2019 NOAA NGS Topobathy Lidar: Puerto Rico

These data were collected by Leading Edge Geomatics using a Riegl VQ-880-G II sensor. The data acqusition began January 20, 2019 through June 2, 2019. The data includes topobathy data in LAS 1.4 format classified as created, never classified (0); unclassified (1); ground (2); noise (7); bathymetric bottom (40); water surface (41); derived water surface (42); submerged object, not otherwise specified (e.g., wreck, rock, submerged piling) (43); International Hydrographic Organization S-57 object, not otherwise specified (44); no bottom found (bathymetric lidar point for which no detectable bottom return was received) (45); bathymetic bottom temporal changes (46) in accordance with project specifications.

Puerto Rico has been identified as having cirtical 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be aware that temporal changes may have occurred since this data set was collected and some parts of this data may no longer represent actual surface conditions.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

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.

Thirty-one checkpoints were photo-identifiable. Using this small sample set of photo-identifiable checkpoints, positional accuracy of this dataset was found to be RMSEx = 31.1 cm and RMSEy = 25.4 cm which equates to ACCURACYr = 69.6 cm at 95% confidence level. The results of the small sample set of checkpoints are within the produced to meet horizontal accuracy criteria.

The vertical accuracy of the classified lidar was tested by Dewberry with 259 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 (131); vegetated terrain, including forest, brush, tall weeds, crops, and high grass (94); and submerged bottom areas (34). 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 Non-vegetated Vertical Accuracy (NVA). Project specifications require a NVA of 0.196 at the 95% confidence level based on RMSEz (0.1) x 1.9600. All checkpoints located in vegetated terrain were used to compute the Vegetated Vertical Accuracy (VVA). Project specifications require a VVA of 0.294 based on the 95th percentile. All checkpoints located in bathymetric areas were used to compute an accuracy value for the bathymetric data. Project specifications require the vertical accuracy for bathymetric data to be 0.363 or better at the 95% confidence level based on RMSEz (0.185) x 1.9600.

his 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 = 0.086, equating to +/- 0.168 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 +/- 25.1 cm at the 95th percentile.

The 5% outliers consisted of 5 checkpoints that are larger than the 95th percentile. These checkpoints had a DZ value of 37.3 cm, 42.3 cm, 35.1 cm, 25.9 cm, and 28.6 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 = 12.1 cm, equating to +/- 0.236 at the 95% confidence level.

Data covers the project boundary.

Not applicable

Data is backed up to tape and to cloud storage.