Applications of Unmanned Aerial Vehicles for Conducting Mesocarnivore and Breeding Waterfowl Surveys in Southern Manitoba
Notice bibliographique
Résumé
Unmanned aerial vehicles (UAVs) are becoming an increasingly important tool for wildlife surveys and equipping UAVs with thermal imaging cameras could make these surveys even more effective. In my thesis I examined the feasibility of using a UAV equipped with a thermal imaging camera to conduct mesocarnivore surveys, search for duck nests built over water, and conduct duck brood surveys in southern Manitoba.\nFor my first objective, I conducted nighttime mesocarnivore surveys with the UAV and thermal camera. I used a modified point-count survey from six waypoints and surveyed 29.5 ha in each replicate. I conducted a total of 200 flights over 53 survey nights during which I detected 32 mesocarnivores of eight different species. The UAV and thermal camera were effective at locating mesocarnivores, however given the large home ranges of mesocarnivores, my surveys should be considered estimates of minimum abundance and not a population census.\nFor my second objective I conducted a two-part survey: 1) I evaluated the effectiveness of a UAV and thermal camera to locate duck nests relative to traditional surveys, and 2) tested the hypothesis that technician visits to nests may influence predation rates. Over the course of my 1st study the UAV located a total of 47 nests that were not located by technicians, however, the technicians located 164 nests missed by the UAV, and both survey methods located 71 of the same nests. There was also no difference in survival rates for nests monitored with the UAV versus those monitored by technicians. Though the UAV completed surveys faster than technicians, the usefulness of this technology was limited, because the UAV has a relatively low detection rates.\nMy third objective was to evaluate the efficacy of using a UAV and thermal camera to conduct brood surveys. In 2018 and 2019, the UAV and thermal camera located a total of 1569 broods, compared to 666 located by ground technicians, and had higher detection rates (0.48 vs. 0.20). The UAV reliably located twice as many broods as ground technicians and completed surveys four times faster, indicating this technology has great utility for waterfowl biologists.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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) | 0,000 | 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 tête enseignante, 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 ».