chaneast_back.txt - Sidescan sonar backscatter image of Channel East in the Nearshore Benthic Habitat Mapping Project S. California map series.

Title:

Abstract:

The sidescan sonar image of the nearshore seafloor (0 to 100 m water depths) of the Santa Barbara Channel area was mosaicked from data collected in 2005. A 234 kHz Interferometric Submetix Swath Bathy Sonar System; SEA Swath Processor, v. 2.05, SEA Grid Processor v. 2.05, was used for geophysical surveying. The 2005 survey was navigated with a CodaOctopus, Model F180, Differential Global Positioning System (DGPS). A KVH Industries Inc. azimuth digital gyro-compass provided ship headings with 0.5 degree accuracy. Navigation data were recorded using Yo-Nav version 1.19 (Gann, 1992). The sidescan fish was towed approximately 30 m above the seafloor. The distance of the fish behind the ship was not known during this survey and must be estimated when the data are processed in order to produce the sidescan image mosaics. The resolution of the processed data mosaics is 0.2 m. The data are presented here at a resolution of 1 m. This is one of a collection of digital files of a geographic information system of spatially referenced data related to the USGS Coastal and Marine Geology Program Nearshore Benthic Habitat Mapping Project. See <http://walrus.wr.usgs.gov/nearshorehab> for more information.

Supplemental_Information:

Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in ArcInfo format, this metadata file may include some ArcInfo-specific terminology.

Started with SXP files that were the output from A 234 kHz Interferometric Submetix Swath Bathy Sonar System. Based on the Julian date at original data collection, individually imported each raw backscatter file into "SEA Swath Processor Real-Time Software System". Configured the way the data was processed in "SEA Swath Processor" by inputting adjustment and offset information, such as ship's motion, tides, velocity of sound, and relative sensor positions.

Used the "SonarWiz.MAP Sonar File Manager" tool: check and correct the navigation data. Manually "bottom-tracked" each line. Applied signal processing functions by setting the Automatic Gain Control (AGC), Beam Angle Correction (BAC), project sonar data using sensor headings. Individually exported the line as a GeoTif file.

Looped through the directory of GeoTif files and convert GeoTif to ArcGIS raster format. Removed "NoData" raster cells using the ArcGIS Con statement; and projected the data to WGS 1984 UTM Zone 11. **** Ascii to ArcRaster & Project Pyramids (Python Script)**** # --------------------------------------------------------------------------- # ascii_to_arcraster_project_pyramids_stand_alone.py # Created on: Tuesday Oct 04 2006 # Nadine Golden # --------------------------------------------------------------------------- # Import system modules import glob, sys, string, os, win32com.client # Create the Geoprocessor object gp = win32com.client.Dispatch("esriGeoprocessing.GpDispatch.1") # Load required toolboxes... gp.AddToolbox("E:/ArcGIS/ArcToolbox/Toolboxes/Data Management Tools.tbx") gp.AddToolbox("E:/ArcGIS/ArcToolbox/Toolboxes/Conversion Tools.tbx") files = glob.glob("F:/test/grids/*asc") for file in files: (basename, ext) = os.path.splitext(file) ingrid = file outgrid = basename + "tmp" os.system("scii_to_arcraster_project_pyramids_stand_alone.py %s %s" % (ingrid, outgrid)) # Process: ASCII to Raster... gp.ASCIIToRaster_conversion(ingrid, outgrid, "INTEGER") ## # Process: Define Projection... ## gp.DefineProjection_management(outgrid, "PROJCS['NAD_1983_UTM_Zone_11N',GEOGCS['GCS_North_American_1983',DATUM['D_North_American_1983', SPHEROID['GRS_1980',6378137.0,298.257222101]], PRIMEM['Greenwich',0.0], UNIT['Degree',0.0174532925199433]], PROJECTION['Transverse_Mercator'], PARAMETER['False_Easting',500000.0], PARAMETER['False_Northing',0.0], PARAMETER['Central_Meridian',-117.0], PARAMETER['Scale_Factor',0.9996], PARAMETER['Latitude_Of_Origin',0.0], UNIT['Meter',1.0]]") # Process: Project Raster... finalgrid = basename gp.ProjectRaster_management(outgrid, finalgrid, "PROJCS['WGS_1984_UTM_Zone_11N',GEOGCS['GCS_WGS_1984',DATUM['D_WGS_1984', SPHEROID['WGS_1984',6378137.0,298.257223563]], PRIMEM['Greenwich',0.0], UNIT['Degree',0.0174532925199433]], PROJECTION['Transverse_Mercator'], PARAMETER['False_Easting',500000.0], PARAMETER['False_Northing',0.0], PARAMETER['Central_Meridian',-117.0], PARAMETER['Scale_Factor',0.9996], PARAMETER['Latitude_Of_Origin',0.0], UNIT['Meter',1.0]]; -10000 -10000 100000;0 100000;0 100000", "NEAREST", "1") # Process: Build Pyramids... gp.BuildPyramids_management(finalgrid) # Clean up the mess gp.Delete(outgrid)

Looped through the directory arcgrids and normalized the backscatter values using the Python script "RasterStats.py". **** Rasters Statistics for Normalizing Data (Python Script)**** # --------------------------------------------------------------------------- # RasterStats.py # To Calculate the mean value of a raster # Created on: Thursday Oct 20, 2006 # Nadine Golden # --------------------------------------------------------------------------- # Import system modules import sys, string, os, win32com.client # Create the Geoprocessor object gp = win32com.client.Dispatch("esriGeoprocessing.GpDispatch.1") # Check out any necessary licenses gp.CheckOutExtension("spatial") # Load required toolboxes... gp.AddToolbox("E:/ArcGIS/ArcToolbox/Toolboxes/Spatial Analyst Tools.tbx") gp.AddToolbox("E:/ArcGIS/RasterStatsTool/RasterStatistics.tbx") #Directory of grids gp.Workspace = "F:\\test" rasterlist = gp.ListRasters() name = rasterlist.Next() while name: print name # Process: RasterBand Statistics... mean = gp.RasterBandStatistics(name, "MEAN") print mean # Process: Minus... (indir,basename) = os.path.split(name) outgrid = os.path.join("F:\\test\\Normalized", basename) gp.Minus_sa(name, mean, outgrid) print outgrid name = rasterlist.Next()

In ArcGIS, imported lines according to track line number and mosaiced adjacent backscatter lines. Mosaicing was done by manually drawing a mask around the best possible data for each line and it's overlapping lines. Best possible data was determined subjectively and included the least no data values as possible in the overlapping areas.

chaneast_back.txt - Sidescan sonar backscatter image of Channel East in the Nearshore Benthic Habitat Mapping Project S. California map series.

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