Imputing Tree Lists for New Brunswick Spruce Plantations Through Nearest-Neighbor Matching of Airborne Laser Scan and Inventory Plot Data
Bibliographic record
Abstract
Light detection and ranging (LiDAR) has greatly improved the spatial resolution and accuracy of operational forest inventories. However, a cost-effective method to impute species-specific tree-level inventory is needed, to be used as input to tree or stand growth models to project single-point-in-time LiDAR estimates. We evaluated a method to match stand structural variables estimated from LiDAR to those in a library of over 5,500 sample plot measurements to impute tree lists for LiDAR grid cells across 83,000 ha of spruce (Picea sp.) plantations. Matches were determined based on planted species and minimum sum of squared difference between 6 inventory variables. Forest inventory variables obtained by the plot matches were highly correlated (r = 0.91–0.99) with those measured on 98 validation plots. Basal area distributions derived from plot matching were statistically equivalent to those observed on the validation plots 86% of the time (α = 0.05). When we aggregated the predictions for all validation plots, there was minimal difference between predicted and actual basal area distributions by planted species and species compositions were similar. Plot matching is a valid method to impute tree lists for LiDAR cells that combine the wealth of existing plot data with high resolution LiDAR-derived variables.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".