Optical delineation of benthic habitat using an autonomous underwater vehicle

To improve understanding and characterization of coastal regions, there has been an increasing emphasis on autonomous systems that can sample the ocean on relevant scales. Autonomous underwater vehicles (AUVs) with active propulsion are especially well suited for studies of the coastal ocean because they are able to provide systematic and near-synoptic spatial observations. With this capability, science users are beginning to integrate sensor suits for a broad range of specific and often novel applications. Here, the relatively mature Remote Environmental Monitoring Units (REMUS) AUV system is configured with multi-spectral radiometers to delineate benthic habitat in Sequim Bay, WA. The vehicle was deployed in a grid pattern along 5 km of coastline in depths from 30 to less than 2 meters. Similar to satellite and/or aerial remote sensing, the bandwidth ratios from the downward looking radiance sensor and upward looking irradiance sensor were used to identify beds of eelgrass on sub-meter scales. Strong correlations were found between the optical reflectance signals and the geo-referenced in situ data collected with underwater video within the grid. Results demonstrate the ability of AUVs to map littoral habitats at high resolution and highlight the overall utility of the REMUS vehicle for nearshore oceanography.

[1]  Hanumant Singh,et al.  Toward large-area mosaicing for underwater scientific applications , 2003 .

[2]  P. Jeremy Werdell,et al.  Remote assessment of benthic substrate composition in shallow waters using multispectral reflectance , 2003 .

[3]  B. Allen,et al.  Remote environmental measuring units , 1994, Proceedings of IEEE Symposium on Autonomous Underwater Vehicle Technology (AUV'94).

[4]  Edward R. Levine,et al.  Turbulence Measurement from an Autonomous Underwater Vehicle , 1999 .

[5]  C. Roman,et al.  Seabed AUV offers new platform for high‐resolution imaging , 2004 .

[6]  A New AUV Platform for Studying Near Shore Bioluminescence Structure , 2002 .

[7]  Frederick Armstrong,et al.  Antarctic Krill Under Sea Ice: Elevated Abundance in a Narrow Band Just South of Ice Edge , 2002, Science.

[8]  M. Moline,et al.  Remote Environmental Monitoring Units: An Autonomous Vehicle for Characterizing Coastal Environments* , 2005 .

[9]  Robert A. Leathers,et al.  Ocean color remote sensing of seagrass and bathymetry in the Bahamas Banks by high‐resolution airborne imagery , 2003 .

[10]  H. Wijesekera,et al.  Effect of surface waves on the irradiance distribution in the upper ocean. , 2005, Optics express.

[11]  Paul G. Fernandes,et al.  Autonomous underwater vehicles: future platforms for fisheries acoustics , 2003 .

[12]  Mark Moline,et al.  An autonomous vehicle approach for quantifying bioluminescence in ports and harbors , 2005, SPIE Defense + Commercial Sensing.

[13]  H. Gordon,et al.  Self‐shading of in‐water optical instruments , 1992 .

[14]  R. Zimmerman,et al.  Effects of epiphyte load on optical properties and photosynthetic potential of the seagrasses Thalassia testudinum Banks ex König and Zostera marina L. , 2003 .

[15]  Richard C. Zimmerman,et al.  A biooptical model of irradiance distribution and photosynthesis in seagrass canopies , 2003 .

[16]  Performed By , 2020 .

[17]  Paul G. Fernandes,et al.  An investigation of avoidance by Antarctic krill of RRS James Clark Ross using the Autosub-2 autonomous underwater vehicle , 2003 .

[18]  J. Boardman Automating spectral unmixing of AVIRIS data using convex geometry concepts , 1993 .