Using multispectral satellite imagery to estimate leaf area and response to silvicultural treatments in loblolly pine stands
Bibliographic record
Abstract
The relationship between leaf area index (LAI) of loblolly pine plantations and the broadband simple ratio (SR) vegetation index calculated from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data was examined. An equation was derived to estimate LAI from readily available Landsat 7 ETM+ data. The equation developed to predict LAI with Landsat 7 ETM+ data was tested with ground LAI measurements taken in 12 plots. The root mean square error of prediction was 0.29, an error of approximately 14% in prediction. The ability of Landsat 7 ETM+ data to consistently estimate SR over time was tested using two scenes acquired on different dates during the winter (December to early March). Comparison between the two images on a pixel-by-pixel basis showed that approximately 96% of the pixels had a difference of <0.5 units of SR (approximately 0.3 units of LAI). When the comparison was made on a stand-by-stand basis (average stand SR), a maximum difference of 0.2 units of SR (approximately 0.12 units of LAI) was found. These results suggest that stand LAI of loblolly pine plantations can be accurately estimated from readily available remote sensing data and provide an opportunity to apply the findings from ecophysiological studies in field plots to forest management decisions at an operational scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".