InSAR-Based Mapping of Tidal Inundation Extent and Amplitude in Louisiana Coastal Wetlands
Bibliographic record
Abstract
The Louisiana coast is among the most productive coastal areas in the US and home to the largest coastal wetland area in the nation. However, Louisiana coastal wetlands have been disappearing at an alarming rate due to natural and anthropogenic processes, including sea level rise, land subsidence and infrastructure development. Wetland loss occurs mainly along the tidal zone, which varies in width and morphology along the Louisiana shoreline. In this study, we use Interferometric Synthetic Aperture Radar (InSAR) observations to detect the extent of the tidal inundation zone and evaluate the interaction between tidal currents and coastal wetlands. Our data consist of ALOS and Radarsat-1 observations acquired between 2006–2011 and 2003–2008, respectively. Interferometric processing of the data provides detailed maps of water level changes in the tidal zone, which are validated using sea level data from a tide gauge station. Our results indicate vertical tidal changes up to 30 cm and horizontal tidal flow limited to 5–15 km from open waters. The results also show that the tidal inundation is disrupted by various man-made structures, such as canals and roads, which change the natural tidal flow interaction with the coast.
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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 | 0.000 |
| 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".