Studying mixed grassland ecosystems I: suitable hyperspectral vegetation indices
Bibliographic record
Abstract
Hyperspectral remote sensing data with a greater number of bands and narrower bandwidths can be effectively exploited for the study of ecosystem patterns and processes. Hyperspectral remote sensing of semiarid mixed grassland faces the following two challenges, however: (i) providing a good understanding of the performance of different vegetation indices (VIs) in estimating biophysical properties of grassland with a small amount of green vegetation, a large amount of dead material on the ground, and variable soil–ground conditions; and (ii) examining the spatial characterization of hyperspectral remotely sensed data to optimize sampling procedures and address scaling issues. Using ground-based hyperspectral and biophysical data, this study has compared the predictive capability of VIs for estimation of grassland leaf area index (LAI) (this paper) and examined the spatial variation of grassland LAI (the companion paper). The results in this paper indicate that the relationships between grassland LAI and VIs are significant. The performance of the renormalized difference vegetation index (RDVI), adjusted transformed soil-adjusted vegetation index (ATSAVI), and modified chlorophyll absorption ratio index 2 (MCARI2) was slightly better than that of the other VIs in the groups of ratio-based, soil-line-related, and chlorophyll-corrected VIs, respectively. By incorporating the cellulose absorption index (CAI) as a litter factor in ATSAVI, a new VI was computed (L-ATSAVI), and it improved the LAI estimation capability in our study area by about 10%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".