Topobathymetric Model of the Northern Gulf of Mexico, 1888 to 2013

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Date that the source FGDC record was last modified.

Converted from FGDC Content Standards for Digital Geospatial Metadata (version FGDC-STD-001-1998) using 'fgdc_to_inport_xml.pl' script. Contact Tyler Christensen (NOS) for details.

Partial upload of Positional Accuracy fields only.

Partial upload to move data access links to Distribution Info.

Nayegandhi, Amar, Bonisteel-Cormier, J.M., Brock, J.C., Sallenger, A.H., Wright, C.W., Nagle, D.B.,

Vivekanandan, Saisudha, Yates, Xan, and Klipp, E.S., 2010, EAARL coastal topography-Chandeleur Islands, Louisiana, 2010:

bare earth: U.S. Geological Survey Data Series 511, 1 DVD. | Source Geospatial Form: document | Type of Source Media: DVD

Office for Coastal Management, National Ocean Service, National Oceanic and Atmospheric Administration, U.S. Department of Commerce. 2009. 2005

United States Army Corps of Engineers (USACE), The Joint Airborne LiDAR Bathymetry Technical Center of Expertise (JABLTCX), 2009 Coastal Survey 2009.,

dataset accessed at December 17, 2014 at https://coast.noaa.gov/

Office for Coastal Management, National Ocean Service, National Oceanic and Atmospheric Administration, U.S. Department of Commerce. 2005. 2005 United States

Army Corps of Engineers (USACE) Post-Hurricane Katrina Levee Surveys, 13 - 18 September 2005., dataset accessed at December 17, 2014 at

https://coast.noaa.gov/. | Source Geospatial Form: raster digital data | Type of Source Media: Download

2010. Atchafalaya LiDAR Data Collection Project (Classified and Unclassified LAS files and Tiled DEMs), Task Order G10PD02495, V1.0, 15

December 2009 to 15 March 2010. U.S. Geological Survey National Geospatial Technical Operations Center, dataset accessed December-17, 2014

at http://earthexplorer.usgs.gov.

2011. ARRA-LA_COASTAL LiDAR Data Collection Project (Classified and Unclassified LAS files and Tiled DEMs), Task Order G10PD02781, V1.0,

V1.0, 15 January 2011 to 15 March 2011. U.S. Geological Survey National Geospatial Technical Operations Center, dataset accessed December 17, 2014

at http://earthexplorer.usgs.gov.

2013. Atchafalaya2 LiDAR Data Collection Project (Classified and Unclassified LAS files and Tiled DEMs), Task Order G12PD00075, V3.0, V1.0, 15

December 2012 to 15 March 2013. U.S. Geological Survey National Geospatial Technical Operations Center, dataset accessed December 17, 2014

at http://earthexplorer.usgs.gov.

2014. Jean Lafitte & Barataria LiDAR Data Collection Project (Classified and Unclassified LAS files and Tiled DEMs), Task Order G13PD00214, V1.0, 5

March to 8 March 2013. U.S. Geological Survey National Geospatial Technical Operations Center, dataset accessed December 17, 2014

at http://earthexplorer.usgs.gov. | Source Geospatial Form: raster digital data | Type of Source Media: Download

The principal methodology for developing the integrated topobathymetric elevation model can be organized into three

main components. The "topography component" consists of the land-based elevation data, which is primarily comprised from

high-resolution LiDAR data. The topographic source data will include LiDAR data from different sensors (Topographic, Bathymetric)

with distinct spectral wavelengths (NIR-1064nm, Green-532nm).

The "bathymetry component" consists of hydrographic sounding (acoustic) data collected using boats rather than bathymetry acquired

from LiDAR. The most common forms of bathymetry that are used include: multi-beam, single-beam, and swath.

The final component, "Integration", encompasses the assimilation of the topographic and bathymetric data along the near-shore based

on a predefined set of priorities. The land/water interface (+1 m- -1.5 m) is the most critical area, and green laser systems, such as the

Experimental Advanced Airborne Research LiDAR (EAARL-B) and the Coastal Zone Mapping and Imaging LiDAR (CZMIL) that

cross the near-shore interface are valuable in developing a seamless transition.

The end product from the topography and bathymetry components is a raster with associated spatial masks and metadata that can be

passed to the integration component for final model incorporation.

Topo/Bathy Creation Steps:

Topography Processing Component:

a) Quality control check the vertical and horizontal datum and projection information of the input lidar source to ensure the data is referenced to NAVD88 and NAD83, UTM. If the source data is not NAVD88, transform the input LiDAR data to NAVD88 reference

frame using current National Geodetic Survey (NGS) geoid models. Likewise, if required, convert the input source data to NAD83 and reproject to UTM.

b) Check the classification of the topographic LiDAR data to verify the data are classified with the appropriate classes.

If the data have not been classified, then classify the raw point cloud data to non-ground (class 1) ground (class 2), and water (class 9)

classes using LP360-Classify.

c) Derive associated breaklines from the classified LiDAR to capture internal water bodies, such as lakes and ponds and inland

waterways. Inland waterways and water bodies will be hydro-flattened where no bathymetry is present.

d) Extract the ground returns from the classified LiDAR data and randomly spatial subset the points into two point sets based

on the criteria of 95 percent of the points for the "Actual Selected" set and the remaining 5 percent for the "Test Control" set.

The "Actual Selected" points will be gridded in the terrain model along with associated breaklines and masks to generate the topographic

surface, while the "Test Control" points will be used to compute the interpolation accuracy (Root Mean Square Error) from the derived surface.

e) Generate the minimum convex hull boundary from the classified ground LiDAR points that creates a mask that extracts

the perimeter of the exterior LiDAR points. The mask is then applied in the terrain to remove extraneous terrain artifacts outside

of the extent of the ground LiDAR points.

f) Using a terrain model based on triangulated irregular networks (TINs), grid the "Actual Selected" ground points using

breaklines and the minimum convex hull boundary mask at a 3-meter spatial resolution using a natural neighbor interpolation algorithm.

g) Compute the interpolation accuracy by comparing elevation values in the "Test Control" points to values extracted from the

derived gridded surface; report the results in terms of Root Mean Square Error (RMSE).

Bathymetry Processing Component:

a) Quality control check the vertical and horizontal datum and projection information of the input bathymetric source to ensure the

data is referenced to NAVD88 and NAD83, UTM. If the source data is not NAVD88, transform the input bathymetric data to NAVD88

reference frame using VDatum. Likewise, if required, convert the input source data to NAD83 and reproject to UTM.

b) Prioritize and spatially sort the bathymetry based on date of acquisition, spatial distribution, accuracy, and point density to eliminate

any outdated or erroneous points and to minimize interpolation artifacts.

c) Randomly spatial subset the bathymetric points into two point sets based on the criteria of 95 percent of the points for the

"Actual Selected" set and the remaining 5 percent for the "Test Control" set. The "Actual Selected" points will be gridded in the

empirical bayesian krigging model along with associated masks to generate the bathymetric surface, while the "Test Control" points

will be used to compute the interpolation accuracy (Root Mean Square Error) from the derived surface.

d) Spatially interpolate bathymetric single-beam, multi-beam, and hydrographic survey source data using an empirical bayesian

krigging gridding algorithm. This approach uses a geostatistical interpolation method that accounts for the error in estimating the underlying

semivariogram (data structure - variance) through repeated simulations.

e) Cross validation - Compare the predicted value in the geostatistical model to the actual observed value to assess the accuracy and

effectiveness of model parameters by removing each data location one at a time and predicting the associated data value. The results will

be reported in terms of RMSE.

f) Compute the interpolation accuracy by comparing elevation values in the "Test Control" points to values extracted from the derived

gridded surface; report the results in terms of RMSE.

Mosaic Dataset Processing (Integration) Component:

a) Determined priority of input data based on project characteristics, including acquisition dates, cell size, retention of features, water

surface treatment, visual inspection and presence of artifacts.

b) Develop an ArcGIS geodatabase (Mosaic Dataset) and spatial seamlines for each individual topographic (minimum convex hull boundary)

and bathymetric raster layer included in the integrated elevation model.

c) Generalize seamline edges to smooth transition boundaries between neighboring raster layers and split complex raster datasets with

isolated regions into individual unique raster groups.

d) Develop an integrated shoreline transition zone from the best available topographic and bathymetric data to blend the topographic and

bathymetric elevation sources. Where feasible, use the minimum convex hull boundary, create a buffer to logically mask input topography/bathymetry

data. Then, through the use of TINs, interpolate the selected topographic and bathymetric points to gap-fill, if required any near-shore holes in

the bathymetric coverage. Topobathymetric LiDAR data sources such as the EAARL-B or CZMIL systems provide up-to-date, high-resolution

data along the critical land/water interface within inter-tidal zone.

e) Prioritize and spatially sort the input topographic and bathymetric raster layers based on date of acquisition and accuracy to sequence

the raster data in the integrated elevation model.

f) Based on the prioritization, spatially mosaic the input raster data sources to create a seamless topobathymetric composite at a cell size

of 3 meters using blending (spatial weighting).

g) Performed a visual quality assurance (Q/A) assessment on the output composite to review the mosaic seams for artifacts.

h) Generate spatially referenced metadata for each unique data source. The spatially reference metadata consists of a group of

geospatial polygons that represent the spatial footprint of each data source used in the generation of the topobathymetric dataset. Each

polygon is to be populated with attributes that describe the source data, such as, resolution, acquisition date, source name, source organization,

source contact, source project, source URL, and data type (topographic LiDAR, bathymetric LiDAR, multi-beam bathymetry, single-beam bathymetry, etc.).

Data were received from USGS by NOAA and ingested into the Digital Coast system for download via custom processing and ftp. Metadata was modified to reflect distribution through this mechanism.