AVIRIS imagery for forest attribute information: anisotropic effects and limitations in multitemporal data
Bibliographic record
Abstract
Hyperspectral data can provide valuable forest information, such as forest species, stand density, biochemistry, and forest structure. It is also well known that optical radiometric properties of forest objects vary with the angles of illumination and view angle. The anisotropy of the forest canopy can restrict the determination of the forest parameters of interest. In high relief areas such as Vancouver Island, Canada the impact of illumination effects presents numerous additional complexities. The authors present the results of a study undertaken to assess forest attribute determination from AVIRIS data acquired over the Greater Victoria Watershed District Test Site (GVWD) on Vancouver Island B.C., Canada on two dates. A comparison of data from a number of test plots is carried out using AVIRIS imagery acquired in 1993 and 1994. Inventory information (such as stem density, species distribution, biomass, etc.) for these plots is known as a result of field sampling and data fusion of the AVIRIS Hyperspectral data with high spatial resolution (1 m) MEIS data and AirSAR data For GVWD, the dominant forest species is Douglas fir. Similarly aged stands on different slopes and at various aspects provide a sampling of view angles. Acquisitions at different times of the day sample the variation in illumination angles. AVIRIS reflectances from 1993 and 1994 are used to determine the limitations imposed by a range of off-nadir angles and BRDF effects.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.001 |
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".