2023 USGS Lidar: Lower Rio Grande, TX
Data Set (DS) | OCM Partners (OCMP)GUID: gov.noaa.nmfs.inport:76197 | Updated: June 23, 2025 | Published / External
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Summary
Short Citation
OCM Partners, 2025: 2023 USGS Lidar: Lower Rio Grande, TX, https://www.fisheries.noaa.gov/inport/item/76197.
Full Citation Examples
Original Data Products: This TX_LowerRioGrande_D22 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at an aggregate nominal pulse spacing (ANPS) of 0.18 meters(30ppsm) and 0.35 meters (8ppsm). Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification 2022 Rev A. The data was developed based on a horizontal projection/datum of NAD83 (2011), UTM14N (EPSG 6343), meters and vertical datum of NAVD88 (GEOID18), meters. Lidar data was delivered as processed Classified LAS 1.4 files, formatted to individual 500 m x 500 m tiles for the 30 ppsm AOI and 1000 m x 1000 m tiles for the 8ppsm AOI, and as tiled intensity imagery and tiled bare earth DEMs; all tiled to the same schemas. Hydro-flattening breaklines, building footprints, building models, vegetation canopy rasters, and ortho imagery (10 cm and 20cm GSDs) were also included in deliverables.
The TX_LowerRioGrande_D22 task is for a high-resolution data set of QL1+ (30ppsm) and QL1 (8ppsm) lidar of approximately 3,122 square miles in four counties in the state of Texas. These counties include parts of Starr, Hidalgo, Willacy, and Cameron.
The dataset is broken up into four blocks. They are:
TX_LowerRioGrande_1 (Work Unit 300257) - data is in Starr and Hidalgo counties
TX_LowerRioGrande_2 (Work Unit 300630) - data is in Cameron and Hidalgo counties
TX_LowerRioGrande_3 (Work Unit 300446) - data is in Cameron, Hidalgo, Starr and Willacy counties
TX_LowerRioGrande_4 (Work Unit 300469) - data is in Cameron, Hidalgo, Starr and Willacy counties
This metadata record supports the data entry in the NOAA Digital Coast Data Access Viewer (DAV). For this data set, the DAV is leveraging the Entwine Point Tiles (EPT) hosted by USGS on Amazon Web Services.
PurposeThis high resolution lidar data will support the United States Geological Survey (USGS) initiatives.
Distribution Information
-
Format: Not Applicable
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.
-
LAS/LAZ - LASer
Bulk download of data files in LAZ format, UTM Zone 14N NAD83(2011) meters coordinates and elevations in NAVD88(GEOID18) meters. Note that the vertical datum (hence elevations) of the files here are different than described in this document. They will be in an orthometric datum.
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.
Controlled Theme Keywords
COASTAL ELEVATION, elevation, TERRAIN ELEVATION
URLs
-
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.
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Link to the USGS Project Report that provides information about the project, vertical accuracy results, and the point classes and sensors used.
-
Link to the reports, breaklines, metadata, and spatial metadata.
-
Link to the lidar report for the TX_LowerRioGrande_1.
-
Link to the lidar report for the TX_LowerRioGrande_2.
-
Link to the lidar report for the TX_LowerRioGrande_3.
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Link to the lidar report for the TX_LowerRioGrande_4.
-
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.
-
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.
-
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.
-
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.
-
Link to view the point cloud, using the Entwine Point Tile (EPT) format, in the 3D Potree viewer.
Child Items
No Child Items for this record.
Contact Information
Point of Contact
NOAA Office for Coastal Management (NOAA/OCM)
coastal.info@noaa.gov
(843) 740-1202
https://coast.noaa.gov
Metadata Contact
NOAA Office for Coastal Management (NOAA/OCM)
coastal.info@noaa.gov
(843) 740-1202
https://coast.noaa.gov
Extents
-99.18° W,
-97.12° E,
26.8° N,
25.83° S
Data extent for the Lower Rio Grande project in Texas.
2023-01-22 - 2023-03-03
Dates of collection for the Lower Rio Grande project in Texas.
Item Identification
| Title: | 2023 USGS Lidar: Lower Rio Grande, TX |
|---|---|
| Short Name: | tx2023_lwr_rio_grd_m10353 |
| Status: | Completed |
| Creation Date: | 2023 |
| Publication Date: | 2024 |
| Abstract: |
Original Data Products: This TX_LowerRioGrande_D22 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at an aggregate nominal pulse spacing (ANPS) of 0.18 meters(30ppsm) and 0.35 meters (8ppsm). Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification 2022 Rev A. The data was developed based on a horizontal projection/datum of NAD83 (2011), UTM14N (EPSG 6343), meters and vertical datum of NAVD88 (GEOID18), meters. Lidar data was delivered as processed Classified LAS 1.4 files, formatted to individual 500 m x 500 m tiles for the 30 ppsm AOI and 1000 m x 1000 m tiles for the 8ppsm AOI, and as tiled intensity imagery and tiled bare earth DEMs; all tiled to the same schemas. Hydro-flattening breaklines, building footprints, building models, vegetation canopy rasters, and ortho imagery (10 cm and 20cm GSDs) were also included in deliverables. The TX_LowerRioGrande_D22 task is for a high-resolution data set of QL1+ (30ppsm) and QL1 (8ppsm) lidar of approximately 3,122 square miles in four counties in the state of Texas. These counties include parts of Starr, Hidalgo, Willacy, and Cameron. The dataset is broken up into four blocks. They are: TX_LowerRioGrande_1 (Work Unit 300257) - data is in Starr and Hidalgo counties TX_LowerRioGrande_2 (Work Unit 300630) - data is in Cameron and Hidalgo counties TX_LowerRioGrande_3 (Work Unit 300446) - data is in Cameron, Hidalgo, Starr and Willacy counties TX_LowerRioGrande_4 (Work Unit 300469) - data is in Cameron, Hidalgo, Starr and Willacy counties This metadata record supports the data entry in the NOAA Digital Coast Data Access Viewer (DAV). For this data set, the DAV is leveraging the Entwine Point Tiles (EPT) hosted by USGS on Amazon Web Services. |
| Purpose: |
This high resolution lidar data will support the United States Geological Survey (USGS) initiatives. |
| Supplemental Information: |
USGS Contract No. 140G0221D0010 CONTRACTOR: Fugro USA Land, Inc. SUBCONTRACTOR: Terrasurv, Inc. Ground control and checkpoints collected by Terrasurv, Inc. All processing was completed by the prime contractor.Point Cloud File Type = LAS v1.4 The following are the USGS lidar fields in JSON: {
"ldrinfo " :{
"ldrspec " :" USGS-NGP Base Lidar Specification 2022 Rev A ", "ldrsens " :" Riegl VQ-1560 II-S ", "ldrmaxnr " :" 4 ", "ldrnps " :" 0.18 ", "ldrdens " :" 30 ", "ldranps " :" 0.18 ", "ldradens " :" 30 ", "ldrfltht " :" 1036 ", "ldrfltsp " :" 168 ", "ldrscana " :" 58.52 ", "ldrscanr " :" 267; 267 ", "ldrpulsr " :" 2000; 2000 ", "ldrpulsd " :" 3 ", "ldrpulsw " :" 0.25 ", "ldrwavel " :" 1064 ", "ldrmpia " :" 1 ", "ldrbmdiv " :" 0.23 ", "ldrswatw " :" 1161 ", "ldrswato " :" 20 ", "ldrgeoid " :" National Geodetic Survey (NGS) GEOID18 " }, "ldraccur " :{
"ldrchacc " :" 0.494 ", "rawnva " :" 0 ", "rawnvan " :" 0 " }, "lasinfo " :{
"lasver " :" 1.4 ", "lasprf " :" 6 ", "laswheld " :" Withheld(ignore) points were identified in these files using the standard LAS Withheld bit. ", "lasolap " :" The overlap bit was not used to identify any points in the lidar point cloud. ", "lasintr " :" 16 bit ", "lasclass " :{
"clascode " :" 1 ", "clasitem " :" Processed but unclassified " }, "lasclass " :{
"clascode " :" 2 ", "clasitem " :" Bare-earth ground " }, "lasclass " :{
"clascode " :" 3 ", "clasitem " :" Low Vegetation (0 – 1 meter; automated classification) " }, "lasclass " :{
"clascode " :" 4 ", "clasitem " :" Medium Vegetation (1-3 meters; automated classification) " }, "lasclass " :{
"clascode " :" 5 ", "clasitem " :" High Vegetation (>3 meters; automated classification) " }, "lasclass " :{
"clascode " :" 6 ", "clasitem " :" Buildings (automated classification) " }, "lasclass " :{
"clascode " :" 7 ", "clasitem " :" Low Noise " }, "lasclass " :{
"clascode " :" 9 ", "clasitem " :" Water " }, "lasclass " :{
"clascode " :" 17 ", "clasitem " :" Bridge Decks " }, "lasclass " :{
"clascode " :" 18 ", "clasitem " :" High Noise (high, manually identified, if necessary) " }, "lasclass " :{
"clascode " :" 20 ", "clasitem " :" Ignored Ground (breakline proximity) " } } } |
Keywords
Theme Keywords
| Thesaurus | Keyword |
|---|---|
| Global Change Master Directory (GCMD) Science Keywords |
EARTH SCIENCE > LAND SURFACE > TOPOGRAPHY > TERRAIN ELEVATION
|
| Global Change Master Directory (GCMD) Science Keywords |
EARTH SCIENCE > OCEANS > COASTAL PROCESSES > COASTAL ELEVATION
|
| ISO 19115 Topic Category |
elevation
|
Spatial Keywords
| Thesaurus | Keyword |
|---|---|
| Global Change Master Directory (GCMD) Location Keywords |
CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA
|
| Global Change Master Directory (GCMD) Location Keywords |
CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > TEXAS
|
| Global Change Master Directory (GCMD) Location Keywords |
VERTICAL LOCATION > LAND SURFACE
|
Instrument Keywords
| Thesaurus | Keyword |
|---|---|
| Global Change Master Directory (GCMD) Instrument Keywords |
LIDAR > Light Detection and Ranging
|
Platform Keywords
| Thesaurus | Keyword |
|---|---|
| Global Change Master Directory (GCMD) Platform Keywords |
Airplane > Airplane
|
Physical Location
| Organization: | Office for Coastal Management |
|---|---|
| City: | Charleston |
| State/Province: | SC |
Data Set Information
| Data Set Scope Code: | Data Set |
|---|---|
| Data Set Type: | Elevation |
| Maintenance Frequency: | None Planned |
| Data Presentation Form: | Model (digital) |
| Distribution Liability: |
Any conclusions drawn from the analysis of this information are not the responsibility of NOAA, the Office for Coastal Management or its partners. |
| Data Set Credit: | USGS |
Support Roles
Data Steward
| Date Effective From: | 2025 |
|---|---|
| Date Effective To: | |
| Contact (Organization): | NOAA Office for Coastal Management (NOAA/OCM) |
| Address: |
2234 South Hobson Ave Charleston, SC 29405-2413 |
| Email Address: | coastal.info@noaa.gov |
| Phone: | (843) 740-1202 |
| URL: | https://coast.noaa.gov |
Distributor
| Date Effective From: | 2025 |
|---|---|
| Date Effective To: | |
| Contact (Organization): | NOAA Office for Coastal Management (NOAA/OCM) |
| Address: |
2234 South Hobson Ave Charleston, SC 29405-2413 |
| Email Address: | coastal.info@noaa.gov |
| Phone: | (843) 740-1202 |
| URL: | https://coast.noaa.gov |
Distributor
| Date Effective From: | 2024 |
|---|---|
| Date Effective To: | |
| Contact (Organization): | U.S. Geological Survey |
| Address: |
12201 Sunrise Valley Drive Reston, VA 20191 USA |
| URL: | USGS Home |
Metadata Contact
| Date Effective From: | 2025 |
|---|---|
| Date Effective To: | |
| Contact (Organization): | NOAA Office for Coastal Management (NOAA/OCM) |
| Address: |
2234 South Hobson Ave Charleston, SC 29405-2413 |
| Email Address: | coastal.info@noaa.gov |
| Phone: | (843) 740-1202 |
| URL: | https://coast.noaa.gov |
Point of Contact
| Date Effective From: | 2025 |
|---|---|
| Date Effective To: | |
| Contact (Organization): | NOAA Office for Coastal Management (NOAA/OCM) |
| Address: |
2234 South Hobson Ave Charleston, SC 29405-2413 |
| Email Address: | coastal.info@noaa.gov |
| Phone: | (843) 740-1202 |
| URL: | https://coast.noaa.gov |
Extents
| Currentness Reference: | Ground Condition |
|---|
Extent Group 1
Extent Group 1 / Geographic Area 1
| W° Bound: | -99.18 | |
|---|---|---|
| E° Bound: | -97.12 | |
| N° Bound: | 26.8 | |
| S° Bound: | 25.83 | |
| Description |
Data extent for the Lower Rio Grande project in Texas. |
|
Extent Group 1 / Time Frame 1
| Time Frame Type: | Range |
|---|---|
| Start: | 2023-01-22 |
| End: | 2023-03-03 |
| Description: |
Dates of collection for the Lower Rio Grande project in Texas. |
Spatial Information
Spatial Representation
Representations Used
| Grid: | No |
|---|---|
| Vector: | Yes |
| Text / Table: | No |
| TIN: | No |
| Stereo Model: | No |
| Video: | No |
Reference Systems
Reference System 1
Coordinate Reference System |
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Reference System 2
Coordinate Reference System |
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Access Information
| Data License: | Universal (CC0 1.0) Public Domain Dedication |
|---|---|
| Data License URL: | https://creativecommons.org/publicdomain/zero/1.0/ |
| Data License Statement: |
To the extent possible under law, the U.S. Government has waived all copyright and related or neighboring rights to this dataset. |
| Security Class: | Unclassified |
| Data Access Procedure: |
Data is available online for bulk and custom downloads. |
| Data Access Constraints: |
None |
| Data Use Constraints: |
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. |
Distribution Information
Distribution 1
| Start Date: | 2025-06-23 |
|---|---|
| End Date: | Present |
| Download URL: | https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=10353 /details/10353 |
| Distributor: | NOAA Office for Coastal Management (NOAA/OCM) (2025 - Present) |
| File Name: | Customized Download |
| Description: |
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. |
| Distribution Format: | Not Applicable |
| Compression: | Zip |
Distribution 2
| Start Date: | 2024 |
|---|---|
| End Date: | Present |
| Download URL: | https://rockyweb.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/TX_LowerRioGrande_D22/ |
| Distributor: | U.S. Geological Survey (2024 - Present) |
| File Name: | Bulk Download |
| Description: |
Bulk download of data files in LAZ format, UTM Zone 14N NAD83(2011) meters coordinates and elevations in NAVD88(GEOID18) meters. Note that the vertical datum (hence elevations) of the files here are different than described in this document. They will be in an orthometric datum. |
| Distribution Format: | LAS/LAZ - LASer |
| Compression: | LAZ |
URLs
URL 1
| URL: | https://coast.noaa.gov/dataviewer/ |
|---|---|
| Name: | NOAA's Office for Coastal Management (OCM) Data Access Viewer (DAV) |
| URL Type: |
Online Resource
|
| File Resource Format: | HTML |
| Description: |
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. |
URL 2
| URL: | https://prd-tnm.s3.amazonaws.com/StagedProducts/Elevation/metadata/TX_LowerRioGrande_D22/USGS_TX_LowerRioGrande_D22_Project_Report.pdf |
|---|---|
| Name: | USGS Project Report |
| URL Type: |
Online Resource
|
| File Resource Format: | |
| Description: |
Link to the USGS Project Report that provides information about the project, vertical accuracy results, and the point classes and sensors used. |
URL 3
| URL: | https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/metadata/TX_LowerRioGrande_D22/ |
|---|---|
| Name: | USGS Additional Info |
| URL Type: |
Online Resource
|
| Description: |
Link to the reports, breaklines, metadata, and spatial metadata. |
URL 4
| URL: | https://prd-tnm.s3.amazonaws.com/StagedProducts/Elevation/metadata/TX_LowerRioGrande_D22/TX_LowerRioGrande_1_D22/reports/lidar_mapping_report/LidarMappingReport_TX_LowerRioGrande_D22_WU_300257.pdf |
|---|---|
| Name: | Lidar Report - TX_LowerRioGrande_1 |
| URL Type: |
Online Resource
|
| File Resource Format: | |
| Description: |
Link to the lidar report for the TX_LowerRioGrande_1. |
URL 5
| URL: | https://prd-tnm.s3.amazonaws.com/StagedProducts/Elevation/metadata/TX_LowerRioGrande_D22/TX_LowerRioGrande_2_D22/reports/lidar_mapping_report/LidarMappingReport_TX_LowerRioGrande_D22_WU_300360.pdf |
|---|---|
| Name: | Lidar Report - TX_LowerRioGrande_2 |
| URL Type: |
Online Resource
|
| File Resource Format: | |
| Description: |
Link to the lidar report for the TX_LowerRioGrande_2. |
URL 6
| URL: | https://prd-tnm.s3.amazonaws.com/StagedProducts/Elevation/metadata/TX_LowerRioGrande_D22/TX_LowerRioGrande_3_D22/reports/lidar_mapping_report/LidarMappingReport_TX_LowerRioGrande_D22_WU_300446.pdf |
|---|---|
| Name: | Lidar Report - TX_LowerRioGrande_3 |
| URL Type: |
Online Resource
|
| File Resource Format: | |
| Description: |
Link to the lidar report for the TX_LowerRioGrande_3. |
URL 7
| URL: | https://prd-tnm.s3.amazonaws.com/StagedProducts/Elevation/metadata/TX_LowerRioGrande_D22/TX_LowerRioGrande_4_D22/reports/lidar_mapping_report/LidarMappingReport_TX_LowerRioGrande_D22_WU_300469.pdf |
|---|---|
| Name: | Lidar Report - TX_LowerRioGrande_4 |
| URL Type: |
Online Resource
|
| File Resource Format: | |
| Description: |
Link to the lidar report for the TX_LowerRioGrande_4. |
URL 8
| URL: | https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_1_D22/ept.json |
|---|---|
| Name: | USGS Entwine Point Tile (EPT) - TX_LowerRioGrande_1 |
| URL Type: |
Online Resource
|
| File Resource Format: | json |
| Description: |
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. |
URL 9
| URL: | https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_2_D22/ept.json |
|---|---|
| Name: | USGS Entwine Point Tile (EPT) - TX_LowerRioGrande_2 |
| URL Type: |
Online Resource
|
| File Resource Format: | json |
| Description: |
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. |
URL 10
| URL: | https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_3_D22/ept.json |
|---|---|
| Name: | USGS Entwine Point Tile (EPT) - TX_LowerRioGrande_3 |
| URL Type: |
Online Resource
|
| File Resource Format: | json |
| Description: |
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. |
URL 11
| URL: | https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_4_D22/ept.json |
|---|---|
| Name: | USGS Entwine Point Tile (EPT) - TX_LowerRioGrande_4 |
| URL Type: |
Online Resource
|
| File Resource Format: | json |
| Description: |
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. |
URL 12
| URL: | https://usgs.entwine.io/data/view.html?r=[%22https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_1_D22/ept.json%22,%22https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_2_D22/ept.json%22,%22https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_3_D22/ept.json%22,%22https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_4_D22/ept.json%22] |
|---|---|
| Name: | 3D View (Potree USGS) |
| URL Type: |
Online Resource
|
| Description: |
Link to view the point cloud, using the Entwine Point Tile (EPT) format, in the 3D Potree viewer. |
Technical Environment
| Description: |
POSPac 8.7 PP-RTX; RiProcess 1.8.3; RiWorld 5.0.2; RiAnalyze 6.2; RiServer 1.99.5; Microstation CONNECT 10.00.00.25; TerraScan 017.039 and 016.013; TerraModeler 016.001; Lasedit 1.35.07; GeoCue 2014.1.21.5; ArcMap 10.6; Global Mapper 17.1.2; ERDAS Imagine 2016; PhotoShop CS8; Fugro proprietary software; Windows 10 64-bit Operating System \\fgaibrowns\lidar\00218030_USGS_TX_LowerRioGrande_D22\Lidar\04_Delivery\Block1\300034\300257\*\*\*.laz, *tif, *shp, *gdb 275 GB |
|---|
Data Quality
| Horizontal Positional Accuracy: |
This data was produced to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 20.2 (cm) RMSEx/RMSEy Horizontal Accuracy Class which equates to Positional Horizontal Accuracy = +/- 49.4 cm at 95% confidence level. |
|---|---|
| Vertical Positional Accuracy: |
This data was produced to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 10-cm RMSEz Vertical Accuracy Class. USGS Determined Vertical Accuracy: Non-Vegetated Vertical Accuracy (NVA) = 3.4 cm RMSE Vegetated Vertical Accuracy (VVA) = 18.40 cm at the 95th Percentile |
| Completeness Report: |
A complete iteration of processing (GNSS/IMU Processing, Raw Lidar Data Processing, and Verification of Coverage and Data Quality) was performed to ensure that the acquired data was complete, uncorrupted, and that the entire project area had been covered without gaps between flight lines. No void areas or missing data exist. The raw point cloud is of good quality and data passes Vertical Accuracy requirements. The Classified Point Cloud data files include all data points collected except the ones from Cross ties and Calibration lines. The points that have been removed or excluded are the points fall outside the project delivery boundary. Points are classified. A visual qualitative assessment was performed to ensure data completeness. No void areas or missing data exist. The classified point cloud is of good quality and data passes Vertical Accuracy requirements.
The Hydro Breakline cover the entire project delivery boundary. A visual qualitative assessment was performed to ensure data completeness. No void areas or missing data exist. The Hydro Breakline product is of good quality.
The DEM raster files cover the entire project delivery boundary. The pixels that fall outside the project delivery boundary are set to Void with a unique NODATA value. The value is identified in the file headers. There are no void pixels inside the project boundary. A visual qualitative assessment was performed to ensure data completeness. No void areas or missing data exist. The bare earth surface is of good quality and passes Vertical Accuracy requirements.
The Intensity Image files cover the entire project delivery boundary. A visual qualitative assessment was performed to ensure data completeness. No void areas or missing data exist. The Intensity Image product is of good quality.
The Vegetation Raster files cover the entire project delivery boundary. A visual qualitative assessment was performed to ensure data completeness. No void areas or missing data exist. The Vegetation Raster product is of good quality.
The Building Vector files cover the entire project delivery boundary. A visual qualitative assessment was performed to ensure data completeness. No void areas or missing data exist. The Building Vector product is of good quality. |
| Conceptual Consistency: |
Compliance with the accuracy standard was ensured by the collection of ground control and utilization of a web based GNSS correction service of a global network of tracking stations to compute corrections of satellite ephemeris, clock information and atmospheric models. Using this method, cm-level trajectory accuracy without the use of local base-stations. The following checks were performed: 1) The lidar data accuracy was validated by performing a full boresight adjustment and then checking it against the ground control prior to generating a digital terrain model (DTM) or other products. 2) Lidar elevation data was validated through an inspection of edge matching and visual inspection for quality (artifact removal). The following software was used for the validation: 1) RiProcess 1.8.3, RiWorld 5.0.2, RiAnalyze 6.2, RiServer 1.99.5; and 2) Fugro proprietary software |
Data Management
| Have Resources for Management of these Data Been Identified?: | Yes |
|---|---|
| Approximate Percentage of Budget for these Data Devoted to Data Management: | Unknown |
| Do these Data Comply with the Data Access Directive?: | Yes |
| Actual or Planned Long-Term Data Archive Location: | NCEI-NC |
| How Will the Data Be Protected from Accidental or Malicious Modification or Deletion Prior to Receipt by the Archive?: |
Data is backed up to cloud storage. |
Lineage
| Lineage Statement: |
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. |
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Sources
USGS AWS Entwine Point Tile (EPT) - TX_LowerRioGrande_1
| Contact Role Type: | Publisher |
|---|---|
| Contact Type: | Organization |
| Contact Name: | USGS |
| Citation URL: | https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_1_D22/ept.json |
| Citation URL Name: | Entwine Point Tile (EPT) for TX_LowerRioGrande_1 |
USGS AWS Entwine Point Tile (EPT) - TX_LowerRioGrande_2
| Contact Role Type: | Publisher |
|---|---|
| Contact Type: | Organization |
| Contact Name: | USGS |
| Citation URL: | https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_2_D22/ept.json |
| Citation URL Name: | Entwine Point Tile (EPT) for TX_LowerRioGrande_2 |
USGS AWS Entwine Point Tile (EPT) - TX_LowerRioGrande_3
| Contact Role Type: | Publisher |
|---|---|
| Contact Type: | Organization |
| Contact Name: | USGS |
| Citation URL: | https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_3_D22/ept.json |
| Citation URL Name: | Entwine Point Tile (EPT) for TX_LowerRioGrande_3 |
USGS AWS Entwine Point Tile (EPT) - TX_LowerRioGrande_4
| Contact Role Type: | Publisher |
|---|---|
| Contact Type: | Organization |
| Contact Name: | USGS |
| Citation URL: | https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_4_D22/ept.json |
| Citation URL Name: | Entwine Point Tile (EPT) for TX_LowerRioGrande_4 |
Process Steps
Process Step 1
| Description: |
Once boresighting was complete for the project, the project was first set up for automatic classification. The lidar data was cut to production tiles. The low noise points, high noise points and ground points were classified automatically in this process. Fugro utilized commercial software, as well as proprietary, in-house developed software for automatic filtering. The parameters used in the process were customized for each terrain type to obtain optimum results. Once the automated filtering was completed, the files were run through a visual inspection to ensure that the filtering was not too aggressive or not aggressive enough. In cases where the filtering was too aggressive and important terrain were filtered out, the data was either run through a different filter within local area or was corrected during the manual filtering process. Bridge deck points were classified as well during the interactive editing process. Interactive editing was completed in visualization software that provides manual and automatic point classification tools. Fugro utilized commercial and proprietary software for this process. All manually inspected tiles went through a peer review to ensure proper editing and consistency. After the manual editing and peer review, all tiles went through another final automated classification routine. This process ensures only the required classifications are used in the final product (all points classified into any temporary classes during manual editing will be re-classified into the project specified classifications). Once manual inspection, QC and final autofilter is complete for the lidar tiles, the LAS data was packaged to the project specified tiling scheme, clipped to project boundary and formatted to LAS v1.4. The file header was formatted to meet the project specification with File Source ID assigned. This Classified Point Cloud product was used for the generation of derived products. This product was delivered in fully compliant LAS v1.4, Point Record Format 6 with Adjusted Standard GPS Time at a precision sufficient to allow unique timestamps for each pulse. Correct and properly formatted georeference information as Open Geospatial Consortium (OGC) well known text (WKT) was assigned in all LAS file headers. Each tile has unique File Source ID assigned. The Point Source ID matches to the flight line ID in the flight trajectory files. Intensity values are included for each point, normalized to 16-bit. The following classifications are included: Class 1 – Processed, but unclassified; Class 2 – Bare earth ground; Class 3 – Low Vegetation (0 – 1 meter; automated classification); Class 4 – Medium Vegetation (1-3 meters; automated classification); Class 5 – High Vegetation (>3 meters; automated classificationClass 6 – Buildings (automated classification; Class 7 – Low Noise; Class 9 – Water; Class 17 – Bridge Decks; Class 18 - High Noise (high, manually identified, if necessary); Class 20 - Ignored ground (breakline proxiity). The classified point cloud data was delivered in tiles without overlap using the project tiling scheme. |
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| Process Date/Time: | 2023-04-06 00:00:00 |
Process Step 3
| Description: |
Original point clouds in LAS/LAZ format were restructured as Entwine Point Tiles and stored on Amazon Web Services. The data were re-projected horizontally to WGS84 web mercator (EPSG 3857) and the vertical units of NAVD88 (Geoid18) meters were retained. |
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| Process Contact: | U.S. Geological Survey |
Process Step 4
| Description: |
The NOAA Office for Coastal Management (OCM) created references to the Entwine Point Tiles (EPT) that were ingested into the NOAA Digital Coast Data Access Viewer (DAV). No changes were made to the data. The DAV will access the point cloud as it resides on Amazon Web Services (AWS) under the usgs-lidar-public container. These are the AWS URLs being accessed: https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_1_D22/ept.json https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_2_D22/ept.json https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_3_D22/ept.json https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_4_D22/ept.json |
|---|---|
| Process Date/Time: | 2025-06-23 00:00:00 |
| Process Contact: | NOAA Office for Coastal Management (NOAA/OCM) |
| Phone (Voice): | (843) 740-1202 |
| Email Address: | coastal.info@noaa.gov |
Catalog Details
| Catalog Item ID: | 76197 |
|---|---|
| GUID: | gov.noaa.nmfs.inport:76197 |
| Metadata Record Created By: | Rebecca Mataosky |
| Metadata Record Created: | 2025-06-23 17:57+0000 |
| Metadata Record Last Modified By: | Rebecca Mataosky |
| Metadata Record Last Modified: | 2025-06-23 20:16+0000 |
| Metadata Record Published: | 2025-06-23 |
| Owner Org: | OCMP |
| Metadata Publication Status: | Published Externally |
| Do Not Publish?: | N |
| Metadata Last Review Date: | 2025-06-23 |
| Metadata Review Frequency: | 1 Year |
| Metadata Next Review Date: | 2026-06-23 |