RPAS-SfM snow depth and snow density mapping in disturbed vegetated mountainous environments of Coastal British Columbia
Notice bibliographique
Résumé
Concurrent advancements in Remotely Piloted Aircraft Systems (RPAS) and Structure from Motion (SfM) processing technologies have added powerful new methods for remotely sensing the cryosphere. Highly accurate snow depth (SD) estimates derived from RPAS-SfM workflows have been attained, however, most studies have examined open, relatively simple terrain. Results from the few RPAS-SfM SD studies that have examined complex vegetated terrain are of insufficient accuracy for meaningful use in water-resource research and management, prompting further development of RPAS-SfM SD and snow water equivalent (SWE) survey methods to better represent such areas. This study researched the use of RPAS-SfM methods to map SD and SWE across a 52 hectare study plot located on Vancouver Island, British Columbia during two snow seasons. This mid-elevation plot contains steep and complex terrain, including roads, ground covering perennial shrubs, regenerating forest, and old-growth forest. Optical imagery was captured using an off-the-shelf RPAS, and processed into digital elevation models (DEMs) using SfM software. Bare earth DEMs were then subtracted from snow surface DEMs to derive SD estimates. Manual SD measurements were used to validate RPAS-SfM SD estimates, and manual SWE measurements were used to estimate SWE across the study area. Additionally, the efficiency and accuracy of a novel, permanent above snow Ground Control Point (GCP) network was assessed. Root mean square error (RMSE) as low as 0.08 m was found in open terrain, which is consistent with previous research. In off-road areas, RMSE initially ranged from 0.36 m to 0.59 m, however, a bias correction based on ground cover classifications was found to be effective for dealing with underestimations of SD values caused by thick perennial vegetation; with vegetation caused bias ranging from -0.25 m to -0.46 m the application of a positive offsets reduced RMSE by up to 0.27 m in off-road sections, resulting in best case RMSE of 0.18 m in such areas. Multi-temporal SD and SWE outputs captured peak and melt period snowpack conditions, presenting highly detailed information on snow distribution, melt dynamics, and total stored water across the study plot. The elevated permanent GCP network was found to greatly improve the efficiency of both field surveys and data processing, while providing sub-centimeter accuracy levels similar to traditional ground level GCPs. Methods developed through this research show that RPAS-SfM techniques can be successfully applied to previously logged areas containing ground covering vegetation, and offer promise for application in water management of such areas.
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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 | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| 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 ».