Sediment-Texture Units of the Sea Floor for Buzzards Bay, Massachusetts (BuzzardsBay_sedcover, polygon shapefile, Geographic, WGS84)

The texture and spatial distribution of sea-floor sediment were qualitatively-analyzed in ArcGIS using several input data sources (listed in the source contribution), including acoustic backscatter, bathymetry, seismic-reflection profile interpretations, bottom photographs, and sediment samples. In order to create the interpretation, first the polygon shapefile of the Massachusetts coast (1:25,000) OUTLINE25K_POLY (http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-geographic-information-massgis/datalayers/outline.html) was reprojected from Massachusetts State Plane NAD83 to WGS84 UTM Zone 19N using ArcMap (version 9.3): ArcToolbox -- Data Management Tools -- Projections and Transformations -- Feature -- Project. The polygon was edited so that it enclosed Buzzards Bay and surrounding coastal embayments. The edited shapefile was imported to a file geodatabase as a feature dataset. Then physiographic zone polygons were created using 'cut polygon' and 'auto-complete polygon' in an edit session. In general, polygon editing was done at scales between 1:5,000 and 1:20,000, depending on the size of the traced feature and the resolution of the source data. Separate polygons exist where the adjacent physiographic zones are the same but have different confidence levels. The following numbered steps outline the workflow of the data interpretation. 1. Backscatter intensity data (available at 1 m resolution) was the first input. Changes in backscatter amplitude were digitized to outline possible changes in sea-floor texture on the basis of acoustic return. Areas of high backscatter (light colors) have strong acoustic reflections and suggests boulders, gravels, and generally coarse sea-floor sediments. Low-backscatter areas (dark colors) have weak acoustic reflections and are generally characterized by finer grained material such as muds and fine sands. 2. The polygons were then refined and edited using gradient, rugosity, and hillshaded relief images derived from interferometric and multibeam swath bathymetry and (available at 10 m resolutions). Areas of rough topography and high rugosity are typically associated with rocky areas, while smooth, low-rugosity regions tend to be blanketed by fine-grained sediment. These bathymetric derivatives helped to refine polygon boundaries where changes from primarily rock to primarily gravel may not have been apparent in backscatter data, but could easily be identified in hillshaded relief and slope changes. 3. The third data input (where available) was the stratigraphic interpretation of seismic-reflection profiles, which further constrained the extent and general shape of sea-floor sediment distributions and rocky outcrops, and also provided insight concerning the likely sediment texture of the feature on the basis of pre-Quaternary, glacial or post-glacial origin. Seismic lines and the surficial geologic maps derived from them and used here as input data were collected at typically 100-meter spacing, with tie-lines generally spaced 1-km apart. 4. After all the sea-floor features were traced from the geophysical data, a new field was created in the shapefile called 'sed_type'. Bottom photographs and sediment samples were used to define sediment texture for the polygons using Barnhardt and others (1998) classification. Some polygons had more than one sample, and some polygons lacked sample information. For multiple samples within a polygon, the dominant sediment texture (or average phi size) was used to classify sediment type (often aided by the 'data join' sediment statistics described in a later processing step). In rocky areas, bottom photos were used in the absence of sediment samples to qualitatively define sediment texture. Polygons that lacked sample information were texturally defined through extrapolation from adjacent or proximal polygons of similar acoustic character that did contain sediment samples. 325 samples of the over 10,625 total within the study area were analyzed in the laboratory for grain size. Samples with laboratory grain size analysis were preferred over visual descriptions when defining sediment texture throughout the study area. Bottom photo stations are typically around 2-km apart, and the density of sediment samples varies throughout the study area, with an average of 1.36 samples per square km, while rocky areas have almost no sediment samples.

After some additional qualitative polygon editing and reshaping was done in order to create a sediment map that was in the best agreement with all input data: lidar, bathymetry, backscatter, seismic interpretations, bottom photographs, and sediment samples, 3 more fields were added (ArcMap version 9.3.1). The first field, 'simple' is just 3 classes: sand, mud, or hardbottom. Another field 'phi_class' was created and defined using the Wentworth (1922) sediment classification, and finally, a field 'Data_confi' was added as a data interpretation confidence, which describes how confident we are in the interpretation the basis of the number and quality of the input data sources (see the entity and attribute sections for more information on these fields). The remaining fields contain sediment texture statistics or mean water depth information and were created and populated using data joins or zonal statistics functions within ArcMap (version 9.3.1). The fields beginning with "Avg_" and the "Count_" field were automatically generated by computing a data join where the CZM sample database (vector points) was edited to include only the samples with laboratory sediment analysis and joined to the qualitatively-derived polygon file. Each polygon was given an average of the numeric attributes of the points (with laboratory grain size analysis) that fall inside it, and the count field shows how many laboratory analyzed points fall inside it. 325 samples were analyzed in the laboratory. Several fields that were not wanted were deleted after the join. A mean water depth (NAVD 88) field was created using ArcMap (version 9.3): ArcToolbox ? Spatial Analyst Tools ? Zonal -- Zonal Statistics as Table, where the mean water depth for each polygon (input zone data using the zone field sed_type) was derived from the input raster topographic and bathymetric grid (<http://pubs.usgs.gov/of/2014/XXX/GIS_catalog/SourceData/bathy/). No data raster values were ignored in determining the output value for each polygon zone. If all raster values were null within a polygon, that zone had a null value (changed to -999) for that zone. The output was saved to a table, which was joined with the sediment type polygon shapefile. All of the joined fields except MEAN were turned off, and the joined shapefile was exported to a new shapefile.

Finally, a feature dataset was generated inside a file geodatabase (ArcCatalog version 9.3.1) and the shapefile was imported to a feature class, and new topology rules were established to make sure that there were no overlapping polygons or accidental gaps between adjacent polygons. The topology error inspector (ArcMap version 9.3.1) was used to find topology errors and fix them. Overlapping polygon errors and gaps were fixed. Area fields were generated automatically for all polygons in the geodatabase. The data were then exported to a shapefile and the polygons were reprojected from UTM Zone 19N, WGS84 to GCS WGS84.

Edits to the metadata were made to fix any errors that MP v 2.9.32 flagged. This is necessary to enable the metadata to be successfully harvested for various data catalogs. In some cases, this meant adding text "Information unavailable" or "Information unavailable from original metadata" for those required fields that were left blank. Other minor edits were probably performed (title, publisher, publication place, etc.). Empty fields were deleted. Links to the data were fixed. The metadata date (but not the metadata creator) was edited to reflect the date of these changes. The metadata available from a harvester may supersede metadata bundled within a download file. Compare the metadata dates to determine which metadata file is most recent.

Added keywords section with USGS persistent identifier as theme keyword.