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TitleUsing topo-bathymetric LiDAR to map near shore benthic environments
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AuthorWebster, T L; McGuigan, K; Crowell, N; Fee, N
SourceProgram and abstracts: 2017 GeoHab Conference, Dartmouth, Nova Scotia, Canada; by Todd, B J; Brown, C J; Lacharité, M; Gazzola, V; McCormack, E; Geological Survey of Canada, Open File 8295, 2017 p. 120, https://doi.org/10.4095/305941 (Open Access)
LinksGeoHab 2017
Year2017
PublisherNatural Resources Canada
Meeting2017 GeoHab: Marine Geological and Biological Habitat Mapping; Dartmouth, NS; CA; May 1-4, 2017
Documentopen file
Lang.English
Mediaon-line; digital
RelatedThis publication is contained in Todd, B J; Brown, C J; Lacharité, M; Gazzola, V; McCormack, E; (2017). Program and abstracts: 2017 GeoHab Conference, Dartmouth, Nova Scotia, Canada, Geological Survey of Canada, Open File 8295
File formatpdf
Subjectsmarine geology; surficial geology/geomorphology; environmental geology; geophysics; mapping techniques; oceanography; marine environments; coastal studies; conservation; marine organisms; marine ecology; resource management; biological communities; environmental studies; ecosystems; nearshore environment; benthos; vegetation; bathymetry; seafloor topography; modelling; geophysical surveys; acoustic surveys, marine; photography; seagrass; geological mapping; geological mapping techniques; biology; habitat mapping; habitat conservation; habitat management; methodology; LiDAR; digital elevation models; image analysis
ProgramOcean Management Geoscience, Offshore Geoscience
Released2017 09 26
AbstractThe 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.
GEOSCAN ID305941