76200 Data Set Published / External 49404 DEMs - partner (no harvest) Project Completed 2023 2024 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 GeoTIFF files hosted by USGS on Amazon Web Services. This high resolution lidar data will support the United States Geological Survey (USGS) initiatives. 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. DEM Raster File Type = GeoTIFF Bit Depth/Pixel Type = 32-bit float Raster Cell Size = 0.25 meter Interpolation or Resampling Technique = Triangulated Irregular Network (TIN) Network Required Vertical Accuracy = 19.6 cm NVA 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 > LAND SURFACE > TOPOGRAPHY > TERRAIN ELEVATION > DIGITAL ELEVATION/TERRAIN MODEL (DEM) 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 > TEXAS 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 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 NOAA, the Office for Coastal Management or its partners. USGS Data Steward 2025 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 2025 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 2024 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 2025 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 2025 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 -99.18 -97.12 26.8 25.83 Data extent for the Lower Rio Grande project in Texas. Range 2023-01-22 2023-03-03 Dates of collection for the Lower Rio Grande project in Texas. Yes No No No No Vertical EPSG:5703 NAVD88 height North American Vertical Datum 1988 1 Gravity-related height H metre up Projected EPSG:6343 NAD83(2011) / UTM zone 14N NAD83(2011) 1 Easting E metre east 2 Northing N metre north Custom Universal (CC0 1.0) Public Domain Dedication https://creativecommons.org/publicdomain/zero/1.0/ To the extent possible under law, the U.S. Government has waived all copyright and related or neighboring rights to this dataset. 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. https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=10354/details/10354 2025 Organization NOAA Office for Coastal Management Customized Download Create custom data files by choosing data area, map projection, file format, etc. A new metadata will be produced to reflect your request using this record as a base. Not Applicable Zip https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/TX_LowerRioGrande_D22/ 2024 Organization U.S. Geological Survey Bulk Download Bulk download of data files in GeoTIFF format, UTM Zone 14N NAD83(2011) meters coordinates and elevations in NAVD88(GEOID18) meters. GeoTIFF 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://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 Lidar Report - TX_LowerRioGrande_1 Online Resource pdf Link to the lidar report for the TX_LowerRioGrande_1. 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 Lidar Report - TX_LowerRioGrande_2 Online Resource pdf Link to the lidar report for the TX_LowerRioGrande_2. 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 Lidar Report - TX_LowerRioGrande_3 Online Resource pdf Link to the lidar report for the TX_LowerRioGrande_3. 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 Lidar Report - TX_LowerRioGrande_4 Online Resource pdf Link to the lidar report for the TX_LowerRioGrande_4. https://prd-tnm.s3.amazonaws.com/StagedProducts/Elevation/metadata/TX_LowerRioGrande_D22/USGS_TX_LowerRioGrande_D22_Project_Report.pdf USGS Project Report Online Resource pdf Link to the USGS Project Report that provides information about the project, vertical accuracy results, and the point classes and sensors used. https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/metadata/TX_LowerRioGrande_D22/ USGS Additional Info Online Resource Link to the reports, breaklines, metadata, and spatial metadata. https://s3-us-west-2.amazonaws.com/usgs-lidar-public/TX_LowerRioGrande_1_D22/ept.json USGS Entwine Point Tile (EPT) - TX_LowerRioGrande_1 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/TX_LowerRioGrande_2_D22/ept.json USGS Entwine Point Tile (EPT) - TX_LowerRioGrande_2 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/TX_LowerRioGrande_3_D22/ept.json USGS Entwine Point Tile (EPT) - TX_LowerRioGrande_3 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/TX_LowerRioGrande_4_D22/ept.json USGS Entwine Point Tile (EPT) - TX_LowerRioGrande_4 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/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] 3D View (Potree USGS) Online Resource Link to view the point cloud, using the Entwine Point Tile (EPT) format, in the 3D Potree viewer. 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 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. 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 DEM Vertical Accuracy: Non-Vegetated Vertical Accuracy (NVA) = 3.27 cm RMSE Vegetated Vertical Accuracy (VVA) = 21.80 cm at the 95th Percentile 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. 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 Yes Unknown Yes NCEI-NC Data is backed up to cloud storage. The NOAA Office for Coastal Management (OCM) ingested references to the USGS GeoTIFF files that are hosted on Amazon Web Services (AWS), into the Digital Coast Data Access Viewer (DAV). The DAV accesses the raster data as it resides on AWS. USGS GeoTIFF Files - TX_LowerRioGrande_1 Organization USGS Publisher https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/TX_LowerRioGrande_D22/TX_LowerRioGrande_1_D22/TIFF/ USGS GeoTIFF files for TX_LowerRioGrande_1 USGS GeoTIFF Files - TX_LowerRioGrande_2 Organization USGS Publisher https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/TX_LowerRioGrande_D22/TX_LowerRioGrande_2_D22/TIFF/ USGS GeoTIFF files for TX_LowerRioGrande_2 USGS GeoTIFF Files - TX_LowerRioGrande_3 Organization USGS Publisher https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/TX_LowerRioGrande_D22/TX_LowerRioGrande_3_D22/TIFF/ USGS GeoTIFF files for TX_LowerRioGrande_3 USGS GeoTIFF Files - TX_LowerRioGrande_4 Organization USGS Publisher https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/TX_LowerRioGrande_D22/TX_LowerRioGrande_4_D22/TIFF/ USGS GeoTIFF files for TX_LowerRioGrande_4 1 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 classification) Class 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 proximity). The classified point cloud data was delivered in tiles without overlap using the project tiling scheme. 2023-04-06T00:00:00 3 The bare earth DEM was generated using the lidar bare earth points and 3D hydro breaklines to a resolution of 0.25 meter. The bare earth points that fell within 1*NPS along the hydro breaklines (points in class 20) were excluded from the DEM generation process. This is analogous to the removal of mass points for the same reason in a traditional photogrammetrically compiled DTM. This process was done in batch using proprietary software. The technicians then used Fugro proprietary software for the production of the lidar-derived hydro flattened bare earth DEM surface in initial grid format at 2 foot GSD. Water bodies (inland ponds and lakes), inland streams and rivers, and island holes were hydro flattened within the DEM. Hydro flattening was applied to all water impoundments, natural or man-made, that are larger than approximately 2 acres in area and to all streams that are nominally wider than 100 feet. This process was done in batch. Once the initial, hydro flattened bare earth DEM was generated, the technicians checked the tiles to ensure that the grid spacing met specifications. The technicians also checked the surface to ensure proper hydro flattening. The entire data set was checked for complete project coverage. Once the data was checked, the tiles were then converted to industry-standard, GIS-compatible, 32-bit floating point raster format. Georeferenced information is included in the raster files. Void areas (i.e., areas outside the project boundary but within the tiling scheme) are coded using a unique NODATA value. 2023-07-17T00:00:00 4 The NOAA Office for Coastal Management (OCM) created references to the USGS GeoTIFF files that were ingested into the NOAA Digital Coast Data Access Viewer (DAV). No changes were made to the data. The DAV will access the raster data as it resides on Amazon Web Services (AWS). These are the GeoTIFF files that are being accessed: https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/TX_LowerRioGrande_D22/TX_LowerRioGrande_1_D22/TIFF/ https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/TX_LowerRioGrande_D22/TX_LowerRioGrande_2_D22/TIFF/ https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/TX_LowerRioGrande_D22/TX_LowerRioGrande_3_D22/TIFF/ https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/TX_LowerRioGrande_D22/TX_LowerRioGrande_4_D22/TIFF/ 2025-07-18T00:00:00 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 76197 Data Set Cross Reference gov.noaa.nmfs.inport:76200 Rebecca Mataosky 2025-06-23T20:50:15 Rebecca Mataosky 2025-07-18T21:53:48 2025-07-18 OCM Partners OCMP 1002 Public No No 2025-06-23 1 Year 2026-06-23