2016 - 2017 IGIC/USGS Lidar: Indiana Central 3 County

Bounding area of all three counties

Dates of collection for Marion County

Date of collection for Marion County

Dates of collection for Hamilton County

Dates of collection for Wayne County

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 for Hamilton County in LAZ format, Indiana State Plane East (NAD83 2011), US feet coordinates, orthometric heights in feet.

Bulk download of data files for Marion County in LAZ format, Indiana State Plane East (NAD83 2011), US feet coordinates, orthometric heights in feet.

Bulk download of data files for Wayne County in LAZ format, Indiana State Plane East (NAD83 HARN), US feet coordinates, orthometric heights 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.

Lidar report for Marion County.

Lidar report for Hamilton County.

Lidar report for Wayne County.

This link is to the reports, breaklines, metadata, and shapefiles.

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

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

Link to view the Wayne County 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.

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 the Entwine Point Tile file for Marion County

Link to the Entwine Point Tile file for Wayne County

Link to the Entwine Point Tile file for Hamilton County

For Hamilton County(1/1):

LAS Point Classification: The point classification was performed as described below. The bare earth surface was manually reviewed to ensure correct classification on the Class 2 (Ground) points. After the bare-earth surface was finalized, it was then used to generate all hydro-breaklines through heads-up digitization. All ground (ASPRS Class 2) LiDAR data inside of the Lake Pond and Double Line Drain hydro-flattened breaklines were then classified to Water (ASPRS Class 9) using TerraScan macro functionality. A buffer of 1 meter was also used around each hydro-flattened feature to classify these ground (ASPRS Class 2) points to Ignored ground (ASPRS Class 10). All Lake Pond Island and Double Line Drain Island features were checked to ensure that the ground (ASPRS Class 2) points were reclassified to the correct classification after the automated classification was completed. All overlap data was processed through automated functionality provided by TerraScan to classify the overlapping flight line data to approved classes by USGS. The overlap data was classified using standard LAS overlap bit. These classes were created through automated processes only and were not verified for classification accuracy. Due to software limitations within TerraScan, these classes were used to trip the withheld bit within various software packages. All data were manually reviewed and any remaining artifacts removed using functionality provided by TerraScan and TerraModeler. Global Mapper was used as a final check of the bare earth dataset. GeoCue was then used to create the deliverable industry-standard LAS files for both the All Point Cloud Data and the Bare Earth. Quantum Spatial, Inc. proprietary software was used to perform final statistical analysis of the classes in the LAS files, on a per tile level to verify final classification metrics and full LAS header information.

For Marion County(1/1):

1. At selected locations throughout the site, accurate GPS coordinates and elevations are surveyed and the points are marked with targets.

2. New LiDAR data is captured for the project area using a Leica ALS70 MPiA 500khz LiDAR sensor with integrated GPS/INS, mounted in a twin engine Piper Navajo aircraft.

3. The airborne GPS data is post-processed in Inertial Explorer 8.6 and TPOS 2.3, then combined with the IMU data in Novatel/Waypoint Inertial Explorer software to determine the LiDAR sensor's angle and orientation in the terrain (project) coordinate system and datums during the survey.

4. The post processed GPS/INS solution is applied to the raw lidar data to orient and project the data points into the project area reference system as an unclassified point cloud.

5. The georeferenced lidar data is then classified and edited in Terrasolid Terrascan software. Data is classified to produce: Class 1: unclassified points, Class 2: ground points, Class 7: low points, Class 9: water points, Class 10: ignored ground points, Class 17: bridge deck points, Class 18: high noise points, and overlap bit Class O1: overlap unclassified points, Class O2: overlap ground points.

6. The ground class of the processed lidar data is then compared to the ground control and elevation differences between the lidar surface and surveyed elevation are recorded in tabular form. Vertical accuracy statistics are then developed to produce vertical RMSE and overall accuracy estimates and reports.

7. After vertical accuracy evaluation the classified lidar data is exported as 5000 X 5000 foot tiles in the LAS format with any or all classes required to produce derivative products.

For Wayne County(1/2):

Base Stations: All LiDAR collected in conjunction with GPS CORS base stations occupying multiple points simultaneously. The guidelines specified in USGS publication: LiDAR Base Specification; Chapter 4 of Section B; U.S. Geological Survey Standards; Book 11, Collection and Delineation of Spatial Data were followed as much as possible in data collection and processing of data for primary project control stations. In addition, all other usable static GNSS data collected during this survey campaign were incorporated in the OPUS Projects solution. GPS CORS base stations were used for LiDAR ABGPS/IMU data processing. The primary project control positions derived from OPUS-Projects were used for the base stations in the LiDAR processing. Static GNSS data for most of the LiDAR calibration and truing (validation) points was processed using TBC (Trimble Business Center) radially from the primary project control stations. In areas where tree canopy prohibited collection of static GNSS data at validation points, a pair of points were set for use as conventional (total station) survey control points. The conventional data collected was processed into coordinates using TBC.

Calibration Procedures: A system calibration of LiDAR sensor and Inertial Measurement Unit (IMU) was performed before beginning data acquisition for project. This was done to correct for any misalignment errors between the sensor and the IMU, as well as any ranging errors within the sensor itself. Before collection of LiDAR, weather conditions were checked to reduce re-flights as a result of clouds, fog, or other issues, and GPS satellite constellation was checked to ensure reliable positional data. No data was collected if PDOP values were above 4. The LiDAR data was reviewed to verify that there was no gap in coverage, as well as verifying adequate point density and point spacing was maintained throughout the project area. After LiDAR data arrived from the field, and copied to the network, then the airborne was processed using Inertial Explorer. GNSS/IMU data was processed, differentially corrected, and its integrity verified. During GNSS/IMU processing several graphs were produced. The most important of these graphs were the detailed base station length, PDOP, and separation plots. These graphs were reviewed to determine data quality. Upon receiving survey data from the field the GNSS log sheets were be checked for accuracy, and the raw GNSS data was converted to RINEX and submitted to OPUS. The opus solution quality indicators were used to access the general data quality. If the station has NGS published positions and/or elevations, the OPUS results were compared to published values. Once, quality airborne solution was achieved, then LiDAR points are generated and flightlines calibrated together based on the system calibration in Leica CloudPro. Alignment, range, and intensity issues were also computed and corrected for in the process. CloudPro applied the projection, GPS timestamp, scan angle, roll compensation, and output the LiDAR data in LAS 1.2 format. Final calibration of the data to the surveyed ground control was done using TerraSolid's TerraMatch software. QA/QC of the calibrated dataset included the review of flight line matches through the use of distance's shading imagery generated in the GeoCue software and the inspection of control reports comparing the calibrated vertical position of the LiDAR point cloud to the surveyed ground control in the area. Ground truth validation was used to assess the data quality and consistency over sample areas of the project. To facilitate a confident evaluation, existing survey control was used to validate LiDAR data. Published survey control, where orthometric height (elevation) has been determined by precise differential leveling or GPS observation was deemed to be suitable.

For Wayne County (2/2):

Lidar Post-Processing: Calibrated and controlled lidar swaths were processed using automatic point classification routines in proprietary software. Routines operate against entire collection (all swaths, all lifts), eliminating character differences between files. Data was then distributed as virtual tiles to experienced lidar analysts for localized automatic classification, manual editing, hydro breakline extraction, and peer-based QC checks. Lakes and Ponds measuring 2 acres or larger, streams 100 feet or wider, were collected as 3D hydrographic breaklines. Points representing bare earth were classified to class 2. Ground points inside of water features were classified to class 9 to represent water and points ground points outside of hydro features but within 2 feet of hydro breaklines were classified to class 10. Points representing bridge decks were classified to class 17. Extreme high points were classified as high noise class 18. Extreme low points were classified as low noise class 7. All remaining points were classified to class 1. Supervisory QC monitoring of work in progress and completed editing ensured consistency of classification character and adherence to project requirements across the entire project. After completion of classification and final QC approval, all deliverable products were produced per SOW. Fully classified LAS data set - All lidar points classified were delivered in tiles based on 5000 x 5000 foot project tile index. Full tiles were given with all points outside of buffered working boundary classified to class 1. All points inside of buffer boundary were classified to one of the following classes: Unclassifed (1), Ground (2), Low Point (7), Water (9), Ignored Ground (10), Bridge Deck (17). High Noise (18). Class 1 was used for points not classified in the other classes. Class 2 was used for points that represent bare-earth. Class 7 was used for points that represent extreme low elevation. Class 9 was used to represent water. Class 10 was used to represent 2 foot buffer area around hydro breaklines to aid in DEM surface creation. Class 17 was used to represent points for bridge decks. Class 18 was used to classify points with extreme high elevations. No points were deleted from the LAS files. Final data set was formatted into Las Version 1.4 with TerraScan version 018.

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: Hamilton County (NAVD88 GEOID12B, feet), Marion County (NAVD88 GEOID12A, feet), Wayne County (NAVD88 GEOID96, feet)

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 AWS URLs being accessed:

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/USGS_LPC_IN_Central_Hamilton_2017_LAS_2019/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/USGS_LPC_IN_Central_MarionCo_2016_LAS_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/USGS_LPC_IN_Central_Wayne_2016_LAS_2019/ept.json