Determining the Contribution of Shaded Elements of a Canopy to Remotely Sensed Hyperspectral Signatures
Bibliographic record
Abstract
Hyperspectral imagery has the potential to become a useful tool for monitoring and extracting biophysical properties of vegetated areas. Exploitation of this potential relies on the ability to relate at-canopy spectral reflectance to biophysical characteristics of vegetation and derive both sunlit and shaded component proportions and spectral profiles. Increased application of hyperspectral imagery to these areas is expected with the advent of space borne hyperspectral sensors (such as EO-1 Hyperion and CHRIS-PROBA). Such imagery of vegetated scenes is influenced however by the well known bidirectional reflectance distribution (BRDF) effect. One method of determining the contribution of shaded overstorey vegetation and background to observed spectral reflectance is to determine, by model inversion, the proportion of shaded surfaces viewed by the sensor, and the relative intensity of the radiative flux incident on these surfaces. This can be achieved by modelling the overall reflectance as composed of mean sunlit and shaded reflectance components, combined with an analytical description of the shaded radiant flux. Assuming a land cover type with consistent mean foliage and background reflectance, inversion of a semi-empirical model can be used to determine BRDF coefficients, which can then be applied to normalize the imagery to a specific view/sun geometry. If the modelled spectral coefficients directly relate to canopy properties, then BRDF normalization can also provide information to help directly relate the canopy architectural and biophysical properties to the remotely sensed signal. One such model, FLAIR, has been successfully used to investigate canopy characteristics from airborne and satellite spectral imagery.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".