2019 - 2020 USGS Lidar: NY FEMA Region 2 Central

Block 5 - Broome, Chemung, Chenango, Cortland, Delaware, Ontario, Otsego, Schuyler, Steuben, Sullivan, Tioga, Yates Counties covering approximately 6,198 square miles.

Dates of collection for Block 5

Block 4 - Albany, Greene, Hamilton and Schenectady Counties covering approximately 3,167 square miles.

Dates of collection for Block 4

Block 3 - Monroe, Ontario, Steuben, Onondaga, Cortland, Chenango, and Madison Counties covering approximately 2,806 square miles.

Dates of collection for Block 3

Block 2 - Lewis, Oneida, Herkimer, Fulton, and Montgomery Counties covering approximately 2,396 square miles.

Dates of collection for Block 2

Block 1 - Niagara, Putnam, and Westchester Counties covering approximately 1,367 square miles.

Dates of collection for Block 1

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, horizontal projection/datum of NAD83 (2011), Albers Equal Area, meters and vertical datum of NAVD88 (GEOID12B), meters.

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

Link to the USGS project report which provides information about the project, the vertical accuracy results, the point classifications and the sensors used.

Link to the Block 1 lidar report.

Link to the Block 2 lidar report.

Link to the Block 3 lidar report.

Link to the Block 4 lidar report.

Link to the Block 5 lidar report.

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 proprietary and commercial 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. 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. Please see the individual block lidar reports (links are in the URL section of this metadata record) for more detailed processing information.

LAS Point Classification: The point classification is performed as described below. The bare earth surface is then manually reviewed to ensure correct classification on the Class 2 (Ground) points. After the bare-earth surface is finalized, it is 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 flattening breaklines were then classified to water (ASPRS Class 9) using TerraScan macro functionality. A buffer of 0.7 meters was also used around each hydro-flattened feature to classify these ground (ASPRS Class 2) points to Ignored ground (ASPRS Class 20). 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 data was 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. The withheld bit was set on the withheld points previously identified in TerraScan before the ground classification routine was performed. The LAS files contain synthetic points. These points were generated by the Riegl processsing software to fill Multiple Time Around (MTA) zones, which are a physical phenomenon that exists in any time of flight lidar system which has multiple pulses in air and wishes to record all of them seamlessly without range gate limitations. The MTA zones only exist in narrow bands of ranges and typically are dependent on flight planning parameters and project area topography. Dewberry proprietary software was then used to create the deliverable industry-standard LAS files for both the All Point Cloud Data and the Bare Earth. Dewberry 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. Please refer to the individual block lidar reports (links to these reports are provided in the URL section of this metadata record) for more detailed processing information.

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 of NAVD88 (GEOID12B), meters,

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/NY_FEMAR2_Central_B1_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/NY_FEMAR2_Central_2_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/NY_FEMAR2_Central_3_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/NY_FEMAR2_Central_4_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/NY_FEMAR2_Central_5_2018/ept.json