Structural Biases and Sensitivities of Vegetation Indices
Bibliographic record
Abstract
Since the epoch of climate change, observation of forest post-disturbance regeneration by satellite remote sensing has become a major research frontier. However, the monotonic saturation effects of specific reflectance bands may hinder the interpretation of post-disturbance vegetation indexing. We examine how spectral vegetation enhancement index limitations negate widespread implementation. The structural biases and sensitivities of four vegetation indices with potential usefulness for observing post-disturbance forest regeneration are assessed and clarified: the normalized difference vegetation index (NDVI), normalized burn ratio (NBR), near-infrared vegetation index (VINIR), and the infrared vegetation index (VIIR). Index structures are partitioned in calculation space to model every possible output. Simulated burned, unburned, and global vegetation computational domains for each index are assessed using complex statistical visualizations. Cross-comparison among indices shows that NDVI and NBR exhibit saturation given the upper range of simulated near-infrared (NIR) reflectance inputs (> 0.30) while VINIR and VIIR display increasing variability given lower inputs in the Green (> 0.07) and Shortwave-infrared (SWIR) (> 0.10), regions of the electromagnetic spectrum. NDVI and NBR display potential for vegetation class separability, while VINIR and VIIR also display a linear association with forest post-disturbance regeneration stages. VINIR and VIIR display significant potential for observing forest post-disturbance regeneration compared to traditional vegetation indices.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".