Ice Roughness Estimation via Remotely Piloted Aircraft and Photogrammetry
Notice bibliographique
Résumé
Abstract. Structure-from-Motion Photogrammetry conducted with images obtained via Remotely Piloted Aircraft (RPA) has revolutionized the field of land surface monitoring. RPA-Photogrammetry can quickly and easily capture a full 3D representation of a study area. The result of this process is a high-definition Digital Elevation Model (DEM) representing the land surface of a given study area. It is particularly useful in applications where land surface data collection would otherwise be expensive or dangerous. The monitoring of fluvial ice covers can be time-intensive, dangerous, and costly, if detailed data are required. Fluvial ice roughness is a sensitive parameter in hydraulic models and is incredibly difficult to measure directly using traditional field methods. This research hypothesized that the surface roughness of a newly-frozen fluvial ice cover is indicative of subsurface roughness. The hypothesis was tested through a comparison of ice roughness determined through the statistical analysis of RPA-photogrammetry DEMs to ice roughness values predicted by the Nezhikhovskiy equation. The Nezhikhovskiy equation is a widely used empirical method for estimating ice roughness based on observed ice thickness. Hydraulic and topographic data were collected over two years of field research on the Dauphin River in Manitoba, Canada. Various statistical metrics were used to represent the roughness of the DEMs. Strong trends were identified in the comparison of ice cover roughness values determined through RPA-photogrammetry and those calculated via the Nezhikhovskiy equation, as well as with ice thickness. The inter-quartile range of observed roughness heights was determined to be the most representative roughness metric. The maximum peak value performed better in some cases, but the fact that this metric would be heavily influenced by outliers led to it being rejected as a representative metric. Three distinct forms of surface ice roughness were noted: rough, smooth, and ridged. Statistical properties of the DEMs of fluvial ice covers were calculated. No DEMs were found to be normally distributed. k-means clustering analysis was used to group sampled data into two categories, which were interpreted as rough and smooth ice. The inter-quartile range of the smooth and rough categories were found to be 0.01–0.05 meters and 0.07–0.12 meters, respectively. RPA-photogrammetry was concluded to be a suitable method for the monitoring of fluvial ice covers. Other applications of RPA-photogrammetry for the characterization of fluvial ice covers are proposed.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».