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www.sea-technology.com September 2015 / st 13 spaced features, allowing for proper attribution and report- ing of the area. Conclusions Properly attributing numerous nearshore features in a post-hurricane, urban setting requires a meticulous ap- proach and multiple tools in order to create the best chart- update products achievable. The methodology OSI utilized to produce accurate survey deliverables, including the ac- quisition of high-resolution, side-looking multibeam soundings along with shoreline video and georefer- enced still photos, proved to be cost-effective and instrumental in product creation. The effcacy of this methodology cannot be over- stated. Interpretation of a complex hydrographic data set can involve much guesswork, especially when sonar targets reside in close proximity to one another or are masked by the acoustic shadowing effect of foreground obstructions. By using the tools available, including sonar data, aerial photogra- phy, satellite imagery, video and still photography, the proper classifcation of features at various states of covering and uncovering was achieved, the true character and above/below water disposition of shoreline features conveyed, and an accurate fea- ture fle was generated to support safe vessel naviga- tion and to assist with cleanup and salvaging opera- tions within the survey area. ST Bonnie L. Johnston is a hydrographer and data analyst with Ocean Surveys, Inc., with 10 years of experience in the hydrographic community, having begun her career in 2004 with NOAA's Offce of Coast Survey. Robert M. Wallace is an ACSM-certifed hydrographer and U.S. Coast Guard licensed captain employed at Ocean Surveys, Inc. since 1995. As a senior hydrographer, he designs and conducts hydrographic surveys for private sector clients, the U.S. Army Corps of Engineers and NOAA. His hydrographic project experience was acquired in the U.S. and overseas. David T. Somers is the data-processing manager for Ocean Surveys, Inc., with 19 years of experience working with hydrographic and geophysical survey data. Wrecks and obstructions identifed in GoPro photos and the MBES point cloud data were correlated during feature clas- sifcation.