2004 Klamath Tribes Lidar DEM: Sprague River, OR

Collection Dates

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Bulk download of DEM files in GeoTIFF format, UTM Zone 10N (NAD83 HARN), meter coordinates, NAVD88 orthometric heights using Geoid03 in 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.

Data report as hosted on the Oregon Department of Geology and Mineral Industries' Oregon Lidar Commission.

Lidar Collection:

The LiDAR system was mounted in the belly of a Cessna Caravan 208. Quality control (QC) flights were performed based on manufacturer's specifications prior to the survey. The QC flight was conducted at the Ashland Airport using known surveyed control points. The positional accuracy of the LiDAR (x, y, z) returns were checked against these known locations to verify the calibration and to report base accuracy. The Optech 3100 system was set to a 50kHz laser repetition rate and flown at 1,000 meters AGL, capturing a 36 o scan width (18 o from NADIR). These settings yielded points with an average spacing of less than 0.68 meters (greater than 2 points per square meter). The entire area was surveyed with opposing flight line overlap of 30% to reduce laser shadowing and increase surface laser painting. While the system allows up to four range measurements per pulse, only the first and last returns were processed in the output.

The data stream from the IMU was stored independently during the flight, and was differentially corrected and integrated with LiDAR pulse data during post processing. Throughout the survey, two dual frequency DGPS Trimble 5700 base stations recorded fast static (1 Hz) data. Two stations were located at National Geodetic Survey (NGS) monuments in Chiloquin and Bly to minimize kinematic solution baselines and increase

GPS data accuracy.

Data Processing:

Laser point return coordinates were computed using the REALM software suite based on independent data from the LiDAR system (pulse time, scan angle), IMU (aircraft attitude), and aircraft position (differentially corrected and optimized using the multiple DGPS base stations data). The inertial measurement data were used to calculate the kinematic corrections for the aircraft trajectories using PosPAC v4.1. Flight lines and LiDAR data were reviewed to ensure complete coverage of the study area and positional accuracy of the laser points.

TerraScan Processing:

To facilitate laser point processing, the first step is to create bins (polygons) that divide the data set into manageable sizes. The entire buffered study area was divided into 465 individual bins with a maximum size of 2.25 km2, capturing all 111 flight lines (see Project Report).

Laser point returns (first and last) are assigned an associated (x, y, z) coordinate, along with unique intensity values. The raw LiDAR points were filtered for noise, pits and birds by screening for absolute elevation limits, isolated points and height above ground. These data have passed initial screening and are deemed accurate; however, ground modeling processing has not been completed on these laser points.

The TerraScan software suite is designed specifically for developing a standard bare earth model to remove buildings, vegetation, and other features. The initial bare earth model retains bridges and overpasses, and these artifacts are removed manually. The high point density and multiple returns result in uncomplicated identification of vegetated and obscured areas using first and last returns. The processing sequence begins by removing all points that are not near the earth based on evaluation of the multi-return layers. The resulting bare earth (ground) model is visually inspected and additional ground modeling is performed in site specific areas (over a 50 meter radius) to improve ground detail. This was only done in areas with known ground modeling deficiencies, such as: bedrock outcrops, cliffs, deeply incised stream banks, and dense vegetation.

Description of Processing Steps:

Units: Meters

Projection: UTM Zone 10, NAD83 NAVD88, Geoid03

1. Import point data into 465 bins

2. Perform relative accuracy testing.

3. Removing False LiDAR Points: False high and low points were removed by establishing thresholds for point removal that are above and below the known terrain elevations.

4. Calculate bare ground model from last return points, with the maximum building size of 100 m2 and maximum terrain angle of 88o . The challenge is to remove buildings and vegetation, but leave rock outcrops and cliffs.

Important: Water points are left in the bare earth model because it is unclear which points are water and which are mud flat, river bed, rocks, etc.

5. Manual removal of bridges and highway spans.

6. Generate TINs within all bins (including points 100 m outside) and export ASCII lattice files for first return and ground points.

7. No weeding or superfluous point removal was performed. The intent of a LiDAR survey is to accurately place points on targets, not remove points. If laser noise is low and internally consistent, aside from pits and birds, it assumed that the remaining laser returns are from targets within the survey area.

8. Create ESRI 1 meter GRIDs

9. Create ½ meter QTViewer GRIDs and point data.) before triangulation.

The NOAA Office for Coastal Management (OCM) received 19 bare earth DEM files in ArcGRID format from DOGAMI. The data were in UTM 10N (NAD 83 HARN), meters coordinates and NAVD88 (Geoid03) elevations in meters. The grid spacing was 1m.

OCM performed the following processing for Digital Coast storage and provisioning purposes:

1. Data were converted to GeoTiff format using GDAL 2.4.0 to comply with the open data policy