Freshwater Fish Habitat Complexity Mapping Using Above and Underwater Structure-From-Motion Photogrammetry
Bibliographic record
Abstract
Substrate complexity is strongly related to biodiversity in aquatic habitats. We illustrate a novel framework, based on Structure-from-Motion photogrammetry (SfM) and Multi-View Stereo (MVS) photogrammetry, to quantify habitat complexity in freshwater ecosystems from Unmanned Aerial Vehicle (UAV) and underwater photography. We analysed sites in the Xingu river basin, Brazil, to reconstruct the 3D structure of the substrate and identify and map habitat classes important for maintaining fish assemblage biodiversity. From the digital models we calculated habitat complexity metrics including rugosity, slope and 3D fractal dimension. The UAV based SfM-MVS products were generated at a ground sampling distance (GSD) of 1.20–2.38 cm while the underwater photography produced a GSD of 1 mm. Our results show how these products provide spatially explicit complexity metrics, which are more comprehensive than conventional arbitrary cross sections. Shallow neural network classification of SfM-MVS products of substrate exposed in the dry season resulted in high accuracies across classes. UAV and underwater SfM-MVS is robust for quantifying freshwater habitat classes and complexity and should be chosen whenever possible over conventional methods (e.g., chain-and-tape) because of the repeatability, scalability and multi-dimensional nature of the products. The SfM-MVS products can be used to identify high priority freshwater sectors for conservation, species occurrences and diversity studies to provide a broader indication for overall fish species diversity and provide repeatability for monitoring change over time.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".