Evaluation of Landsat TM vegetation indices for estimating vegetation cover on semi-arid rangelands: a case study from Australia
Bibliographic record
Abstract
The accurate monitoring of vegetation cover of globally extensive arid and semi-arid environments is important and challenging. This study examined the capacity of five Landsat Thematic Mapper (TM) spectral bands and 17 vegetation indices (VIs) in estimating saltbush and total vegetation cover in semi-arid rangeland environments. It investigated the relationships between ground-surveyed vegetation cover and VIs derived from Landsat TM images and coincident ground reflectance measurements at Lake Mungo and Fowlers Gap, New South Wales, Australia, both vegetated by perennial chenopod shrublands, principally saltbush. In comparison with spectral measures that rely only on visible and near-infrared wavebands, mid-infrared wavelengths and VIs derived from them were better at characterizing vegetation cover. TM5, TM7, SRVI-1, SRVI-2, and SRVI-3 were identified as generally suitable for large-scale surveys in semi-arid rangelands, where a cost-effective evaluation of vegetation cover is required.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".