58957
2018 - 2019 USGS QL2 Lidar: Northern California Wildfires
ca2018_wildfire_ql2_m9036_metadata
Data Set
Published / External
49401
Lidar - partner (no harvest)
Project
Completed
2019-10-01
Product: These lidar data are processed Classified LAS 1.4 files, formatted to individual 1000 m x 1000 m tiles; used to create intensity images, 3D breaklines, and hydro-flattened DEMs as necessary.
Geographic Extent: 27 counties in California, covering approximately 16846 total square miles.
Dataset Description: The Northern California - QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 2011 Contiguous USA Albers, Meter and vertical datum of NAVD88 GEOID 12B, Meter. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 43332 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. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in summer, fall, and winter 2018 and winter, spring, and summer 2019, while no snow was on the ground and 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, Quantum Spatial, Inc. utilized a total of 387 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 678 independent accuracy checkpoints, 380 in Bare Earth and Urban landcovers (380 NVA points), 298 in Tall Weeds categories (298 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 in 8 blocks from this USGS site:
https://rockyweb.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/CA_NoCAL_3DEP_Supp_Funding_2018_D18/
The blocks downloaded were:
CA_NoCAL_Wildfires_B1_2018 Number of files: 7491
CA_NoCAL_Wildfires_B2_2018 Number of files: 10,870
CA_NoCAL_Wildfires_B3_2018 Number of files: 7239
CA_NoCAL_Wildfires_B4_2018 Number of files: 6195
CA_NoCAL_Wildfires_B5a_2018 Number of files: 6302
CA_NoCAL_Wildfires_B5b_2018 Number of files: 4958
CA_NoCal_Wildfires_GMEG_2018 Number of files: 1048
CA_NoCAL_Wildfires_TL_QL2_2018 Number of files: 518
These files were processed to the Data Access Viewer (DAV). The total number of files downloaded and processed was 44,621.
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 DEMs. The purpose of these LiDAR data was to produce high accuracy 3D hydro-flattened digital elevation models (DEMs) with a 1 meter cell size. These raw LiDAR point cloud data were used to create classified LiDAR LAS files, intensity images, 3D breaklines, and hydro-flattened DEMs as necessary.
Contract No. G16PC00016, Task Order No. 140G0218F0251; CONTRACTOR: Quantum Spatial, Inc.
The following are the USGS lidar fields in JSON:
{
"ldrinfo" : {
"ldrspec" : "USGS-NGP Base Specification v1.3",
"ldrsens" : "Riegl VQ1560i",
"ldrmaxnr" : "unlimited",
"ldrnps" : "0.71",
"ldrdens" : "2.0",
"ldranps" : "0.48",
"ldradens" : "4.34",
"ldrfltht" : "2000",
"ldrfltsp" : "160",
"ldrscana" : "29",
"ldrscanr" : "46",
"ldrpulsr" : "100",
"ldrpulsd" : "3",
"ldrpulsw" : "0.36",
"ldrwavel" : "1064",
"ldrmpia" : "1",
"ldrbmdiv" : "0.18",
"ldrswatw" : "2217.0",
"ldrswato" : "55",
"ldrgeoid" : "GEOID 12B",
"ldrcrs" : "NAD 1983 2011 Contiguous USA Albers, Meter"
},
"ldrinfo" : {
"ldrspec" : "USGS-NGP Base Specification v1.3",
"ldrsens" : "Leica ALS70",
"ldrmaxnr" : "4",
"ldrnps" : "0.71",
"ldrdens" : "2.0",
"ldranps" : "0.6",
"ldradens" : "2.78",
"ldrfltht" : "2200",
"ldrfltsp" : "140",
"ldrscana" : "15",
"ldrscanr" : "46",
"ldrpulsr" : "245",
"ldrpulsd" : "4",
"ldrpulsw" : "0.48",
"ldrwavel" : "1064",
"ldrmpia" : "1",
"ldrbmdiv" : "0.22",
"ldrswatw" : "1179.0",
"ldrswato" : "30",
"ldrgeoid" : "GEOID 12B",
"ldrcrs" : "NAD 1983 2011 Contiguous USA Albers, Meter"
},
"ldrinfo" : {
"ldrspec" : "USGS-NGP Base Specification v1.3",
"ldrsens" : "Leica ALS80",
"ldrmaxnr" : "unlimited",
"ldrnps" : "0.71",
"ldrdens" : "2.0",
"ldranps" : "0.6",
"ldradens" : "2.78",
"ldrfltht" : "2300",
"ldrfltsp" : "140",
"ldrscana" : "14",
"ldrscanr" : "60",
"ldrpulsr" : "240",
"ldrpulsd" : "2.5",
"ldrpulsw" : "0.51",
"ldrwavel" : "1064",
"ldrmpia" : "1",
"ldrbmdiv" : "0.22",
"ldrswatw" : "1147.0",
"ldrswato" : "30",
"ldrgeoid" : "GEOID 12B",
"ldrcrs" : "NAD 1983 2011 Contiguous USA Albers, Meter"
},
"ldrinfo" : {
"ldrspec" : "USGS-NGP Base Specification v1.3",
"ldrsens" : "Riegl VQ1560i",
"ldrmaxnr" : "unlimited",
"ldrnps" : "0.71",
"ldrdens" : "2.0",
"ldranps" : "0.6",
"ldradens" : "2.78",
"ldrfltht" : "2300",
"ldrfltsp" : "140",
"ldrscana" : "30",
"ldrscanr" : "165",
"ldrpulsr" : "800",
"ldrpulsd" : "3",
"ldrpulsw" : "0.41",
"ldrwavel" : "1064",
"ldrmpia" : "1",
"ldrbmdiv" : "0.18",
"ldrswatw" : "2656.0",
"ldrswato" : "30",
"ldrgeoid" : "GEOID 12B",
"ldrcrs" : "NAD 1983 2011 Contiguous USA Albers, Meter"
},
"ldraccur" : {
"ldrchacc" : "0",
"rawnva" : "0.087",
"rawnvan" : "374",
"clsnva" : "0.086",
"clsnvan" : "380",
"clsvva" : "0.27",
"clsvvan" : "298"
},
"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" : "In-land Water"
},
"lasclass" : {
"clascode" : "17",
"clasitem" : "Bridge Decks"
},
"lasclass" : {
"clascode" : "18",
"clasitem" : "High Noise"
},
"lasclass" : {
"clascode" : "20",
"clasitem" : "Ignored Ground"
},
"lasclass" : {
"clascode" : "21",
"clasitem" : "Snow (where identifiable)"
},
"lasclass" : {
"clascode" : "22",
"clasitem" : "Temporal Exclusion (if applicable)"
}
}}
Theme
Global Change Master Directory (GCMD) Science Keywords
EARTH SCIENCE > LAND SURFACE > TOPOGRAPHY > TERRAIN ELEVATION
Theme
Global Change Master Directory (GCMD) Science Keywords
EARTH SCIENCE > OCEANS > COASTAL PROCESSES > COASTAL 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 > CALIFORNIA
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
Temporal
2018
Temporal
2019
Spatial
Continent > North America > United States Of America > California > Alameda County
Spatial
Continent > North America > United States Of America > California > Butte County
Spatial
Continent > North America > United States Of America > California > Carson City County
Spatial
Continent > North America > United States Of America > California > Colusa County
Spatial
Continent > North America > United States Of America > California > Contra Costa County
Spatial
Continent > North America > United States Of America > California > El Dorado County
Spatial
Continent > North America > United States Of America > California > Glenn County
Spatial
Continent > North America > United States Of America > California > Humboldt County
Spatial
Continent > North America > United States Of America > California > Lake County
Spatial
Continent > North America > United States Of America > California > Lassen County
Spatial
Continent > North America > United States Of America > California > Marin County
Spatial
Continent > North America > United States Of America > California > Mendocino County
Spatial
Continent > North America > United States Of America > California > Napa County
Spatial
Continent > North America > United States Of America > California > Nevada County
Spatial
Continent > North America > United States Of America > California > Placer County
Spatial
Continent > North America > United States Of America > California > Plumas County
Spatial
Continent > North America > United States Of America > California > Sacramento County
Spatial
Continent > North America > United States Of America > California > San Francisco County
Spatial
Continent > North America > United States Of America > California > Sierra County
Spatial
Continent > North America > United States Of America > California > Solano County
Spatial
Continent > North America > United States Of America > California > Sonoma County
Spatial
Continent > North America > United States Of America > California > Sutter County
Spatial
Continent > North America > United States Of America > California > Tehama County
Spatial
Continent > North America > United States Of America > California > Trinity County
Spatial
Continent > North America > United States Of America > California > Washoe County
Spatial
Continent > North America > United States Of America > California > Yolo County
Spatial
Continent > North America > United States Of America > California > Yuba County
Office for Coastal Management
Charleston
SC
Data Set
Elevation
None Planned
Model (digital)
Any conclusions drawn from the analysis of this information are not the responsibility of Quantum Spatial, Inc., USGS, NOAA, the Office for Coastal Management or its partners.
Quantum Spatial, Inc.
Data Steward
2020
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
2020
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
2020
Organization
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston
VA
20191
USA
https://usgs.gov
USGS Home
Home page for USGS
Online Resource
Metadata Contact
2020
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
2020
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
-124.428962
-119.853493
41.386852
37.727043
Range
2018-07-07
2019-09-05
No
Yes
No
No
No
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.
2020-03-02
https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=9036
2020
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. Change to an orthometric vertical datum is one of the many options.
Zip
Zip
2020
https://rockyweb.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/CA_NoCAL_3DEP_Supp_Funding_2018_D18/
2020
Organization
U.S. Geological Survey
Bulk Download
Bulk download of data files in LAZ format, NAD 1983 2011 Contiguous USA Albers, meters projection and orthometric heights in meters.
LAZ
LAS/LAZ - LASer
LAZ
https://rockyweb.usgs.gov/vdelivery/Datasets/Staged/Elevation/metadata/CA_NoCAL_3DEP_Supp_Funding_2018_D18/CA_NoCAL_Wildfires_B3_2018/
USGS Additional Data
Online Resource
This link is to the reports, breaklines, metadata, spatial metadata, and shapefiles.
https://rockyweb.usgs.gov/vdelivery/Datasets/Staged/Elevation/metadata/CA_NoCAL_3DEP_Supp_Funding_2018_D18/CA_NoCAL_Wildfires_B4_2018/reports/33371_USGS_NoCal_LiDAR_ProjectReport.pdf
Lidar Report
Online Resource
Link to the lidar report.
https://s3-us-west-2.amazonaws.com/usgs-lidar-public/CA_NoCAL_Wildfires_TL_QL2_2018/ept.json
USGS Entwine Point Tile (EPT) - CA_NoCAL_Wildfires_TL_QL2_2018
Online Resource
json
Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.
https://s3-us-west-2.amazonaws.com/usgs-lidar-public/USGS_LPC_CA_NoCAL_Wildfires_B1_2018/ept.json
USGS Entwine Point Tile (EPT) - USGS_LPC_CA_NoCAL_Wildfires_B1_2018
Online Resource
json
Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.
https://s3-us-west-2.amazonaws.com/usgs-lidar-public/USGS_LPC_CA_NoCAL_Wildfires_B2_2018/ept.json
USGS Entwine Point Tile (EPT) - USGS_LPC_CA_NoCAL_Wildfires_B2_2018
Online Resource
json
Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.
https://s3-us-west-2.amazonaws.com/usgs-lidar-public/USGS_LPC_CA_NoCAL_Wildfires_B3_2018/ept.json
USGS Entwine Point Tile (EPT) - USGS_LPC_CA_NoCAL_Wildfires_B3_2018
Online Resource
json
Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.
https://s3-us-west-2.amazonaws.com/usgs-lidar-public/USGS_LPC_CA_NoCAL_Wildfires_B4_2018/ept.json
USGS Entwine Point Tile (EPT) - USGS_LPC_CA_NoCAL_Wildfires_B4_2018
Online Resource
json
Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.
https://s3-us-west-2.amazonaws.com/usgs-lidar-public/USGS_LPC_CA_NoCAL_Wildfires_B5a_2018/ept.json
USGS Entwine Point Tile (EPT) - USGS_LPC_CA_NoCAL_Wildfires_B5a_2018
Online Resource
json
Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.
https://s3-us-west-2.amazonaws.com/usgs-lidar-public/USGS_LPC_CA_NoCAL_Wildfires_B5b_2018/ept.json
USGS Entwine Point Tile (EPT) - USGS_LPC_CA_NoCAL_Wildfires_B5b_2018
Online Resource
json
Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.
https://s3-us-west-2.amazonaws.com/usgs-lidar-public/USGS_LPC_CA_NoCal_Wildfires_GMEG_2018/ept.json
USGS Entwine Point Tile (EPT) - USGS_LPC_CA_NoCal_Wildfires_GMEG_2018
Online Resource
json
Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.
https://usgs.entwine.io/data/view.html?r=[%22https://s3-us-west-2.amazonaws.com/usgs-lidar-public/USGS_LPC_CA_NoCAL_Wildfires_B1_2018/ept.json%22,%22https://s3-us-west-2.amazonaws.com/usgs-lidar-public/USGS_LPC_CA_NoCAL_Wildfires_B2_2018/ept.json%22,%22https://s3-us-west-2.amazonaws.com/usgs-lidar-public/USGS_LPC_CA_NoCAL_Wildfires_B3_2018/ept.json%22,%22https://s3-us-west-2.amazonaws.com/usgs-lidar-public/USGS_LPC_CA_NoCAL_Wildfires_B4_2018/ept.json%22,%22https://s3-us-west-2.amazonaws.com/usgs-lidar-public/USGS_LPC_CA_NoCAL_Wildfires_B5a_2018/ept.json%22,%22https://s3-us-west-2.amazonaws.com/usgs-lidar-public/USGS_LPC_CA_NoCAL_Wildfires_B5b_2018/ept.json%22,%22https://s3-us-west-2.amazonaws.com/usgs-lidar-public/USGS_LPC_CA_NoCal_Wildfires_GMEG_2018/ept.json%22,%22https://s3-us-west-2.amazonaws.com/usgs-lidar-public/CA_NoCAL_Wildfires_TL_QL2_2018/ept.json%22]
USGS 3D View
Online Resource
Link to view the point cloud, using the Entwine Point Tile (EPT) format, in the 3D Potree viewer.
MicroStation Version 8; TerraScan Version 18; TerraModeler Version 18; GeoCue Version 2017.1.14.1; Esri ArcGIS 10.3; Global Mapper 19; Leica Cloud Pro 1.2.4, RiProcess 1.8.5; Windows 10 Operating System
\\point_cloud\tilecls\*.las
The specifications require that only nonvegetated vertical accuracy (NVA) be computed for raw LiDAR data swath files. The vertical accuracy was tested with 374 independent survey points located in open terrain. These check points were not used in the calibration or post processing of the LiDAR data. The survey check points were distributed throughout the project area.
Tested 0.087 meters NVA at a 95% confidence level using 374 independent survey points located in open terrain. The survey check points were distributed throughout the project area. The 374 independent check points were surveyed using GPS techniques. See survey report for additional survey methodologies. Elevations from the unclassified LiDAR surface were measured for the x,y location of each checkpoint. Elevations interpolated from the LiDAR surface were then compared to the elevation values of the surveyed control points. The root mean square error vertical (RMSEz) was computed to be 0.044 meters. AccuracyZ has been tested to meet 19.0 cm NVA at 95-percent confidence level using (RMSEz * 1.9600) as defined by the National Standards for Spatial Data Accuracy (NSSDA); assessed and reported using American Society of Photogrammetry and Remote Sensing (ASPRS) Guidelines.
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 the data pass Non-Vegetated Vertical Accuracy specifications.
Data covers the entire area specified for this project.
Yes
Unknown
Yes
NCEI-CO
Data is backed up to tape and to cloud storage.
The NOAA Office for Coastal Management (OCM) downloaded laz files from the USGS rockyweb site (https://rockyweb.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/CA_NoCAL_3DEP_Supp_Funding_2018_D18/) and processed the data to be available for custom download from the NOAA Digital Coast Data Access Viewer (DAV).
These are the folders that were downloaded:
CA_NoCAL_Wildfires_B1_2018
CA_NoCAL_Wildfires_B2_2018
CA_NoCAL_Wildfires_B3_2018
CA_NoCAL_Wildfires_B4_2018
CA_NoCAL_Wildfires_B5a_2018
CA_NoCAL_Wildfires_B5b_2018
CA_NoCal_Wildfires_GMEG_2018
CA_NoCAL_Wildfires_TL_QL2_2018
Ground Control for the Northern California - QL2 Project
Organization
Quantum Spatial, Inc.
Originator
2019-10-01
Discrete
2018-11-29
This data source was used (along with airborne GPS/IMU data) to georeference the LiDAR point cloud data.
USGS LAZ Data Download
Organization
USGS
Publisher
https://rockyweb.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/CA_NoCAL_3DEP_Supp_Funding_2018_D18/
1
Raw Data and Boresight Processing: The boresight for each lift was done individually as the solution may change slightly from lift to lift. The following steps describe the Raw Data Processing and Boresight process: 1) Technicians processed the raw data to LAS format flight lines using the final GPS/IMU solution. This LAS data set was used as source data for boresight. 2) Technicians first used Quantum Spatial, Inc. proprietary and commercial software to calculate initial boresight adjustment angles based on sample areas selected in the lift. These areas cover calibration flight lines collected in the lift, cross tie, and production flight lines. These areas are well distributed in the lift coverage and cover multiple terrain types that are necessary for boresight angle calculation. The technicians then analyzed the results and made any necessary additional adjustment until it was acceptable for the selected areas. 3) Once the boresight angle calculation was completed for the selected areas, the adjusted settings were applied to all of the flight lines of the lift and checked for consistency. The technicians utilized commercial and proprietary software packages to analyze how well flight line overlaps matched for the entire lift and adjusted as necessary until the results met the project specifications. 4) Once all lifts were completed with individual boresight adjustment, the technicians checked and corrected the vertical misalignment of all flight lines and also the matching between data and ground truth. The relative accuracy was less than or equal to 7 cm RMSEz within individual swaths and less than or equal to 10 cm RMSEz or within swath overlap (between adjacent swaths). 5) The technicians ran a final vertical accuracy check of the boresighted flight lines against the surveyed checkpoints after the z correction to ensure the requirement of NVA = 19.6 cm 95% Confidence Level (Required Accuracy) was met.
2019-01-01T00:00:00
2
LAS Point Classification: The point classification was performed as described below. The bare earth surface was manually reviewed to ensure correct classification on the Class 2 (Ground) points. After the bare-earth surface was finalized, it was then used to generate all hydro-breaklines through heads-up digitization. All ground (ASPRS Class 2) LiDAR data inside of the Lake Pond and Double Line Drain hydro-flattened breaklines were then classified to Water (ASPRS Class 9) using TerraScan macro functionality. A buffer of 1 meter was also used around each hydro-flattened feature to classify these ground (ASPRS Class 2) points to Ignored ground (ASPRS Class 20). All Lake Pond Island and Double Line Drain Island features were checked to ensure that the ground (ASPRS Class 2) points were reclassified to the correct classification after the automated classification was completed. All overlap data was processed through automated functionality provided by TerraScan to classify the overlapping flight line data to approved classes. The overlap data was classified using standard LAS overlap bit. These classes were created through automated processes only and were not verified for classification accuracy. Due to software limitations within TerraScan, these classes were used to trip the withheld bit within various software packages. These processes were reviewed and accepted through numerous conference calls and pilot study areas. All data were manually reviewed and any remaining artifacts removed using functionality provided by TerraScan and TerraModeler. Global Mapper was used as a final check of the bare earth dataset. GeoCue was then used to create the deliverable industry-standard LAS files for both the All Point Cloud Data and the Bare Earth. Quantum Spatial, Inc. proprietary software was used to perform final statistical analysis of the classes in the LAS files, on a per tile level to verify final classification metrics and full LAS header information.
2019-01-01T00:00:00
3
The NOAA Office for Coastal Management (OCM) downloaded this data set in 7 blocks from this USGS site:
https://rockyweb.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/CA_NoCAL_3DEP_Supp_Funding_2018_D18/
The blocks downloaded were:
CA_NoCAL_Wildfires_B1_2018 Number of files: 7491
CA_NoCAL_Wildfires_B2_2018 Number of files: 10,870
CA_NoCAL_Wildfires_B3_2018 Number of files: 7239
CA_NoCAL_Wildfires_B4_2018 Number of files: 6195
CA_NoCAL_Wildfires_B5a_2018 Number of files: 6302
CA_NoCAL_Wildfires_B5b_2018 Number of files: 4958
CA_NoCal_Wildfires_GMEG_2018 Number of files: 1048
The total number of files downloaded and processed was 44,103.
The data were in Albers Equal Area (NAD83 2011), meters coordinates and NAVD88 (Geoid12B) elevations in meters. The data were classified as: 1 - Unclassified, 2 - Ground, 7 - Low Noise, 9 - Water, 17 - Bridge Decks, 18 - High Noise, 20 - Ignored Ground. 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, 17, 18, 20.
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 Albers Equal Area (NAD83 2011) coordinates in meters to geographic coordinates, to assign the geokeys, to sort the data by gps time and zip the data to database.
2020-03-02T00:00:00
Organization
Office for Coastal Management
OCM
2234 South Hobson Avenue
Charleston
SC
29405-2413
https://www.coast.noaa.gov/
4
The NOAA Office for Coastal Management (OCM) downloaded 518 laz files from the TL (tribal lands) folder from the USGS rockyweb site. This data was released after the above blocks of data were processed. This is the URL for that data:
https://rockyweb.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/CA_NoCAL_3DEP_Supp_Funding_2018_D18/CA_NoCAL_Wildfires_TL_QL2_2018/LAZ/
The data were in Albers Equal Area (NAD83 2011), meters coordinates and NAVD88 (Geoid12B) elevations in meters. The data were classified as: 1 - Unclassified, 2 - Ground, 7 - Low Noise, 9 - Water, 17 - Bridge Decks, 18 - High Noise, 20 - Ignored Ground. 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, 17, 18, 20.
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 Albers Equal Area (NAD83 2011) coordinates in meters to geographic coordinates, to assign the geokeys, to sort the data by gps time and zip the data to database.
2023-10-31T00:00:00
Organization
Office for Coastal Management
OCM
2234 South Hobson Avenue
Charleston
SC
29405-2413
https://www.coast.noaa.gov/
59131
Data Set
2018 - 2019 USGS QL1 Lidar: Northern California Wildfires
Cross Reference
gov.noaa.nmfs.inport:58957
Rebecca Mataosky
2020-03-02T10:49:50
Rebecca Mataosky
2023-12-13T16:09:18
2022-03-16
OCM Partners
OCMP
1002
Public
No
2022-03-16
1 Year
2023-03-16