2011 MN DNR Lidar: Twin Cities Metro (Minneapolis/St. Paul & Maple Grove), MN

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.

Simple download of data files.

Link to view the point cloud (using the Entwine Point Tile (EPT) format) in the 3D Potree viewer.

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.

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.

This graphic shows the lidar coverage for the 2011 lidar acquisition for Minneapolis/St. Paul and Maple Grove, MN.

The vertical validation report from the MN DNR.

The vertical validation report from the MN DNR.

VENDOR PROCESSING STEPS:

(See map of block boundaries: ftp://lidar.dnr.state.mn.us/documentation/status/metro_data_delivery_dates.pdf )

1. Specifications for Blocks A-H (1.5 points/square meter):

The settings for the Leica sensor ALS50-II MPiA included acquisition at 6,600' AMT, 130 knots, pulse rate 99,500Hz, scan rate 27.28Hz, 40 degree field of view, 4,805ft swath width, maximum along track spacing (occurs at FOV edge) of 2.45m in overlap areas, maximum cross track spacing (occurs at Nadir) 1.24m, 3sigma post spacing of 1.4m and 3sigma point density of 0.65 points per square meter. This sensor was also equipped with IPAS inertial measuring unit (IMU) and a dual frequency airborne GPS receiver. These settings were used to meet or exceed the following accuracy specification in flat areas with minimal vegetation. 24.5cm ACCz, 95% (12.5cm RMSEz) 29.4cm ACCz, 95% (15.0cm RMSEz) 29.4cm ACCz 95% (15.0cm RMSEz).

2. Specifications for the Dakota Block (2 points/square meter):

The settings for the FLI-MAP sensor included acquisition at 2,700' AMT, 145 knots, 30% sidelap, 150kHz, 60% degree field of View, 3,116ft swath to attain an approximate 8 points per square meter. This sensor was also equipped with an inertial measuring unit (IMU) and a dual frequency airborne GPS receiver. These settings were used to meet or exceed the following accuracy specification in flat areas with minimal vegetation. 17.64cm ACCz 95% (9.0cm RMSEz) 24.5cm ACCz 95% (12.5cm RMSEz) 24.5cm ACCz 95% (12.5cm RMSEz).

3. Specifications for the Metro Block and the Maple Grove Block (8 points/square meter):

The settings for the FLI-MAP sensor included acquisition at 2,100' AMT, 130 knots, 60% sidelap, 200kHz, 60% degree field of View, 2,424ft swath to attain an approximate 8 points per square meter. This sensor was also equipped with an inertial measuring unit (IMU) and a dual frequency airborne GPS receiver. These settings were used to meet or exceed the following accuracy specification in flat areas with minimal vegetation 17.64cm ACCz 95% (9.0cm RMSEz) 24.5cm ACCz 95% (12.5cm RMSEz) 24.5cm ACCz 95% (12.5cm RMSEz).

The data set for each flight line was checked for project area coverage, data gaps between overlapping flight lines, and tension/compression areas (areas where data points are more or less dense that the average project specified post spacing). Using an iterative process that involves analyzing raster difference calculations the omega, phi, kappa angle corrections for the LiDAR instrument were determined. Corrections were applied to the LiDAR data set. Extensive comparisons were made of vertical and horizontal positional differences between points common to two or more LiDAR flight lines. An intensity raster for each flight line was generated and verified that intensity was recorded for each LiDAR point. LiDAR ground points were compared to independently surveyed and positioned ground control points in the project area. Based on the results of these comparisons, the LiDAR data was vertically biased to the ground.

PRE-PROCESSING STAGE LiDAR, GPS and IMU data are processed together using LiDAR processing software. The LiDAR data set for each flight line is checked for project area coverage and LiDAR post spacing is checked to ensure it meets project specifications. The LiDAR collected at the calibration area is used to correct the rotational, atmospheric, and vertical elevation differences that are inherent to LiDAR data. Intensity raster is generated to verify that intensity was recorded for each LiDAR point. LiDAR data is transformed to the specified project coordinate system. By utilizing the ground survey data collected at the calibration site and project area, the LiDAR data is vertically biased to the ground. Comparisons between the biased LiDAR data and ground survey data within the project area are evaluated and a final RMSE value is generated to ensure the data meets project specifications.

VENDOR DELIVERABLES Deliverables for the LiDAR are in UTM15N NAD83/HARN NAVD88 Geiod09 meters. Each geodatabase is named for its respective USGS quarter-quarter quad name; there is a tiling scheme feature class in the elevation_data file geodatabase. Every geodatabase contains a feature dataset called "terrain_data". The terrain_data feature dataset contains up to two feature classes: Bare_Earth_Points and Hydro_Breaklines (when applicable). The Bare-Earth_Points feature class is comprised of MultipointZ shapefiles extracted from the Ground and KeyPoint LAS files. Both the Bare_Earth_Points and Hydro-Breaklines have been clipped to the USGS quarter-quarter quad extent. Each geodatabase also contains a DEM. The DEM was created from a terrain using bare-earth multipoint PointZ and hydro-breaklines. The Terrain was then converted to a Raster DEM using a 1-meter cell-size, then clipped to an adjusted, quarter-quarter quad minimum-bounding rectangle and buffered an additional 50 meters. The naming convention for all DEMs is "DEM01". LAS files are clipped to the provided USGS quarter-quarter quad. For the higher density blocks (Dakota Block; Metro Block; Maple Grove Block), the vendor tiled the las files further, breaking each standard tile into 16 additional tiles. They are simply appended an A,B,C, or D starting in the upper left and proceeding in a clockwise direction. A sample image ( http://www.mngeo.state.mn.us/chouse/elevation/16tile_naming_convention.jpg ) taken from the tile index map that is on the FTP site, shows a single tile with the sub-block lettering scheme. A sample tile name would be: 4243-02-30_a_a.laz Water edges were created using proprietary processes to create an accurate 3D representation of water features. Further hands-on evaluations are performed to ensure compliance with USGS V13 regarding Hydro-Flattening. Once the waterbodies are finalized, LAS bare-earth points within the extents of the water polygons are classified to Class 9 (Water). Bare-earth points within 1-meter of the water polygons are re-classified to Class 10 (Ignored Class). ADDITIONAL PRODUCTS GENERATED BY MINNESOTA DNR STAFF: These products are in the geodatabase for each of the tiles: 1. Two-foot contours were created by resampling the 1-meter DEM to 3 meters, then smoothing the 3-meter grid using a neighborhood average routine, and then creating contours from this surface using standard ArcGIS processing tools. 2. Building outlines were created by extracting from the LAS files those points with Classification 6 (buildings), then grouping those points within 3 meters of each other into a single cluster and then creating an outline around those points. This was done using standard ArcMap tools. 3. Hillshades were created from the one- and three-meter DEMs using standard ArcMap tools. Azimuth value = 215, Altitude = 45, Z-Factor = 1

The NOAA Office for Coastal Management (OCM) downloaded 1776 laz point data files from these USGS sites:

https://rockyweb.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/USGS_LPC_MN_Phase4_Metro_Metro_2011_LAS_2016/laz/ (1536 laz files)

https://rockyweb.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/USGS_LPC_MN_Phase4_Metro_MapleGrove_2011_LAS_2016/laz/ (240 laz files)

The data were in UTM Zone 15N NAD83 (HARN), meters coordinates and NAVD88 (Geoid09) elevations in meters. The data were classified as: 1 - Unclassified, 2 - Ground, 3 - Low Vegetation, 4 - Medium Vegetation, 5 - High Vegetation, 6 - Buildings, 7 - Low Noise, 8 - Model Key Point, 9 - Water, 10 - Ignored Ground, 14 - Bridge Decks. OCM processed all classifications of points to the Digital Coast Data Access Viewer (DAV). Classes available on the DAV are: 1,2,3,4,5,6,7,8,9,10,14.

OCM performed the following processing on the data for Digital Coast storage and provisioning purposes:

1. Internal OCM scripts were run to check the number of points by classification and by flight ID and the gps, elevation, and intensity ranges.

2. Internal OCM scripts were run on the laz files to:

a. Convert from orthometric (NAVD88) elevations to NAD83 (2011) ellipsoid elevations using the Geoid09 model

b. Convert the laz files from UTM Zone 15N NAD83 (HARN) meters coordinates to geographic coordinates

c. Assign the geokeys, sort the data by gps time and zip the data to database.