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2009 Puget Sound LiDAR Consortium (PSLC) Topographic LiDAR: Snohomish River Estuary

browse graphicThis kmz file shows the extent of coverage for the 2012 PSLC Snohomish River Estuary, WA lidar data set.
Watershed Sciences, Inc. (WS) co-acquired Light Detection and Ranging (LiDAR) data and Truecolor Orthophotographs of the Snohomish River Estuary, WA on July 20 & 21, 2009. The original requested survey area (26,150 acres) was expanded, at the client?s request, to include more of the valley lowland areas in the SW and SE edge of the original AOI as well as additional creeks on the northern edge of the survey (Figure 1). The total area of delivered LiDAR and True-color Orthophotographs, including the expansion and 100 m buffer, is 32,140 acres.

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    Distribution Formats
    • LAZ
    Distributor DOC/NOAA/NOS/CSC > Coastal Services Center, National Ocean Service, National Oceanic and Atmospheric Administration, U.S. Department of Commerce
    Point of Contact Diana Martinez
    Puget Sound Lidar Consortium (PSLC)
    206-971-3052
    dmartinez@psrc.org
    Associated Resources
    • Lidar Dataset Supplemental Information
    Originator
    • DOC/NOAA/NOS/CSC > Coastal Services Center, National Ocean Service, National Oceanic and Atmospheric Administration, U.S. Department of Commerce
    Originator
    • Diana Martinez
      Puget Sound Lidar Consortium (PSLC)
    Publisher
    • DOC/NOAA/NOS/CSC > Coastal Services Center, National Ocean Service, National Oceanic and Atmospheric Administration, U.S. Department of Commerce
    Date(s)
    • publication: 2013-11-14
    Data Presentation Form: Digital image
    Dataset Progress Status Complete
    Data Update Frequency: As needed
    Purpose: The LAS files can be used to create DEMs and also to extract topographic data in software that does not support raster data. Other surface features can also be extracted with custom applications. LiDAR data has a wide range of uses such as earthquake hazard studies, hydrologic modeling, forestry, coastal engineering, roadway and pipeline engineering, flood plain mapping, wetland studies, geologic studies and a variety of analytical and cartographic projects.
    Use Limitations
    • These data depict the elevations at the time of the survey and are only accurate for that time. Users should be aware that temporal changes may have occurred since this data set was collected and some parts of this data may no longer represent actual surface conditions. Users should not use this data for critical applications without a full awareness of its limitations. Any conclusions drawn from analysis of this information are not the responsibility of NOAA or any of its partners. These data are NOT to be used for navigational purposes.
    Time Period: 2009-07-20  to  2009-07-21
    Spatial Reference System: urn:ogc:def:crs:EPSG::4269 Ellipsoid in Meters
    Spatial Bounding Box Coordinates:
    N: 48.06306836
    S: 47.85093689
    E: -122.0545681
    W: -122.2683624
    Spatial Coverage Map:
    Themes
    • Topography
    • Elevation
    • Model
    • LiDAR
    • LAZ
    • LAS
    • Remote Sensing
    Places
    • US
    • Washington
    • Snohomish County
    Use Constraints No constraint information available
    Fees Fee information not available.
    Lineage Statement Lineage statement not available.
    Processor
    • DOC/NOAA/NOS/CSC > Coastal Services Center, National Ocean Service, National Oceanic and Atmospheric Administration, U.S. Department of Commerce
    • DOC/NOAA/NESDIS/NGDC > National Geophysical Data Center, NESDIS, NOAA, U.S. Department of Commerce
    Processing Steps
    • Point Generation. The points are generated as Terrascan binary Format using Terrapoint?s proprietary Laser Postprocessor Software. This software combines the Raw Laser file and GPS/IMU information to generate a point cloud for each individual flight. All the point cloud files encompassing the project area were then divided into quarter quad tiles. The referencing system of these tiles is based upon the project boundary minimum and maximums. This process is carried out in Terrascan. The bald earth is subsequently extracted from the raw LiDAR points using Terrascan in a Microstation environment. The automated vegetation removal process takes place by building an iterative surface model. This surface model is generated using three main parameters: Building size, Iteration angle and Iteration distance. The initial model is based upon low points selected by a roaming window and are assumed to be ground points. The size of this roaming window is determined by the building size parameter. These low points are triangulated and the remaining points are evaluated and subsequently added to the model if they meet the Iteration angle and distance constraints (fig. 1). This process is repeated until no additional points are added within an iteration. There is also a maximum terrain angle constraint that determines the maximum terrain angle allowed within the model. Multiple process dates, report compiled 20050331.
    • Applications and Work Flow Overview 1. Resolved kinematic corrections for aircraft position data using kinematic aircraft GPS and static ground GPS data. Software: Waypoint GPS v.8.10, Trimble Geomatics Office v.1.62 2. Developed a smoothed best estimate of trajectory (SBET) file that blends post-processed aircraft position with attitude data Sensor head position and attitude were calculated throughout the survey. The SBET data were used extensively for laser point processing. Software: IPAS v.1.4 3. Calculated laser point position by associating SBET position to each laser point return time, scan angle, intensity, etc. Created raw laser point cloud data for the entire survey in *.las (ASPRS v1.1) format. Software: ALS Post Processing Software v.2.69 4. Imported raw laser points into manageable blocks (less than 500 MB) to perform manual relative accuracy calibration and filter for pits/birds. Ground points were then classified for individual flight lines (to be used for relative accuracy testing and calibration). Software: TerraScan v.9.001 5. Using ground classified points per each flight line, the relative accuracy was tested. Automated line-to-line calibrations were then performed for system attitude parameters (pitch, roll, heading), mirror flex (scale) and GPS/IMU drift. Calibrations were performed on ground classified points from paired flight lines. Every flight line was used for relative accuracy calibration. Software: TerraMatch v.9.001 6. Position and attitude data were imported. Resulting data were classified as ground and nonground points. Statistical absolute accuracy was assessed via direct comparisons of ground classified points to ground RTK survey data. Data were then converted to orthometric elevations (NAVD88) by applying a Geoid03 correction. Ground models were created as a triangulated surface and exported as ArcInfo ASCII grids at a 1-meter pixel resolution. Software: TerraScan v.9.001, ArcMap v9.3, TerraModeler v.9.001 Laser point coordinates were computed using the IPAS and ALS Post Processor software suites based on independent data from the LiDAR system (pulse time, scan angle), and aircraft trajectory data (SBET). Laser point returns (first through fourth) were assigned an associated (x, y, z) coordinate along with unique intensity values (0-255). The data were output into large LAS v. 1.2 files; each point maintains the corresponding scan angle, return number (echo), intensity, and x, y, z (easting, northing, and elevation) information. These initial laser point files were too large for subsequent processing. To facilitate laser point processing, bins (polygons) were created to divide the dataset into manageable sizes (< 500 MB). Flightlines and LiDAR data were then reviewed to ensure complete coverage of the survey area and positional accuracy of the laser points. Laser point data were imported into processing bins in TerraScan, and manual calibration was performed to assess the system offsets for pitch, roll, heading and scale (mirror flex). Using a geometric relationship developed by Watershed Sciences, each of these offsets was resolved and corrected if necessary. LiDAR points were then filtered for noise, pits (artificial low points) and birds (true birds as well as erroneously high points) by screening for absolute elevation limits, isolated points and height above ground. Each bin was then manually inspected for remaining pits and birds and spurious points were removed. In a bin containing approximately 7.5-9.0 million points, an average of 50-100 points are typically found to be artificially low or high. Common sources of non-terrestrial returns are clouds, birds, vapor, haze, decks, brush piles, etc. LiDAR Data Acquisition and Processing: Snohomish River Estuary, WA Prepared by Watershed Sciences, Inc. Internal calibration was refined using TerraMatch. Points from overlapping lines were tested for internal consistency and final adjustments were made for system misalignments (i.e., pitch, roll, heading offsets and scale). Automated sensor attitude and scale corrections yielded 3-5 cm improvements in the relative accuracy. Once system misalignments were corrected, vertical GPS drift was then resolved and removed per flight line, yielding a slight improvement (<1 cm) in relative accuracy. The TerraScan software suite is designed specifically for classifying near-ground points (Soininen, 2004). The processing sequence began by ?removing? all points that were not ?near? the earth based on geometric constraints used to evaluate multi-return points. The resulting bare earth (ground) model was visually inspected and additional ground point modeling was performed in site-specific areas to improve ground detail. This manual editing of grounds often occurs in areas with known ground modeling deficiencies, such as: bedrock outcrops, cliffs, deeply incised stream banks, and dense vegetation. In some cases, automated ground point classification erroneously included known vegetation (i.e., understory, low/dense shrubs, etc.). These points were manually reclassified as non-grounds. Ground surface rasters were developed from triangulated irregular networks (TINs) of ground points.
    • The NOAA Coastal Services Center (CSC) downloaded topographic files in text format from PSLC's website. The files contained lidar easting, northing, elevation, intensity, return number, class, scan angle and GPS time measurements. The data were received in Washington State Plane North Zone 4601, NAD83 coordinates and were vertically referenced to NAVD88 using the Geoid03 model. The vertical units of the data were feet. CSC performed the following processing for data storage and Digital Coast provisioning purposes: 1. The All-Return ASCII txt files were parsed to convert GPS Week Time to Adjusted Standard GPS Time. 2. The All-Return ASCII files were converted from txt format to las format using LASTools' txt2las tool and reclassified to fit the CSC class list, N=1 (unclassified), G=2 (ground). 3. The las files were converted from orthometric (NAVD88) heights to ellipsoidal heights using Geoid03. 4. The las files' vertical units were converted from feet to meters and filtered to remove bad elevations. 5. The las files were converted from a Projected Coordinate System (WA SP North) to a Geographic Coordinate system (NAD83) 6. The data were converted to converted to LAZ format 7. The laz tiles containing only water areas were removed and remaining tiles were clipped to remove excess noise.
    • The NOAA National Geophysical Data Center (NGDC) received lidar data files via ftp transfer from the NOAA Coastal Services Center. The data are currently being served via NOAA CSC Digital Coast at http://www.csc.noaa.gov/digitalcoast/. The data can be used to re-populate the system. The data are archived in LAS or LAZ format. The LAS format is an industry standard for LiDAR data developed by the American Society of Photogrammetry and Remote Sensing (ASPRS); LAZ is a loseless compressed version of LAS developed by Martin Isenburg (http://www.laszip.org/). The data are exclusively in geographic coordinates (either NAD83 or ITRF94). The data are referenced vertically to the ellipsoid (either GRS80 or ITRF94), allowing for the ability to apply the most up to date geoid model when transforming to orthometric heights.

    Metadata Last Modified: 2013-11-19

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