Recent advancements in optical field leaf area index, foliage heterogeneity, and foliage angular distribution measurements
Bibliographic record
Abstract
In-situ estimations of leaf area index (LAI), leaf clumping, and leaf angular distribution are often performed from canopy gap fraction measurements with optical sensors. Two new procedures are used in this study to improve the estimation of gap fraction from digital camera photographs,: 1) the Digital Number (DN) of mixed sky-canopy pixels is use to estimate the within pixel gap fraction instead of the usual threshold used to separate a pixel in gap or a foliage pixel, and 2), the within pixel gap fraction is calculated at different view zenith and azimuth angles to take into account multiple scattering effects. To estimate foliage clumping, a gap size distribution is calculated from a narrow view zenith angle range (less than 1°). The clumping index is then extracted using 3 methods: 1) a refined gap size distribution theory developed for the TRAC instruments; 2) The Lang and Xiang logarithm gap fraction averaging and 3) a combination of 1) and 2). Clumping index variations with view zenith angle in the range from 15° to 70° are derived using the individual and combined methods. Analysis of the digital hemispherical photographs shows that 1) the three methods give different clumping estimates, but the angular variation patterns are similar, and 2) canopies with significant angular variation in clumping can induce large errors in the inverted leaf angle distribution when the clumping angular variation is not included in the retrieval. The practical implication of these findings is that LAI, clumping index, and foliage orientation can all be reliably retrieved using digital hemispherical photographs, considerably reducing the number and cost of instruments needed in fieldwork.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".