Woody-to-total area ratio determination with a multispectral canopy imager
Bibliographic record
Abstract
Leaf area index (LAI) - defined as one half of the total green leaf area per unit ground surface area - can be determined by direct or indirect methods. Three major sources of errors exist in indirect LAI measurements: within-shoot clumping, beyond-shoot clumping and non-photosynthetic components. The effect of non-photosynthetic components on LAI measurements can be described by the woody-to-total area ratio, alpha; however, no convenient and efficient indirect methods have been developed to estimate alpha, especially the variations in alpha with zenith angle , alpha(theta). We describe the development and use of a multispectral canopy imager (MCI) to estimate alpha and alpha(theta) by considering the effects of non-random distributions of canopy elements and woody components and the overestimation of needle-to-shoot area ratio on woody components. The MCI, which mainly comprises a near-infrared band camera (Fujifilm IS-1), two visible band cameras (Canon 40D), filters and a pan tilt, was developed to measure clumping index, woody-to-total area ratio and geometric parameters of isolated trees. Two typical sampling plots (Plots 1 and 5) chosen from among 16 permanent forest experiment plots were selected for the estimation of alpha and alpha(theta). The non-random distributions of canopy elements and woody components were estimated separately at eight zenith angles (from 0 degrees to 70 degrees in increments of 10 degrees) using MCI images based on the gap size distribution theory. The visible/near-infrared image pairs captured by the MCI were able to discriminate among sky, leaves, cloud and woody components. Based on three methods of estimation, we obtained woody-to-total area ratios of 0.24, 0.19, 0.19 for Plot 1 and 0.23, 0.18, 0.17 for Plot 5. If clumping effects were ignored, alpha values were overestimated by as much as 21% and 24% at Plots 1 and 5, respectively. We demonstrated that alpha(theta) varied with the zenith angle, with variations in the range of 3-33% at Plot 1 and 2-65% at Plot 5. A new formula for the precise determination of LAI is proposed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".