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ftp://ftp.coast.noaa.gov/pub/benthic/Benthic_Cover_Data/DE_DelawareBay.zip |
Bulk Download |
FTP download of data files. |
download |
| 1 |
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http://www.cmecscatalog.org/ |
CMECS Catalog |
Searchable online catalog of CMECS units, descriptions, and source references |
download |
| 1 |
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https://coast.noaa.gov/ |
NOAA's Office for Coastal Management (OCM) website |
Information on the NOAA Office for Coastal Management (OCM) |
download |
| 1 |
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https://coast.noaa.gov/digitalcoast/ |
NOAA's Office for Coastal Management (OCM) Digital Coast Data section |
The website provides not only coastal data, but also the tools, training, and information
needed to make these data truly useful. Content comes from many sources, all of which
are vetted by NOAA. |
download |
| 4 |
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https://coast.noaa.gov |
NOAA Office for Coastal Management Website |
NOAA Office for Coastal Management Home Page |
information |
| 1 |
|
https://forum.earthdata.nasa.gov/app.php/tag/GCMD%2BKeywords |
GCMD Keyword Forum Page |
Global Change Master Directory (GCMD). 2024. GCMD Keywords, Version 19. 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 |
| 1 |
|
https://iocm.noaa.gov/cmecs |
CMECS Home Page |
Information and resources on the CMECS standard and how to apply it |
download |
| 1 |
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https://www.fisheries.noaa.gov/inport/item/47869 |
Full Metadata Record |
View the complete metadata record on InPort for more information about this dataset. |
information |
| 1 |
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https://www.fisheries.noaa.gov/inportserve/waf/noaa/nos/ocm/dmp/pdf/47869.pdf |
NOAA Data Management Plan (DMP) |
NOAA Data Management Plan for this record on InPort. |
information |
| 1 |
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2008-01-01T00:00:00 |
Bivalve Reef (Oyster, Identified Oyster, and Corbicula) polygons were derived from
the bottom sediment map that were constructed by the utilization of a Roxann Seabed
Classification System and extensive sediment grab samples bottom sediment map that
were constructed by the utilization of a Roxann Seabed Classification System and extensive
sediment grab samples. Corbicula fluminea beds were identified on the Roxann output
by the occurrence of data point which had moderate to high hardness return (E2) and
an anomalously high roughness return (E1), as compared to the adjacent sediments.
The sediments surrounding these beds are usually of a finer (silt to clay) grain size,
with low roughness and hardness values. The regions where these types of returns were
encountered were then sampled with a grab sampler. Several samples (2 to 3 samples)
were collected at each station to increase the likely hood that Corbicula would be
encountered, if it was indeed located in that region. Corbicula beds can have varying
densities, distributions, and bed configurations; hence this sampling scheme was enacted
to account for this spatial variability.
Submersed Rooted Vascular Plants (Vallisneria Americana) beds outlines were identified
through the same bottom sediment map used for delineating bivalve reef. SRV beds
were identified on the Roxann output by the occurrence of data point which had very
low hardness returns (E2) and an anomalously high roughness return (E1), as compared
to the adjacent sediments. The regions where these types of returns were encountered,
where then sampled with a grab sampler. Several samples (2 to 3 samples) were collected
at each station to increase the likely hood that SAV would be encountered, if it was
indeed located in that region. SAV beds can have varying densities, distributions,
and bed configurations; hence the sampling scheme was enacted to account for this
spatial variability.
Outcrop areas consist of Cretaceous sediment that is at the river bottom surface (or
near the surface ~1 to 2 cm). These areas are scour or erosional zones within the
river. The outcropping material consists of highly compacted/de-watered silty fine
sand to fine sandy silts, which contain relict burrow casts and glauconite. Outcrop
boundaries were derived from the bottom sediment map raster grid.
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| 1 |
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2008-01-01T00:00:00 |
The bottom sediment map was constructed by the utilization of a Roxann Seabed Classification
System and extensive
sediment grab samples. Data was collected in a gridded trackline configuration, with
tracklines spacing of 100 meters parallel to
the shoreline and 200 meters perpendicular to the shoreline. This project is an extension
of the work currently being performed in
Delaware waters by DNREC's Delaware Coastal Program's Delaware Bay Benthic Mapping
Project. The bottom sediment point data, which
has been classified according to the existing benthic mapping Roxann box plot, are
converted from a number that categorizes the
point according to its corresponding box (in the Roxann) into a number which reflects
the sediment properties of each box in
relation to one another. A ranking scale is used to allow a statistical gridding
scheme to interpolate between sediment data
points, while minimizing erroneous sediment classifications and allowing gradational
sediment deposits to be gridded. A ranking
scale from 0 to 28 was used for this project, with 0 representing the finest grained
classifications (fluidized clay) and 28
representing the coarsest grained classifications (dense shell material). Table 1
illustrates the distribution of sediment
classifications along the ranking scale, which takes into account the relation of
sediment types and grain sizes to one another
using both the Wentworth Scale and Shepard's classification system. Finer grains
are more similar in their deposition
environments, such as clay and silts, because they reflect similar current regimes,
sorting, and reworking patterns (Poppe et al.,
2003). While coarse sediments are much more dissimilar to finer grains, with respect
to current velocities, sorting, and winnowing,
the finer grains are much more closely related in their sediment diameters that the
coarser grains as you increase in Phi size
and/or diameter. These account for the close clustering of coarse grained deposit
descriptions at the upper end of the ranking
scale, while the finer grained sediments show a gradation as you increase in the
rating scale.
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2008-01-01T00:00:00 |
The bottom sediment data is gridded in Surfer degrees 8, a surface and terrain modeling
program, using block kriging and a nugget effect. This statistical griding technique
estimates the average value of a variable within a prescribed local area (Isaaks and
Srivastava, 1989). Block kriging utilizes the existing point data values, weights
the values of the data depending upon the proximity to the point being estimated,
to discretize the local area into an array of estimated data value points and then
averaging those individual point estimates together to get an average estimated value
over the area of interest (Isaaks and Srivastava, 1989). A variogram is constructed
for the data, and the resultant spatial model that is developed from the variogram
is used in the block kriging surface model to more accurately interpolate the sediment
data . The fitted model was a nugget effect (with an error variance of 21.8%) and
a linear model (with a slope of 0.00286 and an anisotropy of 1, which represents a
complete lack of spatial correlation).The accuracy of the estimation is dependent
upon the grid size of the area of interpolation, the size of each cell within the
grid, and the number of discretized data points that are necessary to estimate the
cells within that grid spacing. The grid size that was used to interpolate the bottom
sediment maps was 442 lines x 454 lines, with a cell size of 44.93 m2. The nugget
effect is added to allow the griding to assume there is very little, if any, lateral
correlation or trends within the bottom sediment (Isaaks and Srivastava, 1989). The
nugget effect model entails a complete lack of spatial correlation; the point data
values at any particular location bear no similarity even to adjacent data values
(Isaaks and Srivastava, 1989). Without the nugget effect the gridding would assume
that you could only have a linear progression of sediment types and would insert all
the sediment types along the scale between two sediment types (i.e. silty fine to
medium sands and fine to medium sand with varying amounts of pebbles would be inserted
between fine sand and coarse sand even though that is not what is occurring along
the bottom. The sediment data is gridded with no drift for the data interpolation,
also helping to minimize erroneous classifications. Sediment Classification Ranking
Sediment Description 0-11-2 Clay, 2-33-44-55-66-7 Silt,7-88-9 Sandy Silts, 9-1010-11
Fine Sand, 11-1212-13 Silty Fine to Medium Sands, 13-14 Silty Medium Sand, 14-1515-16
Fine to Medium Sand, 16-1717-18 Fine to Medium Sand with abundant shell material and/or
pebbles, 18-1919-20 Coarse Sand with varying amounts of pebbles, 20-2121-2222-23 Moderate
Shell Material/Sandy Pebbles, 23-2424-2525-26 Abundant Shell Material/Gravel, 26-2727-28
Dense Oyster Shell.
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| 1 |
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2012-01-01T00:00:00 |
Separate shapefiles for oyster beds, identified oyster beds, SAV, corbicula, outcroppings,
and depositional zones were integrated into a single feature layer to produce a comprehensive
benthic cover polygon data set using the ArcMap 10 Merge tool. A review of the
bottom sediment raster data sets (Delaware River/Bay, Upper Shelf, and Roxann 2004
which lies along the Delaware near-shore area) indicated that they consisted entirely
of various types of unconsolidated sediments ranging from fluidized clay to oyster
shells. Each of these rasters were converted into a single Unconsolidated Sediments
polygon layer, merged together and then joined with the other features (oyster, SAV,
etc.) to form a continuous benthic cover layer for the entire project area. As a
final step habitat classes from the Florida System for Classifying Habitats in Estuarine
and Marine Environments (SCHEME) were added to the attribute table for this single
polygon file to ensure consistency with other Digital Coast benthic cover data sets.
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| 1 |
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2012-01-01T00:00:00 |
The bottom sediment maps (source for the unconsolidated sediments polygons in the
vector data set) were constructed by
the utilization of a Roxann Seabed Classification System and extensive sediment grab
samples. Data was collected in a gridded
trackline configuration, with tracklines spacing of 100 meters parallel to the shoreline
and 200 meters perpendicular to the
shoreline.This project is an extension of the work currently being performed in Delaware
waters by DNREC's Delaware Coastal
Program's Delaware Bay Benthic Mapping Project.The bottom sediment point data, which
has been classified according to the existing
benthic mapping Roxann box plot, are converted from a number that categorizes the
point according to its corresponding box (in the
Roxann) into a number which reflects the sediment properties of each box in relation
to one another. A ranking scale is used to
allow a statistical griding scheme to interpolate between sediment data points, while
minimizing erroneous sediment classifications
and allowing gradational sediment deposits to be gridded. A ranking scale from 0
to 28 was used for this project, with 0
representing the finest grained classifications (fluidized clay) and 28 representing
the coarsest grained classifications (dense
shell material). This ranking scale takes into account the relation of sediment types
and grain sizes to one another using both the
Wentworth Scale and Shepard's classification system. Finer grains are more similar
in their deposition environments, such as
clay and silts, because they reflect similar current regimes, sorting, and reworking
patterns (Poppe et al., 2003). While coarse
sediments are much more dissimilar to finer grains, with respect to current velocities,
sorting, and winnowing, the finer grains
are much more closely related in their sediment diameters that the coarser grains
as you increase in Phi size and/or diameter.
These account for the close clustering of coarse grained deposit descriptions at
the upper end of the ranking scale, while the
finer grained sediments show a gradation as you increase in the rating scale.
|
| 1 |
|
2012-01-01T00:00:00 |
The bottom sediment data is gridded in Surfer degrees 8, a
surface and terrain modeling program, using block kriging and a nugget effect. This
statistical griding technique estimates the
average value of a variable within a prescribed local area (Isaaks and Srivastava,
1989). Block kriging utilizes the existing point
data values, weights the values of the data depending upon the proximity to the point
being estimated, to discretize the local area
into an array of estimated data value points and then averaging those individual
point estimates together to get an average
estimated value over the area of interest (Isaaks and Srivastava, 1989). A variogram
is constructed for the data, and the resultant
spatial model that is developed from the variogram is used in the block kriging surface
model to more accurately interpolate the
sediment data . The fitted model was a nugget effect (with an error variance of 21.8%)
and a linear model (with a slope of 0.00286
and an anisotropy of 1, which represents a complete lack of spatial correlation).The
accuracy of the estimation is dependent upon
the grid size of the area of interpolation, the size of each cell within the grid,
and the number of discretized data points that
are necessary to estimate the cells within that grid spacing. The grid size that
was used to interpolate the bottom sediment maps
was 442 lines x 454 lines, with a cell size of 44.93 m2. The nugget effect is added
to allow the griding to assume there is very
little, if any, lateral correlation or trends within the bottom sediment (Isaaks
and Srivastava, 1989). The nugget effect model
entails a complete lack of spatial correlation; the point data values at any particular
location bear no similarity even to
adjacent data values (Isaaks and Srivastava, 1989). Without the nugget effect the
griding would assume that you could only have a
linear progression of sediment types and would insert all the sediment types along
the scale between two sediment types (i.e. silty
fine to medium sands and fine to medium sand with varying amounts of pebbles would
be inserted between fine sand and coarse sand
even though that is not what is occurring along the bottom. The sediment data is
gridded with no drift for the data interpolation,
also helping to minimize erroneous classifications. Sediment Classification Ranking
Sediment Description: 0-11-2 Clay, 2-33-44-55-66-7
Silt, 7-88-9 Sandy Silts, 9-1010-11 Fine Sand, 11-1212-13 Silty Fine to Medium Sands,
13-14 Silty Medium Sand, 14-1515-16 Fine to
Medium Sand, 16-1717-18 Fine to Medium Sand, with abundant shell material and/or
pebbles, 18-1919-20 Coarse Sand, with varying
amounts of pebbles 20-2121-2222-23 Moderate Shell Material / Sandy Pebbles, 23-2424-2525-26
Abundant Shell Material / Gravel 26-2727-28 Dense Oyster Shell
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| 1 |
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2015-01-01T00:00:00 |
The data were converted from a single ESRI polygon shapefile classified according
to the System for Classifying Habitats in Estuarine and Marine Environments (SCHEME)
to the Coastal and Marine Ecological Classification Standard (CMECS) 2012 format (which
can be found at https://coast.noaa.gov/digitalcoast/tools/cmecs-crosswalk) which produces
separate geoform, geoform, and geoform feature layers from the original input benthic
habitat dataset. This geoform feature layer contains CMECS geoform component attributes
where an "Equal" or "Nearly Equal" SCHEME value was present in the original data.
Polygons for which no geoform information was present have been removed. No other
changes to the original polygon boundaries or any other alterations of the original
SCHEME data were made during this process.
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