An Integrated System for Estimating Forest Basal Area from Spherical Images
Bibliographic record
Abstract
Basal area is one of the most important parameters in forest inventory, but data collection by traditional methods is often time consuming and labor intensive. This study uses a new, portable, and relatively inexpensive 360° spherical camera to estimate stand basal area and make permanent digital forest visual records. Forty-five plots in Newfoundland and eighty-three plots in New Brunswick were used to compare traditional field inventory with spherical photo inventory and to analyze potential factors impacting results. Results showed that 1) photo estimated basal area is similar to traditional methods measured by diameter tape and fixed-area plots or by angle gauge counting; 2) better accuracy and precision can be achieved when adding multiple digital sample locations to avoid effects of hidden trees caused by nearby trunks; 3) understory tree and shrub density did not significantly influence stem visibility; and 4) differences among different users were tested and shown to not be significant. An open-source software package was developed to make the implementation of our technique easy and efficient.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.009 | 0.003 |
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".