to be undetected when 40% or more of crown radius is hidden, performs well compared with the estimator formed directly from the number of algorithmically detected trees. It produces a 54% reduction in the root mean square error and shifts the mean of errors notably towards zero in the modelling data. Small variations in allowed visible crown radius do not seem to impact the accuracy of the estimates. Generalization of the results remains as a topic of future research.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".