Sampling gap fraction and size for estimating leaf area and clumping indices from hemispherical photographs
Bibliographic record
Abstract
Hemispherical photography is becoming a popular technique for gap fraction measurements to characterize biophysical parameters and solar radiation in plant canopies. One of the crucial steps in the measurement of canopy gap fraction using hemispherical photography is determining the resolution of the sampling grid. In this work, the effects of varying resolutions of sampling grids by modifying the angle widths of zenithal annuli and azimuthal sectors were evaluated for leaf area and clumping indices computations. Sensitivity analysis was performed to test these effects using artificial photographs simulating ideal canopies with varying leaf area index and aggregation levels of foliage elements. Contrasting forest types, including natural tropical cloud forest and exotic plantations, were tested as real canopies. Results indicate that leaf area and clumping indices estimates are significantly affected by the variation of sampling grids. A new approach to solve the problem of null-gap segments, obscured completely by foliage, is proposed. However, the determination of optimal combinations of zenithal annuli and azimuthal sector angular widths that suit all canopy types remains a difficult practical problem that is often overlooked. Finally, theoretically sound gap fraction and size sampling regions were demonstrated for reliable estimates of canopy biophysical parameters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 teacher head, 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".