Terrestrial laser scanning for quantifying small-scale vertical movements of the ground surface in Arctic permafrost regions
Bibliographic record
Abstract
Three-dimensional data acquired by terrestrial laser scanning (TLS) provides an accurate representation of the Earth’s surface, which is commonly used to detect and quantify topographic changes on a small scale. However, in Arctic permafrost regions TLS-based monitoring of thaw subsidence is challenging due to vegetation and the micro-topographic characteristics (e.g. dense moss-lichen layer, hummocks etc.). In this presentation, we focus, firstly, on the evaluation of raster- and point-based TLS methods for quantifying small-scale thaw subsidence within the continuous permafrost zone. Secondly, a new filter strategy is presented that reduces spatial sampling effects caused by various factors such as vegetation, micro-topography and scan-setup. Our study site is located at the Trail Valley Creek research watershed, 50 km north-east of Inuvik, Northwest Territories, Canada. Three field campaigns took place in 2015 and 2016. Besides capturing TLS data, at-point real-time kinematic (RTK) Global Navigation Satellite System (GNSS) measurements and manual subsidence measurements were gathered. To achieve a highly accurate registration (on mm-scale) of the three TLS campaigns, co-registration of the georeferenced point clouds is performed based on the stable fix points in the otherwise highly dynamic permafrost environment. Then, different methods to quantify vertical ground movements are applied and evaluated. The result reveals limitations of standard raster-based DEM differencing, but also of point-based distance calculation for detecting spatial patterns of small-scale thaw subsidence. In the Arctic tundra ecosystem, TLS-based deformation analysis is strongly affected by occlusion and spatial sampling effects, even if data acquisition is repeated from similar scan positions. We show that the mentioned errors can be reduced by capturing the ground surface from more than one TLS scan position. Our filter strategy allows to identify TLS points which are suitable for multi-temporal deformation analyses, and results in an average seasonal subsidence rate (2015/06-2015/08) of about -2.0 cm at our study site. The derived subsidence maps deliver highly accurate ground-truth data, which is needed to improve area-wide subsidence monitoring methods such as SAR interferometry. This leads to a deeper understanding of permafrost-related subsidence processes.
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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 | 0.002 |
| 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) | 0.001 | 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".