Relationship between leaf area index and Landsat Operational Land Imager equivalent reduced simple ratio vegetation index for the Athabasca oil sands region, northern Alberta
Bibliographic record
Abstract
This document describes the production of a regression relationship between leaf area index and the reduced simple ration vegetation index (RSR) for Landsat Operational Land Imager spectral bands over the Athabasca Oil Sands region of Alberta. from satellite imagery using standard Canada Centre for Remote Sensing algorithms. 245 Elementary Sampling Units (ESUs) were specified based on a stratification of both land cover and spectral reflectance in the vicinity of Fort McKay, Alberta, Canada. ESU LAI was estimated using in-situ digital hemispherical photographs acquired during the 2012 and 2013 growing seasons. The estimation used the CCRS Line Transect protocol followed by processing using CANEYEV6.3 software. Empirical corrections for shoot clumping are documented. In-situ Lai ranged from 0.09 to 6.08. A SPOT 5 satellite image was acquired within two weeks of each of the 2012 and 2013 field campaigns, orthorectified to within 10m (1 standard deviation) and radiometrically normalized to invariant targets in a surface reflectance Landsat OLI image acquired within 1 week of the 2013 SPOT image. The RSR was derived from both normalized SPOT5 images and sampled over each ESU. A Thiel-Sen linear regression was applied to generate a relationship to predict LAI given RSR across all sampled land cover conditions with a root mean square error of 0.49.
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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 | 0.001 | 0.002 |
| 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) | 0.002 | 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".