2018-2019 NOAA NGS Topobathy Lidar Post Hurricane Harvey: Galveston to Corpus Christi, TX

These data were collected by Leading Edge Geomatics using a Leica Chiroptera II Bathymetric & Topographic Sensor. The data were acquired from October 22, 2018 through May 31, 2019. The data include topobathy data in LAS 1.4 format classified as created, unclassified (1); ground (2); noise (low or high) (7); bathymetric bottom (40); water surface (41); derived water surface (42); submerged object (43); IHO S-57 object (44); no bathymetric bottom found (45); and bathymetric bottom temporal changes (46) in accordance with project specifications. The project consists of approximately 942 square miles of data along the Gulf shore of Texas from Galveston to Corpus Christi. This full project delivery consists of 3813 1000 m x 1000 m lidar tiles in five blocks:

Block 01 delivery consists of 322 1000 m x 1000 m lidar tiles.

Block 02 delivery consists of 871 1000 m x 1000 m lidar tiles.

Block 03 delivery consists of 789 1000 m x 1000 m lidar tiles.

Block 04 delivery consists of 557 1000 m x 1000 m lidar tiles.

Block 05 delivery consists of 1274 1000 m x 1000 m lidar tiles.

The eastern coastline of Texas has been identified as having critical topographic and bathymetric data gaps by NOAA. This lidar data will fill those critical gaps.

The full workflow used for this project is found in the final project report submitted to NOAA.

Any conclusions drawn from the analysis of this information are not the responsibility of NOAA, the Office for Coastal Management or its partners

Data is available online for bulk or custom downloads

None

Users should be aware that temporal changes may have occurred since this data set was collected and some parts of this data may no longer represent actual surface conditions. Users should not use this data for critical applications without a full awareness of its limitations.

OS Independent

All checkpoints located in non-vegetated terrain were used to compute the Non-vegetated Vertical Accuracy (NVA). Project specifications required a NVA of 19.6 cm at the 95% confidence level based on RMSEz (10 cm) x 1.9600. All checkpoints located in vegetated terrain were used to compute the Vegetated Vertical Accuracy (VVA). Project specifications required a VVA of 30.0 cm based on the 95th percentile. All checkpoints located in bathymetric areas were used to compute an accuracy for the bathymetric data. Project specifications required bathymetric data to meet or exceed 35.3 cm at the 95% confidence level based on RMSEz (18.5 cm) x 1.9600.

Only checkpoints photo-identifiable in the intensity imagery can be used to test the horizontal accuracy of the lidar. Photo-identifiable checkpoints in intensity imagery typically include checkpoints located at the ends of paint stripes on concrete or asphalt surfaces or checkpoints located at 90 degree corners of different reflectivity, e.g. a sidewalk corner adjoining a grass surface. The xy coordinates of checkpoints, as defined in the intensity imagery, are compared to surveyed xy coordinates for each photo-identifiable checkpoint. These differences are used to compute the tested horizontal accuracy of the lidar. As not all projects contain photo-identifiable checkpoints, the horizontal accuracy of the lidar cannot always be tested.

Lidar vendors calibrate their lidar systems during installation of the system and then again for every project acquired. Typical calibrations include cross flights that capture features from multiple directions that allow adjustments to be performed so that the captured features are consistent between all swaths and cross flights from all directions. Horizontal positional accuracy is achieved through rigorous processing of airborne GPS and IMU, use of control, and calibration procedures. This data set was produced to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 41 cm RMSEx/RMSEy Horizontal Accuracy Class which equates to Positional Horizontal Accuracy = +/- 1 meter at a 95% confidence level.

This lidar dataset was tested to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 10 cm RMSEz Vertical Accuracy Class. Actual NVA accuracy was found to be RMSEz= 7.1 cm, equating to +/- 13.8 cm at 95% confidence level.

This lidar dataset was tested to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 10 cm RMSEz Vertical Accuracy Class. Actual VVA accuracy was found to be +/- 26.4 cm at the 95th percentile. The 5% outliers consisted of 3 checkpoints with DZ values larger than the 95th percentile. The larger DZ values were recorded at 37.9 cm, 30.0 cm, and 27.7 cm.

This lidar dataset was tested to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 18.5 cm RMSEz Vertical Accuracy Class. Actual Bathymetric accuracy was found to be RMSEz = 14.9 cm, equating to +/- 29.2 cm at 95% confidence level.

The vertical accuracy of the lidar was tested by Dewberry with 134 independent survey checkpoints. The survey checkpoints are located in areas of non-vegetated terrain, including bare earth, open terrain, and urban terrain (72), vegetated terrain, including forest, brush, tall weeds, crops, and high grass (50), and submerged bottom areas (12). The vertical accuracy is tested by comparing survey checkpoints to a triangulated irregular network (TIN) that is created from the lidar ground and submerged bottom points. Checkpoints are always compared to interpolated surfaces created from the lidar point cloud because it is unlikely that a survey checkpoint will be located at the location of a discrete lidar point. The 134 survey checkpoints were not evenly distributed throughout the project area. The lack of municipal development surrounding Matagorda Bay hindered accessibility to this area.

Data covers the project boundary.

Not applicable

Because swath data is tested prior to any classification, only non-vegetated (open terrain, bare earth and urban) checkpoints are used in this testing. All checkpoints located in non-vegetated terrain were used to compute the NVA of the unclassified swath data. Project specifications require a NVA of 19.6 cm at the 95% confidence level based on RMSEz (10 cm) x 1.9600. The unclassified swath lidar dataset was tested to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 10 cm RMSEz Vertical Accuracy Class.

Data is backed up to tape and to cloud storage.