Sediment-Texture Units of the Sea Floor for Vineyard and western Nantucket Sounds, Massachusetts (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. The interpretation was initiated by digitizing a polygon shapefile (file > new > shapefile in ArcCatalog 9.3.1, then editor > 'create new feature' in ArcMap 9.3.1) around the extent of the regional bathymetric DEM (see vns10m_navd88 in the larger work citation). The polygon was then partitioned into multiple sediment texture polygons 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. Some areas interpreted as a single sediment textural unit may contain multiple polygons that indicate different interpretation 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 suggest 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, lower-relief 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'. Seafloor composition observations from sediment samples and bottom photographs/video were used to define sediment texture for the polygons using Barnhardt and others (1998) classification. 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. Some polygons contained more than one sample with grain-size statistics, while others contained none. 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. 219 samples within the study area were analyzed in the laboratory for grain size. Bottom photo stations are typically around 2-km apart, and the density of sediment samples varies throughout the study area.

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 'ConfLevel' was added as a data interpretation confidence, which describes how confident we are in the interpretation on 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. 219 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 regional bathymetric DEM (see vns10m_navd88 in the larger work citation). 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.

The polygon shapefile containing sediment texture units with joined sediment sample lab statistics was imported as a feature class within a file geodatabase feature dataset, and topological rules were established (ArcCatalog 9.3.1). Topological errors, primarily overlaps and gaps, were identified and remedied using the topology toolbar in ArcMap (9.3.1).

The sediment texture polygon feature class was exported back to a shapefile and the 'Shape_Area' and 'Shape_Length' fields were deleted from its attribute table (ArcCatalog and ArcMap 9.3.1). XTools Pro (7.1.0) was then used to add and populate a new attribute field containing polygon area in square kilometers based on UTM, zone 19 N, WGS84. Finally, the shapefile was reprojected from UTM zone 19 N, WGS84 to GCS WGS84 using ArcToolbox > Data Management Tools > Projections and Transformations > Feature > Project.

Added keywords section with USGS persistent identifier as theme keyword.