Seasonal leaf area index variations derived from needle growth and fall measurements in two eastern white pine (Pinus Strobes L.) stands
Bibliographic record
Abstract
Existing global leaf area index (LAI) products have a common problem of too large seasonal variations for conifer stands. The seasonal variation of remotely sensed LAI is often distorted due to many confounding factors. We propose a methodology to quantify the LAI seasonal pattern based on needle lifespan and meteorological conditions. In this paper, we test this methodology in two conifer stands in Ontario, Canada. Needle growth and senescence rates were quantified from needle elongation measurements and litterfall collection. By measuring the average needle lifespan and assuming a dynamic equilibrium of LAI for the full annual cycle, the amount of needles renewed annually can be estimated. Then it is seasonally ‘distributed’ based on these two rates in order to reckon seasonal variations of new and old needle cohorts. These measured LAI seasonal trajectories were compared with combined measurements by TRAC (Tracing Radiation and Architecture of Canopies) and LAI 2000 plant canopy analyzer. The difference between peak LAI from needle growth and fall and that from optical instruments is constrained within 15%. Meanwhile, LAI values from needle growth and fall beyond the growing season are not fallaciously low like LAI from remote sensing data, resulting in a more realistic seasonal variation of needles than that is retrieved from algorithms using remote sensing data. We believe this study can be a positive step towards improving global LAI mapping.
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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) | 0.000 | 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".