A canopy reflectance model based topographic normalisation approach for forested terrain
Bibliographic record
Abstract
In most forestry remote sensing applications in steep terrain, simple photometric and empirical corrections are confounded as a result of variable stand and species structure with terrain and the anisotropic reflective properties of vegetated surfaces. To address these problems, we test two new topographic correction approaches based on Sun-Canopy-Sensor (SCS) geometry. SCS is more appropriate than strictly terrain-based corrections in forested areas since it preserves the geotropic nature of trees (vertical growth with respect to the geoid) regardless of terrain, view and illumination angles. The first SCS approach accounts for diffuse atmospheric irradiance based on the C-correction (SCS+C). Secondly, a new multiple forward mode (MFM) canopy reflectance model based correction (MFM-TOPO-COR) is introduced which normalizes topographically induced signal variance as a function of forest stand structure and sub-pixel scale components, while also maintaining proper SCS geometry. These two new techniques are compared to existing correction methods (cosine, c correction, Minnaert, statistical-empirical, SCS, and b correction) in a Rocky Mountain forest setting in western Canada. The ability of these eight correction methods are tested and compared for removing topographically induced variance and for improving the classification accuracy of a SPOT image over this sub-alpine and alpine forest area. The new MFM-TOPO-COR canopy reflectance model correction method is shown to provide the greatest improvement in classification accuracy within a species and stand density based class structure. The potential and limitations of this new approach are critically discussed.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".