Forest structure without ground data: Adaptive Full-Blind Multiple Forward-Mode reflectance model inversion in a mountain pine beetle damaged forest
Bibliographic record
Abstract
A new approach for using canopy reflectance models (CRMs) is presented that requires no field data or knowledge about the study area or imagery. Multiple Forward-Mode Adaptive Full-Blind (MFM-AFB) modelling provides forest biophysical structural information (BSI), and can also be used for classification and spectral mixture analysis at sub-pixel scales without user-specified model inputs, training data or endmember spectra, as these are instead automatically derived. In an example application using 2007 Landsat imagery of forest damaged by a mountain pine beetle (MPB) epidemic in British Columbia, Canada, overall BSI accuracy was within ±1000 stems ha–1 for stand density, ±0.5 m for crown radius and ±1 m tree height for healthy and MPB stands. MFM-AFB software is suitable for regional, multi-temporal and unknown imagery and areas. By not requiring user-specified a priori model inputs to infer BSI, the MFM-AFB approach may help enable mainstream use of diverse and advanced CRMs for image analysis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".