78379 Data Set Published / External 49401 Lidar - partner (no harvest) Project Completed 2020 2023 Project Description for the Original Data: The Goodhue County lidar project area covers approximately 941 square miles which includes a 100 meter buffer around the county boundary. The airborne lidar data was acquired at an aggregate nominal point density (ANPD) of 30 points per square meter. Project specifications are based on Goodhue County requirements and on the U.S. Geological Survey National Geospatial Program LiDAR Base Specification, Version 2.1. The data was developed based on a horizontal projection/datum of NAD83(HARN) Adj MN Goodhue Co (ftUS), and vertical datum of NAVD88 - Geoid12B (Feet). LiDAR data was acquired using a Riegl VQ 1560i sensor with serial number 4040 from April 08, 2020 to May 03, 2020 in 13 total lifts. Acquisition occurred with leaves absent from deciduous trees, when no snow was present on the ground, and with rivers at or below normal levels. 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 data, along with its derivatives, is the result of a countywide elevation mapping with cooperative partnerships from Goodhue County, Minnesota DOA, and the USGS 3DEP program. This data was produced from lidar data collected in May 2020, which was processed and delivered in 2021. Raster File Type = TIFF Bit Depth/Pixel Type = 32-bit float Raster Cell Size = 1 foot Interpolation or Resampling Technique = Triangulated Irregular Network Required Vertical Accuracy = 9.8 cm NVA Theme Global Change Master Directory (GCMD) Science Keywords EARTH SCIENCE 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 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 > MINNESOTA 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. Ayres Associates, 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 2023 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 -93.097105 -92.220453 44.721758 44.165595 Range 2020-04-08 2020-05-03 Dates of collection for Goodhue County Yes No No No No Vertical EPSG:6360 NAVD88 height (ftUS) North American Vertical Datum 1988 1 Gravity-related height H US survey foot up Custom 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=13709/details/13709 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/MN_GoodhueCounty_2020_A20/MN_GoodhueCo_1_2020/TIFF/ 2023 Organization U.S. Geological Survey Bulk Download Bulk download of data files in the original projection/datum of NAD83(HARN) Adj MN Goodhue Co (ftUS), and vertical datum of NAVD88 - Geoid12B (Feet). GeoTIFF https://prd-tnm.s3.amazonaws.com/StagedProducts/Elevation/metadata/MN_GoodhueCounty_2020_A20/MN_GoodhueCo_1_2020/reports/Processing%20Report%20Goodhue%20County%203DEP%20Lidar_Ayres.pdf Lidar Processing Report pdf Link to the Ayres lidar processing report. 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/MN_GoodhueCounty_2020_A20/MN_GoodhueCo_1_2020/reports/Collection%20Report%20Goodhue%20County%203DEP%20Lidar_Ayres.pdf Lidar Collection Report Online Resource pdf Link to the Ayres lidar collection report. https://prd-tnm.s3.amazonaws.com/StagedProducts/Elevation/metadata/MN_GoodhueCounty_2020_A20/USGS_MN_GoodhueCounty_2020_A20_ProjectReport.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/MN_GoodhueCounty_2020_A20/MN_GoodhueCo_1_2020/ USGS Additional Info Online Resource Link to the reports, breaklines, metadata, and spatial metadata. https://s3-us-west-2.amazonaws.com/usgs-lidar-public/MN_GoodhueCo_1_2020/ept.json USGS Entwine Point Tile (EPT) 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=https://s3-us-west-2.amazonaws.com/usgs-lidar-public/MN_GoodhueCo_1_2020/ept.json USGS 3D View Online Resource Link to view the point cloud, using the Entwine Point Tile (EPT) format, in the 3D Potree viewer. Terrasolid Not provided This data set was produced to meet ASPRS Positional Accuracy Standard for Digital Geospatial Data (2014) for a 10-cm RMSEz Vertical Accuracy Class. USGS Determined DEM Vertical Accuracy: Tested NVA RMSEz = 2.59 cm Tested 95th percentile value for VVA is : 13.62 cm For more information see the USGS project report and the lidar report. The links to these reports are provided in the URL section of this metadata record. Datasets contain complete coverage of tiles. No points have been removed or excluded. A visual qualitative assessment was performed to ensure data completeness. There are no void areas or missing data. The raw point cloud is of good quality and data passes Non-Vegetated Vertical Accuracy specifications. Data covers the entire area specified for this project. 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 AWS GeoTIFF Files - MN_GoodhueCo_1_2020 Organization USGS Publisher https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/MN_GoodhueCounty_2020_A20/MN_GoodhueCo_1_2020/TIFF/ USGS AWS GeoTIFF Files for MN_GoodhueCo_1_2020 1 The boresight for each lift was done individually as the solution may change slightly from lift to lift. The following steps describe the Raw Data Processing and Boresight process: 1) Technicians processed the raw data to LAS format flight lines using the final GPS/IMU solution. This LAS data set was used as source data for boresight. 2) Technicians first used RIEGL RiPROCESS software to calculate initial boresight adjustment angles based on sample areas selected in the lift. These areas cover calibration flight lines collected in the lift, cross tie and production flight lines. These areas are well distributed in the lift coverage and cover multiple terrain types that are necessary for boresight angle calculation. The technician then analyzed the results and made any necessary additional adjustment until it is acceptable for the selected areas. 3) Once the boresight angle calculation was completed for the selected areas, the adjusted settings were applied to all of the flight lines of the lift and checked for consistency. The technicians utilized commercial and proprietary software packages to analyze how well flight line overlaps match for the entire lift and adjusted as necessary until the results met the project specifications. 4) Once all lifts were completed with individual boresight adjustment, the technicians checked and corrected the vertical misalignment of all flight lines and also the matching between data and ground truth. The relative accuracy was less than or equal to 7 cm RMSEz within individual swaths and less than or equal to 10 cm RMSEz or within swath overlap (between adjacent swaths). 5) The technicians ran a final vertical accuracy check of the boresighted flight lines against the surveyed check points after the z correction to ensure the requirement of NVA = 9.8 cm 95% Confidence Level (Required Accuracy) was met. Point classification was performed according to USGS Lidar Base Specification 2.1, and breaklines were collected for water features. Bare earth DEMs were exported from the classified point cloud using collected breaklines for hydroflattening. 2020-06-23T00:00:00 2 LAS Point Cloud Classification: LiDAR data processing for the point cloud deliverable consists of classifying the LiDAR using a combination of automated classification and manual edit/reclassification processes. On most projects the automated classification routines will correctly classify 90-95 percent of the LiDAR points. The remaining 5-10 percent of the bare earth ground class must undergo manual edit and reclassification. Because the classified points serve as the foundation for the Terrain, DEM and breakline products, it is necessary for the QA/QC supervisor to review the completed point cloud deliverables prior to the production of any additional products. The following workflow steps are followed for automated LiDAR classification: 1. Lead technicians review the group of LiDAR tiles to determine which automated classification routines will achieve the best results. Factors such as vegetation density, cultural features, and terrain can affect the accuracy of the automated classification. The lead technicians have the ability to edit or tailor specific routines in order to accommodate the factors mentioned above, and achieve the best results and address errors. 2. Distributive processing is used to maximize the available hardware resources and speed up the automated processing as this is a resource-intensive process. 3. Once the results of the automated classification have been reviewed and passed consistent checks, the supervisor then approves the data tiles for manual classification. The following workflow steps are followed for manual edits of the LiDAR bare earth ground classification: 1. LiDAR technicians review each tile for errors made by the automated routines and correctly address errors any points that are in the wrong classification. By methodically panning through each tile, the technicians view the LiDAR points in profile, with a TIN surface, and as a point cloud. 2. Any ancillary data available, such as Google Earth, is used to identify any features that may not be identifiable as points so that the technician can make the determination to which classification the feature belongs. The QA/QC processes for the LiDAR processing phase consist of: 1. The lead technician reviews all automated classification results and adjust the macros as necessary to achieve the optimal efficiency. This is an iterative process, and the technician may need to make several adjustments to the macros, depending upon the complexity of the features in the area being processed.  During the manual editing process, the LiDAR technicians use a system of QA, whereby they check each other’s edits. This results in several benefits to the process:  There is a greater chance of catching minor blunders  It increases communication between technicians on technique and appearance  Solutions to problems are communicated efficiently  To ensure consistency across the project area, the supervisor reviews the data once the manual editing is complete. For this phase of a project, the following specifications are checked against: • Point cloud – all points must be classified according to the USGS classification standard for LAS. The all-return point cloud must be delivered in fully-compliant LAS version 1.4. • LAS files will use the Spatial Reference Framework according to project specification and all files shall be projected and defined. • General Point classifications:  Class 1. Processed, but unclassified  Class 2. Bare Earth  Class 5. High Vegetation  Class 6. Building  Class 7. Noise  Class 9. Water  Class 17. Bridge Decks  Class 18. High Noise  Class 20. Ignored ground (Breakline proximity)  Class 22. Temporaral Exclusion • Outliers, noise, blunders, duplicates, geometrically unreliable points near the extreme edge of the swath, and other points deemed unusable are to be identified using the "Withheld" flag. This applies primarily to points which are identified during pre-processing or 2020-06-23T00:00:00 3 LiDAR processing utilizes several software packages, including GeoCue and the TerraSolid suite of processing components. The GeoCue software is a database management system for housing the LiDAR dataset (usually multiple gigabytes in size). GeoCue incorporates a thorough checklist of processing steps and quality assurance/quality control (QA/QC) procedures that assist in the LiDAR workflow. The TerraSolid software suite is used to automate the initial classification of the LiDAR point cloud based on a set of predetermined parameters. Lidar technicians refer to ground cover research (natural and cultural features) within the project area and determine algorithms most suitable for the initial automated LiDAR classification. (Some algorithms/filters recognize the ground in forests well, while others have greater capability in urban areas). During this process each point is given an initial classification (e.g., as ground, vegetation, or noise) based on the point's coordinates and the relation to its neighbors. Classifications to be assigned include all those outlined by ASPRS standards. The initial classifications produce a coarse and inexact dataset, but offer an adequate starting point for the subsequent manual classification procedure. During this step, "overlap" points are automatically classified (those originating from neighboring flightlines) using information gathered from the ABGPS and IMU data. Any duplicate points existing from adjacent flightlines are removed during this process. Hydrographic breaklines are collected using LiDARgrammetry to ensure hydroflattened water surfaces. This process involves manipulating the LiDAR data's intensity information to create a metrically sound stereo environment. From this generated "imagery", breaklines are photogrammetrically compiled. Breakline polygons are created to represent open water bodies. The LiDAR points that fall within these areas are classified as "water." All hydrographic breaklines include a 3.125 foot buffer, with the Class 2 (bare earth) points being re-classified as Class 20 (ignored ground). TerraSolid is further used for the subsequent manual classification of the LiDAR points allowing technicians to view the point cloud in a number of ways to ensure accuracy and consistency of points and uniformity of point coverage. The TIN was processed to create a GRID or digital elevation model (DEM) with 2 foot pixels. 2020-06-23T00:00:00 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/MN_GoodhueCounty_2020_A20/MN_GoodhueCo_1_2020/TIFF/ 2025-10-08T00:00:00 Organization Office for Coastal Management OCM https://ror.org/05v14bq57 2234 South Hobson Avenue Charleston SC 29405-2413 https://www.coast.noaa.gov/ 72333 Data Set Cross Reference gov.noaa.nmfs.inport:78379 Rebecca Mataosky 2025-10-07T19:04:48 SysAdmin InPortAdmin 2026-03-04T19:17:17 2025-11-20 OCM Partners OCMP 1002 Public No No 2024-03-20 1 Year 2025-03-20