2019 NOAA NGS Topobathy Lidar DEM: Tampa Bay, FL

These data were collected by Leading Edge Geomatics using a RIEGL VQ880-G / RIEGL VQ880-GII topobathymertic lidar system. The data were acquired from November 22, 2019 to December 20, 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); no bathymetric bottom found (45); and temporal (46) in accordance with project specifications. The project was delivered in two blocks:

Block 1: The project consists of approximately 311 square miles of data within the Tampa Bay inlet, to include Old Tampa Bay, Hillsborough Bay and the Interbay Peninsula. This dataset contains 883 1000 m x 10000 m lidar tiles. The Block 1 delivery consists of 419 tiles for the eastern half of the AOI.

Block 2: The project consists of approximately 311 square miles of data within the Tampa Bay inlet, to include Old Tampa Bay, Hillsborough Bay, and the Interbay Peninsula. This dataset contains 905 1000 m x 1000 m lidar tiles. The Block 2 delivery consists of 486 tiles for the western half of the AOI.

The Tampa Bay inlet has been identified as having critical topographic and bathymetric data gaps by NOAA. This lidar data will fill those critical gaps.

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be aware that temporal changes may have occurred since this data set was collected and some parts of this data may no longer represent actual surface conditions.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

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.

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 DEM was tested by Dewberry with 57 independent survey 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 located in areas of non-vegetated terrain, including bare earth, open terrain, and urban terrain (32), vegetated terrain, including forest, brush, tall weeds, crops, and high grass (18), and submerged bottom areas (7). 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. The 57 survey checkpoints were evenly distributed throughout the project area.

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.

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 = 3.1 cm, equating to +/- 6.1 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 +/- 14.4 cm at the 95th percentile.

The 5% outliers consisted of 1 checkpoint with DZ values larger than the 95th percentile. The larger DZ value for this checkpoint was recorded at 24.8 cm.

This DEM 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 = 21.1 cm, equating to +/- 41.4 cm at 95% confidence level.

Data covers the project boundary.

Not applicable

Data is backed up to tape and to cloud storage.