50097 usda2008_or_sandy_river_m1432_metadata Data Set Published / External 49401 Lidar - partner (no harvest) Project Completed 2012 Watershed Sciences, Inc. collected Light Detection and Ranging (LiDAR) data for the Sandy River study area in collaboration with the USDA Forest Service. The areas for LiDAR collection have been designed as part of a collaborative effort of state, federal, and local agencies in order to meet a wide range of project goals. This LiDAR data set was collected Sept 29 - Oct 1, 2008 and falls mainly in Multnomah County, but also in small portions of Clark and Clackamas Counties, Oregon. This data set consists of bare earth and unclassified points. The average pulse density is 8.04 pulses per square meter over terrestrial surfaces. The area of interest (AOI) encompasses approximately 34,021 acres and the total area flown (TAF) covers 35,873 acres. The TAF acreage is greater than the original AOI acreage due to buffering and flight planning optimization. In some areas of heavy vegetation or forest cover, there may be relatively few ground points in the LiDAR data. Elevation values for open water surfaces are not valid elevation values because few lidar points are returned from water surfaces. Lidar intensity values were also collected. Original contact information: Contact Name: Ian Madin Contact Org: DOGAMI Phone: 971-673-1542 Email: ian.madin@dogami.state.or.us Provide high resolution terrain elevation and land cover elevation data. 10741 The final LiDAR Report for the Sandy River study area may be accessed at: https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/1432/supplemental/or2008_usda_sandy_river_m1432.pdf A footprint of this data set may be viewed in Google Earth at: https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/1432/supplemental/2008_USDA_Forest_Service_Sandy_River_Lidar.kmz Theme ISO 19115 Topic Category elevation Theme Bare earth Theme Bare ground Theme DOGAMI Theme High-resolution Theme Light Detection and Ranging Office for Coastal Management Charleston SC Data Set None Planned Lidar points in LAZ format Classed 1,2 none Any conclusions drawn from the analysis of this information are not the responsibility of the USDA Forest Service, the Office for Coastal Management or its partners. USDA Forest Service Data Steward 2012 Organization NOAA Office for Coastal Management NOAA/OCM coastal.info@noaa.gov 2234 South Hobson Ave Charleston SC 29405-2413 (843) 740-1202 https://coast.noaa.gov NOAA Office for Coastal Management Home Page Online Resource Distributor 2012 Organization NOAA Office for Coastal Management NOAA/OCM coastal.info@noaa.gov 2234 South Hobson Ave Charleston SC 29405-2413 (843) 740-1202 https://coast.noaa.gov NOAA Office for Coastal Management Home Page Online Resource Metadata Contact 2012 Organization NOAA Office for Coastal Management NOAA/OCM coastal.info@noaa.gov 2234 South Hobson Ave Charleston SC 29405-2413 (843) 740-1202 https://coast.noaa.gov NOAA Office for Coastal Management Home Page Online Resource Point of Contact 2012 Organization NOAA Office for Coastal Management NOAA/OCM coastal.info@noaa.gov 2234 South Hobson Ave Charleston SC 29405-2413 (843) 740-1202 https://coast.noaa.gov NOAA Office for Coastal Management Home Page Online Resource Ground Condition -122.427072 -122.104372 45.575607 45.369807 Range 2008-09-29 2008-10-01 Yes Unclassified This data can be obtained on-line at the following URL: https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=1432 This data set is dynamically generated based on user-specified parameters. ; None 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. https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=1432 Customized Download Create custom data files by choosing data area, product type, map projection, file format, datum, etc. https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/1432/index.html Bulk Download Simple download of data files. https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/1432/supplemental/2008_USDA_Forest_Service_Sandy_River_Lidar.kmz Browse Graphic Browse Graphic kmz This graphic shows the lidar coverage for the Sandy River Study Area. https://coast.noaa.gov Online Resource https://coast.noaa.gov/dataviewer Online Resource 2016-05-23 Date that the source FGDC record was last modified. 2017-11-14 Converted from FGDC Content Standards for Digital Geospatial Metadata (version FGDC-STD-001-1998) using 'fgdc_to_inport_xml.pl' script. Contact Tyler Christensen (NOS) for details. 2018-02-08 Partial upload of Positional Accuracy fields only. 2018-03-13 Partial upload to move data access links to Distribution Info. Microsoft Windows XP Version 5.1 (Build 2600) Service Pack 2; ESRI ArcCatalog 9.2.0.1324 Not specified in final report, assumed to be 1 m. The Root Mean Square Error (RMSE) of the data is 0.051 meters. Accuracy was assessed using 506 ground survey RTK (real time kinematic) points. These ground survey points are distributed throughout the Sandy River study area. The final LiDAR Report for the Sandy River study area may be accessed at: https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/1432/supplemental/sandy_report.doc LiDAR data has been collected and processed for all areas within the project study area. LiDAR flight lines have been examined to ensure that there is at least 50% sidelap, that there are no gaps between flightlines, and that overlapping flightlines have consistent elevation values. Shaded relief images have been visually inspected for data errors such as pits, border artifacts, gaps, and shifting. 1 No metadata was provided to NOAA OCM with this data set. The following process step is derived from the Watershed Sciences, Inc. lidar report. This report may be accessed at: https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/1432/supplemental/sandy_report.doc Acquisition 1. The lidar data were collected between Sept 29 - Oct 1, 2008. 2. The survey used a Leica ALS50 Phase II sensor. 3. Near nadir scan angles were used to increase penetration of vegetation to ground surfaces. 4. Ground level GPS and aircraft IMU were collected during the flight. Processing 1. Laser point coordinates were computed using the REALM software 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. 1.1 files; each point maintains the corresponding scan angle, return number (echo), intensity, and x, y, z (easting, northing, and elevation) information. 2. 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 (less than 500 MB). Flightlines and LiDAR data were then reviewed to ensure complete coverage of the study area and positional accuracy of the laser points. 3. 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. 4. 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. 5. 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 (less than 1 cm) in relative accuracy. 6. 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. 2 The NOAA Office for Coastal Management (OCM) received the files in las format. The files contained LiDAR elevation and intensity measurements. The data were in UTM Zone 10 NAD83(CORS96) projection, NAVD88 (Geoid03) vertical datum and units were in meters. OCM performed the following processing for data storage and Digital Coast provisioning purposes: 1. The data were converted from UTM Zone 10 coordinates to geographic coordinates. 2. The data were converted from NAVD88 (orthometric) heights to GRS80 (ellipsoid) heights using Geoid03. 3. The data were sorted by time and zipped to laz format. 2012-12-01T00:00:00 gov.noaa.nmfs.inport:50097 Anne Ball 2017-11-15T15:24:15 SysAdmin InPortAdmin 2022-08-09T17:11:37 2022-03-16 OCM Partners OCMP 1002 Public No No 2022-03-16 1 Year 2023-03-16