Shoreline Change and Coastal Vulnerability Characterization with Landsat Imagery: A Case Study in the Outer Hebrides, Scotland
Bibliographic record
Abstract
Observation of cause–effect patterns of change in coastal environments provides insights into vulnerable areas and supports prediction and adaptation to flooding and erosion. Historic and periodic (6–8 year intervals) imagery from the Landsat archive is used to investigate transformations in the Atlantic coast of two Scottish islands over the period 1989–2011. Supervised classification of spectrally normalized images followed by change detection and spatial analysis reveals the patterns of change and the location of the most dynamic coastal areas. Quantitative measures of recent shifts and movement rates of relevant coastal lines, such as the lower limit of land-based vegetation, are assessed with the Digital Shoreline Analysis System. While very low rates are indicated for horizontal changes in the position of the lower limit of land-based vegetation (0.3 m y−1), specific areas have been subjected to high rates of coastal progradation as well as erosion (e.g. 2.5 m y−1 at Stilligarry). Information derived from satellite data supports the characterization of geomorphologically dynamic coasts at regional scales. With a rich and open access archive of imagery, a commitment to continuity, and compatibility with the Earth observation missions of other space programs, the Landsat mission offers useful and otherwise unavailable data for monitoring of coastal areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".