The relationship between effective plant area index and Landsat spectral response across elevation, solar insolation, and spatial scales in a northern Idaho forest
Bibliographic record
Abstract
Leaf area index (LAI) is an important forest characteristic related to photosynthesis and carbon sequestration, and gains in efficiency for LAI measurements are possible using remotely sensed imagery. However, the potential effects of complex topography on this measurement system are not well understood. Our objective was to understand how complex terrain and measurement aggregation influence the relationship between LAI and remotely sensed vegetation indices across a mountainous conifer forest. We identified NDVIc, a middle-infrared (MIR) correction to NDVI (Normalized Difference Vegetation Index), as the vegetation index providing the best prediction of effective plant area index (PAIe), used to approximate LAI. We tested formal hypotheses to identify how elevation and solar insolation gradients and spatial scale of measurement aggregation affected the PAIeNDVIc relationship and found that it changed across elevation at one spatial scale. Comparisons of NDVIc with NDVI revealed that vegetation index choice is important in complex terrain, and we concluded that the MIR correction improves the PAIeNDVI relationship by explaining variation related to solar insolation. Our results suggest that NDVIc calculated from Landsat ETM+ provides a practical estimate of PAIeacross our northern Idaho study area and potentially other conifer forests in complex terrain.
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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 | 0.000 |
| 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) | 0.001 | 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".