1888 - 2014 USGS CoNED Topobathy DEM (Compiled 2015): New Jersey and Delaware

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Spatial metadata in geodatabase format.

Spatial metadata in geopackage format.

GDAL VRT format xml describing all the tiles to allow streaming access.

Date that the source FGDC record was last modified.

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

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Light Detection and Ranging (LiDAR) data is remotely sensed high-resolution elevation data collected by an airborne collection platform. LiDAR was flown for approximately 1675 square miles in Passaic, Sussex, Bergen, Morris, Warren, Hunterdon, and Somerset Counties in New Jersey and a small part of Orange County, New York with an Root Mean Square Error (RMSE) of 18.5 centimeters vertically and 95% confidence level. | Type of Source Media: onLine

This data set includes lidar intensity values and encoded RGB image values. Fire Island, NY data collected on Nov 25, 2011 and the Cape May to Absecon Inlet, NJ data was collected on July 16, 2011. | Type of Source Media: onLine

These remotely sensed, geographically referenced elevation measurements of lidar-derived submerged topography datasets were produced by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida. | Type of Source Media: onLine

Historical and current 3 Bare-earth DEMs (3D) National Elevation Data | Source Geospatial Form: raster digital data | Type of Source Media: onLine

High-resolution geophysical data collected along the Delmarva Peninsula 2014, U.S. Geological Survey Field Activity 2014-002-FA | Source Geospatial Form: raster digital data | Type of Source Media: onLine

Developed for the Pacific Marine Environmental Laboratory (PMEL), NOAA Center for Tsunami Research program in support of NOAA degrees's tsunami forecasting and warning efforts. | Source Geospatial Form: raster digital data | Type of Source Media: onLine

NOAA's National Geophysical Data Center (NGDC) is developing a suite of digital elevation models (DEMs) of the U.S. Atlantic Coast impacted by Hurricane Sandy in October 2012. The DEMs telescope from the deep ocean floor to the coastal zone in 3, 1, 1/3, and 1/9 arc-second cell sizes. Only the 1/9 arc-second DEMs integrate both bathymetric and topographic data; the remaining DEMs map bathymetry only. DEMs are tiled to enable targeted, rapid updates as new data become available. Source topographic and bathymetric data utilized for DEM creation were contributed by a variety of sources, including the NOAA Office of Coast Survey, NOAA National Geodetic Survey, NOAA Coastal Services Center, U.S. Geological Survey, and the U.S. Army Corps of Engineers. The DEMs are referenced vertically to the North American Vertical Datum of 1988. The vertical units of the data are meters. The DEMs are referenced horizontally to the North American Datum of 1983. The horizontal units of the data are decimal degrees. | Type of Source Media: onLine

These remotely sensed, geographically referenced elevation measurements of lidar-derived bare-earth (BE) and first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI. This project provides highly detailed and accurate datasets of a portion of the Sandy Hook Unit of Gateway National Recreation Area in New Jersey, acquired post-Nor'Ida (November 2009 nor'easter) on December 4, 2009. | Type of Source Media: dvd

These hydrographic data are made available to the public for a wide variety of uses, such as sea-bottom characterization, habitat classification, coastal studies, boundary issues, and tsunami forecasting. These data are not intended to be used for navigation. Nautical charts produced and certified by NOAA should be used for navigation purposes. 1950 | Type of Source Media: onLine

This DVD contains lidar-derived bare-earth (BE) and first-surface (FS) topography GIS datasets of a portion of the Gateway National Recreation Area in New Jersey and New York. These datasets were acquired July 8-9, 2009. | Source Geospatial Form: raster digital data | Type of Source Media: dvd

Developed for the Pacific Marine Environmental Laboratory (PMEL), NOAA Center for Tsunami Research program in support of NOAA degrees's tsunami forecasting and warning efforts. | Source Geospatial Form: raster digital data | Type of Source Media: onLine

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 http. Metadata was modified to reflect distribution through this mechanism.

USGS CoNED noticed a versioning discrepancy in the New Jersey / Delaware TBDEM dataset, and an updated TBDEM product has been implemented.

Updated data were received from USGS and the version 2 data were ingested into the Data Access Viewer. The single file from USGS was retiled into 8192 x 8192 pixel tiles and stored as cloud optimized geotiffs. The original source data remain the same.