Comparing the Use of Terrestrial LiDAR Scanners and Pin Profilers for Deriving Agricultural Roughness Statistics
Bibliographic record
Abstract
Adequate descriptions of soil surface roughness are vital for the accurate retrieval of soil moisture maps from remote sensing products. Terrestrial laser scanners (TLSs) have the potential to offer a more comprehensive method of measuring surface roughness than traditional techniques, but are underused in this application. This research examines the use of TLSs for measuring the surface roughness of bare agricultural fields in Elora, Ontario. Through the development and application of an innovative plug-in called Roughness from Point Cloud Profiles (RPCP), this research compares TLS surface roughness characterizations to those derived from a pin profiler. In most cases, the root mean square height (RMSH) measurements obtained from the pin profiler are within 1.5 cm of those obtained from Light Detection and Ranging (LiDAR). Nearly 90% of the l measurements obtained from the TLS were >5 cm larger than those from the pin profiler. Discrepancies between pin profiler and TLS roughness characterizations can be partially explained by LiDAR shadowing in some cases, but are likely caused by other factors such as de-trending techniques, profile length, and profile orientation. The results of this research illustrate roughness variations across fields and between roughness profile orientations according to tillage structures.
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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 | 0.003 |
| 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 | 0.001 |
| 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".