gov.noaa.nmfs.inport:27377 eng UTF8 dataset GIS dataset Point, Terrain, Raster Alaska Regional Office (907)586-7202 709 W. 9th St, P.O. Box 21668 Juneau AK 99802-1668 USA https://alaskafisheries.noaa.gov/ WWW:LINK-1.0-http--link Website Website for this organization information 8:00 am - 4:30 pm, Mon-Fri. resourceProvider Lewis, Steve 907-586-7858 907-586-7255 P.O. Box 21668 Juneau AK 99801 USA steve.lewis@noaa.gov email pointOfContact 2025-05-15T19:15:56 ISO 19115-2 Geographic Information - Metadata Part 2 Extensions for imagery and gridded data ISO 19115-2:2009(E) Bathymetry (Alaska and surrounding waters) Bathymetry Alaska 2017 publication NOAA/NMFS/EDM 27377 https://www.fisheries.noaa.gov/inport/item/27377 WWW:LINK-1.0-http--link Full Metadata Record View the complete metadata record on InPort for more information about this dataset. information Lewis, Steve 907-586-7858 907-586-7255 P.O. Box 21668 Juneau AK 99801 USA steve.lewis@noaa.gov web author https://alaskafisheries.noaa.gov/arcgis/rest/services/bathy_40m/MapServer WWW:LINK-1.0-http--link Citation URL REST Service download mapDigital 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 Editing processes included: deleting erroneous and superseded values, digitizing missing values, and referencing all data sets to a common, modern datum.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 Steve Lewis, Jarvis Shultz underDevelopment Lewis, Steve 907-586-7858 907-586-7255 P.O. Box 21668 Juneau AK 99801 USA steve.lewis@noaa.gov email pointOfContact Lewis, Steve 907-586-7858 907-586-7255 P.O. Box 21668 Juneau AK 99801 USA steve.lewis@noaa.gov email custodian quarterly Alaska Analytical Purposes Only Bathy Bathymetry Depth Ocean SE Alaska Southeast theme DOC/NOAA/NMFS/AKRO > Alaska Regional Office, National Marine Fisheries Service, NOAA, U.S. Department of Commerce dataCentre Global Change Master Directory (GCMD) Data Center Keywords 2017-04-24 publication 8.5 Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA) https://forum.earthdata.nasa.gov/app.php/tag/GCMD%2BKeywords GCMD Keyword Forum Page Global Change Master Directory (GCMD). 2025. GCMD Keywords, Version 22. Greenbelt, MD: Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA). URL (GCMD Keyword Forum Page): https://forum.earthdata.nasa.gov/app.php/tag/GCMD+Keywords information custodian AKRO Sustainable Fisheries (SF) Division Metadata Portfolio project InPort 2013-01-01T00:00:00 Outlier removal processes Lewis, Steve user otherRestrictions Cite As: Alaska Regional Office, [Date of Access]: Bathymetry (Alaska and surrounding waters) [Data Date Range], https://www.fisheries.noaa.gov/inport/item/27377. NOAA provides no warranty, nor accepts any liability occurring from any incomplete, incorrect, or misleading data, or from any incorrect, incomplete, or misleading use of the data. It is the responsibility of the user to determine whether or not the data is suitable for the intended purpose. otherRestrictions Access Constraints: via REST Services. Not for navigation. Analysis only. otherRestrictions Distribution Liability: for analytical purposes only. NOT FOR NAVIGATION unclassified NA NA NOAA Data Management Plan (DMP) NOAA/NMFS/EDM 27377 https://www.fisheries.noaa.gov/inportserve/waf/noaa/nmfs/akro/dmp/pdf/27377.pdf WWW:LINK-1.0-http--link NOAA Data Management Plan (DMP) NOAA Data Management Plan for this record on InPort. information crossReference eng; US oceans In progress. -133 170 40 88 Alaska and surrounding waters Continual | Currentness: Ground Condition 2013 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). ESRI REST Service 10.3.1 Uncompressed ESRI REST Service Lewis, Steve 907-586-7858 907-586-7255 P.O. Box 21668 Juneau AK 99801 USA steve.lewis@noaa.gov email distributor 8.2 https://alaskafisheries.noaa.gov/arcgis/rest/services WWW:LINK-1.0-http--link ShoreZoneFlex REST Service download https://alaskafisheries.noaa.gov/arcgis/rest/services/bathy_40m/MapServer WWW:LINK-1.0-http--link ALaskaBathy REST Service for Alaska Fisheries bathymetry download 8.2 https://alaskafisheries.noaa.gov/arcgis/rest/services/bathy_40m/MapServer WWW:LINK-1.0-http--link ALaskaBathy REST Service for Alaska Fisheries bathymetry download dataset Representativeness Data was extracted, parsed, and groomed by each of the individual 84,009+ tracklines using statistical analysis and visual inspection with some imputation 86,600 MB tracklines were extracted and processed at full resolution, often less than 1 meter resolution We are currently downloading a multi-national bathymetric data with another 160,000 surveys getting ready to be added, Completeness Measure Multibeam tracks are often prone to conal outliers. However, these outliers and investigated both using various GIS analytical tools Field Precision 1/10 of a meter. Sensitivity Multibeam tracks are often prone with conal outliers. -Currently, our process keeps a record of the survey from which that point originated. Some older data does not have this level of metadata. Servers are crawled for relevant data, and a list of download URLS to data files is returned. Data files are then retrieved using custom python scripts. Raw data is downloaded from online NCEI web servers. Data is converted to CSV or XYZ files Points missing one or more of their XYZ points values are removed and archived. Data is evaluated as a component of the bathymetry map to identify outliers (instances where data point(s) are not consistent with the expected variability of the surrounding environments) using a variety of statistical and manual methods. K-Natural Neighbors (KNN) - Python and SciPy Percentiles with Standard Deviations - Python and SciPy Slope and Neighbors - ArcGIS Models Manual/visual selection Human Imputation of points to make them consistent with surrounding terrain tracklines and satellite altimetry. Upon integration into the dataset, between 0-25% of the deepest and shallowest points are immediately removed from each trackline based on the StdDev of each induvial trackline. All data is archived; a dataset with the outliers could be built within 7 weeks. The percentage of points removed (R) is determined by a non-linear function of the datasets standard deviation (s), and can be seen below: R=-4.746813 +30.059/(1+(s/9.584625)^0.9983 ) This function was derived using a best-fit curve tool, which was instructed to return a naturally-logarithmic function which was equal to ~25 when s=0, and which decreased asymptotically to 0 as s grew larger. The function was then tailored to have what the developer felt was a reasonable slope The logic here is that datasets with low standard deviations would be relatively flat and featureless. Since they have a lower level of topographic complexity, they can undergo a higher rate of removal while still retaining the essential topographic character of the surface they represent. Data is then organized into a Kd-Tree structure in which data points are organized based on their values with the data sorted between levels in the tree (i.e., the first level is split along the x axis, the next level is split along the y axis, the next along the z axis, and then the fourth along the x axis again. The result was a tree which can be searched in O (log(n)) time, and which was optimized for quick spatial searches, critical for the next step. K-Nearest-Neighbors (KNN) statistical model is used on the data. This uses the value of each data point’s K spatially nearest neighbors (k-value) to produce an ‘expected value.’ The expected value is then subtracted from the point’s observed value, and the absolute value of this difference is the point’s ‘residual.’ After calculating the residuals for all of the points in the data set, we remove the 5% of points with the highest residuals. After these steps are completed, the remaining data are converted to Feature Classes. This data structure is composed of not only the raw data, but also a host of metadata calculated from the raw data, such as vessel name and tracklines number. Spatial indexes are added to the data to optimize operations. All internal data is point data and is stored in Alaska Albers project. ArcMap and Arc Pro Slope and Neighbor Outlier Tools. The ArcMap and ArcPro function analyzes each data point based on the slope of the rendered terrain polygon and the point’s immediate adjacent neighbors. If a sufficient portion of these slopes exceeded a manually pre-defined threshold, the point is flagged as a potential outlier but not removed. After this function has identified all potential outliers, the set is visually reviewed and flagged. Flagged points are manually removed from the terrain but stored as an independent shapefile and thus no data removed from the active dataset were truly deleted. otherRestrictions Access Constraints: By Raster format download |