Sediment Texture Units of the Sea Floor from Nahant to Northern Cape Cod Bay, Massachusetts (NAH_CCB_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, Lidar, seismic-reflection profile interpretations, bottom photographs, and sediment samples. In order to create the interpretation, first a polygon shapefile of the study area was created (with ArcMap version 9.3.1) and merged with the published Boston Harbor interpretive polygons (Ackerman and others, 2006). Then sediment texture polygons were created using 'cut polygon' and 'auto-complete polygon' in an edit session. In general, polygon editing was done at scales between 1:8,000 and 1:20,000, depending on the size of the traced feature and the resolution of the source data. The following numbered steps outline the workflow of the data interpretation. 1. Backscatter intensity data (available at 1 to 10 m resolutions) was the first input. Changes in backscatter amplitude were digitized to outline possible changes in seafloor texture based on acoustic return. Areas of high backscatter (light colors) have strong acoustic reflections and suggests boulders, gravels, and generally coarse seafloor 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 Lidar (available at 2.5 - 30 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 seafloor sediment distributions and rocky outcrops, and also provided insight concerning the likely sediment texture of the feature based on the 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 seafloor features were traced from the geophysical data, new fields were created in the shapefile called 'sed_type' and 'shep_code'. Bottom photographs and sediment samples were used to define sediment texture for the polygons using Barnhardt and others (1998) and Shephard code (Shepard, 1954) classifications. 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. 615 samples of the over 5,500 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 photos are typically around 2-km apart, and the density of sediment samples varies throughout the study area, with Boston Harbor having a high density of samples, 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, 4 more fields were added (ArcMap version 9.3.1). The first field, 'simple' is just 3 classes: sand, mud, or hardbottom. Another field 'bedforms' defines the presence or absence of bedforms features in the geophysical data, which helps determine seabed mobility. Another filed '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 based on 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. 615 samples were analyzed in the laboratory. Several fields that were not wanted were deleted after the join. Finally, a mean water depth field was created using zonal statistics, where the mean water depth for each polygon was derived from the topographic and bathymetric grid (http://pubs.usgs.gov/of/2012/1157/GIS_catalog/SourceData/bathy/).

Finally, a feature dataset was generated inside a geodatabase (ArcCatalog version 9.3.1), 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 were fixed, and several gaps were marked as exceptions since there are several islands and significant data gaps within the study area. Very small (less than 1-square meter) gaps and triangular jogs in polygon boundaries within Boston Harbor exist where data published by Ackerman and others (2006) was used. These small topology feature errors were not corrected. Length and 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 2.9.30 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.). The distribution format was modified in an attempt to be more consistent with other metadata files of the same data format. 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.