Investigating the use of Landsat thematic mapper data for estimation of forest leaf area index in southern Sweden
Bibliographic record
Abstract
The study aims at investigating the use of Landsat thematic mapper (TM) for mapping leaf area index (LAI) in coniferous and deciduous forests in southern Sweden. LAI has been estimated in the field with optical measurements, allometric equations, and litter-trap data, and empirical relationships between LAI estimates and satellite-measured reflectances have been analysed. Several common vegetation indices and multiple regressions where estimated LAI is predicted as a function of various spectral bands are tested. The results indicate significant relationships between Landsat TM reflectances and parameters related to LAI, and the relationships are improved when separating coniferous and deciduous stands. The best relationships occur between Landsat TM data and the product of effective LAI as estimated with the LAI-2000 instrument and a needle clumping factor (LG), which explains about 80% of the variation in coniferous stands and about 50% of the variation in deciduous stands. The best single bands in coniferous stands are the middle-infrared bands (TM5 and TM7), and the best vegetation index is the moisture stress index (TM5/TM4). The best single band in deciduous stands is TM4, and the best vegetation index is the simple ratio (SR).
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".