gov.noaa.nmfs.inport:76197 eng UTF8 dataset Elevation OCM Partners resourceProvider NOAA Office for Coastal Management (843) 740-1202 2234 South Hobson Ave Charleston SC 29405-2413 coastal.info@noaa.gov https://coast.noaa.gov WWW:LINK-1.0-http--link NOAA Office for Coastal Management Website NOAA Office for Coastal Management Home Page information pointOfContact 2026-04-30T00:00:00 ISO 19115-2 Geographic Information - Metadata Part 2 Extensions for imagery and gridded data ISO 19115-2:2009(E) EPSG::5703 EPSG::3857 2023 USGS Lidar: Lower Rio Grande, TX tx2023_lwr_rio_grd_m10353 2023 creation 2024 publication NOAA/NMFS/EDM 76197 https://www.fisheries.noaa.gov/inport/item/76197 WWW:LINK-1.0-http--link Full Metadata Record View the complete metadata record on InPort for more information about this dataset. information https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_4_D22/ept.json WWW:LINK-1.0-http--link USGS Entwine Point Tile (EPT) - 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. download https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_3_D22/ept.json WWW:LINK-1.0-http--link USGS Entwine Point Tile (EPT) - TX_LowerRioGrande_3 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. download https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_2_D22/ept.json WWW:LINK-1.0-http--link USGS Entwine Point Tile (EPT) - TX_LowerRioGrande_2 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. download https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_1_D22/ept.json WWW:LINK-1.0-http--link USGS Entwine Point Tile (EPT) - TX_LowerRioGrande_1 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. download https://prd-tnm.s3.amazonaws.com/StagedProducts/Elevation/metadata/TX_LowerRioGrande_D22/USGS_TX_LowerRioGrande_D22_Project_Report.pdf WWW:LINK-1.0-http--link USGS Project Report Link to the USGS Project Report that provides information about the project, vertical accuracy results, and the point classes and sensors used. download 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 WWW:LINK-1.0-http--link Lidar Report - TX_LowerRioGrande_1 Link to the lidar report for the TX_LowerRioGrande_1. download 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 WWW:LINK-1.0-http--link Lidar Report - TX_LowerRioGrande_2 Link to the lidar report for the TX_LowerRioGrande_2. download 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 WWW:LINK-1.0-http--link Lidar Report - TX_LowerRioGrande_3 Link to the lidar report for the TX_LowerRioGrande_3. download 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 WWW:LINK-1.0-http--link Lidar Report - TX_LowerRioGrande_4 Link to the lidar report for the TX_LowerRioGrande_4. download https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/metadata/TX_LowerRioGrande_D22/ WWW:LINK-1.0-http--link USGS Additional Info Link to the reports, breaklines, metadata, and spatial metadata. download 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] WWW:LINK-1.0-http--link 3D View (Potree USGS) Link to view the point cloud, using the Entwine Point Tile (EPT) format, in the 3D Potree viewer. download https://coast.noaa.gov/dataviewer/ WWW:LINK-1.0-http--link NOAA's Office for Coastal Management (OCM) Data Access Viewer (DAV) 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. download 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. This high resolution lidar data will support the United States Geological Survey (USGS) initiatives. USGS completed NOAA Office for Coastal Management (843) 740-1202 2234 South Hobson Ave Charleston SC 29405-2413 coastal.info@noaa.gov https://coast.noaa.gov WWW:LINK-1.0-http--link NOAA Office for Coastal Management Website NOAA Office for Coastal Management Home Page information pointOfContact NOAA Office for Coastal Management (843) 740-1202 2234 South Hobson Ave Charleston SC 29405-2413 coastal.info@noaa.gov https://coast.noaa.gov WWW:LINK-1.0-http--link NOAA Office for Coastal Management Website NOAA Office for Coastal Management Home Page information custodian notPlanned EARTH SCIENCE > LAND SURFACE > TOPOGRAPHY > TERRAIN ELEVATION EARTH SCIENCE > OCEANS > COASTAL PROCESSES > COASTAL ELEVATION theme Global Change Master Directory (GCMD) Science Keywords 23.3 CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > TEXAS VERTICAL LOCATION > LAND SURFACE place Global Change Master Directory (GCMD) Location Keywords 23.3 LIDAR > Light Detection and Ranging instrument Global Change Master Directory (GCMD) Instrument Keywords 23.3 Airplane > Airplane platform Global Change Master Directory (GCMD) Platform Keywords 23.3 Lidar - partner (no harvest) project InPort otherRestrictions Cite As: OCM Partners, [Date of Access]: 2023 USGS Lidar: Lower Rio Grande, TX [Data Date Range], https://www.fisheries.noaa.gov/inport/item/76197. NOAA provides no warranty, nor accepts any liability occurring from any incomplete, incorrect, or misleading data, or from any incorrect, incomplete, or misleading use of the data. It is the responsibility of the user to determine whether or not the data is suitable for the intended purpose. license To the extent possible under law, the U.S. Government has waived all copyright and related or neighboring rights to this dataset. otherRestrictions Access Constraints: None otherRestrictions 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. otherRestrictions 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. unclassified 2023 USGS Lidar DEM: Lower Rio Grande, TX NOAA/NMFS/EDM 76200 crossReference vector eng; US elevation 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 -99.18 -97.12 25.83 26.8 Data extent for the Lower Rio Grande project in Texas. Dates of collection for the Lower Rio Grande project in Texas. | Currentness: Ground Condition 2023-01-22 2023-03-03 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) " } } } Zip LAS/LAZ - LASer LAZ NOAA Office for Coastal Management (843) 740-1202 2234 South Hobson Ave Charleston SC 29405-2413 coastal.info@noaa.gov https://coast.noaa.gov WWW:LINK-1.0-http--link NOAA Office for Coastal Management Website NOAA Office for Coastal Management Home Page information distributor U.S. Geological Survey 12201 Sunrise Valley Drive Reston VA 20191 USA https://usgs.gov WWW:LINK-1.0-http--link USGS Home Home page for USGS information distributor https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=10353 /details/10353 WWW:LINK-1.0-http--link 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. download https://rockyweb.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/TX_LowerRioGrande_D22/ WWW:LINK-1.0-http--link Bulk Download 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. download dataset 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 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. 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. 2023-04-06T00:00:00 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. U.S. Geological Survey processor 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 2025-06-23T00:00:00 NOAA Office for Coastal Management (843) 740-1202 coastal.info@noaa.gov processor USGS AWS Entwine Point Tile (EPT) - TX_LowerRioGrande_1 USGS https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_1_D22/ept.json WWW:LINK-1.0-http--link Entwine Point Tile (EPT) for TX_LowerRioGrande_1 Source Citation URL information publisher USGS AWS Entwine Point Tile (EPT) - TX_LowerRioGrande_2 USGS https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_2_D22/ept.json WWW:LINK-1.0-http--link Entwine Point Tile (EPT) for TX_LowerRioGrande_2 Source Citation URL information publisher USGS AWS Entwine Point Tile (EPT) - TX_LowerRioGrande_3 USGS https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_3_D22/ept.json WWW:LINK-1.0-http--link Entwine Point Tile (EPT) for TX_LowerRioGrande_3 Source Citation URL information publisher USGS AWS Entwine Point Tile (EPT) - TX_LowerRioGrande_4 USGS https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_4_D22/ept.json WWW:LINK-1.0-http--link Entwine Point Tile (EPT) for TX_LowerRioGrande_4 Source Citation URL information publisher