2015 PA DEP Lidar: Lake Erie Watershed, PA

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, geographic coordinates, orthometric heights. Note that the vertical datum (hence elevations) of the files here are different than described in this document. They will be in an orthometric datum.

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 displays the footprint for this lidar data set.

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.

Link to custom download, from the Data Access Viewer (DAV), the raster Digital Elevation Model (DEM) data that were created from this lidar data set.

Link to the Woolpert lidar report.

Link to the hydro breaklines.

Using a Leica ALS70 (lidar) systems on board Woolpert aircraft , high density data, at a nominal pulse spacing (NPS) of 0.7 meters, were collected for this task order (approximately 512 square miles). Leica Specs- AGL = 6,500 ft - Aircraft Speed = 150 Knots, Field of View (Full) = 40 degrees, Pulse Rate = 272 kHz, Scan Rate = 41 Hz, with an average side lap of 25 %. Multiple returns were recorded for each laser pulse along with an intensity value for each return. Three (3) missions were flown between April 29, 2015 and May 7, 2015. One (1) Global Navigation Satellite System (GNSS) Base Station was used in support of the lidar data acquisition. Specific information regarding latitude, longitude, and ellipsoid height to the L1 phase center is included in the lidar processing report. The geoid used to reduce satellite derived elevations to orthometric heights was GEOID09. Data for the task order is referenced to the to NAD 83 (2007) Pennsylvania State Plane North, NAVD88, US Survey Feet. Once the data acquisition and GPS processing phases are complete, the lidar data was processed immediately to verify the coverage had no voids. The GPS and IMU data was post processed using differential and Kalman filter algorithms to derive a best estimate of trajectory. The quality of the solution was verified to be consistent with the accuracy requirements of the project. The SBET was used to reduce the lidar slant range measurements to a raw reflective surface for each flight line. The coverage was classified to extract a bare earth digital elevation model (DEM) and separate last returns. The ALS70 calibration and system performance is verified on a periodic basis using Woolpert's calibration range. The calibration range consists of a large building and runway. The edges of the building and control points along the runway have been located using conventional survey methods. Inertial measurement unit (IMU) misalignment angles and horizontal accuracy are calculated by comparing the position of the building edges between opposing flight lines. The scanner scale factor and vertical accuracy is calculated through comparison of lidar data against control points along the runway. Field calibration is performed on all flight lines to refine the IMU misalignment angles. IMU misalignment angles are calculated from the relative displacement of features within the overlap region of adjacent (and opposing) flight lines. The raw lidar data is reduced using the refined misalignment angles.

For this survey, Woolpert field crews utilized two (2) Woolpert-owned, Trimble Navigation R8 Model 3 series dual frequency GPS receivers, and one (1) Woolpert-owned, Trimble Navigation R8 Model 2 series dual frequency GPS receiver. The field crew utilized Real-Time Kinematic (RTK) GPS surveying throughout the ground control data collection process. Using RTK GPS techniques, observations were performed on LiDAR quality control points. The survey was conducted using a 1-second epoch rate, in a fixed solution RTK mode, with each observation lasting 180 seconds. Each station was occupied twice to ensure the necessary horizontal and vertical accuracies were being met for this project.

The individual flight lines were inspected to ensure the systematic and residual errors have been identified and removed. Then, the flight lines were compared to adjacent flight lines for any mismatches to obtain a homogenous coverage throughout the project area. The point cloud underwent a classification process to determine bare-earth points and non-ground points utilizing "first and only" as well as "last of many" lidar returns. This process determined Default points (Class 1), Ground points (Class 2), Low vegetation (Class 3), Medium vegetation (Class 4), High vegetation (Class 5), Building (Class 6), Noise (Class 7), Water (Class 9), Ignored Ground (Class 10), Bridge deck (Class 13), Overlap Default (Class 17), Overlap Ground (Class 18). The bare-earth (Class 2 - Ground) lidar points underwent a manual QA/QC step to verify the quality of the DEM as well as a peer-based QC review. This included a review of the DEM surface to remove artifacts and ensure topographic quality. Classification of water (class 9) and ignored ground (class 10) was completed via the use of the hydrologic breaklines collected for the hydro-flattening phase. The overlap classes were determined by first identifying the overlapping areas and reclassifying the LAS data by offset from a corridor. This allows the returns located on the edge of the swath to be removed from the bare earth coverage in an effort to produce a more uniform data density. The surveyed ground control points are used to make vertical adjustments to the data set and to perform the accuracy checks and statistical analysis of the lidar dataset. Supervisory QC monitoring of work in progress and completed editing ensured consistency of classification character and adherence to project requirements across the entire project area. The resulting deliverables for this task order consist of classified LAS file in LAS 1.2 format, 2.5 ft pixel size DEM files in ERDAS IMG format and Hydrologic Breakline data in ESRI shape file format. The method used to create the DEM consisted of isolating the ground (class 2) points and introducing 3-d breakline data derived from delineating hydrologic features and applying USGS v1.0 hydro flattening specification z-values. This combined dataset was then processed into a gridded format for additional QAQC to ensure DEM quality. Following QAQC process completion, a gridded raster file was created on a per tile basis. The task order projection information was applied to the final raster dataset. Prior to delivery, all raster files were visually reviewed to ensure coverage and reviewed using automated processes to ensure proper formatting.

The compilation procedure included use of lidar intensity, bare earth surface model, point cloud data, and open source imagery in an effort to manually compile hydrologic features in a 2-d environment. Following the compilation phase, a separate process was used to adjust the breakline data to best match the water level at the time of the lidar collection. Any ponds and/or lakes were adjusted to be at or just below the bank and to be at a constant elevation. Any streams were adjusted to be at or just below the bank and to be monotonic. Manual QAQC and peer-based QC review was performed on all delineated data to ensure horizontal placement quality and on all adjusted data to ensure vertical placement quality. The final hydrologic breakline product was delivered in ESRI shape file format and was also used in the processing of the DEM deliverable.

Tile Size: 2,500ft x 2,500ft. The tile file name was derived from the southwest corner of each tile. Tile index and extent are provided in shape file format.

The NOAA Office for Coastal Management (OCM) downloaded 2606 las point data files from this PASDA site:

https://www.pasda.psu.edu/download/seagrant/2015/LiDAR/las/

The data were in Pennsylvania State Plane North (NAD83 HARN), US survey feet coordinates and NAVD88 (Geoid09) elevations in feet. The data were classified as: 1 - Unclassified, 2 - Ground, 3 - Low Vegetation, 4 - Medium Vegetation, 5 - High Vegetation, 6 - Buildings, 7 - Low Noise, 9 - Water, 10 - Ignored Ground,13 - Bridge Deck, 17 - Overlap Default, 18 - Overlap Ground. OCM processed all classifications of points to the Digital Coast Data Access Viewer (DAV). Points that were classified as 13 were re-classified to Class 14. Classes available on the DAV are: 1, 2, 3, 4, 5, 6, 7, 9, 10, 14, 17, 18.

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 Geoid09 model

b. Convert from Pennsylvania State Plane North (NAD83 HARN), US survey feet coordinates to geographic coordinates

c. Re-classify points that were class 13 to class 14

d. Convert the elevations from feet to meters.

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