<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<metadata xml:lang="en">
<Esri>
<CreaDate>20230519</CreaDate>
<CreaTime>16583500</CreaTime>
<ArcGISFormat>1.0</ArcGISFormat>
<SyncOnce>TRUE</SyncOnce>
<MapLyrSync>TRUE</MapLyrSync>
</Esri>
<dataIdInfo>
<idCitation>
<resTitle>BenthicMapping_STEER_Dynamic</resTitle>
</idCitation>
<idAbs>&lt;div style='text-align:Left;font-size:12pt'&gt;&lt;div&gt;&lt;p&gt;&lt;span&gt;This dataset contains information about the shallow-water (&amp;lt;40 meters) geology and biology of the seafloor in Coral Bay, St. John in the U.S. Virgin Islands (USVI). It was created by manually delineating and classifying habitats visible in a 0.3x0.3 meter aerial photograph mosaic, and by using edge detection algorithms and boosted regression trees to automatically delineate and classify habitat features visible in 0.3x0.3 meter LiDAR surfaces. Habitat features less than 100 square meters were not delineated from the orthomosaic, and were removed from habitat polygons derived from the LiDAR surfaces using ET Geowizards ArcGIS extension. Manually delineated habitat polygons were digitized at a scale of 1:1,000. Habitat polygon boundaries derived from the LiDAR surfaces were smoothed in ArcGIS to more closely match the 1:1,000 scale used for manual digitizing. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span /&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Georeferenced underwater video &amp;amp; photos were used to train the analyst and algorithm to classify the major and detailed geomorphological structure, percent hard bottom, major and detailed biological cover and live coral cover for each polygon. The thematic accuracy of the map was assessed qualitatively by local experts and quantitatively using randomly sampled locations stratified by detailed geomorpholoigcal structure type. Thematic accuracies for major and detailed geomorphological structure, percent hardbottom, major and detailed biological cover, live coral cover and dominant coral type were: 93.0%, 75.1%, 86.2%, 86.5%, 74.5%, 83.3% and 88.2%, respectively. These thematic accuracies are similar to the thematic accuracies reported for other NOAA benthic habitat mapping efforts around Buck Island in St. Croix (&amp;gt;81.4%), in St. John (&amp;gt;80%), in the Main Eight Hawaiian Islands (&amp;gt;84.0%) and in the Republic of Palau (&amp;gt;80.0%).&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span /&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;For more information, visit the project website at https://coastalscience.noaa.gov/project/habitat-mapping-us-virgin-islands/&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;</idAbs>
<searchKeys>
<keyword>benthic habitat</keyword>
<keyword>zone</keyword>
<keyword>structure</keyword>
<keyword>cover</keyword>
<keyword>coral. USVI</keyword>
</searchKeys>
<idPurp>Benthic Habitats of Fish Bay, Coral Bay, and the St. Thomas East End Reserve in the U.S. Virgin Islands to Support Coral Reef Conservation</idPurp>
<idCredit>National Oceanic and Atmospheric Administration (NOAA)/National Ocean Service (NOS)/National Centers For Coastal and Ocean Science (NCCOS)/Marine Spatial Ecology (MSE)/Biogeography Branch</idCredit>
<resConst>
<Consts>
<useLimit/>
</Consts>
</resConst>
</dataIdInfo>
<Binary>
<Thumbnail>
<Data EsriPropertyType="PictureX">iVBORw0KGgoAAAANSUhEUgAAASwAAADICAYAAABS39xVAAAAAXNSR0IB2cksfwAAAAlwSFlzAAAO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==
</Data>
</Thumbnail>
</Binary>
</metadata>
