Estimating Tree Diameter and Volume with a Taper Model and Large-Scale Photo Measurements
Bibliographic record
Abstract
Abstract Large-scale photo (LSP) mensurational procedures were developed, in part, to reduce field costs by replacing much of the ground sampling with less expensive photo measurements. The conventional LSP approach uses photo measurements of tree height and crown area, which serve as independent variables in models, to predict tree diameter or volume. This study compared 18 linear and nonlinear model forms for estimating tree diameters and assessed the use of a provincial taper model to estimate total tree volume from LSP data. On average, linear models produce R2, root mean square error, and mean bias values that were at least equivalent to, if not statistically better than, nonlinear models for the range of data evaluated. For lodgepole pine, white spruce and a composite of two deciduous species (trembling aspen and balsam poplar), total volume estimates were not statistically different from those estimated from field measurements. A comparative analysis of LSP and field sampling costs suggests the use of taper models in LSP mensuration could save considerable cost and effort in data collection and model development. This finding may result in an increased use of LSP in operational forest inventory work. North J. Appl. For. 18(4):110–118.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".