2023 USGS Lidar DEM: Lower Rio Grande, TX

Data extent for the Lower Rio Grande project in Texas.

Dates of collection for the Lower Rio Grande project in Texas.

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.

Bulk download of data files in GeoTIFF format, UTM Zone 14N NAD83(2011) meters coordinates and elevations in NAVD88(GEOID18) meters.

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.

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.

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.

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.

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.

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/