Employing ground-based spectroscopy for tree-species differentiation in the Gulf Islands National Park Reserve
Bibliographic record
Abstract
Airborne hyperspectral data is a promising tool to map species distribution; however, the large number of input bands can be highly correlated and potentially noisy. Ground-based spectrometer data can identify spectral regions that are optimal for species differentiation, and therefore provide a logical initial step for species mapping endeavours employing airborne hyperspectral data. This study used reflectance collected by an Analytical Spectral Devices (ASD) spectrometer to differentiate between tree species common to the Canadian Gulf Islands. Baseline ASD reflectance and its derivatives were used as input for forward stepwise discriminant analyses to identify wavelengths that minimize within-species variance while maximizing between-species variance. Identified wavelengths were then used as input for normal discriminant analyses, which confirmed through cross-validation classifications that, at the leaf scale, species could be differentiated with an overall accuracy > 98% and individual accuracies > 85% using 40 optimal wavelengths. Accuracies slightly decreased when using derivatives, but only for certain species. Results indicate that wavelengths in the ranges 501–550, 681–740 and 1401–1800 nm exhibited the most significance. The selected bands form the basis of ongoing mapping efforts using airborne hyperspectral imagery.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".