Evaluating the recovery of beach-dune systems from the 2016 El Niño using unmanned aerial systems (UAS) and terrestrial laser scanning (TLS)
Bibliographic record
Abstract
This paper compares and contrasts UAS-based Structure from Motion (SfM) and TLS survey methods as applied to evaluate the impacts of, and recovery from, the extreme El Niño 2015-16 on the seasonal geomorphic and sediment budget responses of an embayed, high-energy beach-dune system on the central coast of British Columbia, Canada. TLS and UAS mapping campaigns over a two-year period provided seasonal bare-earth digital terrain models (DTMs) and orthophoto mosaics. Spatial-temporal change detection methods were used to quantify volumes of significant erosion and deposition within the beach-dune system. The frequency and magnitude of erosive events and aeolian activity were also estimated from oblique, time-lapse photography. During the 2015-16 El Niño season, elevated water levels and storm waves eroded the foredune and lowered the beach surface by ~ 1m. Erosion was greatest in the middle of the beach with dune scarping of over 2m where wave energy was focused. Minor accretion occurred during the summer of 2016 on the upper beach, and ramp rebuilding was observed mostly from slumping and avalanching of existing dune sands. The following winter 2017 storm season led to minor erosion on the beach and extensive incipient dune development and sand ramp recovery fronting the foredune to an extent close to pre-El Niño elevations. Comparison of change surfaces between methods revealed limitations in the SfM method, namely due to vegetation effects on DTM generation, which limit its ability to detect change in the coastal environment. The costs, time, logistics, and accuracy for both SfM and TLS survey methodologies for coastal geomorphic change detection analysis is also evaluated. Combined, the UAS and SfM workflow provides a competitive solution to more expensive and time-consuming survey methods, such as TLS and aerial LiDAR, but its utility and accuracy is highly dependent on research objectives and post-processing techniques.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".