2018 USGS Lidar: Sabine River, LA

Product: The lidar data were processed and classified according to project specifications. Detailed breaklines and bare-earth Digital Elevation Models (DEMs) were produced for the project area. Data was formatted according to tiles with each tile covering an area of 1000m by 1000m. The total project encompasses an area of approximately 10,535 sq. miles.

Dataset Description: The LA Sabine River Lidar project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 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), Universal Transverse Mercator, meters and vertical datum of NAVD88 (GEOID12B), meters. Lidar data was delivered as processed Classified LAS 1.4 files, formatted to 28119 individual 1000 m x 1000 m tiles, as tiled Intensity Imagery, and as tiled bare earth DEMs; all tiled to the same 1000 m x 1000 m schema.

Ground Conditions: Lidar was collected between January 12, 2018 and Dec 1, 2018, while no snow was on the ground and rivers were at or below normal levels.

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 0.5 meter cell size. These lidar point cloud data were used to create intensity images, 3D breaklines, and hydro-flattened DEMs as necessary.

USGS Contract No. G16PC00020 CONTRACTOR: Dewberry SUBCONTRACTOR: Airborne Imaging Inc, Lidar data were acquired and calibrated by Airborne Imaging Inc. 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 VQ-1560i",

"ldrmaxnr" : "7",

"ldrnps" : "0.35",

"ldrdens" : "8",

"ldranps" : "0.35",

"ldradens" : "8",

"ldrfltht" : "1400",

"ldrfltsp" : "160",

"ldrscana" : "60",

"ldrscanr" : "361",

"ldrpulsr" : "2000",

"ldrpulsd" : "3",

"ldrpulsw" : "0.9",

"ldrwavel" : "1064",

"ldrmpia" : "1",

"ldrbmdiv" : "0.25",

"ldrswatw" : "1569",

"ldrswato" : "30",

"ldrgeoid" : "National Geodetic Survey (NGS) Geoid12B"

},

"ldraccur" : {

"ldrchacc" : "0",

"rawnva" : "0",

"rawnvan" : "0",

"clsnva" : "0",

"clsnvan" : "0",

"clsvva" : "0",

"clsvvan" : "0"

},

"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 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.

Riegl RiProcess; GeoCue; Microstation; Terrascan; ArcGIS 10.4; Windows 7; .las 3.66 TB

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. Dewberry reviews all NVA checkpoints to determine which, if any, of these checkpoints are located on photo-identifiable features in the intensity imagery. This subset of checkpoints was then used for horizontal accuracy testing. Thirteen checkpoints were determined to be photo-identifiable in the intensity imagery and were used to test the horizontal accuracy of the lidar dataset.

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 41 cm RMSEx/RMSEy Horizontal Accuracy Class which equates to Positional Horizontal Accuracy = +/- 1 meter at a 95% confidence level. Using this small sample set of photo-identifiable checkpoints, positional accuracy of this dataset was found to be RMSEx = 22.9 cm and RMSEy = 21.7 cm which equates to +/- 54.5 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 more information see the project report. The link to the report is provided in the URL section of this metadata record.

Dewberry Determined Vertical Accuracy

Number of NVA checkpoints: 209

Number of VVA checkpoints: 150

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 =4.8 cm, equating to +/- 9.4 cm at 95% confidence level. Actual VVA accuracy was found to be +/- 14.4 cm at the 95th percentile.

For more information see the project report. The link to the report is provided in the URL section of this metadata record.

USGS Determined Vertical Accuracy

Number of NVA checkpoints: 209

Number of VVA checkpoints: 150

NVA RMSEz = 4.89 cm, NVA Accuracyz at 95th confidence interval=9.58 cm

VVA= 15.16 cm at the 95th percentile

For more information see the USGS work package report and the project report. The links to these reports are provided in the URL section of this metadata record.

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.

The NOAA Office for Coastal Management (OCM) ingested references to the USGS Entwine Point Tiles (EPT) hosted on Amazon Web Services (AWS) into the Digital Coast Data Access Viewer (DAV). The DAV accesses the point cloud as it resides on AWS under the usgs-lidar-public-container.