Spectral Diversity as a Predictor of Tree Diversity: Exploring Challenges and Opportunities Across Forest Ecosystems
Bibliographic record
Abstract
Forests are crucial for ecosystem health, climate regulation, and biodiversity. However, many tree species face extinction threats, requiring active monitoring for conservation. The spectral variation hypothesis (SVH) suggests that spectral diversity can serve as a proxy for ground-measured biodiversity. Despite its promise, SVH’s application has shown inconsistent results, complicating its use in biodiversity monitoring. This study examines the relationship between tree diversity and Sentinel-2-derived spectral diversity across Quebec’s forests, analyzing 2531 inventory plots using a combination of spectral analysis, cluster analysis and random forest (RF) regressions. We evaluate four biodiversity indices: species richness, Shannon diversity, functional dispersion, and percent conifer. Our analysis reveals overlapping spectral signatures that make it challenging to differentiate between varying levels of species richness, Shannon diversity, and functional dispersion. However, percent conifer shows spectral separability and can be stratified using unsupervised k-means clustering. Using RF regression models, only the percent conifer models demonstrated strong performance (R2 = 0.77), while models for the other biodiversity indices did not exceed an R2 of 0.46. This study highlights the complex relationship between spectral diversity and tree diversity, and suggests that future research should aim to improve the understanding of the relationship, or lack thereof, between ground-measured biodiversity indices and relatable spectral metrics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".