Multispectral remote sensing of landscape level foliar moisture: techniques and applications for forest ecosystem monitoring
Bibliographic record
Abstract
Broad-scale monitoring of varying moisture levels of leaves has ramifications for understanding fire potential, biogeochemistry, and ecosystem dynamics. Five different shortwave infrared (SWIR)-derived spectral indices, principal components analysis (PCA), and the tasseled cap transformation (TCT), derived from Landsat Thematic Mapper (TM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data, were used to quantify landscape-level foliar moisture in an ecosystem dominated by Pinus ponderosa P. & C. Lawson. Landsat TM data demonstrated stronger correlations with in situ calculations of foliar moisture than did ASTER data. The second principal component correlated strongly with ground data (r2 = 0.765). The Landsat-derived TCT wetness component was significantly correlated with ground data (r2 = 0.638) as well as a normalized difference NIR/SWIR ratio (r2 = 0.834). The spectral indices and TCT are more practical for ecosystem moisture monitoring than PCA because of the empirical nature of PCA. Based on these results, we recommend modifications to existing methods of satellite-based fire susceptibility monitoring to account for primary effects of vegetation curing and temporal variation in ground fuels.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".