1930 - 2014 USGS CoNED Topobathy DEM (Compiled 2016): Southern Coast of CA & Channel Islands

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Information on the NOAA Office for Coastal Management (OCM)

The Data Access Viewer (DAV) allows a user to search for and download elevation, imagery, and land cover data for the coastal U.S. and its territories. The data, hosted by the NOAA Office for Coastal Management, can be customized and requested for free download through a checkout interface. An email provides a link to the customized data, while the original data set is available through a link within the viewer.

This graphic displays the footprint for this lidar data set.

Link to the geospatial metadata.

This data is first-return surface (vegetation is in the dataset) X-band Interferometric Synthetic Aperture Radar (IfSAR). Products include a digital elevation model (DEM) and an orthorectified radar reflectance image. This data was collected to support an array of studies and analyses that include hydrologic modeling, watershed delineation and water quality assessment.

The dataset covers the entire state of CA, from +10m elevation to the 3-nautical mile limit.

This dataset was only used to fill in where higher resolution topographic data were not available.

This dataset was used in the following areas: Canyon Fire, Day Fire, Baja California, Orange County, Poway Quadrangle, City of San Diego. These datasets were only included when no higher resolution data existed.

This dataset was used to cover three small areas where the landward extent of the OPC DEM did not cover the +10m elevation contour, which is needed for modeling.

U.S. Geologicial Survey (USGS) historical and current 3D Elevation Program (3DEP) Bare-earth DEMs

High-density LIDAR (Light Detection and Ranging), was acquired in conjunction with 4-inch ortho-photography data. This source was used for the majority of urban/suburban areas of the County in mainland (approximately 2,898 square miles) and Catalina Island covering approximately 75 square miles.

"Data presented in the SFML DATA LIBRARY include a series of remotely sensed images (multibeam, side scan sonar), derived data (bathymetric contours, grid analyses, etc.), habitat analyses, associated data sets (survey footprints, coastline) with FGDC metadata, and grouped by survey location. Data are presented in ESRI ArcGIS format (shapefiles, grids). Multibeam and sidescan images are Geotiffs. These data are NOT to be used for navigational purposes."

Deveraux Slough.

This dataset covers the northern Channel Islands region in southern California.

Inner Continental Borderland, Southern California .

In 2010 and 2011, scientists from the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC), acquired bathymetry and acoustic-backscatter data from the outer shelf region of the eastern Santa Barbara Channel, California. These surveys were conducted in cooperation with the Bureau of Ocean Energy Management (BOEM). BOEM is interested in maps of hard-bottom substrates, particularly natural outcrops that support reef communities in areas near oil and gas extraction activity. The surveys were conducted using the USGS R/V Parke Snavely, outfitted with an interferometric sidescan sonar for swath mapping and real-time kinematic navigation equipment. This report provides the bathymetry and backscatter data acquired during these surveys in several formats, a summary of the mapping mission, maps of bathymetry and backscatter, and Federal Geographic Data Committee (FGDC) metadata.

This dataset covers the Newport Harbor.

These surveys cover San Pedro Bay and Los Angeles and Long Beach Harbors.

South and west of the northern Channel Islands.

The only NOS Hydrographic Survey (H08920) used was used in Mission Bay.

The San Diego NAVD88 DEM covers the coastal area surrounding Camp Pendleton, California including the communities of Emerald Bay in the north to San Diego in the south. The DEM has a 1/3 arc-second (~10 meter) cell size, and is referenced to a vertical datum of NADV88. It was built in 2012 to support NOAA's Tsunami Program.

As specified in Chaytor, J.D. et al. 2008, during October 2003 and September 2004, multibeam bathymetry surveys were carried out around the Northern Channel Islands and the Pilgrim-Kidney Banks using a pole-mounted Simrad Mesotech SM2000 imaging sonar coupled to a WAAS-enable DGPS receiver and Seatex MRU-4 attitude sensor onboard the the R/V Velero IV. For depths ranging between 30 to 400-meters, the nominal pixel spacing is 10-meters; while in areas with water depths shallower than 150-meters, the vertical accuracy is better than 3-meters.

"The creation of a high-resolution 1:24,000 scale geologic and habitat base map series covering all of California's 14,500 km2 state waters out to the 3 mile limit, and support of the state's Marine Life Protection Act Initiative (MLPA) goal to create a statewide network of Marine Protected Areas (MPAs)."

The Santa Barbara MHW DEM covers the coastal area surrounding Santa Barbara, California including the communities of San Augustine in the west to Oxnard in the east. The DEM has a 1/3 arc-second (~10 meter) cell size, and is referenced to a vertical datum of MHW. It was built in 2008 2012 to support NOAA's Tsunami Program.

"The Santa Monica MHW DEM covers the coastal area surrounding Santa Monica, California including the communities of Los Angeles, Malibu, Marina del Rey, Redondo Beach, Long Beach, and Huntington Beach. The DEM has a 1/3 arc-second (~10 meter) cell size, and is referenced to a vertical datum of NADV88. It was built in March 2010 to support NOAA's Tsunami Program."

Southwest Santa Rosa Island.

The bathymetry in this dataset were used to fill in gaps in the study area where no higher resolution data existed.

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 and VDatum. 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 1-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 1 meter 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.).

The NOAA Office for Coastal Management (OCM) received the Digital Elevation Model (DEM) file from USGS. The data were in UTM Zone 11 (NAD83) coordinates and NAVD88 elevations in meters. The bare earth raster files were at a 1 meter grid spacing.

OCM performed the following processing on the data for Digital Coast storage and provisioning purposes:

1. Tiled the large DEM file into smaller files using Global Mapper.

2. Copied the files to https