2018 USGS Lidar: Matagorda Bay, TX

Collection Dates

Collection dates.

Collection dates.

Collection dates.

Collection dates.

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This graphic displays the footprint for this lidar data set.

Link to data set report.

Link to data set report.

Link to data set report.

Link to data set report.

Link to data set report.

Under Fugro’s direction, all surveying activities were performed by Fugro's approved ID/IQ subcontractor Terrasurv, Inc. A total of 12 ground control points to support the lidar collection, along with 45 NVA and 35 VVA checkpoints were collected. The National Spatial Reference System (NSRS) was used to provide control for the network. Three Continuously Operating Reference Stations (CORS) were included in the network, as well as two existing NSRS benchmarks. The horizontal datum was the North American Datum of 1983 – NAD83 (2011), epoch 2010.0. The vertical datum was the North American Vertical Datum of 1988 (NAVD88), realized with GEOID12B.

Fugro collected ALS-derived lidar over the TX_Matagorda Bay_2018_D18 AOI with 1.0 meter ANPS. Data was collected when environmental conditions meet the criteria specified. To be specific, the following conditions existed prior to launch of the aircraft: 1) Cloud and fog-free between the aircraft and ground, 2) Snow free, 3) No unusual flooding or inundation, and 4) Leaf off. The collection for the TX_Matagorda Bay_2018_D18 AOI was accomplished on January 24, 25, 27, 28 and 29; data was acquired in 7 lifts. Lift 190124_133_33780026_01; lift 190125_130_33780026_01; lift 190125_130_33780026_02, lift 190125_133_33780026_02, lift 190127_130_33780026_03, lift 190128_130_33780026_04, and lift 190129_130_33780026_05. The collection was performed using Leica ALS80 lidar systems, serial numbers 130 and 133.

Flight status was communicated during data collection. All acquired lidar data went through a preliminary review to assure that complete coverage had been obtained and that there were no gaps between flight lines before the flight crew left the project site. Once back in the office, the data was run through a complete iteration of processing to ensure that it is complete, uncorrupted, and that the entire project area has been covered without gaps between flight lines. There are essentially three steps to this processing: 1) GPS/IMU Processing - Airborne GPS and IMU data was processed using the airport GPS base station data. The following GPS base station was utilized: Fugro (FGI_8003, TXPV, TXBC, FUSAL_100, temp_LBX). 2) Raw Lidar Data Processing - Technicians processed the raw data to LAS format flight lines with full resolution output before performing QC. A starting configuration file is used in this process, which contains the latest calibration parameters for the sensor. The technicians also generated flight line trajectories for each of the flight lines during this process. 3) Verification of Coverage and Data Quality - Technicians checked trajectory files to ensure completeness of acquisition for the flight lines, calibration lines, and cross flight lines. The intensity images were generated for the entire lift at the required 0.5 meter ANPS. Visual checks of the intensity images against the project boundary were performed to ensure full coverage to the 100 meter buffer beyond the project boundary. The intensity histogram was analyzed to ensure the quality of the intensity values. The technician also thoroughly reviewed the data for any gaps in project area. The technician generated a sample TIN surface to ensure no anomalies were present in the data. Turbulence was inspected for each flight line; if any adverse quality issues were discovered, the flight line was rejected and re-flown. The technician also evaluated the achieved post spacing against project specified 1.0 meter ANPS as well as making sure there is no clustering in point distribution.

The boresight for each lift was done individually as the solution may change slightly from lift to lift. The following steps describe the Raw Data Processing and Boresight process: 1) Technicians processed the raw data to LAS format flight lines using the final GPS/IMU solution. This LAS data set was used as source data for boresight. 2) Technicians first used Fugro proprietary and commercial software to calculate initial boresight adjustment angles based on sample areas within the lift. These areas cover calibration flight lines collected in the lift, cross tie and production flight lines. These areas are well distributed in the lift coverage and cover multiple terrain types that are necessary for boresight angle calculation. The technician then analyzed the results and made any necessary additional adjustment until it is acceptable for the selected areas. 3) Once the boresight angle calculation was completed for the selected areas, the adjusted settings were applied to all of the flight lines of the lift and checked for consistency. The technicians utilized commercial and proprietary software packages to analyze the matching between flight line overlaps for the entire lift and adjusted as necessary until the results met the project specifications. 4) Once all lifts were completed with individual boresight adjustment, the technicians checked and corrected the vertical misalignment of all flight lines and also the matching between data and ground truth. The relative accuracy was 6 cm within individual swaths (smooth surface repeatability) and 8 cm RMSD within swath overlap (between adjacent swaths) with a maximum difference of ± 16 cm. 5) The technicians ran a final vertical accuracy check of the boresighted flight lines against the surveyed check points after the z correction to ensure the requirement of RMSEz (non-vegetated) 10 cm, NVA 19.6 cm 95% Confidence Level was met.

Once boresighting was complete for the project, the project was first set up for automatic classification. The lidar data was cut to production tiles. The low noise points, high noise points and ground points were classified automatically in this process. Fugro utilized commercial software, as well as proprietary, in-house developed software for automatic filtering. The parameters used in the process were customized for each terrain type to obtain optimum results. Once the automated filtering was completed, the files were run through a visual inspection to ensure that the filtering was not too aggressive or not aggressive enough. In cases where the filtering was too aggressive and important terrain were filtered out, the data was either run through a different filter within local area or was corrected during the manual filtering process. Bridge deck points were classified as well during the interactive editing process. Interactive editing was completed in visualization software that provides manual and automatic point classification tools. Fugro utilized commercial and proprietary software for this process. All manually inspected tiles went through a peer review to ensure proper editing and consistency. After the manual editing and peer review, all tiles went through another final automated classification routine. This process ensures only the required classifications are used in the final product (all points classified into any temporary classes during manual editing will be re-classified into the project specified classifications). Once manual inspection, QC and final autofilter is complete for the lidar tiles, the LAS data was packaged to the project specified tiling scheme, clipped to project boundary including the 100 meter buffer and formatted to LAS v1.4. It was also re-projected to UTM Zone 14 north; NAD83 (2011), meters; NAVD88 (GEOID12B), meters. The file header was formatted to meet the project specification with File Source ID assigned. This Classified Point Cloud product was used for the generation of derived products. This product was delivered in fully compliant LAS v1.4, Point Record Format 6 with Adjusted Standard GPS Time at a precision sufficient to allow unique timestamps for each pulse. Correct and properly formatted georeference information as Open Geospatial Consortium (OGC) well known text (WKT) was assigned in all LAS file headers. Each tile has unique File Source ID assigned. The Point Source ID matches to the flight line ID in the flight trajectory files. Intensity values are included for each point, normalized to 16-bit. The following classifications are included: Class 1 – Processed, but unclassified; Class 2 – Bare earth ground; Class 3, Low Vegetation; Class 4, Medium Vegetation; Class 5, High Vegetation; Class 6, Building; Class 7 – Low Noise; Class 9 – Water; Class 10 – Ignored Ground; Class 14, Culverts; Class 17 – Bridge Decks; and Class 18 – High Noise. The classified point cloud data was delivered in tiles without overlap using the project tiling scheme.

NOAA's OCM retrieved the data from the USGS RockyFTP website. Data were in UTM zones 14 & 15N and had the following classificaitons; 1 - Never Classified, 2 - Ground, 3 - Low Vegetation, 4 - Medium Vegetation, 5 - High Vegetation, 6 - Building, 7 - Low Noise, 9 - Water, 10 - Ignored Ground, 14 - Culvert, 17 - Bridge Deck. All files and classifications were processed to the Digital Coast. Derived products may be retrieved from the USGS National Map.

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

1. An internal OCM script was run to check the number of points by classification and by flight ID and the gps and intensity ranges.

2. Internal OCM scripts were run on the laz files to convert from orthometric (NAVD88) elevations to ellipsoid elevations using the Geoid12b model, to convert from UTM 14 & 15, NAD83 (2011), meters coordinates to geographic coordinates, to assign the geokeys, to sort the data by gps time and zip the data to database and to http.