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Lidar_HydroFlat_BareEarth_HS_2013_WM
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WGS_1984_Web_Mercator_Auxiliary_Sphere
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FGDC
WSI
503-505-5100
503-546-6801
421 SW 6th Ave, Suite 800
Portland
OR
97204
US
20201215
ArcGIS Metadata
1.0
Sonoma County Agricultural Preservation and Open Space District
(707) 565-7369
747 Mendocino Ave, Suite 100
Santa Rosa
CA
95401
US
Raster Dataset
Lidar_HydroFlat_BareEarth_HS_2013_WM
2014-03-21
Sonoma County Vegetation Mapping and LiDAR Program
remote-sensing image
<div style='text-align:Left;'><p style='margin:0 0 11 0;'><span>The hillshade is a greyscale image showing topography in the landscape. In this case it is created from a LiDAR derived </span><span>hydro-flattened </span><span>bare earth digital elevation model illuminated by hypothetical light source shining from the north west. A </span><span>hydro flattened </span><span>bare earth digital elevation model (DEM) represents the earth's surface with all vegetation and human-made structures removed. In addition bodies of waters 2acres or larger have been smoothed.</span></p><p style='margin:0 0 11 0;'><span>The DEM used to create this hillshade is described as a bare earth digital elevation model (DEM) representing the earth's surface with all vegetation and human-made structures removed. The bare earth DEMs were derived from LiDAR data using triangulated irregular network (TIN) processing of the ground point returns. Each image corresponds to a 37,800-square-foot tile. Each pixel is 3 feet and represents an average elevation for that area.</span></p></div>
A high resolution LiDAR derived hillshade facilitates the visualization of the topography of a landscape at a variety of scales. This hillshade which was created from a LiDAR derived hydro-flattened bare earth digital elevation model shows the signal returns without any vegetation or human-made structures. In addition to that, bodies of water have been smoothed. This layer may be used on its own or in conjunction with other data.
The Sonoma County Vegetation Mapping and LiDAR Program (http://sonomavegmap.org) and the University of Maryland (under grant NNX13AP69G from NASA’s Carbon Monitoring System, Dr. Ralph Dubayah, PI) contracted LiDAR and orthophoto data collection for all of Sonoma County in late 2013. Also included in the data collection were two areas in Mendocino County - the Soda Spring Creek-Dry Creek Watershed and Lake Mendocino. This fine scale data will help provide an accurate, up-to-date inventory of the county’s landscape features, ecological communities and habitats. Project funders include: NASA, the University of Maryland, the Sonoma County Agricultural Preservation and Open Space District, the Sonoma County Water Agency, the California Department of Fish and Wildlife, the United States Geological Survey, the Sonoma County Information Systems Department, the Sonoma County Transportation and Public Works Department, the Nature Conservancy, and the City of Petaluma.
Sonoma County Vegetation Mapping and LiDAR Consortium, NASA, University of Maryland, Watershed Sciences, Inc., Tukman Geospatial LLC
Sonoma County
Mendocino Lake
Dry Creek
Sonoma County
WSI
Airborne Survey
Light Detection and Ranging
LiDAR Rectified Orthophotographs
Ortho
Digital Orthophoto
Orthophotography
high resolution color imagery
color infrared
CIR
NIR
near-infrared
aerial photographs
ISO 19115 Topic Categories
imageryBaseMapsEarthCover
imageryBaseMapsEarthCover
Sonoma County
Mendocino Lake
Dry Creek
Sonoma County
WSI
Airborne Survey
Light Detection and Ranging
LiDAR Rectified Orthophotographs
Ortho
Digital Orthophoto
Orthophotography
high resolution color imagery
color infrared
CIR
NIR
near-infrared
aerial photographs
Data were provided by the University of Maryland and the Sonoma County Vegetation Mapping and LiDAR Program (http://sonomavegmap.org) under grant NNX13AP69G from NASA’s Carbon Monitoring System (Dr. Ralph Dubayah, PI). This data is available for unrestricted public use. However, users should acknowledge the source of the data in any reports, publications, or presentations where the data is used.
<div style='text-align:Left;'><div><p><span>Data were provided by the University of Maryland and the Sonoma County Vegetation Mapping and LiDAR Program (http://sonomavegmap.org) under grant NNX13AP69G from NASA’s Carbon Monitoring System (Dr. Ralph Dubayah, PI). This data is available for unrestricted public use. However, users should acknowledge the source of the data in any reports, publications, or presentations where the data is used.</span></p></div></div>
-123.548077
-122.345272
38.098597
39.246035
Ground Condition - LiDAR: Leica ALS50 & Leica ALS70
2013-10-01T00:00:00
2013-10-28T00:00:00
Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; Esri ArcGIS 10.7.1.11595
1
-123.565035
-122.224633
39.342443
38.086873
Flight lines have been examined to ensure that there was at least 60% sidelap, there are no gaps between flight lines, and overlapping flight lines have consistent elevation values. All orthophotographs have been processed and reviewed for contracted accuracy and completeness. Orthophoto mosaics were reviewed at a scale of 1:2000 and reviewed against the LiDAR intensities to ensure no seam artifacts or offsets.
Raw acquired images were radiometrically and geometrically corrected using the camera’s calibration files. The resulting radiometry is then manually edited by digitally adjusting output levels, exposure and intensity, atmospheric haze correction, and applying hotspot dodging. This ensures that each image has the appropriate tone, no pixels are clipped, and that each image frame is blended with its neighbors. Once radiometry has been edited, separate RGBI and Panchromatic images are blended together to form single pan-sharpened 4 band TIFF images. During the mosaicking stage, orthorectified frames are color balanced a second time using an automated global tilting method and in certain cases, further manual balancing. Despite these extensive radiometric blending techniques, occasional frame to frame radiometric discrepancy may exist within the final mosaics due to bi-directional solar reflectance, changing sun angle, or difficult atmospheric conditions.
To assess the spatial accuracy of the orthophotographs, they were compared against check points identified from the LiDAR intensity images. The checkpoints were distributed evenly across the total acquired area, and were measured on surface features such as painted road lines and fixed high-contrast objects on the ground surface. Horizontal accuracy applies only to the portion of the swath that was orthorectified to the LiDAR-derived DEM. Outside of that, accuracy and radiometry at ortho seams are not guaranteed. The accuracy of the final mosaic was calculated in relation to the LiDAR-derived control points.
The orthophoto horizontal accuracy is reported as the root-mean-square error (RMSE).
0.27 meters
Acquisition. Orthophoto data acquisition was conducted from October 1 to October 28, 2013. The survey utilized a UltraCam Eagle 260 megapixel camera system mounted in a Cessna 208-B Grand Caravan.
2013-10-28
Processing. 1. Resolve GPS kinematic corrections for aircraft position data using kinematic aircraft GPS (collected at two hertz) and static ground GPS (one hertz) data collected over geodetic controls. 2. Develop a smoothed best estimate of trajectory (SBET) file that blends post-processed aircraft position with attitude data. Sensor heading, position, and attitude will be calculated throughout the survey. 3. Create an exterior orientation (EO) files for each photo image with omega, phi, and kappa. 4. Convert “Level 00” raw imagery into geometrically corrected “Level 02” image files. 5. Apply radiometric adjustments to “Level 02” image files to create “Level 03” Pan-sharpened tiffs. 6. Apply EO to photos, measure ground control points, and perform aerial triangulation. 7. Import DEM, orthorectify, and clip triangulated photos to specified area of interest. 8. Mosaic orthorectified imagery, blending seams between individual photos and correcting for radiometric differences between photos.
2014-03-21
2
1
0
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-13605983.888269 4591705.376964
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499738
0.298582
599961
0.298582
This raster data set represents high resolution orthophotographs acquired during an aerial survey.
WSI
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8.8(9.3.1.2)
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