57818
2017 USGS Lidar: Palm Beach County, FL
fl2017_palm_beach_m8881_metadata
Data Set
Published / External
49401
Lidar - partner (no harvest)
Project
Completed
2019-10-01
2018-05
Axis Geospatial, LLC collected 1,994 square miles in Palm Beach County, Florida. The nominal pulse spacing for this project was 1 point every 0.7 meters. Dewberry used proprietary procedures to classify the LAS according to project specifications: 1-Processed, but unclassified, 2- Bare-earth ground, 7-Low Noise (low, manually identified, if necessary), 9-Water, 10-Ignored Ground due to breakline proximity, 17- Bridge Decks, 18-High Noise (high, manually identified, if necessary). Dewberry produced 3D breaklines and combined these with the final lidar data to produce seamless hydro flattened DEMs for the project area. The data was formatted according to the tile naming convention provided by the client with each tile covering an area of 2,500 ft by 2,500 ft. A total of 9,222 LAS tiles and 9,222 DEM tiles were produced for the entire project.
In addition to these lidar point data, the hydro breaklines are also available. These data are available for download at the link provided in the URL section of this metadata record.
The purpose of this lidar data was to produce high accuracy 3D elevation products, including tiled lidar in LAS 1.4 format, 3D breaklines, and 2.5 foot cell size hydro flattened Digital Elevation Models (DEMs). All products follow and comply with USGS Lidar Base Specification Version 1.2.
A complete description of this dataset is available in the Final Project Report that was submitted to the U.S. Geological Survey.
The following are the USGS lidar fields in JSON:
{
"ldrinfo" : {
"ldrspec" : "USGS-NGP Lidar Base Specification V1.2",
"ldrsens" : "Riegl LMS-Q1560",
"ldrmaxnr" : "unlimited",
"ldrnps" : "0.7",
"ldrdens" : "2.04",
"ldranps" : "0.7",
"ldradens" : "2.04",
"ldrfltht" : "907",
"ldrfltsp" : "140",
"ldrscana" : "58.52",
"ldrscanr" : "267",
"ldrpulsr" : "800",
"ldrpulsd" : "3",
"ldrpulsw" : "0.51",
"ldrwavel" : "1064",
"ldrmpia" : "1",
"ldrbmdiv" : "0.25",
"ldrswatw" : "1016",
"ldrswato" : "30",
"ldrcrs" : "NAD 1983 (2011) State Plane Florida East U.S. Survey Feet",
"ldrgeoid" : "National Geodetic Survey (NGS) Geoid12B"
},
"ldraccur" : {
"ldrchacc" : "0.485",
"rawnva" : "0.104",
"rawnvan" : "79",
"clsnva" : "0.094",
"clsnvan" : "79",
"clsvva" : "0.180",
"clsvvan" : "61"
},
"lasinfo" : {
"lasver" : "1.4",
"lasprf" : "6",
"laswheld" : "Withheld 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 due to breakline proximity"
},
"lasclass" : {
"clascode" : "17",
"clasitem" : "Bridge decks"
},
"lasclass" : {
"clascode" : "18",
"clasitem" : "High noise"
}
}}
Theme
Global Change Master Directory (GCMD) Science Keywords
EARTH SCIENCE > LAND SURFACE > TOPOGRAPHY > TERRAIN ELEVATION
Theme
ISO 19115 Topic Category
elevation
Spatial
Global Change Master Directory (GCMD) Location Keywords
CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA
Spatial
Global Change Master Directory (GCMD) Location Keywords
CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > FLORIDA
Spatial
Global Change Master Directory (GCMD) Location Keywords
VERTICAL LOCATION > LAND SURFACE
Instrument
Global Change Master Directory (GCMD) Instrument Keywords
LIDAR > Light Detection and Ranging
Platform
Global Change Master Directory (GCMD) Platform Keywords
Airplane > Airplane
Theme
Bare earth
Theme
beach
Theme
erosion
Office for Coastal Management
Charleston
SC
Data Set
Elevation
As Needed
Model (digital)
Any conclusions drawn from the analysis of this information are not the responsibility of USGS, NOAA, the Office for Coastal Management or its partners.
U.S. Geological Survey
Data Steward
2019
Organization
NOAA Office for Coastal Management
NOAA/OCM
coastal.info@noaa.gov
2234 South Hobson Ave
Charleston
SC
29405-2413
(843) 740-1202
https://coast.noaa.gov
NOAA Office for Coastal Management Home Page
Online Resource
Distributor
2019
Organization
NOAA Office for Coastal Management
NOAA/OCM
coastal.info@noaa.gov
2234 South Hobson Ave
Charleston
SC
29405-2413
(843) 740-1202
https://coast.noaa.gov
NOAA Office for Coastal Management Home Page
Online Resource
Metadata Contact
2019
Organization
NOAA Office for Coastal Management
NOAA/OCM
coastal.info@noaa.gov
2234 South Hobson Ave
Charleston
SC
29405-2413
(843) 740-1202
https://coast.noaa.gov
NOAA Office for Coastal Management Home Page
Online Resource
Point of Contact
2019
Organization
NOAA Office for Coastal Management
NOAA/OCM
coastal.info@noaa.gov
2234 South Hobson Ave
Charleston
SC
29405-2413
(843) 740-1202
https://coast.noaa.gov
NOAA Office for Coastal Management Home Page
Online Resource
Ground Condition
-80.889069
-80.025918
26.972147
26.318379
Range
2016-12-28
2017-03-10
No
Yes
No
No
No
Yes
75583484635
Geographic 3D
EPSG:6319
NAD83(2011)
NAD83 (National Spatial Reference System 2011)
GRS 1980
6378137
298.257222101
1
Geodetic Latitude
Lat
degree
north
2
Geodetic Longitude
Lon
degree
east
3
Elipsoidal height
h
metre
up
Unclassified
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.
2019-10-01
https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=8881
2019
Organization
NOAA Office for Coastal Management
Customized Download
Create custom data files by choosing data area, product type, map projection, file format, datum, etc. A new metadata will be produced to reflect your request using this record as a base.
Zip
Zip
2019-10-01
https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid12b/8881/index.html
2019
Organization
NOAA Office for Coastal Management
Bulk Download
Bulk download of data files in LAZ format, geographic coordinates, orthometric heights. Note that the vertical datum (hence elevations) of the files here are different than described in this document.
LAZ
LAS/LAZ - LASer
Zip
https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid12b/8881/supplemental/fl2017_palm_beach_m8881.kmz
Browse graphic
Browse Graphic
KML
This graphic displays the footprint for this lidar data set.
https://coast.noaa.gov/dataviewer/
NOAA's Office for Coastal Management (OCM) Data Access Viewer (DAV)
Online Resource
HTML
The Data Access Viewer (DAV) allows a user to search for and download elevation, imagery, and land cover data for the coastal U.S. and its territories. The data, hosted by the NOAA Office for Coastal Management, can be customized and requested for free download through a checkout interface. An email provides a link to the customized data, while the original data set is available through a link within the viewer.
https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid12b/8881/breaklines/index.html
Online Resource
Link to the hydro breaklines.
https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid12b/8881/supplemental/LiDAR_GCP_Survey_Report_Palm_Beach_County.pdf
Dataset report
Online Resource
PDF
Link to data set report.
https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid12b/8881/supplemental/Palm_Beach_County_Project_Report.pdf
Dataset report
Online Resource
PDF
Link to data set report.
Microsoft Windows 7 Enterprise Service Pack 1; ESRI ArcCatalog 10.4.1
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. Qualitative value: 0.485, Test that produced the valued: 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 1.35 ft (41 cm) RMSEx/RMSEy Horizontal Accuracy Class which equates to Positional Horizontal Accuracy = +/- 3.28 ft (1 meter) at a 95% confidence level.
Eight (8) 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 = 0.79 ft (24.1 cm) and RMSEy = 0.47 ft (14.3 cm) which equates to +/- 1.59 ft (48.5 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.
The vertical accuracy of the classified lidar and final bare earth DEMs was tested by Dewberry with 140 independent survey checkpoints. The survey checkpoints are evenly distributed throughout the project area and are located in areas of non-vegetated terrain (79 checkpoints), including bare earth, open terrain, and urban terrain, and vegetated terrain (61 checkpoints), including forest, brush, tall weeds, crops, and high grass.The vertical accuracy of the lidar is tested by comparing survey checkpoints to a triangulated irregular network (TIN) that is created from the lidar ground 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 location of a discrete lidar point. The vertical accuracy of the final bare earth DEMs 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. Accuracy results may vary between the source lidar and final DEM deliverable. DEMs are created by averaging several lidar points within each pixel which may result in slightly different elevation values at each survey checkpoint when compared to the source LAS, which is tested by comparing survey checkpoints to TINs. TINs do not average several lidar points together but interpolate (linearly) between two or three points to derive an elevation value. The accuracy results reported for the overall project are the accuracy results of the source lidar. Bare earth DEM accuracy results are reported in the DEM metadata file.
All checkpoints located in non-vegetated terrain were used to compute the Non-vegetated Vertical Accuracy (NVA). Project specifications required a NVA of 0.64 ft (19.6 cm) at the 95% confidence level based on RMSEz (0.33 ft/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 0.96 ft (29.4 cm) based on the 95th percentile.Qualitative value:0.094 0.180, Test that produced the value: The following result is from testing the source lidar data. DEM accuracy results are in the DEM metadata file.
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 = 0.16 ft (4.9 cm), equating to +/- 0.31 ft (9.4 cm) at 95% confidence level. The following result is from testing the source lidar data. DEM accuracy results are in the DEM metadata file.
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 VVA accuracy was found to be +/- 0.59 ft (18.0 cm)at the 95th percentile.
The 5% outliers consisted of 3 checkpoints that is larger than the 95th percentile. These checkpoints have DZ values ranging between 0.73 ft (22.3 cm) and 2.09 ft (63.7cm).
A visual qualitative assessment was performed to ensure data completeness and bare earth data cleanliness. No void or missing data and data passes vertical accuracy specifications.
Data covers the project boundary.
Yes
Unknown
Yes
NCEI-CO
Data is backed up to tape and to cloud storage.
1
Data for Palm Beach QL2 Lidar Project was acquired by Axis Geospatial, LLC.
The project area included approximately 1,994 contiguous square miles or 5164.44 square kilometers for Palm Beach County, Florida.
Lidar sensor data were collected with the Riegl LMS-Q1560 lidar system. The data was delivered in the State Plane coordinate system, U.S. Survey Feet, Florida East, horizontal datum NAD83(2011), vertical datum NAVD88, Geoid 12B. Deliverables for the project included a raw (unclassified) calibrated lidar point cloud, survey control, and a final acquisition/calibration report.
The calibration process considered all errors inherent with the equipment including errors in GPS, IMU, and sensor specific parameters. Adjustments were made to achieve a flight line to flight line data match (relative calibration) and subsequently adjusted to control for absolute accuracy. Process steps to achieve this are as follows:
Rigorous lidar calibration: all sources of error such as the sensor's ranging and torsion parameters, atmospheric variables, GPS conditions, and IMU offsets were analyzed and removed to the highest level possible. This method addresses all errors, both vertical and horizontal in nature. Ranging, atmospheric variables, and GPS conditions affect the vertical position of the surface, whereas IMU offsets and torsion parameters affect the data horizontally. The horizontal accuracy is proven through repeatability: when the position of features remains constant no matter what direction the plane was flying and no matter where the feature is positioned within the swath, relative horizontal accuracy is achieved.
Absolute horizontal accuracy is achieved through the use of differential GPS with base lines shorter than 25 miles. The base station is set at a temporary monument that is 'tied-in' to the CORS network. The same position is used for every lift, ensuring that any errors in its position will affect all data equally and can therefore be removed equally.
Vertical accuracy is achieved through the adjustment to ground control survey points within the finished product. Although the base station has absolute vertical accuracy, adjustments to sensor parameters introduces vertical error that must be normalized in the final (mean) adjustment.
The withheld and overlap bits are set and all headers, appropriate point data records, and variable length records, including spatial reference information, are updated in GeoCue software and then verified using proprietary Dewberry tools.
2017-03-01T00:00:00
2
Dewberry utilizes a variety of software suites for inventory management, classification, and data processing. All lidar related processes begin by importing the data into the GeoCue task management software. The swath data is tiled according to project specifications (2,500 ft x 2,500 ft). The tiled data is then opened in Terrascan where Dewberry identifies edge of flight line points that may be geometrically unusable with the withheld bit. These points are separated from the main point cloud so that they are not used in the ground algorithms. Overage points are then identified with the overlap bit. Dewberry then uses proprietary ground classification routines to remove any non-ground points and generate an accurate ground surface. The ground routine consists of three main parameters (building size, iteration angle, and iteration distance); by adjusting these parameters and running several iterations of this routine an initial ground surface is developed. The building size parameter sets a roaming window size. Each tile is loaded with neighboring points from adjacent tiles and the routine classifies the data section by section based on this roaming window size. The second most important parameter is the maximum terrain angle, which sets the highest allowed terrain angle within the model. As part of the ground routine, low noise points are classified to class 7 and high noise points are classified to class 18. Once the ground routine has been completed, bridge decks are classified to class 17 using bridge breaklines compiled by Dewberry. A manual quality control routine is then performed using hillshades, cross-sections, and profiles within the Terrasolid software suite. After this QC step, a peer review is performed on all tiles and a supervisor manual inspection is completed on a percentage of the classified tiles based on the project size and variability of the terrain. After the ground classification and bridge deck corrections are completed, the dataset is processed through a water classification routine that utilizes breaklines compiled by Dewberry to automatically classify hydrographic features. The water classification routine selects ground points within the breakline polygons and automatically classifies them as class 9, water. During this water classification routine, points that are within 1x NPS or less of the hydrographic features are moved to class 10, an ignored ground due to breakline proximity. A final QC is performed on the data. All headers, appropriate point data records, and variable length records, including spatial reference information, are updated in GeoCue software and then verified using proprietary Dewberry tools.
The data was classified as follows:
Class 1 = Processed, but unclassified. This class includes vegetation, buildings, noise etc.
Class 2 = Bare-earth ground
Class 7 = Low Noise (low, manually identified, if necessary)
Class 9 = Water
Class 10 = Ignored Ground due to breakline proximity
Class 17 = Bridge Decks
Class 18 = High Noise (high, nmanually identified, if necessary)
The LAS header information was verified to contain the following:
Class (Integer)
Adjusted GPS Time (0.0001 seconds)
Easting (0.003 m)
Northing (0.003 m)
Elevation (0.003 m)
Echo Number (Integer)
Echo (Integer)
Intensity (16 bit integer)
Flight Line (Integer)
Scan Angle (degree)
2017-04-01T00:00:00
3
The NOAA Office for Coastal Management (OCM) downloaded 9222 laz files from: ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/USGS_LPC_FL_PalmBeachCo_2016_LAS_2019/
The data were in FL State Plane East (NAD83 2011) coordinates and NAVD88 (Geoid12B) elevations in US Survey feet. The data were classified as: 1 - Unclassified, 2 - Ground, 7 - Low Noise, 9 - Water, 10 - Ignored Ground, 17 - Bridge Decks, 18 - High Noise. OCM processed all classifications of points to the Digital Coast Data Access Viewer (DAV). Classes available on the DAV are: 1, 2, 3, 7, 9, 10, 17, 18.
OCM performed the following processing on the data for Digital Coast storage and provisioning purposes:
1. An internal OCM script was run to check the number of points by classification and by flight ID and the gps and intensity ranges.
2. Internal OCM scripts were run on the laz files to convert from orthometric (NAVD88) elevations to ellipsoid elevations using the Geoid 12B model, to convert from FL State Plane East (NAD83 2011) coordinates in US survey feet to geographic coordinates, to convert vertical units from feet to meters, to assign the geokeys, to sort the data by gps time and zip the data to database and to http.
2019-09-30T00:00:00
Organization
Office for Coastal Management
OCM
2234 South Hobson Avenue
Charleston
SC
29405-2413
https://www.coast.noaa.gov/
gov.noaa.nmfs.inport:57818
Rebecca Mataosky
2019-10-01T08:58:37
SysAdmin InPortAdmin
2023-10-17T16:12:19
2022-03-16
OCM Partners
OCMP
1002
Public
No
2022-03-16
1 Year
2023-03-16