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SCWA_SonomaCreek_Lidar_BareEarth_DEM_2021.tif
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Version 6.2 (Build 9200) ; Esri ArcGIS 10.7.1.11595
SCWA_SonomaCreek_Lidar_BareEarth_DEM_2021
NV5 Geospatial
1100 Circle Blvd. Suite 126
Corvallis
OR
97330
US
541-752-1204
http://www.nv5.com/geospatial
NV5 Geospatial
2021-04-21
1
-122.591899
-122.409513
38.448803
38.227525
Sonoma Creek Topobathymetric Lidar
2021-02-04
2021-02-06
Provide high resolution terrain elevation data from the Sonoma Creek Topo bathymetric dataset.
<div style='text-align:Left;'><div><div><p><span>The topo bathymetric bare earth digital elevation model (DEM) represents the earth's surface with all water, vegetation and anthropogenic features removed. It was derived from Green and NIR Lidar data using TIN processing of the ground and bathymetric bottom point returns. This version of the topo bathymetric model has not been clipped. Areas of bathymetric voids were triangulated from the nearest bathymetric returns. Users should be aware that bathymetric voids can indicate deeper areas beyond the penetration capabilities of the laser and view these as low confidence areas. A bathymetric coverage polygon has been provided as a separate deliverable indicating where there was a lack of bathymetric returns. The horizontal datum for this dataset is NAD83 (2011), the vertical datum is NAVD88, Geoid 18, and the data is projected in California State Plane, Zone 2. Units are in US Survey Feet. NV5 Geospatial collected the Sonoma Creek Topo bathymetric Lidar data for Sonoma Water between 02/04/2021 and 02/06/2021.</span></p></div></div></div>
Sonoma Water
lidar
Light Detection and Ranging
elevation data
topography
bare earth
DEM
digital elevation model
Sonoma County
California
Environmental Science Associates
lidar, Light Detection and Ranging, high-resolution, elevation data, topography, bare earth, DEM, digital elevation model
Sonoma County
California
Environmental Science Associates
3
This data is assembled by AOI and projected in California State Plane, Zone 2.
Carlos Diaz
Sonoma Water
404 Aviation Blvd
Santa Rosa
California
95403
carlos.diaz@scwa.ca.gov
US
707-547-1956
Carlos Diaz
Please contact Sonoma Water for information regarding the use of this data.
<div style='text-align:Left;'><div><div><p><span>In some areas of heavy vegetation or forest cover, there may be relatively few ground points in the lidar data. TINing the points produces large triangles and hence the elevations may be less accurate within such areas. </span></p><p><span style='font-weight:bold;'>SUBJECT: PUBLIC INFORMATION REQUEST –LiDAR Data</span></p><p><span>The attached information was retrieved from Sonoma County Water Agency CAD-Drafting/GIS Records in connection with a Request for Public Information. These records and the drawings upon which they are based, were produced or conducted solely for the technical and/or functional needs and purposes of the Sonoma County Water Agency and/or other entities which it now acts, or may have acted on behalf of in the past. </span></p><p><span>The Sonoma County Water Agency makes no assertions or representations regarding the sufficiency, accuracy or applicability of the information and/or records provided for any purpose beyond that for which they were originally produced.</span></p><p><span>The information contained herein is for the use and information of the party to whom it was directly sent. The redistribution or “sharing”of this information is strictly prohibited. </span></p></div></div></div>
Raster Dataset
1
Carlos Diaz
Sonoma Water
404 Aviation Blvd
Santa Rosa
California
95403
carlos.diaz@scwa.ca.gov
US
707-547-1956
dataset
EPSG
8.9.3(10.3.1)
2
1
0
6392151.000000 1845231.000000
6392151.000000 1925532.000000
6444024.000000 1925532.000000
6444024.000000 1845231.000000
6418087.500000 1885381.500000
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3.000000
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32
ft_us
Bare earth unclipped
BE_unclipped
Digital Elevation Model
False
False
False
False
False
20221210
NV5 Geospatial
1100 Circle Blvd. Suite 126
Corvallis
OR
97330
US
541-752-1204
http://www.nv5.com/geospatial
NV5 Geospatial
NV5 Geospatial
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 0.066 ft (0.020 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
0.066 ft (0.020 m)
The Non-vegetated Vertical Accuracy (NVA) of this dataset, tested at 95% confidence level is 0.122 ft (0.037 m) versus the classified point cloud. 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
0.122 ft (0.037 m)
The Non-vegetated Vertical Accuracy (NVA) of this dataset, tested at 95% confidence level is 0.115 ft (0.035 m) versus the bare earth digital elevation model. 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
0.115 ft (0.035 m)
The Vegetated Vertical Accuracy (VVA) of this dataset, evaluated at the 95th percentile is 0.588 ft (0.179 m) versus the classified point cloud. Please see the lidar data report for a discussion of the statistics related to this dataset.
Vegetated Vertical Accuracy was assessed using 24 VVA check points. These check points were not used in the calibration or post processing of the lidar point cloud data.
VVA
ft_us
0.588 ft (0.179 m)
The Vegetated Vertical Accuracy (VVA) of this dataset, evaluated at the 95th percentile is 0.611 ft (0.186 m) versus the bare earth digital elevation model. Please see the lidar data report for a discussion of the statistics related to this dataset.
Vegetated Vertical Accuracy was assessed using 24 VVA check points. These check points were not used in the calibration or post processing of the lidar point cloud data.
VVA
ft_us
0.611 ft (0.186 m)
Vertical accuracy was also assessed using ground control points that were used in the calibration and post processing of the lidar point cloud as they still serve as a good indication of the overall accuracy of the lidar dataset. The Root Mean Square Error (RMSE) of the vertical accuracy of the lidar dataset as compared to ground control points is 0.068 ft (0.021 m). Please see the lidar data report for a discussion of the statistics related to this dataset.
12 ground control points were collected and utilized in the calibration and post processing of the lidar data point cloud.
RMSE
0.068 ft (0.021 m)
ft_us
Vertical accuracy was also assessed using bathymetric bottom check points collected over submerged bathymetric terrain. The bathymetric check point accuracy of this dataset, tested at 95% confidence level is 0.544 ft (0.166 m). Please see the lidar data report for a discussion of the statistics related to this dataset.
Bathymetric bottom vertical accuracy was assessed using 81 bathymetric bottom check points. These check points were not used in the calibration or post processing of the lidar point cloud data.
95% Confidence
ft_us
0.544 ft (0.166 m)
Vertical accuracy was also assessed using wetted edge check points collected at the interface between water and terrain. The wetted edge check point accuracy of this dataset, tested at 95% confidence level is 0.369 ft (0.112 m). Please see the lidar data report for a discussion of the statistics related to this dataset.
Wetted edge vertical accuracy was assessed using 16 wetted edge check points. These check points were not used in the calibration or post processing of the lidar point cloud data.
95% Confidence
ft_us
0.369 ft (0.112 m)
Acquisition. NV5 Geospatial collected the Sonoma Creek Topobathymetric Lidar data between 02/04/2021 and 02/06/2021. The survey used a Riegl VQ-880-GII-G laser system mounted in a Cessna Caravan. Ground level GPS and aircraft IMU were collected during the flight. Sensor: Riegl VQ-880-GII-G
Maximum returns: 15
Nominal pulse density: 15 pulses/m^2
Nominal pulse spacing: 0.26 m
AGL: 450 m
Speed: 145 knots
FOV: 40°
Scan frequency: 80 Lines Per Second hz
Pulse rate: 200 kHz
Pulse duration: 1.5 ns
Pulse footprint: 31.5 cm
Wavelength: 532 nm
Pulses in air mode: Multiple Times Around
Beam divergence: 0.7 mrads
Swath width: 328 m
Overlap: 30%
Sensor: Riegl VQ-880-GII-IR
Maximum returns: 15
Nominal pulse density: 15 pulses/m^2
Nominal pulse spacing: 0.26 m
AGL: 450 m
Speed: 145 knots
FOV: 42°
Scan frequency: Uniform Point Spacing hz
Pulse rate: 300 kHz
Pulse duration: 3 ns
Pulse footprint: 9 cm
Wavelength: 1064 nm
Pulses in air mode: Multiple Times Around
Beam divergence: 0.2 mrads
Swath width: 345 m
Overlap: 30%
2021-02-06
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 V1.8.5 and POSPac MMS V.8.5 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. Data was processed with a combination of manual and automated techniques using both Riegl software and in-house proprietary software to refract water returns and classify ground and bathymetric returns. 7. 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. 8. Using TerraScan and Microstation, ground classifications utilized custom settings appropriate to the study area. 9. The corrected and filtered return points were compared to the RTK ground survey points collected to verify the vertical accuracy. 10. TIN processing of the ground and bathymetric bottom point returns was used to create this clipped bare earth DEM.
2021-04-21
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