An optimized Enhanced Vegetation Index for Sparse Tree Cover Mapping across a Mountainous Region
Bibliographic record
Abstract
In mountainous areas with sparse woody vegetation, tree cover is among the most crucial indicators for landscape conservation and ecosystem stability. It is thus essential to be continuously monitored, which is yet seriously hurdled by harsh topography and inaccessibility. Field-based methods are time consuming and costly and are thus particularly prohibitive across large areas. Multispectral remote sensing data sources have been historically used via deriving various vegetation indices to enhance the visual interpretability as well as to deliver practical products from vegetation cover. Whereas indices like NDVI or even more empirically-parametrized indices provide convenient and easy-to-use proxies for large area cover mapping, their robustness to issues raised by shadow, atmospheric and illumination conditions and thus their interpretability for regional cover mapping in sparse vegetation cover are highly questioned. Here, we introduce an efficient method to optimize the enhanced vegetation index (EVI) by particle swarm optimization (PSO). We compared the efficiency of the optimized EVI against the NDVI and the traditional EVI across a portion of remote, sparsely vegetated and mountainous Zagros region in western Iran. The results based on Sentinel 2 sensor data show clear advantages of locallyoptimized EVI over the conventional indices in both visual interpretability and explained variance.
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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 | 0.001 |
| 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 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".