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TitreUsing topo-bathymetric LiDAR to map near shore benthic environments
TéléchargerTéléchargement (publication entière)
LicenceVeuillez noter que la Licence du gouvernement ouvert - Canada remplace toutes les licences antérieures.
AuteurWebster, T L; McGuigan, K; Crowell, N; Fee, N
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. 120, (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; études côtières; conservation; organismes marins; écologie marine; gestion des ressources; peuplements biologiques; etudes de l'environnement; écosystèmes; milieu littoral; benthos; végétation; bathymétrie; topographie du fond océanique; établissement de modèles; levés géophysiques; levés acoustiques marins; photographie; 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)
The Chiroptera II shallow water topo-bathymetric LiDAR sensor has been used to survey several coastal areas in Maritime Canada since 2014. In addition to the production of seamless elevation models, the green laser reflectance amplitude has been used in combination with the seabed roughness to map seagrass beds. The LiDAR is coupled with a 5 MPIX quality assurance camera and a 60 MPIX RCD30 multispectral camera (RGB+NIR). Utilizing the camera information with the LiDAR derivatives has allowed us to improve our bottom mapping capabilities and expand the number of benthic classes that can be derived. The amplitude of the green laser, 515 nm, decays exponentially with water depth. The strength of the signal is dependent on several factors including: water surface specular reflection, local incidence angle (scan angle + aircraft orientation), water column properties and the seabed material. We have developed a method to normalize the amplitude of the green laser points between flight lines. The energy of the light exponentially decays with depth and the amplitude of the signal is not scaled accordingly. We have developed an empirical method to depth normalize the amplitude image so it can be used in image classification.
We will present various classification methods using the LiDAR and photo derived products to map the benthic environment. These methods include: semi-empirical, pattern recognition (maximum likelihood, k-means), machine learning such as random forest and object based segmentation. The results are validated using drop camera point based ground truth or echosounding data from a Biosonics system. We will also present research related to extracting attributes directly from the waveform of the green laser return that offers additional potential for habitat classification.