2019 WROC Lidar: WI Counties (Adams, Jefferson, Lafayette, Monroe, Pepin)

WI_Adams_2019 (Work Unit 177786)

Date of collection for WI_Adams_2019 (Work Unit 177786)

Date of collection for WI_Adams_2019 (Work Unit 177786)

WI_Monroe_2019 (Work Unit 177789)

Date of collection for WI_Monroe_2019 (Work Unit 177789)

Date of collection for WI_Monroe_2019 (Work Unit 177789)

Date of collection for WI_Monroe_2019 (Work Unit 177789)

WI_Pepin_2019 (Work Unit 177780)

Date of collection for WI_Pepin_2019 (Work Unit 177780)

WI_Lafayette_2019 (Work Unit 177783)

Date of collection for WI_Lafayette_2019 (Work Unit 177783)

WI_Jefferson_2019 (Work Unit 173957)

Dates of collection for WI_Jefferson_2019 (Work Unit 173957)

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.

Bulk download of data files in LAZ format, WISCRS NAD83(2011), US survey feet coordinates and NAVD88 (Geoid12B) elevations in feet.

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, the point classes and sensors used.

Link to the reports, breaklines, metadata, and spatial metadata.

Link to the Ayres lidar report for WI_Adams_2019

Link to the Ayres lidar report for WI_Jefferson_2019

Link to the Ayres lidar report for WI_Lafayette_2019

Link to the Ayres lidar report for WI_Monroe_2019

Link to the Ayres lidar report for WI_Pepin_2019

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.

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.

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 Leica LMS 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 = 19.6 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.

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)

• 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 r

Original point clouds in LAS/LAZ format were restructured as Entwine Point Tiles and stored on Amazon Web Services. The data were re-projected horizontally to WGS84 web mercator (EPSG 3857) and no changes were made to the vertical elevations in NAVD88 (GEOID12B).

The NOAA Office for Coastal Management (OCM) created references to the Entwine Point Tile (EPT) 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 point cloud as it resides on Amazon Web Services (AWS) under the usgs-lidar-public container.

These are the URLs being accessed:

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/WI_Adams_2019/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/WI_Jefferson_2019/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/WI_Lafayette_2019/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/WI_Monroe_2019/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/WI_Pepin_2019/ept.json