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Enregistrement W2802195938 · doi:10.7939/r3416t597

Evaluation of Radar and Cameras as Tools for Automating the Monitoring of Waterbirds at Industrial Sites

2014· article· en· W2802195938 sur OpenAlexaboutno aff
Sarina Loots

Notice bibliographique

RevueUniversity of Alberta Library · 2014
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueOil Spill Detection and Mitigation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRemote sensingRadarComputer scienceGeographyTelecommunications

Résumé

récupéré en direct d'OpenAlex

Conflict occurs between people and birds at industrial sites around the world, where birds can endanger human lives (e.g. airports) and where bird populations are endangered by human activities (e.g. wind farms). Mitigating these conflicts requires accurate detection of birds and measures of their abundance and distribution. At industrial sites, detection of flying birds and the deployment of deterrents are often automated through detection by avian radar. Such sites include the various oil sands mining operations in northern Alberta, where operators are required to protect migrating waterfowl from landing on potentially toxic waste-water ponds. I tested two technologies for detecting birds in this context, one for flying birds (radar), and one for birds that have landed (cameras). I tested radar to establish its accuracy for detecting flying birds, based on birds detected by paired human observers. I used X-band marine radar and tested two types of radar antennas, one parabolic and one open-array, across a range of conditions at both process-affected water ponds and freshwater ponds. I found that the two antennas failed to detect about half of all detections confirmed by visual observers, both when they were each in operation separately (open-array antenna failed to detect 43% of targets that were confirmed as birds; parabolic antenna failed to detect 56.4% of targets that were confirmed as birds) and when they were in operation together (both antennas operating simultaneously on two radars failed to detect 43% of targets that were confirmed as birds by the visual observers). My results suggest that antenna type, height of radar station, substrate around the station, and site-specific knowledge of target birds should be more explicitly addressed when marine radar is used as part of bird protection programs. A combination of radar types, antennas, and other detection methods may be needed to achieve more comprehensive bird detection strategies at industrial sites. I also tested cameras to monitor birds in the context of industrial ponds. Birds that have landed on ponds are not detectable by radar, and standardised monitoring by human observers has documented tens of thousands of birds landing annually on oil sands process-affected water ponds. Such counts provide information on bird abundance, but there is considerable variation between observers and sites. To overcome these limitations, I evaluated the potential for cameras to monitor birds on industrial water bodies. I compared counts from high-resolution panoramic photos and photos taken by conventional remote cameras to counts conducted by field observers. I also tested the success of a computer algorithm to process photos automatically. High-resolution panoramas recorded two-thirds of bird counts recorded simultaneously by field observers, for distances of approximately 500 m from survey stations. Conventional remote cameras recorded two-thirds of birds in photos clearly, but only to a distance of 100 m. Both single-frame SLR panoramas and single-frame wildlife photos failed to capture birds that dove, birds that were behind other birds, and birds with oblique aspects to the camera. The presence of these birds could be revealed by capturing bird motion with multiple photo frames in short succession (time-interval). Automated processing of time-interval photos produced a very high true negative rate (95%), suggesting that it can substantially reduce the time spent by humans to process photos. The combined application of high resolution photos taken at frequent intervals and a specialized bird detection code makes cameras a viable alternative to human observers. Understanding the distributions and abundance of migratory waterfowl in the oil sands is in the interest of hunters, naturalists and citizens across North America. Radar and cameras can both contribute to this understanding, while simultaneously improving human safety, reducing cost and inter-observer variation, and increasing the duration and frequency of monitoring.

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 machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,010
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,026

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,010
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0010,002
Science ouverte0,0020,001
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,026
Tête enseignante GPT0,210
Écart entre enseignants0,184 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2014
Routes d'admission1
Résumé présentoui

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