2018 USGS Lidar: Upper St. Johns, FL

Product: These lidar data are processed Classified LAS 1.4 files, formatted to 973 individual 5000 ft x 5000 ft tiles; used to create intensity images, 3D breaklines and hydro-flattened DEMs as necessary.

Geographic Extent: Upper St. Johns LiDAR, covering approximately 687 square miles.

Dataset Description: Upper St. Johns 2018 LiDAR project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.25 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011), State Plane Florida East, U.S. survey feet and vertical datum of NAVD88 (GEOID12B), U.S. survey feet. Lidar data was delivered as processed Classified LAS 1.4 files, formatted to 973 individual 5000 ft x 5000 ft tiles, as tiled Intensity Imagery, and as tiled bare earth DEMs; all tiled to the same 5000 ft x 5000 ft schema. Ground Conditions: Lidar was collected in early 2018, while rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Dewberry established a total of 30 ground control points that were used to calibrate the lidar to known ground locations established throughout the Upper St. Johns LiDAR project area. An additional 90 independent accuracy checkpoints, 50 in Bare Earth and Urban landcovers (50 NVA points), 40 in Tall Grass and Brushland/Low Trees categories (40 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.

The NOAA Office for Coastal Management (OCM) downloaded this data set from this USGS site:

ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/USGS_LPC_FL_Upper_Saint_Johns_2017_2019

These files were processed to the Data Access Viewer (DAV) and https. The total number of files downloaded and processed was 973.

The breaklines were also downloaded and are available for download at the link provided in the URL section of this metadata record. Please note that this product has not been reviewed by the NOAA Office for Coastal Management (OCM) and any conclusions drawn from the analysis of this information are not the responsibility of NOAA or OCM.

The purpose of this Lidar project was to acquire detailed surface elevation data for use in conservation planning, design, research, floodplain mapping, dam safety assessments and elevation modeling, etc. Classified LAS files are used to show the manually reviewed bare earth surface. This allows the user to create Intensity Images, Breaklines and Raster DEM. The purpose of these lidar data was to produce high accuracy 3D hydro-flattened Digital Elevation Model (DEM) with a 2.5 feet cell size. These lidar point cloud data were used to create intensity images, 3D breaklines, and hydro-flattened DEMs as necessary. All products follow and comply with USGS Lidar Base Specification, Techniques and Methods 11-B4, Version 1.3.

USGS Contract No. G16PC00020 CONTRACTOR: Dewberry SUBCONTRACTOR: Leading Edge Geomatics (LEG). Lidar data were acquired and calibrated by Leading Edge Geomatics (LEG). All follow-on processing was completed by the prime contractor.

The following are the USGS lidar fields in JSON:

{

"ldrinfo" : {

"ldrspec" : "U.S. Geological Survey (USGS) - National Geospatial Program (NGP) Lidar Base Specification v1.2",

"ldrsens" : "Riegl VQ1560i",

"ldrmaxnr" : "7",

"ldrnps" : "0.26",

"ldrdens" : "14.8",

"ldranps" : "0.2",

"ldradens" : "25",

"ldrfltht" : "1300",

"ldrfltsp" : "120",

"ldrscana" : "57",

"ldrscanr" : "74",

"ldrpulsr" : "2000",

"ldrpulsd" : "3",

"ldrpulsw" : "0.8994",

"ldrwavel" : "1064",

"ldrmpia" : "1",

"ldrbmdiv" : "0.25",

"ldrswatw" : "1412",

"ldrswato" : "55",

"ldrgeoid" : "National Geodetic Survey (NGS) GEOID12B"

},

"ldraccur" : {

"ldrchacc" : "0.28",

"rawnva" : "0.165",

"rawnvan" : "50",

"clsnva" : "0.155",

"clsnvan" : "50",

"clsvva" : "0.157",

"clsvvan" : "40"

},

"lasinfo" : {

"lasver" : "1.4",

"lasprf" : "6",

"laswheld" : "Withheld (ignore) points were identified in these files using the standard LAS Withheld bit.",

"lasolap" : "Swath "overage" points were identified in these files using the standard LAS overlap bit.",

"lasintr" : "16",

"lasclass" : {

"clascode" : "1",

"clasitem" : "Processed, but Unclassified"

},

"lasclass" : {

"clascode" : "2",

"clasitem" : "Bare Earth Ground"

},

"lasclass" : {

"clascode" : "7",

"clasitem" : "Low Noise"

},

"lasclass" : {

"clascode" : "9",

"clasitem" : "Water"

},

"lasclass" : {

"clascode" : "10",

"clasitem" : "Ignored Ground"

},

"lasclass" : {

"clascode" : "17",

"clasitem" : "Bridge Decks"

},

"lasclass" : {

"clascode" : "18",

"clasitem" : "High Noise"

}

}}

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

Data is available online for bulk and 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.

Horizontal accuracy testing requires well-defined checkpoints that can be identified in the dataset. Elevation datasets, including lidar datasets, do not always contain well-defined checkpoints suitable for horizontal accuracy assessment. However, the ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) recommends at least half of the NVA vertical check points should be located at the ends of paint stripes or other point features visible on the lidar intensity image, allowing them to double as horizontal check points.

Nineteen checkpoints were determined to be photo-identifiable in the intensity imagery and were used to test the horizontal accuracy of the lidar dataset. As only nineteen (19) checkpoints were photo-identifiable, the results are not statistically significant enough to report as a final tested value, but the results of the testing are provided. Using NSSDA methodology (endorsed by the ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014)), horizontal accuracy at the 95% confidence level (called ACCURACYr) is computed by the formula RMSEr * 1.7308 or RMSExy * 2.448. No horizontal accuracy requirements or thresholds were provided for this project. However, lidar datasets are generally calibrated by methods designed to ensure a horizontal accuracy of 1 meter or less at the 95% confidence level.

This data set was produced to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 1.34 ft (41 cm) RMSEx/RMSEy Horizontal Accuracy Class which equates to Positional Horizontal Accuracy = +/- 3.28 ft (1 meter) at a 95% confidence level. Nineteen (19) checkpoints were photo-identifiable but do not produce a statistically significant tested horizontal accuracy value. Using this small sample set of photo-identifiable checkpoints, positional accuracy of this dataset was found to be RMSEx = 0.35 ft (11 cm) and RMSEy = 0.40 ft (12 cm) which equates to +/- 0.92 ft (28 cm) at 95% confidence level. While not statistically significant, the results of the small sample set of checkpoints are within the produced to meet horizontal accuracy.

For the vertical accuracy assessment, ninety (90) check points were surveyed for the project and are located within bare earth/open terrain, grass/weeds/crops, and forested/fully grown land cover categories. Please see the survey report which details and validates how the survey was completed for this project. Checkpoints were evenly distributed throughout the project area so as to cover as many flight lines as possible using the “dispersed method” of placement.

NVA (Non-vegetated Vertical Accuracy) is determined with check points located only in nonvegetated terrain, including open terrain (grass, dirt, sand, and/or rocks) and urban areas, where there is a very high probability that the lidar sensor will have detected the bare-earth ground surface and where random errors are expected to follow a normal error distribution. The NVA determines how well the calibrated lidar sensor performed. With a normal error distribution, the vertical accuracy at the 95% confidence level is computed as the vertical root mean square error (RMSEz) of the checkpoints x 1.9600. For the Upper Saint Johns lidar project, vertical accuracy must be 0.64 ft (19.6 cm) or less based on an RMSEz of 0.33 ft (10 cm) x 1.9600.

VVA (Vegetated Vertical Accuracy) is determined with all checkpoints in vegetated land cover categories, including tall grass, weeds, crops, brush and low trees, and fully forested areas, where there is a possibility that the lidar sensor and post-processing may yield elevation errors that do not follow a normal error distribution. VVA at the 95% confidence level equals the 95th percentile error for all checkpoints in all vegetated land cover categories combined. The Upper Saint Johns lidar project VVA standard is 0.96 ft (29.4 cm) based on the 95th percentile. The VVA is accompanied by a listing of the 5% outliers that are larger than the 95th percentile used to compute the VVA; these are always the largest outliers that may depart from a normal error distribution. Here, Accuracyz differs from VVA because Accuracyz assumes elevation errors follow a normal error distribution where RMSE procedures are valid, whereas VVA assumes lidar errors may not follow a normal error distribution in vegetated categories, making the RMSE process invalid.

This lidar dataset was tested to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 0.33 ft (10 cm) RMSEz Vertical Accuracy Class.

Actual NVA accuracy was found to be RMSEz =0.26 ft (7.9 cm), equating to +/- 0.51 ft (15.5 cm) at 95% confidence level.

Actual VVA accuracy was found to be +/- 0.52 ft (15.8 cm) at the 95th percentile.

These LAS data files include all data points collected. No points have been removed or excluded. A visual qualitative assessment was performed to ensure data completeness. No void areas or missing data exist. The raw point cloud is of good quality and data passes Non-Vegetated Vertical Accuracy specifications.

Data covers the entire area specified for this project.

Data is backed up to tape and to cloud storage.