Evaluation of species-specific tree density and height interpreted from airborne digital stereo imagery in young boreal stands in Northwestern Ontario
Bibliographic record
Abstract
Monitoring silviculture through accurate assessment of the density and height of trees in young (10–25 years) stands is a critical component of sustainable forest management. Reporting species composition and height of young stands that regenerate after harvest and renewal treatments ensures planned targets have been achieved. In the boreal regions of Ontario, operational assessment of young stand conditions is conducted using qualitative visual and/or higher cost quantitative plot-based field assessments. High resolution three-dimensional digital imagery can be collected using manned aircraft across large forest management areas for stereo-photo interpretation. Our objective was to evaluate the accuracy of stereo-photo interpretation of species-specific tree counts and height in plots in digital imagery, and the effect of resolution on interpretation accuracy. Digital imagery (7-cm and 10-cm resolution) was acquired across nine stands representing common regeneration types. Prior to imagery acquisition, marked plots were established and assessed in the field; 177 plots were used in the analysis. Higher resolution imagery improved accuracy of total and conifer tree counts and conifer heights. Interpreted counts of white birch trees and height estimates of deciduous stems were not accurate using the leaf-off imagery, and interpreters could not differentiate spruce from fir.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".