Mapping canopy foliar functional traits in a mixed temperate forest using imaging spectroscopy
Bibliographic record
Abstract
Abstract Foliar functional traits are key drivers of ecological processes in forests. Despite progress in forest foliar trait mapping from imaging spectroscopy, there is a need to build environment-specific, spectra-trait models trained from tree-level measurements to improve the accuracy of local trait maps. We mapped 12 foliar functional traits in a mixed temperate forest using airborne imaging spectroscopy. Top-of-canopy foliar samples from tree crowns (n = 166), representing a total of 16 species, were collected using a drone platform to measure foliar traits for individual trees, from which tree-level crown spectra were also determined. Partial least squares regression (PLSR) models were used to predict foliar traits from tree-level reflectance spectra (400-2400 nm). These models predicted leaf mass per area (LMA), specific leaf area (SLA) and equivalent water thickness (EWT) with high accuracy (R 2 > 0.8, %RMSE < 15). Models for pigment, nitrogen and cellulose concentrations showed a moderate performance (R 2 = 0.53–0.68, %RMSE = 17.24–21.31). Poorest performance was observed for lignin, carbon, leaf dry mass content (LDMC) and hemicellulose (R 2 = 0.24–0.44, %RMSE = 20.67–26.13). High-resolution (1.25 m pixel -1 ) foliar trait maps were produced for the entire 16-km 2 study area. Our study adds to the extensive research aiming to use remote sensing to monitor forest functional trait biodiversity at larger scales and provides models that capture intraspecific variation across many tree species from a mixed temperate forest in eastern Canada.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".