2022 NOAA NGS Topobathy Lidar of Indian River Lagoon, FL

These data were collected by Dewberry using a CZMIL Super Nova system. The data were acquired from 20220224 through 20220601. The data include topobathy data in LAS 1.4 format classified as unclassified (1); ground (2); low noise (7); high noise (18); bathymetric bottom (40); water surface (41); derived water surface (42); submerged object, not otherwise specified (e.g., wreck, rock, submerged piling) (43); no bottom found (bathymetric lidar point for which no detectable bottom return was received) (45); submerged aquatic vegetation (64); and temporal bathymetric bottom (65) in accordance with project specifications. The project consists of approximately 615 square miles of data along the shores of Indian River Lagoon and contains 8,786 500 m x 500 m lidar tiles.

The National Geodetic Survey (NGS) Remote Sensing Division (RSD) Coastal Mapping Program (CMP) requires the collection of airborne topographic/bathymetric Light Detection and Ranging (lidar) and digital camera imagery data to enable accurate and consistent measurement of the national shoreline.

Data include all lidar returns. Outliers or false returns (e.g., returns from birds, atmospheric particles, and/or system noise) may be present in the data. An automated ground classification algorithm was used to determine bare earth point classification. It should be noted that not all returns were correctly classified; therefore the user should examine for acceptability.

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

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be aware that temporal changes may have occurred since this data set was collected and some parts of this data may no longer represent actual surface conditions.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

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. 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

The vertical accuracy of the classified lidar will be tested by Dewberry with 86 independent survey checkpoints. The survey checkpoints are evenly distributed throughout the project area and are located in areas of non-vegetated terrain, including bare earth, open terrain, and urban terrain (46); vegetated terrain, including forest, brush, tall weeds, crops, and high grass (33); and submerged bottom areas (7). 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 precise location of a discrete lidar point. All checkpoints located in non-vegetated terrain were used to compute the NVA. Project specifications require 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 VVA. Project specifications require a VVA of 30.0 cm based on the 95th percentile. All checkpoints located in bathymetric areas were used to compute an accuracy value for the BVA. Project specifications require the vertical accuracy for bathymetric data to be 58.8 cm or better at the 95% confidence level based on depth-dependent RMSEz (30.0 cm) x 1.9600.

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 = 4.4 cm, equating to +/- 8.6 cm at the 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 +/- 20.7 cm at the 95th percentile. The 5% outliers consisted of 2 checkpoints larger than the 95th percentile. These checkpoints have DZ values of 20.9 cm and 21.0 cm.

This lidar dataset was tested to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 30.0 cm RMSEz Vertical Accuracy Class. Actual Bathymetric accuracy was found to be RMSEz = 14.8 cm, equating to +/- 29.1 cm at the 95% confidence level.

Data covers 8,786 500 m x 500 m lidar tiles.

Not applicable

Data is backed up to tape and to cloud storage.