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Watershed Sciences, Inc. (WSI)
503-505-5100
503-546-6801
421 S.W. 6th Ave., Suite 800
Portland
OR
97204
US
ArcGIS Metadata
1.0
Sonoma County Vegetation Mapping and LiDAR Program
False
Sonoma County Vegetation Mapping and LiDAR Program
Raster Dataset
2014-02-19T00:00:00
2013-09-28T00:00:00
Sonoma County Vegetation Mapping and LiDAR Program
raster digital data
Lidar_Highest_Hit_HS_2013_WM
<div style='text-align:Left;'><div><div><p style='margin:0 0 11 0;'><span>Hillshade of the highest hit digital elevation model using the Sonoma Veg Map LiDAR data. The Mosaic Reproject function was used to reproject the raster mosaic to Web Mercator. The Mosaic hillshade function was applied to generate this hillshade. The default values were used except for the Z value. A value of .4 was used for the Z value. The raster cache was generated from the prevous item.</span></p><p style='margin:0 0 11 0;'><span>The DEM used to create this hillshade is described as a Highest Hit or First Return digital elevation model (DEM) represents the earth’s surface with the base or bare-earth DEM values subtracted from the first returns, with the resulting raster being the height of any vegetation, structure, or the ground for those areas lacking in vegetation or structures for the subject area. Values are in feet. Each cell in the GRID is three feet and has a value that represents an average vegetation height at that location. The purpose of the data is to provide users with a very accurate view of the vegetation height in the subject area for the date of data capture.</span></p></div></div></div>
A high resolution LiDAR derived hillshade facilitates the visualization of the topography of a landscape at a variety of scales. This layer may be used on its own or in conjunction with other data. This hillshade which was created from a LiDAR derived highest hit digital elevation model shows the signal returns that were the highest above the ground in a given location. This provides the viewer a hillshade display of the tree canopy or structures at the time of data capture. 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, Lake Mendocino, Sonoma Lake Watershed
LiDAR, Light Detection And Ranging
LiDAR
Light Detection And Ranging
Sonoma County
Lake Mendocino
Sonoma Lake Watershed
Elevation
Canopy Height
Highest Hit
First Return.
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><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).</span></p><p><span>You are free to: Share - copy and redistribute the data in any medium or format. Adapt - You may make derivative works, transform, and build upon the data for any purpose, even commercial. The licensor cannot revoke these freedoms as long as you follow the license terms.</span></p><p><span>License terms: Attribution - You must give appropriate credit (if supplied, you must provide the name of the creator and attribution parties, a copyright notice, a license notice, a disclaimer notice and a link to the material) and indicate if any changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you, your organization, or your use of the data. ShareAlike - if you modify, transform, or build on the data, you must distribute your contributions under the same license as the original.</span></p><p><span>No additional Restrictions - You may not apply legal terms or technological measures that legally restrict others form doing anything the license permits.</span></p><p><span>Notices: You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation. No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the data.</span></p><p><span>EXCEPT TO THE EXTENT REQUIRED BY APPLICABLE LAW, IN NO EVENT WILL LICENSOR BE LIABLE TO YOU ON ANY LEGAL THEORY FOR ANY SPECIAL, INCIDENTAL, CONSEQUENTIAL, PUNITIVE OR EXEMPLARY DAMAGES ARISING OUT OF THIS LICENSE OR THE USE OF THE DATA, EVEN IF LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.</span></p><p><span>The above is an easily understandable summary of and not a substitute for the license and disclaimer for the Attribution-ShareAlike 3.0 United States (CC BY-SA 3.0 US) the full text is available at creativecommons.org.</span></p><p><span>https://creativecommons.org/licenses/by-sa/3.0/us/legalcode</span></p></div></div></div>
Ground Condition - LiDAR: Leica ALS50 & Leica ALS70
2013-09-28T00:00:00
2013-11-26T00:00:00
1
-123.565035
-122.224633
38.086873
39.342443
Sonoma Veg Map
Sonoma County Vegetation Mapping & LiDAR Program
staff
http://sonomavegmap.org/
747 Mendocino Ave, Suite 100
Santa Rosa
CA
95401
openspace@sonoma-county.org
US
707-565-7360
707-565-7359
M-F 9am to 4pm
Watershed Sciences, Inc. (WSI)
503-505-5100
503-546-6801
421 S.W. 6th Ave., Suite 800
Portland
OR
97204
US
Watershed Sciences, Inc. (WSI)
Version 6.2 (Build 9200) ; Esri ArcGIS 10.7.1.11595
1
-123.565035
-122.224633
39.342443
38.086873
LiDAR flight lines have been examined to ensure that there was at least 60% sidelap, there are no gaps between flightlines, and overlapping flightlines have consistent elevation values. Shaded relief images have been visually inspected for data errors such as pits, border artifacts, gaps, and shifting. The data were examined at a 1:3000 scale.
LiDAR has been collected and processed for all areas within the project study Area. In some areas of heavy vegetation and forest cover, there may be relatively few ground points in the LiDAR data. Some elevation units have been interpolated across areas in the ground model where there are no elevation data (e.g., over water, over dense vegetation). TINing the points produces large triangles and hence the elevations may be less accurate within such areas. Some elevation units have been interpolated across areas in the ground model where there are no elevation data (e.g., over water, over dense vegetation).
Relative Accuracy measures the divergence between points from different flightlines. Relative Accuracy median is 0.05 meters (0.17 feet) out of 106,255,686,985 laser points over 4,133 flightlines. For more information regarding the internal consistency between ground-classified points from different overlapping flightlines please see LiDAR data report.
The Fundamental Vertical Accuracy (FVA) of the data set is 0.03 meters (0.09 feet). Accuracy was assessed using 9,685 ground control points (GCP). These ground control points are distributed through out the project study area.
The FVA was tested using 851 independent ground control points (GCPs) located in open terrain. The GCPs were distributed throughout the project area. Elevations from the unclassified LiDAR surface were measured for the x,y location of each check point. Elevations interpolated from the LiDAR surface were then compared to the elevation values of the surveyed control. The Root-Mean-Square (RMSE) was computed to be 0.03 m (0.09 ft.). AccuracyZ has been tested to meet 0.05 m (0.17 ft.) FVA at 95 Percent confidence level using RMSE(z) x 1.9600 as defined by the National Standards for Spatial Data Accuracy (NSSDA); assessed and reported using National Digital Elevation Program (NDEP)/ASRPS Guidelines.
0.05 meters RMSEz at 95 percent Confidence Interval.
Acquisition. LiDAR data acquisition was started September 28, 2013 and was completed on November 26, 2013. The survey is utilizing a Leica ALS50 or ALS70 laser system mounted in a Piper Navajo PA-31-325 or Cessna Caravan 208B. Near nadir scan angles were used to increase penetration of vegetation to ground surfaces. Ground level GPS and aircraft IMU were collected during the flight.
2014-02-19
LiDAR Data Processing. Flight lines and data were reviewed to ensure complete coverage of the study area and positional accuracy of the laser points. Laser point return coordinates were computed using ALS Post Processor software and IPAS Pro GPS/INS software, based on independent data from the LiDAR system, IMU, and aircraft. The raw LiDAR file was assembled into flight lines per return with each point having an associated x, y, and z coordinate. Visual inspection of swath to swath laser point consistencies within the study area were used to perform manual refinements of system alignment. Custom algorithms were designed to evaluate points between adjacent flight lines. 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. Noise (e.g., pits and birds) was filtered using ALS post-processing software, based on known elevation ranges and included the removal of any cycle slips. Using TerraScan and MicroStation, ground classifications utilized custom settings appropriate to the study area. The corrected and filtered return points were compared to the RTK ground survey points collected to verify the vertical and horizontal accuracies. Points were output as laser points, TINed and GRIDed surfaces.
2014-10-24
499738
0.298582
599961
0.298582
2
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0
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Band_1
32
3 foot pixel resolution raster cache.
False
False
False
False
False
This raster data set represents ground surface elevations derived from ground classified LiDAR point data.
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