Responses of spectral indices to heat and drought differ by tree size in Douglas-fir
Bibliographic record
Abstract
The frequency of extreme heat and drought events is predicted to increase in temperate forests throughout the 21st century. The effects of these stressors on established trees with ranging height and crown conditions, and the potential trade-offs between rapid growth and vulnerability to extremes, are poorly understood – even for drought-adapted species. In this study, we use drone-mounted LiDAR and multispectral sensors to analyze spectral responses by tree size in 24-year-old coastal Douglas-fir during the summer of 2022, which experienced a heat event with temperatures reaching 38°C and a seasonal drought where soil water content (m3/m3) fell by over 65% to 0.06 m3/m3. Comparing pre-heat to heat event vegetation indices, all structural clusters show significant (α = 0.01) decreases in the photochemical reflectance index (PRI), slope of the red edge (RE slope), and normalized difference red edge index (NDRE717); small trees express the greatest drop in PRI and large trees show the greatest decrease in RE slope and NDRE717. This suggests that small trees experience a greater photoprotective response via decreases in photosynthetic activity and large trees undergo a greater response in terms of structural health. Under drought conditions, all structural clusters show significant (α = 0.01) declines in the green chromatic coordinate (GCC), chlorophyll carotenoid index (CCI), and RE slope. Declines in GCC and CCI suggest an onset of yellowing and shifts in seasonal pigment pools from chlorophylls to carotenoids, however this response is not able to be pinpointed to drought as it cannot be untangled from the natural physiological response of pigment shifts and yellowing that occur in the late summer. Large trees experience a greater decrease in RE slope between pre- and mid-drought acquisitions compared to medium and small trees; however, the RE slope of small trees is significantly (α = 0.01) lower than both medium and large trees mid-drought. This suggests that although large trees undergo a greater decrease in RE slope, they are able to maintain more photosynthetic pigments and greater structural health throughout the drought period compared to small trees. Overall, this work demonstrates the potential for multispectral phenotyping of heat response in various sizes of costal Douglas-fir.
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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 | 0.000 | 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) | 0.001 | 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".