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            <gco:CharacterString>These data are not to be used for navigational purposes.
NOAA makes no warranty regarding these data, expressed or
implied, nor does the fact of distribution constitute such a
warranty. NOAA cannot assume liability for any damages caused by
any errors or omissions in these data, nor as a result of the
failure of these data to function on a particular system.</gco:CharacterString>
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            <gco:CharacterString>Use Constraints: These data are not to be used for navigation purposes. Please acknowledge the NOAA Pacific Islands Fisheries Science Center (PIFSC) Coral Reef Ecosystem Division (CRED) and the Pacific Islands Benthic Habitat Mapping Center (PIBHMC), and the Joint Institute for Marine and Atmospheric Research (JIMAR) and School of Ocean and Earth Science and Technology (SOEST), University of Hawaii as the sources of this information.</gco:CharacterString>
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        <gco:CharacterString>Gridded multibeam data were collected aboard the R/V AHI (Acoustic Habitat Investigator), a 25' survey launch owned and operated by the NOAA Pacific Islands Fisheries Science Center in Honolulu, HI. The process for deriving the 5-m gridded multibeam bathymetry is described in Rose_5m.asc.txt, available on www.soest.hawaii.edu/pibhmc. The resulting grid (rose_5m.grd) was clipped using the ESRI ArcToolbox tool 'Extract by Mask' leaving only the multibeam data collected within the lagoon and excluding the narrow band of data around the upper slopes of the atoll. Classification was carried out just for this lagoon area, as classification using the full multibeam dataset was heavily affected by artifacts within this narrow band of data. The process for deriving the gridded multibeam backscatter is described in rose-8101-bs-1m.grd.txt. These data are gridded at 1m. Prior to classification the backscatter data were re-gridded at 5m, using the resample tool in the Arc Toolbox. The rugosity data used in this classification is also available on the same website, metadata describing the process of producing the rugosity grid is described in Rose_5m_rugosity.txt. The bathymetric variance grids were generated for use in the classification process and are not served separately. See the process description portion of this document for additional informaiton about how the variance and unsupervised layers were derived. A second classification was carried out using data collected on the slopes surrounding the atoll, and these are served separately on www.soest.hawaii.edu/pibhmc. Metadata for this second classification product is described in Rose_40m_hardsoft.txt.</gco:CharacterString>
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                <gco:CharacterString>Processing steps used to generate the backscatter, bathymetry, and bathymetric rugosity grids used in the unsupervised classification process are described in their own metadata that can be found at www.soest.hawaii.edu/pibhmc. Bathymetric variance was calculated from the 5 m multibeam bathymetry in a two-step process using ArcMap Version 9.2. The first step involved calculating the standard deviation of the bathymetry layer using the Neighborhood Statistic tool from the Spatial Analyst pull-down menu in ArcMap Version 9.2. Rectangular neighborhood settings were set to a height and width of 3 grid cells to create a small-scale variance layer. The standard deviation output was then squared using the ArcMap Raster Calculater tool. A large-scale variance layer was also generated using a rectangular neighborhood height and width of 5 grid cells. The fine-scale variance is useful for defining small features that may indicate the presence of sediment ripples or possible coral-rich areas whereas the large-scale variance is useful for defining seafloor ridges, pinnacles, and significant changes in slope. Bathymetric variance proved to be a useful tool in previous efforts to map seafloor habitats by Dartnell and Gardner (2004). Their publication can be downloaded from www.soest.hawaii.edu/pibhmc/pibhmc_documentation.htm. Bathymetric rugosity was calculated using the ArcGIS Benthic Terrain Modeler (D.J. Wright et al., 2005) accessible at https://coast.noaa.gov/digitalcoast//tools/btm/index.html. The layers were then opened as ESRI GRIDS in the image processing software package ENVI (Environment for Visualizing Images) Version 4.3. A multi-band images were then created with the backscatter, small- and large-scale variance, and rugosity. A mask was also created in ENVI for the dataset. Image classification was performed on the multi-band image using the K-Means unsupervised classification method, which calculates initial class means evenly distributed in the data space and then iteratively clusters the pixels into the nearest class using a minimum distance technique. More information about K-Means algorithm can be found in the ENVI documentation available at www.ittvis.com/envi/. The number of classes was set to two and all other options were set to default. Class color mapping was performed on the resulting classified images. The substrate types of hard and soft were assigned based on a visual interpretation of backscatter intensity and corresponding classes.</gco:CharacterString>
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