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TitreQuality of image-based manganese nodule abundance assessment
TéléchargerTéléchargement (publication entière)
LicenceVeuillez noter que la Licence du gouvernement ouvert - Canada remplace toutes les licences antérieures.
AuteurSchoening, T; Greinert, J
SourceProgram and abstracts: 2017 GeoHab Conference, Dartmouth, Nova Scotia, Canada; par Todd, B J; Brown, C J; Lacharité, M; Gazzola, V; McCormack, E; Commission géologique du Canada, Dossier public 8295, 2017 p. 108, (Accès ouvert)
LiensGeoHab 2017
ÉditeurRessources naturelles Canada
Réunion2017 GeoHab: Marine Geological and Biological Habitat Mapping; Dartmouth, NS; CA; mai 1-4, 2017
Documentdossier public
Mediaen ligne; numérique
Référence reliéeCette publication est contenue dans Todd, B J; Brown, C J; Lacharité, M; Gazzola, V; McCormack, E; (2017). Program and abstracts: 2017 GeoHab Conference, Dartmouth, Nova Scotia, Canada, Commission géologique du Canada, Dossier public 8295
Sujetstechniques de cartographie; océanographie; milieux marins; conservation; organismes marins; écologie marine; gestion des ressources; peuplements biologiques; etudes de l'environnement; écosystèmes; gisements minéraux; nodules de manganèse; sédiments marins; faunes; méthodes d'échantillonage; levés géophysiques; levés acoustiques marins; bathymétrie; configurations lit; biologie; ressources renouvelables; géologie économique; géologie marine; géologie des dépôts meubles/géomorphologie; géologie de l'environnement; géophysique
Illustrationsdigital images
ProgrammeGéoscience en mer, Géoscience de la gestion des océans
Diffusé2017 09 26
Résumé(disponible en anglais seulement)
Manganese nodules are a marine mineral resource and are considered for deep sea mining operations. These nodules constitute an important element of the deep sea habitats they occur in and their abundance and size frequencies have an impact on occurring fauna. Assessing the distribution of nodules is traditionally done with a combination of large-aerial hydro-acoustic mapping linked with ground-truthing by physical sampling. While hydro-acoustics provide large aerial coverage (km2/h) with low resolution (m/px), physical sampling provides low aerial coverage (cm2/h) with high resolution (mm/px). To bridge these two separate data domains, optical imaging has successfully been applied as it provides medium aerial coverage (ha/h) and resolution (cm/px).
Extracting quantitative data from optical images is traditionally done by effortful manual image annotation. More recently, multiple automated and semi-automated image analysis algorithms have been proposed. These algorithms are usually tuned for one specific data set or use case. The application of these algorithms to other optical imagery data sets is one necessity to prove their robustness. As manual annotations of manganese nodules are scarce and focus on nodule counts rather than exact nodule delineations, quantitative assessment of the quality of detection algorithms in the form of e.g. precision and recall is not possible at the moment.
Apart from the within-data comparison, a link to the traditional sampling strategies is required. These strategies are the de-facto standard for aerial mapping of habitats and assessing seafloor substrate composition (including manganese nodules). In the case of physical sampling, statistic variations in the natural nodule abundance can bias the sampling outcome. In the case of hydro-acoustic sampling, small-scale natural variations in abundance that are relevant to mining as well as habitat composition can be occluded due to the limited resolution. Using optical imaging as a bridge technology enables to extract more robust nodule abundance data.
This presentation will include results on comparing different nodule detection algorithms, and will show the challenges in correlating physical sampling derived data with optical imagery data and shows potential applications for habitat assessment using the presented algorithms.