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.
Cochrane, Guy R. , Harney, Jodi, Dartnell, Pete, Golden, Nadine, and Chezar, Hank, 2005, Glacier Bay Habitat polygons:.This is part of the following larger work.Online Links:
- <http://pubs.usgs.gov/of/2006/1081/catalog.html>
- <http://pubs.usgs.gov/of/2006/1081/habitat.html/gbhab.tgz>
Cochrane, Guy R. , Harney, Jodi, Dartnell, Pete, Golden, Nadine, and Chezar, Hank, 2005, Geologic characteristics of benthic habitats in Glacier Bay, southeast Alaska Edition: 1.0: Open-File Report USGS OFR 2006-1081, U.S. Geological Survey, Coastal and Marine Geology Program, Western Coastal and Marine Geology, Santa Cruz, CA.Online Links:
This is a Vector data set. It contains the following vector data types (SDTS terminology):
The map projection used is NAD_1983_UTM_Zone_8N.
Planar coordinates are encoded using coordinate pair
Abscissae (x-coordinates) are specified to the nearest 0.000064
Ordinates (y-coordinates) are specified to the nearest 0.000064
Planar coordinates are specified in meters
The horizontal datum used is North American Datum of 1983.
The ellipsoid used is Geodetic ReferenceSystem 80.
The semi-major axis of the ellipsoid used is 6378137.
The flattening of the ellipsoid used is 1/298.2572222.
FID Alias: Shape Data type: Geometry Width: 0 Precision: 0 Scale: 0 Definition: Feature geometry. Definition Source: ESRI
FID Alias: FID Data type: Number Width: 6 Definition: Internal feature number. Definition Source: ESRI
GRIDCODE Alias: GRIDCODE Data type: Number Width: 10
MEGA_ID Alias: MEGA_ID Data type: String Width: 10
BOTTOM_ID Alias: BOTTOM_ID Data type: String Width: 10
MSO_MCR_ID Alias: MSO_MCR_ID Data type: String Width: 10
MDFIR_ID Alias: MDFIR_ID Data type: String Width: 10
SLOPE_ID Alias: SLOPE_ID Data type: String Width: 10
COMPLEX_ID Alias: COMPLEX_ID Data type: String Width: 10
Area Alias: Area Data type: String Width: 200
COMMENT Alias: COMMENT Data type: String Width: 25
bathyclass Alias: bathyclass Data type: String Width: 50
MEGA Alias: MEGA Data type: Float Width: 19 Number of decimals: 11
BOTTOM Alias: BOTTOM Data type: String
MSO_MCR Alias: MSO_MCR Data type: String Width: 50
MDFR Alias: MDFR Data type: String Width: 50
HAB_TYPE Alias: HAB_TYPE Data type: String Width: 50
SLOPE Alias: SLOPE Data type: String Width: 50
COMPLEXITY Alias: COMPLEXITY Data type: String Width: 50
Benthic habitat classification attributes: megahabitat, bottom induration, meso-macrohabitat, and modifiers from Green and others, 1999. CODE is a combination of the habitat attributes. MEGA_ID is I for “Inland seas, fjords.” BOTTOM_ID is h for hard bottom, m for mixed hard and soft bottom, or s for soft sediment bottom MSO_MRC_ID are macrohabitats described in Greene and others 1999. MDFR_ID are modifiers to describe the texture or lithology of the seafloor and appear in the code preceded by an underscore (_). Including; bimodal (_b), interface (_i), ripples (_r), heavily bioturbated (_t), nearshore bathy class >-75m(_x), and nearshore bathy class <= -75m and >-200m (_y).
The authors would like to thank Gerry Hatcher, and Paul Carlson of the USGS Western Region Coastal and Marine Geology Program (in Santa Cruz and Menlo Park, CA) for field support and GIS assistance. Kevin O’Toole, Mike Boyle, Jerry O’Brien, and others at the USGS Marine Facility contributed equipment and logistical support. Ecologists Lisa Etherington, Jennifer Mondragon, and Alex Andrews from the Alaska Science Center (Gustavus and Juneau, AK) provided invaluable biological expertise during data collection. The R/V Gyre was expertly skippered by Jim De la Breure (also of the Alaska Science Center in Gustavus). Student assistants in Janine Bird and Angela Lam (in Santa Cruz, CA) contributed to GIS and laboratory analyses.
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These data are intended for science researchers, students, policy makers, and the general public. The data can be used with geographic information systems (GIS) software to display geologic and oceanographic information.
Geomorphic Classification Methods: The Glacier Bay multibeam data were first analyzed using a hierarchical decision-tree classification process. The classification used four images, the original backscatter-intensity image, seafloor slope, and two derivative raster images calculated from the original bathymetry and backscatter images; a 3x3-filtered bathymetry-variance image and a 3x3-filtered backscatter-variance image. Variance was calculated as the variability of bathymetry or backscatter within a kernel. An area with a large range of bathymetric relief, such as a rocky outcrop, would have a large bathymetry variance. A smooth area would have low bathymetry variance. Backscatter was parsed in a similar fashion; an area with high backscatter variability, such as an outcrop (high BS) with pockets of sediment (low BS) would have a large backscatter variance, whereas a flat, uniformly sedimented seafloor would have a low backscatter variance. The variance images were calculated by generating two intermediate images, a maximum image and a minimum image. The maximum image was calculated by running a filter (3x3 cells) that returned the maximum value within a kernel to the center cell. The minimum image was calculated by running a filter that returned the minimum value within a kernel to the center cell. The variance images were created from the difference between the maximum and minimum images. Unsupervised classifications run on the two variance images, on the original backscatter-intensity image, and on the seafloor slope image clustered the pixels into five groups numbering one to five, with one representing a very low variance/intensity/slope, 2 representing a low variance/intensity/slope, 3 representing a medium variance/intensity/slope, 4 representing a high variance/intensity/slope, and 5 representing a very high variance/intensity/slope.
The four unsupervised classified images were then analyzed using a hierarchical decision-tree classification that is part of the ERDAS Imagine 8.4 software package (ERDAS, 1999). The classification is a rules-based approach that uses a hierarchy of conditions to parse the input data into a set of classes. The decision-tree framework was developed from empirically determined textural rules, variables, and hypotheses. An hypothesis is an output-geomorphic class, such as fine-grained homogeneous mud, a variable is a raster image of derived values (i.e. bathymetry variance), and a rule is a conditional statement about the variable’s pixel (data) values that describes the hypothesis. Because the four unsupervised classified images are co-registered with one another, rules can be established that relate pixel values within or between images that will ultimately classify a new seafloor geomorphic image. Multiple rules and hypotheses can be linked together into a hierarchy that describes the hypothesis.
Rules for the decision-tree classification process were based on seafloor video observations. Rules were developed to correctly classify the seafloor over a camera transect. The areas that were previously unknown were similarly classified based on these same rules.
Results: The combination of hypotheses, rules, and variables in the hierarchical decision tree produced a map of the Glacier Bay geomorphic provinces. Areas classified as ‘High complexity/high slope/boulder or cobble” correlated with very low- to very high-backscatter intensity (Table 1), low- to very high-backscatter variance, and medium- to very high-bathymetry variance. Areas of “High complexity/low slope/boulder or cobble” correlated with very low- to very high-backscatter intensity, low- to very high-backscatter variance, medium- to very high-bathymetry variance, and very low- slope. Areas of “fine-grained homogeneous mud” correlated with medium- to very low-backscatter intensity, medium- to very low-backscatter variance, and very high- to very low-bathymetry variance. Finally, areas of “unsorted, unconsolidated sediment, sand to boulder-sized glacial till” correlated with very low- to very high-backscatter intensity, very low- to very high-backscatter variance, and low- to very low-bathymetry variance.
References: ERDAS Field Guide, 1999, ERDAS Inc, Atlanta Georgia. 672p.
//nibble grid 1) Reclassed data values: 0 --> NoData 1 --> 3 (hard) 2 --> 4 (hard) 3 --> 2 (mixed) 4 --> 1 (soft)
//filtered grid Used spatial analyst tool "filter" one pass on low. Reclassified with spatial analyst "reclassify tool" to assign grid range values back to 1 through 4. Assigned filter values grid map: .5 - -1.5 = 1 1.5 - 2.5 =2 2.5 - 3.3 = 3 3.3 - 4.5 = 4
//converted grid to poly Used spatial analyst tool "raster to feature" to convert grid to polygon shapefile.
//created bathy polygon of 3 contour values: 75 meters, 200 meters, 400 meters. Created polygon file from bathymetry grid using spatial analyst "raster to feature" tool. Selected for contours of 75 meters, 200 meters, 400 meters. Exported selected data to new polygon shapefile.
//merged fourclass poly and contour poly Used spatial analyst "union" tool to merge fourclass polygon and 3 value contour polygon. Note: union intersects the polygons of the input grid_1 (fourclass) everywhere the input gid_2 (contour polygon) intersects.
//manual edit and clean polygons Added bathymetry column to new, merged fourclass polygon using "Hawth's Tools--> Intersect Point Tool." Added all Green habitat code (1999) ID and definition columns to polygons. Used select by attribute, location, and manual tools to query and assign Green habitat code attributes.
//eliminated border polygons remaining from filter Selected polygons with areas less or equal to 10 sq meters (note: I selected for area <=1, <=2, etc...up to <=10 and ran the eliminate tool for each selection set). Used the "Eliminate" tool from the "Data Management Tools" --> "Generalation" --> "Eliminate." Note: Eliminate tool merges the selected polygons with neighboring polygons with the largest area.
United States Geological Survey, Coastal and Marine Geology (CMG), 2005, USGS CMG Glacier Bay, Alaska Habitat Metadata.This is part of the following larger work.Online Links:
United States Geological Survey, Coastal and Marine Geology (CMG), 2005, USGS CMG InfoBank.Online Links:
Habitat polygons dervived in ArcGIS 9.1 from a georefereced sidescan sonar mosaic tiff.
% gravel, sand, silt and clay and % fine grain for 1144 samples
No additional checks for topological consistency were performed on this data.
Are there legal restrictions on access or use of the data?
- Access_Constraints: None
- Use_Constraints: Not suitable for navigation
(831) 427-4754 (voice)
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Please recognize the U.S. Geological Survey (USGS) as the source of this information.Although these data have been used by the U.S. Geological Survey, U.S. Department of the Interior, no warranty expressed or implied is made by the U.S. Geological Survey as to the accuracy of the data.
The act of distribution shall not constitute any such warranty, and no responsibility is assumed by the U.S. Geological Survey in the use of this data, software, or related materials.
(831) 427-4754 (voice)
(831) 427-4748 (FAX)
gcochrane@usgs.gov