Bathymetry (Alaska and surrounding waters)

A Raster having 20 m resolution with decimal values was assembled from 18.6 billion bathymetric soundings that were obtained from the National Center for Environmental Information (NCEI) https://www.ncei.noaa.gov. Bathymetric soundings extends from Kuril-Kamchatka Trench in the Bearing Sea along the Aleutian Trench to the Gulf of Alaska, and in the Arctic Ocean from Prince Patrick Island to the International Date Line. Bathymetric soundings were scrutinized for accuracy using statistical analysis and visual inspection with some imputation. Editing processes included: deleting erroneous and superseded values, digitizing missing values, and referencing all data sets to a common, modern datum.

Cartographic products for the entire Alaska Exclusive Economic Zone (AEEZ) and surrounding waters

Time Period of Content Date: Various dates

Maintenance and Update Frequency: As needed

Spatial Extent of Data: Alaska and surrounding waters

Keywords (Place): - Alaska Exclusive Economic Zone (AEEZ)

(Theme): - Bathymetry

- Depth,

- Shaded relief

- Alaska - Arctic Ocean

- Aleutian Islands

Attributes

Table Detail - The attribute tables associated with each data point has four fields:

(1) OBJECTID (is a unique, system-defined, object identifier number for each row in the table)

(2) Value (Value of ocean depth multiplied by 100)

(3) Count (the number of cells in the raster dataset with the cell value in the VALUE column

(4) Z (the approximate depth of the water column at that spatial location). (Use Z value for depth)

Data Quality

Data Source(s): 86,000 separate bathymetric surveys including trackline geophysics, hydrographic surveys, gridded multi-beam NET CDF, MB-58 multi-beam sonar data, and satellite altimetry.

Completeness: This dataset is kept up to date with data from National Center for Environmental Information (NCEI) data services

Direct Spatial Reference Method: Raster

Raster Object Type: Grid Cell

Row Count: 338352

Column Count: 316476

XY Coordinate system NAD_1983_Alaska_Albers

Datum D_North_American_1983

Linear Unit Meter (1.000000)

Vertical Coordinate System NAD_1983

Spatial Domain Bounding Coordinates Southern Region: Kuril-Kamchatka Trench to Gulf of Alaska

West_Bounding_Coordinate: 150

East_Bounding_Coordinate: -150

North_Bounding_Coordinate: 63

South_Bounding_Coordinate: 40

Bounding Coordinates Northern Region: Prince Patrick Island to the International Date Line

West_Bounding_Coordinate: 150

East_Bounding_Coordinate: 0

North_Bounding_Coordinate: 80

South_Bounding_Coordinate: 71

This bathymetry grid was created in several steps:

1) Data were downloaded from NCEI and other online sources and parsed using NCEI Auto Grid, WGET with Python 2.7, Python 3.4, PostgreSQL DBMS, Quantum GIS, and ArcGIS. NetCDF, Grid, ASC and other data was converted into XYZ format. The XYZ coordinate files were projected to Albers Equal Area Conic projection and imported into an ESRI file geodatabase (FGDB) or individual tracklines.

2) The KNN model was set for one-hundred and fifty nearest neighbors and to delete the top 3% of those “outliers”. Percentiles were applied to clean data on a sliding scale based on standard deviation (StDEV). If the StDEV was lower – as in mud-flats or other relatively flat terrain, then a higher amount of the data’s trackline was selected for removal. Where the StDEV was high in high-relief areas, a smaller amount of the data was removed. The percentile technique was most effective in removing cross-band concavity or convexity in the survey’s trackline. Z-Score was often used to guide the GIS analyst in looking for areas of high variability that may warrant data imputation. PostGRESQL database functions such as ST_DWithin was used to perform the data removal because of its highly optimized indexing system, which allows it to perform the deletes at a rate several orders of magnitude greater than ArcGIS. PostGRESQL was not used for the actual Geodatabase because of its ArcGIS to DBMS interface overhead. When PostGRESQL was using to process data, it was all exported to ArcGIS File geodatabase.

3) Feature classes were combined using the ArcGIS Terrain data type to create a surface. The terrain feature type is based on triangulated irregular network (TIN). Varying terrains have been created based on the spacing of the data points. This includes 5 meter, 25 meter, and 50 meter. The 40 meter grid export is based on the 25 meter terrain.

4) The terrain has built in tools for locating integrated outliers using natural-neighbors (NN), slope and absolutes. Several iterations of NN and slope were applied to find potential outliers. Actual outliers were selected manually from the algorithm, cataloged, archived and then deleted from the dataset. The Terrain is rebuilt after each set of deletions. The external bounds of the TIN were trimmed to reflect the actual spatial extent of the point data. TIN was converted into a grid (raster with 200 meter resolution).