Multi-Angle Measurements with Chris for Forest Parameters
Bibliographic record
Abstract
It is with high spectral resolution, medium spatial resolution, and multi-directional imagery from CHRIS that forest structural parameters can be retrieved over our test site, the Greater Victoria Watershed District. This analysis requires an understanding of the anisotropic nature of forest canopies as measured by spaceborne hyperspectral sensors and modeled by radiative transfer models. The Evaluation and Validation of CHRIS for National Forests Project (EVC) was selected by ESA's science team for their hyperspectral sensor, CHRIS as part of the PROBA mission. On September 2nd to 4th, 2006, a triplet acquisition over the Greater Victoria Watershed District (GVWD), taken in Mode-1, provided us with 15 look angles. The Minimum Zenith Angles (MZA) for each date were +20°, -2° and -23° respectively, each of which has five nominal Fly-by Zenith Angles (FZA) of ±55°, ±36° and 0°. This triplet has been processed and analyzed in order to assess the utility of CHRIS data for mapping forest parameters. CHRIS algorithms for producing accurate estimates of forest parameters such as conifer forest species and biomass were compared with 5-Scale Model simulations. The spectral information content provides information on the content of the forest canopy while the multi-angle imagery offers information on the structural components of the forest canopy [2]. This paper provides an update on the status of this work in progress.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".