Quantifying spatial complexity in submerged aquatic vegetation landscapes using remote sensing: Lessons from simulated and real landscapes
Bibliographic record
Abstract
Abstract The spatial organization of vegetation has been shown to be a strong indicator of ecological state in multiple ecosystems. In this study, we analyze the relationships between spatial complexity metrics in submerged aquatic vegetation (SAV) landscapes, and we explore the potential of satellite remote sensing to quantify these metrics in submerged environments. To do so, we estimated an array of complexity metrics over both simulated and real SAV landscapes of contrasted spatial organization. All these landscapes were artificially manipulated to (i) simulate remote sensing noise associated with the low signal‐to‐noise ratio (SNR) of aquatic environments and environmental noise generated by wind and waves, and (ii) reduce their spatial resolution from very high (2 m) to medium (30 m). Among these treatments, spatial resolution and low SNR (represented by sensor noise) had the strongest impacts on the perceived spatial complexity of the landscapes, while the impact of environmental noise was highly dependent on resolution. Although single metrics were deemed insufficient to characterize the spatial complexity of a landscape, a combination of informational complexity metrics such as the clumpy index, mean information gain, landscape shape index, and edge density provided a robust explanation of variation in the real and simulated datasets. These findings suggest that remote sensing has a strong potential for the ecological monitoring of SAV by contributing to establishing the link between SAV spatial structure and ecological status.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| 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".