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(WSI) 503-505-5100 503-546-6801 421 S.W. 6th Ave., Suite 800 Portland OR 97204 US 20140522 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 CANOPY_DENSITY 2014-02-19 Sonoma County Vegetation Mapping and LiDAR Program raster digital data The canopy density digital elevation model (DEM) represents tree percent canopy cover. Each image corresponds to a 37,800-square-foot tile. Each pixel is 3 feet and represents an average canopy density for that area. The specified coordinate system for this dataset is California State Plane Zone II (FIPS 0402), NAD83 (2011), with units in US Survey Feet for horizontal, and vertical units are NAVD88 (12A) US Survey Feet. The dataset encompasses all of Sonoma County and portins of Mendocino County. WSI collected the LiDAR and created this data set for the Sonoma County Vegetation Mapping and LiDAR Program. 2013 LiDAR Derived Canopy Density - Sonoma County CA 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 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. LiDAR data and orthophotography were provided by the University of Maryland under grant NNX13AP69G from NASA's Carbon Monitoring System (Dr. Ralph Dubayah and Dr. George Hurtt, Principal Investigators). This grant also funded the creation of derived forest cover and land cover information, including a countywide biomass and carbon map, a canopy cover map, and DEMs. The Sonoma County Vegetation Mapping and LiDAR Program funded LiDAR derived products in the California State Plane Coordinate System, such as DEMs, hillshades, building footprints, 1-foot contours, and other derived layers. The entirety of this data is freely licensed for unrestricted public use, unless otherwise noted. Any use of these data, including value-added products, within reports, papers, and presentations must acknowledge NASA Grant NNX13AP69G, the University of Maryland, and the Sonoma Vegetation Mapping and LiDAR Program as their sources. Ground Condition - LiDAR: Leica ALS50 & Leica ALS70 2013-09-28 2013-11-26 -123.238581 -122.390681 38.298129 38.511668 Esri ArcGIS 10.2.0.3397 1 -123.565031 -122.224633 39.342439 38.086871 LiDAR has been collected and processed throughout study area. 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). In some areas of heavy vegetation and 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. In some areas with large bodies of water, competing water surface levels may be visible. This is due to seasonal water level fluctuation and intervals of time between acquisition of an area. LiDAR has been collected and processed for all areas within the project study Area. For Canopy Cover, First-Return, only the highest hits, are shown (e.g. tree-tops, roofs, etc.). 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 was examined at a 1:3000 scale. Relative Accuracy measures the divergence between points from different flightlines. Relative Accuracy median is 0.05 meters (0.17 feet) out of 106,255,665,985 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 (real time kinematic) points. These ground control points are distributed through out the project study area. Supplemental Vertical Accuracy (SVA) is reported as the deviation between landcover classified laser points and landclass checkpoints at the 95th percentile. The SVA for individual land classes are 0.27 meters for shrub, 0.09 meters for short grass, 0.19 meters for tall grass, 0.19 meters for mixed forest, 0.05 meters for developed areas, 0.12 for herbaceous upland natural areas, 0.05 for non-natural woody areas and 0.07 for barren areas. The supplemental vertical accuracies were calculated using 417 individual landclass checkpoints. Consolidated Vertical Accuracy (CVA) is reported as the deviation between both ground and landcover classified laser points from all survey checkpoints, reported at the 95th percentile. CVA for this dataset is 0.06 meters, and was calculated from 10,102 ground and landclass checkpoints. See LiDAR data report. 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 (0.18 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. 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. Vegetation-classified Points were output as laser points, TINed and GRIDed surfaces. 2014-02-19 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 2 1 0 6119016.999954 1797202.000025 6119016.999954 2250802.000025 6497016.999954 2250802.000025 6497016.999954 1797202.000025 6308016.999954 2024002.000025 126000 3.000000 151200 3.000000 This raster data set represents vegetation heights derived from ground classified LiDAR point data. 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