Data Management Plan

This Southeastern Wisconsin Regional Planning Commission (SEWRPC) high density lidar project encompassed the entirety of Milwaukee County, Wisconsin, which covers approximately 250 square miles and includes a 333 foot 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 SEWRPC requirements. The data was developed and delivered in State Plane Coordinates, Wisconsin South zone, with a horizontal datum of NAD83(2011) and vertical datum of NAVD88 - Geoid12B, with horizontal and vertical units in US Survey Feet. LiDAR data was acquired using a Riegl VQ 1560i sensor, serial number 4040, from April 2, 2020 to April 3, 2020 in three total lifts. Lidar acquisition occurred with leaves absent from deciduous trees, when no snow was present on the ground and with rivers at or below normal levels. The lidar data was calibrated, processed, and delivered in 2020. The data deliverables include classified point cloud, breaklines, and a digital elevation model. The lidar data is not to be used for purposes other than those outlined by SEWRPC for its partners and stakeholders on this project.

This metadata record supports the data entry in the NOAA Digital Coast Data Access Viewer (DAV).

The NOAA Office for Coastal Management (OCM) received las point data files from the Milwaukee County Land Information Office. The data were processed to the NOAA Digital Coast Data Access Viewer (DAV) to make the data available for bulk and custom downloads. In addition to these lidar point data, the bare earth Digital Elevation Models (DEM) created from the lidar point data are also available. These data are available for custom download at the link provided in the URL section of this metadata record.

Lineage Statement:
Data were collected for the Southeastern Wisconsin Regional Planning Commission (SEWRPC) and were provided to the NOAA Office for Coastal Management (OCM) by the Milwaukee County Land Information Office. OCM processed the data to make it available for custom download from the NOAA Digital Coast Data Access Viewer (DAV) and for bulk download from S3 AWS.

Process Steps:

• 2020-10-05 00:00:00 - 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 2 cm RMSEz within individual swaths and less than or equal to 5 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 1.3, and breaklines were collected for water features. Bare earth DEMs were exported from the classified point cloud using collected breaklines for hydroflattening.
• 2021-01-14 00:00:00 - Steps followed by Ayres for 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 7. Noise  Class 9. Water  Class 17. Bridge Decks  Class 18. High Noise  Class 20. Ignored ground (Breakline proximity) • 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 through automated post-processing routines. Subsequently ide
• 2023-06-01 00:00:00 - The NOAA Office for Coastal Management (OCM) received 1204 las point data files from the Milwaukee County Land Information Office. The data were in Wisconsin South State Plane (NAD83 2011), US Survey Feet coordinates and NAVD88 (Geoid12b) elevations in US Survey Feet. The data were classified as: 1 - Unclassified, 2 - Ground, 7 - Low Noise, 9 - Water, 17 - Bridge Decks, 18 - High Noise, 20 - Ignored Ground. OCM processed all classifications of points to the Digital Coast Data Access Viewer (DAV). Classes available on the DAV are: 1, 2, 7, 9, 17, 18, 20. 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 las files to: a. Convert from orthometric (NAVD88) elevations to NAD83 (2011) ellipsoid elevations using the Geoid12b model b. Convert the las files from Wisconsin South State Plane (NAD83 2011), US Survey Feet coordinates to geographic coordinates c. Convert from elevations in feet to meters. d. Assign the geokeys, sort the data by gps time and zip the data to database.

Data is available online for bulk and custom downloads.

Data is backed up to tape and to cloud storage.