AUV-based bed roughness mapping over a tropical reef
- 292 Downloads
Identifying fixed bed roughness scales of hydrodynamic relevance to waves and currents is challenging around coral reefs due to their highly inhomogeneous bathymetry. In order to characterize the spatial variability in reef roughness, a quantitative analysis of high-resolution sidescan sonar backscatter is performed for the identification of distinct substrates around a tropical reef and is related to echo sounder-based roughness measurements. Data were collected in the vicinity of the Kilo Nalu Observatory on the south shore of Oahu using sidescan sonar and a narrow beam echo sounder incorporated in a REMUS-100 (Remote Environmental Monitoring UnitS) autonomous underwater vehicle (AUV). With basic statistics and principal component analysis of variables derived from the backscatter data, it is possible to discriminate between areas of rough reef, bare reef, and rippled sand. Echo sounder-derived spectral analysis did not reveal dominant length scales. However, by combining the seabed classification obtained from sidescan measurements with echo sounder data, spectral root mean square (RMS) height values of approximately 3.3 cm and 7.3 cm are assigned to the bare reef and rough reef areas, respectively, for roughness with wavelengths between 0.2 and 6 m.
KeywordsSeabed roughness Sidescan sonar Substrate classification Autonomous underwater vehicle
The authors thank Judith Wells, Jonathan Fram, and Kumar Rajagopalan for their helpful suggestions during our weekly meetings. We are indebted to Roy Wilkens and Roger Davis for providing the sidescan preprocessing software and for their assistance in its use. We are also thankful to Jennifer Patterson, Chris Colgrove, Kimball Millikan, and Brian McLaughlin for their support during field operations and to Alyssa Glass for her help with the visual verification of sidescan data. This work was carried out with funding from the Office of Naval Research Coastal Geosciences Program (Grants N00014-07-1-1182 and N00014-10-1-0414).
- Bendat JS, Piersol AG (1971) Random data: analysis and measurement procedures. Wiley-Interscience, New YorkGoogle Scholar
- Ferrall CC Jr (1976) Subsurface geology of Waikiki, Moiliili and Kakaako with engineering application. M.S. thesis, University of Hawaii, HonoluluGoogle Scholar
- Gonzalez RC, Woods RE, Eddins SL (2003) Digital image processing using MATLAB. Prentice-Hall, Inc, Englewood CliffsGoogle Scholar
- Hearn CJ (2008) The dynamics of coastal models, 1st edn. Cambridge Univ. Press, New YorkGoogle Scholar
- Hearn CJ (2010) Hydrodynamics of coral reef systems, Encyclopedia of modern corals. Springer, BerlinGoogle Scholar
- Hoppner F, Klawonn F, Kruse R, Runkler T (1999) Fuzzy cluster analysis. Wiley, ChichesterGoogle Scholar
- Jackson DR, Richardson MD (2007) High-frequency seafloor acoustics. Springer, New YorkGoogle Scholar
- Kundu P (1990) Fluid mechanics. Academic Press, LondonGoogle Scholar
- Nikuradse J (1933) Stromungsgesetz in rauhren rohren, vdi-forschungsheft 361. (English translation: Laws of flow in rough pipes), 1950. Technical report, NACA Technical Memo 1292. National Advisory Commission for Aeronautics, Washington, DCGoogle Scholar
- Pawlak G, De Carlo EH, Fram JP, Hebert AB, Jones CS, McLaughlin BE, McManus MA, Millikan KS, Sansone FJ, Stanton TP, Wells JR (2009) Development, deployment, and operation of Kilo Nalu nearshore cabled observatory. In: Proceedings IEEE OCEANS 2009 conference, Bremen, May 2009, pp 1–10Google Scholar
- Rajagopalan K (2010) Large eddy simulation of turbulent boundary layers over rough bathymetry. PhD thesis, University of Hawaii at ManoaGoogle Scholar