A Multi-Scale Analytical Canopy (MAC) Reflectance Model Based on the Angular Second Order Gap Size Distribution
Bibliographic record
Abstract
Anisotropy in direction hemispherical reflectance of vegetation canopies has been exploited both as a source of information regarding canopy structure and has been considered a source of noise. Geometric optics canopy reflectance models have had some success in relating canopy structure to observed top-of-canopy anisotropy, especially within wavelengths where multiple scattering is not important. However, these models are often scale specific. Furthermore, most models use simplified methods for treating the hotspot effectwhere both the view and illumination direction vectors from a point in the canopy pass through the same gap in the vegetation. We present a multi-scale analytical canopy model (MAC) that deals with an arbitrary number of scales of canopy organisation. The angular gap size distribution defined as the probability of a gap of length along the plane containing both view and illumination direction vectors is applied to describe the extent of the hotspoteffect in a manner that includes all scales. Validation of the MAC model is presented over boreal stands in terms of both top-of-canopy BRDF's as well as sub-canopy gap size distribution. The possibility of using high-resolution imagery to characterize crown clumping using second order reflectance distributions is discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".