gov.noaa.nmfs.inport:50159 eng UTF8 dataset OCM Partners resourceProvider NOAA Office for Coastal Management (843) 740-1202 2234 South Hobson Ave Charleston SC 29405-2413 coastal.info@noaa.gov https://coast.noaa.gov WWW:LINK-1.0-http--link NOAA Office for Coastal Management Website NOAA Office for Coastal Management Home Page information pointOfContact 2024-02-29T00:00:00 ISO 19115-2 Geographic Information - Metadata Part 2 Extensions for imagery and gridded data ISO 19115-2:2009(E) NAD83(NSRS2007) / UTM zone 10N2008-11-12publicationEuropean Petroleum Survey Grouphttps://apps.epsg.org/api/v1/CoordRefSystem/3717/export/?format=gmlurn:ogc:def:crs:EPSG:37176.18.3 2009 Puget Sound LiDAR Consortium (PSLC) Topographic LiDAR: Snohomish River Estuary wa2009_pslc_snohomishriverestuary_m2590_metadata 2013-11-14 publication NOAA/NMFS/EDM 50159 https://www.fisheries.noaa.gov/inport/item/50159 WWW:LINK-1.0-http--link Full Metadata Record View the complete metadata record on InPort for more information about this dataset. information https://coast.noaa.gov/dataviewer WWW:LINK-1.0-http--link Citation URL Online Resource download https://coast.noaa.gov WWW:LINK-1.0-http--link Citation URL Online Resource download 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. This data set contains point cloud data(' in LAZ (compressed LAS) format', ''). The data may have been reprojected or otherwise modified from the original data in an automated process. Disregard projection information in this abstract and refer to the spatial reference section. 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. Please credit the Puget Sound LiDAR Consortium (PSLC) for these data. The PSLC is supported by the Puget Sound Regional Council, the National Aeronautical and Space Administration (NASA), the United States Geological Survey (USGS) and numerous partners in local, state, and tribal government. The custom download may be cited as National Oceanic and Atmospheric Administration (NOAA) Digital Coast Data Access Viewer. Charleston, SC: NOAA Office for Coastal Management. Accessed 2025-04-19 at https://coast.noaa.gov/dataviewer. completed NOAA Office for Coastal Management (843) 740-1202 2234 South Hobson Ave Charleston SC 29405-2413 coastal.info@noaa.gov https://coast.noaa.gov WWW:LINK-1.0-http--link NOAA Office for Coastal Management Website NOAA Office for Coastal Management Home Page information pointOfContact NOAA Office for Coastal Management (843) 740-1202 2234 South Hobson Ave Charleston SC 29405-2413 coastal.info@noaa.gov https://coast.noaa.gov WWW:LINK-1.0-http--link NOAA Office for Coastal Management Website NOAA Office for Coastal Management Home Page information custodian asNeeded https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/2590/supplemental/wa2009_pslc_snohomishriverestuary.KMZ This graphic shows the lidar coverage for the 2009 Snohomish River Estuary collection area in Washington. kmz LAZ theme Lidar - partner (no harvest) project InPort otherRestrictions This data set was compiled dynamically. It will be removed 10 days after 2025-04-19 from the NOAA system. Cite As: OCM Partners, [Date of Access]: 2009 Puget Sound LiDAR Consortium (PSLC) Topographic LiDAR: Snohomish River Estuary [Data Date Range], https://www.fisheries.noaa.gov/inport/item/50159. NOAA provides no warranty, nor accepts any liability occurring from any incomplete, incorrect, or misleading data, or from any incorrect, incomplete, or misleading use of the data. It is the responsibility of the user to determine whether or not the data is suitable for the intended purpose. otherRestrictions This data set was compiled dynamically. It will be removed 10 days after 2025-04-19 from the NOAA system. Access Constraints: None otherRestrictions This data set was compiled dynamically. It will be removed 10 days after 2025-04-19 from the NOAA system. Use Constraints: 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. These data depict the heights at the time of the survey and are only accurate for that time. otherRestrictions This data set was compiled dynamically. It will be removed 10 days after 2025-04-19 from the NOAA system. Distribution Liability: Any conclusions drawn from the analysis of this information are not the responsibility of Terrapoint, PSLC, NOAA, the Office for Coastal Management or its partners. unclassified NOAA Data Management Plan (DMP) NOAA/NMFS/EDM 50159 https://www.fisheries.noaa.gov/inportserve/waf/noaa/nos/ocmp/dmp/pdf/50159.pdf WWW:LINK-1.0-http--link NOAA Data Management Plan (DMP) NOAA Data Management Plan for this record on InPort. information crossReference vector eng; US elevation -122.2683624 -122.0545681 47.85093689 48.06306836 | Currentness: Ground Condition 2009-07-20 2009-07-21 A footprint of this data set may be viewed in Google Earth at: https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/2590/supplemental/wa2009_pslc_snohomishriverestuary.KMZ Reports explaining collection and quality assurance is available at: https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/2590/supplemental/wa2009_pslc_snohomishriverestuary.pdf false eng false none NOAA Office for Coastal Management (843) 740-1202 2234 South Hobson Ave Charleston SC 29405-2413 coastal.info@noaa.gov https://coast.noaa.gov WWW:LINK-1.0-http--link NOAA Office for Coastal Management Website NOAA Office for Coastal Management Home Page information distributor https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=2590 WWW:LINK-1.0-http--link Customized Download Create custom data files by choosing data area, product type, map projection, file format, datum, etc. download https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/2590/index.html WWW:LINK-1.0-http--link Bulk Download Simple download of data files. download dataset Accuracy Elevations are recorded in floating-point meters and the vertical datum is ellipsoidal (GEOID03). Horizontal Positional Accuracy Horizontal positional accuracy for LiDAR is dependent upon the quality of the GPS/INS solution, sensor calibration and ground conditions at the time of data capture. The standard system results for horizontal accuracy are less than 1 meter. ; Quantitative Value: 1.0 meters, Test that produced the value: Lidar horizontal accuracy was not reported. Vertical Positional Accuracy To enable assessment of LiDAR data accuracy, ground truth points were collected using GPS based real-time kinematic (RTK) surveying. For an RTK survey, the ground crew uses a roving unit to receive radio-relayed corrected positional coordinates for all ground points from a GPS base station set up over a survey control monument. Instrumentation includes multiple Trimble DGPS units (R8). RTK surveying allows for precise location measurements with an error (s) of = 1.5 cm (0.6 in). Figure 2 below portrays a distribution of hard surface RTK point locations used for the survey areas. Additional RTK surveys were taken by Watershed Sciences (in low grass vegetation) and the client (in high marsh vegetation) to compare absolute accuracy amongst land covers; this data is presented in Table 4. To assess spatial accuracy of the orthophotographs they are compared against control points identified from the LIDAR intensity images. The control points were collected\measured on surface features such as painted road-lines, and boulders in the stream beds. The accuracy of the final mosaic, expressed as root mean square error (RMSE), was calculated in relation to the LiDAR-derived control points. Figure 3 displays the co-registration between orthorectified photographs and LiDAR intensity images. ; Quantitative Value: 0.03 meters, Test that produced the value: The vertical accuracy of the LiDAR data is described as the mean and standard deviation (sigma ~ s) of divergence of LiDAR point coordinates from RTK ground survey point coordinates. To provide a sense of the model predictive power of the dataset, the root mean square error (RMSE) for vertical accuracy is also provided. Completeness Report LiDAR data has been collected and processed for all areas within the project study area. Conceptual Consistency LiDAR flight lines have been examined to ensure that there was at least 50% 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. 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 Office for Coastal Management (OCM) 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. OCM 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 OCM 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, removing bad elevations. 5. The las files were converted from a Projected Coordinate System (WA SP North) to a Geographic Coordinate system (NAD83) 6. The las files' horizontal units were converted from feet to decimal degrees and converted to laz format. 7. The laz tiles containing only water areas were removed and remaining tiles were clipped to remove excess noise. 2013-11-14T00:00:00 The dataset has been mosaiced, reprojected, and clipped as needed from the original input files using PDAL and GDAL commands. 2025-04-19NOAA Office for Coastal Managementcoastal.info@noaa.govprocessor