GEOSCAN, résultats de la recherche


TitreHow to homogeneously map adjacent backscatter datasets at regional scale - a case study from the southern Adriatic Sea (Italy)
TéléchargerTéléchargement (publication entière)
LicenceVeuillez noter que la Licence du gouvernement ouvert - Canada remplace toutes les licences antérieures.
AuteurPrampolini, M; Foglini, F; Angeletti, L; Campiani, E; Grande, V; Mercorella, A
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. 95, (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
Lat/Long OENS 13.5000 21.0000 43.0000 39.7500
Sujetstechniques de cartographie; océanographie; milieux marins; études côtières; conservation; organismes marins; écologie marine; gestion des ressources; peuplements biologiques; etudes de l'environnement; écosystèmes; benthos; interprétations géophysiques; levés acoustiques marins; levés au sonar; sonar latéral; méthodes statistiques; bathymétrie; topographie du fond océanique; biologie; méthodologie; géologie marine; géologie des dépôts meubles/géomorphologie; géologie de l'environnement; géophysique
ProgrammeGéoscience en mer, Géoscience de la gestion des océans
Diffusé2017 09 26
Résumé(disponible en anglais seulement)
Benthic habitat mapping is a field of marine research highly developed in a small time-span. Classification of backscatter data constitutes a key approach, and one of the most widespread approaches, for seabed and benthic habitat mapping and a large number of methodologies are described in literature. The first method used is the visual interpretation of backscatter imagery, but it is subjective and time-consuming. Then, taking inspiration from the terrestrial remote sensing, automatic classifications have been developed based both on signal (e.g. ARA) and/or image analysis (e.g. TexAn, Principal Component Analysis, Neural Network). Image analysis is the most applied approach, both for supervised and unsupervised classification because it describes large-scale organizations of seafloor substrate and benthic habitats better than backscatter signal analysis. However, any type of image segmentation based on pixels as units of analysis may lead to some disadvantages such as noisy results, uni-scale approach, texture considerations, context and shape and, finally, pixels are not true geographical objects. For this reason Object-Based Image Analysis (OBIA) is getting more and more success since it is devoted to segment the backscatter image in "meaningful image objects" and should be able to overcome the differences among backscatter datasets acquired with different instruments.
In the last ten years, a large amount of high resolution bathymetry, backscatter data and seafloor samples have been acquired in the Southern Adriatic Sea (Italy), a physiographically complex basin hosting a variety of benthic habitats. The latter constitutes an ideal laboratory for integrated methodologies aiming at habitat mapping at different scales, in different seafloor settings and including heterogeneous datasets. The most challenging aspect of benthic habitat mapping is given by the necessity to produce an integrated map that could unify different datasets, showing comparable results. Within this framework, we present the classification of the backscatter data of some key areas of the Adriatic seafloor: we chose to apply the OBIA classification since it could be the most suitable approach in order to overcome the differences in backscatter intensity and imagery due the use of different devices.