Application of terrestrial laser scanning to quantify surface changes in restored and degraded blanket bogs
Bibliographic record
Abstract
Many recognised areas of blanket bog are degraded, but the inventory and rate of loss of blanket bog globally is not fully known. Rapid identification of the rate and drivers of erosion and peat loss in blanket bogs more widely could inform localised approaches to protection and restoration of these important ecosystems. This study developed the application of Terrestrial Laser Scanning (TLS) to quantify the rate of surface change in restored and degraded blanket bogs by adopting a single scan strategy with fixed ground markers for repeat scanner location and fixed reference markers for scan alignment. Three recently mapped and remote areas of blanket bog in the Cantabrian Mountains (northern Spain) were scanned in May 2017 and July 2017 with a portable TLS (FARO X330) and 3D change in exposed peat surfaces was determined using a mesh to cloud (M2C) algorithm. The mean resolution of scan data across the sites was <3 mm, and where reference markers remained visible the maximum error of scan alignment was <1 mm, increasing to 6.5 mm where markers were obscured or lost. The rate of erosion determined over two months at Zalama (a protected blanket bog where reference markers were not disturbed) was -5.9 ± 4.6 mm (mean ± SD), but significantly higher (p < 0.001) rates of erosion and peat loss were determined for two unprotected blanket bogs under grazing regimes at Ilsos de Zalama (-22.9 ± 20.5 mm) and Collado de Hornaza (-35.7 ± 37 mm). This rate of change is already equal to the mean annual rate of erosion reported for bare peat in England and Wales (22.4–23.1 mm yr-1) and for Scotland (36.3 mm yr-1). This study demonstrates that portable TLS units can be used to make rapid assessment of surface change (erosion and peat loss) in blanket bog and indicates that trampling by cattle and horses is significantly increasing the rate of peat surface change in unprotected blanket bog in north Spain. This technique has direct application for peatlands under grazing regimes globally, and further installation of fences around blanket bog in northern Spain may be required imminently to reduce the loss of peat, the associated carbon store and priority habitat.
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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 | 0.002 | 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".