2008 MN DNR Lidar: Southeast Minnesota

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 2008 lidar acquisition for Southeast Minnesota.

The lidar data was captured using fixed wing aircraft equipped with lidar systems. The lidar system included a differential GPS unit and inertial measurement system to provide superior accuracy. Both AeroMetric, Inc. and Surdex Corporation acquired lidar data over the project area. Surdex's data was post-processed to a raw point cloud by Surdex and then delivered to AeroMetric to be merged into one raw point cloud dataset.

Acquisition parameters:

1. Scanners - Optech ALTM Gemini (AeroMetric) and Leica ALS50-2 (Surdex)

2. Flight Height - 2400 meters above mean terrain

3. Swath Width - 32 degrees

4. Sidelap - 60%

5. Nominal Post Spacing - 1.0 meter

GPS and IMU processing parameters:

1. Processing Programs - AeroMetric= Applanix - POSGPS and POSProc; Surdex= GravNav GNSS and Leica IPAS

2. Maximum baseline length - Not greater than 30km.

3. Number of base stations during lidar collection - A minimum of 2 MnDOT CORS stations were occupied during any of the lifts to acquire the lidar data. The following were the occupied base stations: BLUE, CLDN, DDGC, ELKT, EYTA, LCHI, LCRS, NALB, PRSP, REDW, RSHF, STWV, TWNL, WBSH, WINO, and WSCA.

4. GPS and IMU processing monitored for consistency and smoothness - Yes.

Point Cloud Processing:

1. Program - AeroMetric= Optech Dashmap; Surdex= Leica ALS Post Processor

2. Horizontal Datum - NAD83(NSRS2007)

3. Horizontal Coordinates - UTM, Zone 15, in meters

4. Vertical Datum - NAVD88

5. Geoid Model used to reduce satellite derived elevations to orthometric heights - NGS Geoid03.

Lidar Processing:

1. Processing Programs and versions - TerraSolid TerraScan (version 009.010), TerraModeler (version 009.002) and TerraMatch (version 009.003) and Intergraph MicroStation (version.08.01.02.15).

2. Point Cloud data was imported to TerraScan in a Microstation V8 (V) CAD environment.

3. The data is projected to the horizontal project coordinate system of UTM - Zone 15 in meters.

4. Analyzed the data for overall completeness and consistency. This was to ensure that there are no voids in the data collection.

5. Inspected for calibration errors in the dataset using the TerraMatch software. This was accomplished by sampling the data collected across all flight lines and classifying the individual lines to ground. The software used the ground-classified lines to compute corrections (Heading, Pitch, Roll, and Scale).

6. Orientation corrections (i.e., calibration corrections) were then applied to the entire dataset.

7. Automatic ground classification was performed using algorithms with customized parameters to best fit the project area. Several areas of varying relief and planimetric features were inspected to verify the final ground surface.

8. AeroMetric provided Quality Assurance and Quality Control (QA/QC) data for this project. AeroMetric captured 127 QA/QC points in multiple land cover categories that were used to test the accuracy of the lidar ground surface. TerraScan's Output Control Report (OCR) was used to compare the QA/QC data to the lidar data. This routine searches the lidar dataset by X and Y coordinate, finds the closest lidar point and compares the vertical (Z) values to the known data collected in the field. Based on the QA/QC data, a bias adjustment was determined, and the results were applied to the lidar data. A final OCR was performed with a resulting RMSE of 0.109 meters.

9. Once the automatic processing and the testing of lidar was complete, AeroMetric meticulously reviewed the generated bare-earth surface data to insure that proper classification was achieved as part of a Quality Control process.

10. Final deliverables were generated and cut out according to the MnDNR tiling and naming scheme (1/16 USGS 7.5 minute quadrangles).

Geodatabase Creation:

1. The final geodatabase for each of the 1544 tiles (1/16 USGS 7.5 minute quadrangles) was created using Esri ArcInfo software.

2. Each MicroStation contour file was converted into shapefile format. The contours were created using only the keypoint point features from the LAS data files.

3. The shapefile was inserted into the geodatabase as a feature class named 'Contours' inside the defined feature dataset 'Contour_Data'.

4. Topology checks were done to look for dangles, crossing and intersecting contours, as well as other anomalies. All errors were fixed and a second topology check was done to verify an error free dataset.

5. The contour features have two attributes, 'Contour_Type', which identifies the contour type (index, intermediate, depression, and depression_index) and 'ELEVATION' which indicates the contour elevation in U.S. feet.

6. The BareEarth data within the LAS point files was imported into a feature class named 'Bare_Earth_Points' inside the defined feature dataset 'Terrain_Data'. The points were created as multipoint features which group the LAS points into blocks of data in order to reduce the number of records in the geodatabase.

7. The breaklines were imported as polylines into a feature class named 'Hydro_Breaklines' inside the defined feature dataset 'Terrain_Data'.

8. The 1.0 meter DEM data was imported as raster points into the defined feature dataset 'DEM01'. Each point in the DEM file became a pixel with an assigned elevation in meters.

Final Deliverables:

1. One paper copy of the Lidar Accuracy Assessment Report.

2. One firewire hard drive containing the following data:

a. Point Cloud Data in LAS 1.1 format for each of the 1544 tiles (1/16 USGS 7.5 minute quadrangles). x=Easting (0.01 resolution), y=Northing (0.01 resolution), z=Elevation (0.01 resolution), i=Intensity (0.1 resolution) LAS data classified using the following codes: 0, 2, 5, 6, 8, 9, 10, 12 according to ASPRS LAS format classification table. Units in meters.

b. Geodatabase files for each of the 1544 tiles (1/16 USGS 7.5 minute quadrangles) including the following feature classes:

i. Bare_Earth_Points - LAS Point Cloud Data

ii. Hydro_Breaklines - Water/shoreline breakline data

iii. Hydro_Breaklines - Enhanced breakline data included, but was not limited to, retaining walls, road edges, ridge lines, dams for Project Area B only.

iv. DEM01 - Bare-Earth DEM raster at 1.0 meter resolution per tile. Vertical units in meters at 0.01 meter resolution.

v. Contours - Vector contours at 2 foot intervals represented in U.S. feet vertically and meters horizontally.

3. FGDC Compliant metadata for the Point Cloud Data LAS delivery.

4. FGDC Compliant metadata for the Geodatabase delivery.

MnDNR reviewed the deliverables for content and accuracy. Any needed corrections to the content of the dataset were addressed and corrected by AeroMetric and redelivered.

MnDNR created the 3-meter DEM using the following method:

- Step 1: Mosaic all the tiles that make up the county into a file geodatabase.

- Step 2: The 1-meter grid is then "filled" to fill in any NO DATA cells that have data around them. This is common when grid tiles are merged. The commmand that was used is the focalmean tool within the con tool in the Spatial Analyst | Map Algebra tool. The equation used is: con(isnull(dem01),focalmean dem01,rectangle,3,3,data),dem01). The DEM01 grid is then deleted and replaced with the output from this step.

- Step 3: The filled raster is then reduced to 3-meter resolution using the Spatial Analyst, Aggregate Tool. A 3x3 window of 1-meter cells are combined into one 3x3 meter cell and assigned a value based on the mean of the 9 cells.

The NOAA Office for Coastal Management (OCM) downloaded 1545 laz point data files from this USGS site:

https://rockyweb.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/MN_SEMN_2008/laz/

The data were in UTM Zone 15N NAD83 (NSRS2007), meters coordinates and NAVD88 (Geoid03) elevations in meters. The data were classified as: 0 - Never Classified, 2 - Ground, 5 - Vegetation, 6 - Buildings, 8 - Model Key Point, 9 - Water, 10 - Bridge Decks, 12 - Overlap. OCM processed all classifications of points to the Digital Coast Data Access Viewer (DAV). Classes available on the DAV are: 0,2,5,6,8,9,10,12.

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 Geoid03 model

b. Convert the laz files from UTM Zone 15N NAD83 (NSRS2007) meters coordinates to geographic coordinates

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