20230906
11395300
1.0
FGDC CSDGM Metadata
FALSE
Headworks.SDE.HydrolicStreamNetwork
214.807
1386118.735893
1524244.398819
44244.051000
131402.368212
1
002
Projected
GCS_North_American_1983_HARN
Linear Unit: Foot_US (0.304801)
NAD_1983_HARN_StatePlane_Washington_North_FIPS_4601_Feet
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20210521
13074000
20210521
13074000
150000000
5000
FGDC
Quantum Spatial
1100 NE Circle Blvd, Ste. 126
Corvallis
OR
97330
US
541-752-1204
http://www.quantumspatial.com
Quantum Spatial
Quantum Spatial
20210521
1
Enterprise Geodatabase Feature Class
Abigail Gleason
Washington Department of Natural Resources
1111 Washington St. SE MS 47007
Olympia
WA
98504
Abigail.Gleason@dnr.wa.gov
US
253-441-4860
214.807
vector digital data
2019-03-22T00:00:00
Quantum Spatial
1100 NE Circle Blvd, Ste. 126
Corvallis
OR
97330
US
541-752-1204
http://www.quantumspatial.com
Quantum Spatial
Headworks.SDE.HydrolicStreamNetwork
Green River Watershed LiDAR
2017-12-10
2018-11-12
Provide support for high resolution terrain elevation data from the Green River Watershed dataset.
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>This shapefile represents the stream network for the Green River Watershed. Data was processed in reference to NAD83 (CORS96), however the horizontal datum for this dataset is defined as NAD83 (HARN) as the difference is generally small and allows for greater ease of use with data in different datums. The vertical datum is NAVD88, Geoid 12B, and the data is projected in Washington State Plane North. Units are in US survey foot. Quantum Spatial collected the Green River Watershed LiDAR data for Washington Department of Natural Resources between 12/10/17 and 11/12/18.</SPAN></P></DIV></DIV></DIV>
Washington Department of Natural Resources
Light Detection and Ranging
LiDAR
Boundary
Shapefile
Pierce County
Washington
Lester
Howard A. Hanson Reservoir
Eagle Lake
hydroenforcement
stream network
Light Detection and Ranging, LiDAR, Shapefile, hydroenforcement
Pierce County
Washington
Lester, Howard A. Hanson Reservoir, Eagle Lake
This data is projected in Washington State Plane North
Abigail Gleason
Washington Department of Natural Resources
1111 Washington St. SE MS 47007
Olympia
WA
98504
Abigail.Gleason@dnr.wa.gov
US
253-441-4860
Please contact Washington Department of Natural Resources for information regarding the use of this data.
Esri ArcGIS 12.7.3.26828
1
-121.859279
-121.299912
47.359261
47.116665
LiDAR data has been collected and processed for all areas within the project study area.
Flight plans are designed with sufficient sidelap to ensure there are no gaps between flightlines. Shaded relief images have been visually inspected for gaps.
Shaded relief images have been visually inspected for data errors such as pits, border artifacts, and shifting. LiDAR flightlines have been examined to ensure consistent elevation values across overlapping flightlines. The Root Mean Square Error (RMSE) of line to line relative accuracy for this dataset is 0.180 ft (0.055 m). Please see the LiDAR data report for a discussion of the statistics related to this dataset.
Data was examined at a 1:2000 scale. Relative accuracy of the flightlines was assessed in Microstation using TerraMatch.
RMSE
ft_us
0.180 ft (0.055 m)
The Non-vegetated Vertical Accuracy (NVA) of this dataset, tested at 95% confidence level is 0.313 ft (0.095 m). Please see the LiDAR data report for a discussion of the statistics related to this dataset.
Non-vegetated Vertical Accuracy was assessed using 8 ground check points. These check points were not used in the calibration or post processing of the LiDAR point cloud data.
NVA
ft_us
0.313 ft (0.095 m)
The stream network for the Green River Watershed site was generated from the LiDAR-derived bare earth DEMs using ArcHydro 2.0. An initial network was generated by filling all sinks (depressions) in the model and identifying all paths of flow with an accumulation threshold of at least 2.5 acres. This initial stream network was inspected for artificial obstructions to the flow (e.g., culverts beneath roads that allow flow but are not reflected in the normal bare earth model). Small hydro-enforcement breaklines were then incorporated into the bare earth model at obstruction locations to enforce the appropriate flow path. ArcHydro was then re-run on the resulting hydro-enforced bare earth model. The resulting stream network of all flow paths with a flow accumulation of at least 2.5 acres was then manually edited to remove stream segments where no channelization was evident in the ground model. The final stream network has been checked for topological consistency and compared to the National Hydrography dataset.
Acquisition. Quantum Spatial collected the Green River Watershed LiDAR data between 12/10/17 and 11/12/18. The survey used a Leica ALS80 & Riegl VQ1560i laser systems mounted in a Cessna Caravan. Ground level GPS and aircraft IMU were collected during the flight.
Sensor: Leica ALS80
Maximum returns: Unlimited
Nominal pulse density: 8 pulses/m^2
Nominal pulse spacing: 0.35 m
AGL: 1,600 m
Speed: 145 knots
FOV: 36°
Scan frequency: 46.0 hz
Pulse rate: 347.2 kHz
Pulse duration: 2.5 ns
Pulse width: 35 cm
Wavelength: 1064 nm
Pulses in air mode: Multi Pulse in Air (2PiA)
Beam divergence: 22 mrads
Swath width: 1,040 m
Overlap: 63%
Sensor: Riegl VQ1560i
Maximum returns: Unlimited
Nominal pulse density: 8 pulses/m^2
Nominal pulse spacing: 0.35 m
AGL: 1,934 m
Speed: 130 knots
FOV: 58.5°
Scan frequency: Uniform Point Spacing
Pulse rate: 500 kHz
Pulse duration: 3 ns
Pulse width: 34.8 cm
Wavelength: 1064 nm
Pulses in air mode: Multiple Times Around
Beam divergence: 0.18 mrads
Swath width: 2,166.2 m
Overlap: 55 %
2018-11-12T00:00:00
1. Flightlines and data were reviewed to ensure complete coverage of the study area and positional accuracy of the laser points.
2. Laser point return coordinates were computed using RiProcess v.1.8.5 and Terra software based on independent data from the LiDAR system, IMU, and aircraft.
3. The raw LiDAR file was assembled into flightlines per return with each point having an associated x, y, and z coordinate.
4. Visual inspection of swath to swath laser point consistencies within the study area were used to perform manual refinements of system alignment.
5. Custom algorithms were designed to evaluate points between adjacent flightlines. Automated system alignment was computed based upon randomly selected swath to swath accuracy measurements that consider elevation, slope, and intensities. Specifically, refinement in the combination of system pitch, roll, and yaw offset parameters optimize internal consistency.
6. Noise (e.g., pits and birds) was filtered using post-processing software, based on known elevation ranges and included the removal of any cycle slips.
7. Using TerraScan and Microstation, ground classifications utilized custom settings appropriate to the study area.
8. The corrected and filtered return points were compared to the ground survey points collected to verify the vertical accuracy.
2019-02-22T00:00:00
Headworks.SDE.HydrolicStreamNetwork
Feature Class
0
Green River Watershed Stream Network
Quantum Spatial
OBJECTID
OBJECTID
OID
4
10
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Shape
Shape
Geometry
8
0
0
Feature geometry.
Esri
Coordinates defining the features.
Z_Min
Z_Min
Double
8
38
8
StreamName
Stream Name
String
25
0
0
Z_Max
Z_Max
Double
8
38
8
Shape.STLength()
Shape.STLength()
Double
0
0
0
dataset
EPSG
6.3(9.0.0)
0
Simple
FALSE
0
TRUE
TRUE