A large-scale estimation method for beach slopes using ICESat-2 altimeter: A case study of New Zealand
Bibliographic record
Abstract
Beach slope is critical for monitoring coastline erosion and assessing coastal vulnerability. However, accurately estimating beach slopes at large scales remains a great challenge due to the dynamic nature of tidal exposure, the limited availability of in-situ topographic data, and the coarse resolution or coverage gaps of traditional DEM products. This study proposed a novel method driven by the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) ATL03 product for estimating coastal slopes of sandy beaches by considering ICESat-2 photon characteristics and beach morphology. First, we corrected the ICESat-2 photons along satellite trajectories to the direction perpendicular to the coastline, and then preliminarily eliminated sea surface photons. Second, we combined the adaptive threshold OPTICS (Ordering Points To Identify the Clustering Structure) algorithm and the moving window filter to accurately extract sandy beach photons. Third, horizontal ranges of sandy beaches were determined by the quadratic polynomial model, and coastal slopes were estimated using a linear regression model. The proposed large-scale method for deriving beach slopes relies primarily on ICESat-2 photon counting light detection and ranging (LiDAR) data and auxiliary vector data, which does not require in-situ measurements. We applied this method to calculate coastal slopes for 212 sandy beaches with 1,297 profiles in New Zealand and conducted a validation using 1 m high-accuracy airborne Digital Elevation Model (DEM) data. Our results indicate that the inverted beach slopes from our method exhibit a high accuracy (root mean square error ( RMSE ) = 0.06, determination coefficient ( R 2 ) = 0.76). In the future, this method has a large potential for global applications using ICESat-2 data, serving as a valuable tool for estimating coastal slopes for sandy beaches worldwide.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".