2021 NOAA NGS Topobathy Lidar DEM Southern Tampa Bay, Florida

These data were collected by Dewberry using a CZMIL Nova system. The data were acquired from 20210126 - 20210227. 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); and no bottom found (bathymetric lidar point for which no detectable bottom return was received) (45) in accordance with project specifications; temporal surface not used in bathymetric classification (65). This dataset consists of approximately 225 square miles of data along the shores of Tampa Bay and contains 52 5000 m x 5000 m DEM tiles.

This lidar data are an ancillary product of NOAA's Coastal Mapping Program (CMP), created through a wider Integrated Ocean and Coastal Mapping initiative to increase support for multiple uses of the data. It is not intended for mapping, charting, or navigation.

Data are generated from 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.

This data can be obtained on-line at the following URL: https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=9481;

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be aware that temporal changes may have occurred since this data set was collected and some parts of this data may no longer represent actual surface conditions.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

The DEMs are derived from the source lidar and inherit the accuracy of the source data. The DEMs are created using controlled and tested methods to limit the amount of error introduced during DEM production. 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. 11 photo-identifiable checkpoints were used to test horizontal accuracy for this lidar dataset. Horizontal accuracy from this small dataset cannot be considered statistically significant; however, it was still calculated and found to be 26.2 cm.

The DEMs are derived from the source lidar and inherit the accuracy of the source data. The DEMs are created using controlled and tested methods to limit the amount of error introduced during DEM production so that any differences identified between the source lidar and final DEMs can be attributed to interpolation differences. DEMs are created by averaging several lidar points within each pixel which may result in slightly different elevation values at a given location when compared to the source LAS, which does not average several lidar points together but may interpolate (linearly) between two or three points to derive an elevation value. The vertical accuracy of the final topobathymetric DEMs was tested by Dewberry with 51 independent checkpoints. The same checkpoints that were used to test the source lidar data were used to validate the vertical accuracy of the final DEM products. 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 (30); vegetated terrain, including forest, brush, tall weeds, crops, and high grass (17); and submerged bottom areas (4). The vertical accuracy is tested by extracting the elevation of the pixel that contains the x/y coordinates of the checkpoint and comparing these DEM elevations to the surveyed elevations. All checkpoints located in non-vegetated terrain were used to compute the Non-vegetated Vertical Accuracy (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 Vegetated Vertical Accuracy (VVA). Project specifications require a VVA of 29.4 cm based on the 95th percentile. All checkpoints located in bathymetric areas were used to compute an accuracy for the bathymetric data. 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 DEM 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 = 5.7 cm, equating to +/- 11.2 cm at 95% confidence level.

This DEM 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 +/- 15.1 cm at the 95th percentile. The 5% outliers consisted of 1 checkpoint that is larger than the 95th percentile. This checkpoint has a DZ value of 30.2 cm.

This DEM 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 = 15.5 cm, equating to +/- 30.4 cm at 95% confidence level.

Data covers 52 5000 m x 5000 m DEM tiles.

Not applicable

Data is backed up to tape and to cloud storage.