Numerical and Experimental Evaluation of Terrestrial LiDAR for Parameterizing Centimeter-Scale Sea Ice Surface Roughness
Bibliographic record
Abstract
Terrestrial light detection and ranging (LiDAR) offers significant advantages over conventional techniques for measuring the centimeter-scale surface roughness of natural surfaces, such as sea ice. However, the laser scanning technique is inherently limited, principally by the following: 1) the high inclination scanning angle of the sensor with respect to nadir; 2) the precision of the laser ranging estimate; and 3) the beam divergence of the laser. In this paper, we introduce a numerical model that has been designed to simulate the acquisition of LiDAR data over a regular rough surface. Results from the model compare well (r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.97) with LiDAR observations collected over two experimental surfaces of known roughness that were constructed from medium-density fibreboard using a computer numerical control three-axis router. The model demonstrates that surface roughness parameters are not sensitive to minor variations in the LiDAR sensor's range and laser beam divergence, but are slightly sensitive to the precision of the ranging estimate. The model also demonstrates that surface roughness parameters are particularly sensitive to the inclination angle of the LiDAR sensor. The surface RMS height is underestimated, and the correlation length is overestimated as either the inclination angle of the sensor or the true roughness of the surface increases. An isotropic surface is also increasingly observed as an anisotropic surface as either the inclination angle or the true surface roughness increases. Based on the model results, we propose a set of calibration functions that can be used to correct in situ LiDAR measurements of surface roughness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".