Empirical models for tracing seasonal changes in leaf area index in deciduous broadleaf forests by digital hemispherical photography
Bibliographic record
Abstract
Accurate estimation of seasonal leaf area index (LAI) variations is essential for predicting forest growth, but rapid and reliable methods for obtaining such estimates have rarely been reported. In this study, direct measurements of LAI seasonal variations in deciduous broadleaf forests in China were made through leaf seasonality observations in the leaf-out season and litter collection in the leaf-fall season. Meanwhile, indirect LAI measurements were made using a digital hemispherical photography (DHP) method. Our objectives were to explore the relationship between direct and indirect LAI measurements and to recommend a rapid and reliable method to determine the seasonal variation of LAI in forests. To achieve these objectives, we first evaluated seasonal variations of the biases due to key factors (woody materials, clumping effects and incorrect automatic exposure) known to influence the estimation of LAI by DHP. The results showed that the biases due to these factors exhibited different seasonal variation patterns, and the total contribution of these factors could explain 72% of the difference between direct LAI and DHP LAI throughout the entire growing season. Second, linear regression models between direct and DHP LAI were first constructed for each 10-day period as well as the entire growing season. Significance tests were made to the differences among the models for different dates, and models for estimating LAI based on DHP in each date were aggregated to 4 periods with R 2 and RMSE values of 0.91 and 0.22, 0.79 and 0.29, 0.81 and 0.14, 0.97 and 0.14, respectively. There was no significant difference between direct LAI and estimated LAI using the four models in each aggregated period ( p < 0.01). Thus, we confirm that these models can fully simulate the seasonal variations in LAI from the initial leaf emergence to leaf fall in deciduous broadleaf forests.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".