2015 St. Marys River Topobathymetric Lidar DEM

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This graphic shows the lidar footprint for the 2015 USACE LRE St Marys River topobathy lidar project in Michigan

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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This data source was used to populate the lidar point cloud data. | Source Geospatial Form: lidar data | Type of Source Media: online

The Riegl VQ-820-G was selected as the hydrographic airborne laser scanner for the St. Marys River project based on fulfillment of several considerations deemed necessary for effective mapping of the project site. A high repetition pulse rate, high scanning speed, small laser footprint, and wide field of view allow for seamless collection of high resolution data of both topographic and bathymetric surfaces. A short laser pulse length allows for discrimination of underwater surface expression in shallow water, critical to shallow and dynamic environments. The Riegl system has demonstrated hydrographic depth ranging capability up to 1 Secchi depth on bright reflective surfaces.

The LiDAR survey was accomplished using a Leica ALS50 system dually mounted with a Riegl VQ-820-G topobathymetric sensor in a Cessna Caravan 208B. The Riegl VQ-820-G uses a green wavelength (lambda=532 nm) laser that is capable of collecting high resolution vegetation and topography data, as well as penetrating the water surface with minimal spectral absorption by water. The recorded waveform enables range measurements for all discernible targets for a given pulse. The typical number of returns digitized from a single pulse range from 1 to 7 for the St. Marys River project area. The Leica ALS50 laser system records up to four range measurements (returns) per pulse. It is not uncommon for some types of surfaces (e.g., dense vegetation or water) to return fewer pulses to the LiDAR sensor than the laser originally emitted. The discrepancy between first return and overall delivered density will vary depending on terrain, land cover, and the prevalence of water bodies. All discernible laser returns were processed for the output dataset.

Processing workflow was as follows:

Resolve kinematic corrections for aircraft position data using kinematic aircraft GPS and static ground GPS data. Develop a smoothed best estimate of trajectory (SBET) file that blends post-processed aircraft position with sensor head position and attitude recorded throughout the survey.

Calculate laser point position by associating SBET position to each laser point return time, scan angle, intensity, etc. Create raw laser point cloud data for the entire survey in *.las (ASPRS v. 1.2) format. Convert data to orthometric elevations by applying a geoid12b correction.

Import raw laser points into manageable blocks to perform manual relative accuracy calibration and filter erroneous points. Classify ground points for individual flight lines.

Using ground classified points per each flight line, test the relative accuracy. Perform automated line-to-line calibrations for system attitude parameters (pitch, roll, heading), mirror flex (scale) and GPS/IMU drift. Calculate calibrations on ground classified points from paired flight lines and apply results to all points in a flight line. Use every flight line for relative accuracy calibration.

Apply refraction correction to all subsurface returns.

Classify resulting data to ground and other client designated ASPRS classifications . Assess statistical absolute accuracy via direct comparisons of ground classified points to ground control survey data.

Generate bare earth models as triangulated surfaces. Generate highest hit models as a surface expression of all classified points. Export all surface models as ESRI GRIDs at a 1 meter pixel resolution.

Data were received by the NOAA Office for Coastal Management from the USACE Detroit District in the form described above. It was ingested into the Digital Coast Data Access Viewer system for distribution by converting the ESRI GRID raster format to GeoTIFF using gdal_translate. This was done to provide an open format.