Digital surface model (DSM) for the intertidal zone at Lone Tree Point, Kiket Bay, WA, 2019-06-05

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Frequently anticipated questions:


What does this data set describe?

Title:
Digital surface model (DSM) for the intertidal zone at Lone Tree Point, Kiket Bay, WA, 2019-06-05
Abstract:
This portion of the data release presents a digital surface model (DSM) and hillshade image of the intertidal zone at Lone Tree Point, Kiket Bay, WA. The DSM has a resolution of 4 centimeters per pixel and was derived from structure-from-motion (SfM) processing of aerial imagery collected with an unmanned aerial system (UAS) on 2019-06-05. Unlike a digital elevation model (DEM), the DSM represents the elevation of the highest object within the bounds of a cell. Vegetation, buildings and other objects have not been removed from the data. In addition, data artifacts resulting from noise in the original imagery have not been removed. The raw imagery used to create the DSM was acquired using a UAS fitted with a Ricoh GR II digital camera featuring a global shutter. The UAS was flown on pre-programmed autonomous flight lines spaced to provide approximately 70 percent overlap between images from adjacent lines. The camera was triggered at 1 Hz using a built-in intervalometer. The UAS was flown at an approximate altitude of 70 meters above ground level (AGL), resulting in a nominal ground-sample-distance (GSD) of 1.8 centimeters per pixel. The raw imagery was geotagged using positions from the UAS onboard single-frequency autonomous GPS. Eighteen temporary ground control points (GCPs) were distributed throughout the survey area to establish survey control. The GCPs consisted of a combination of small square tarps with black-and-white cross patterns and "X" marks placed on the ground using temporary chalk. The GCP positions were measured using post-processed kinematic (PPK) GPS, using corrections from a GPS base station located approximately 16 kilometers from the study area. The DSM and hillshade images have been formatted as cloud optimized GeoTIFFs with internal overviews and masks to facilitate cloud-based queries and display.
Supplemental_Information:
Additional information about the field activity from which these data were derived is available online at:
https://cmgds.marine.usgs.gov/fan_info.php?fan=2019-623-FA
Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
  1. How might this data set be cited?
    Logan, Joshua B., Grossman, Eric E., and VanArendonk, Nathan R., 20210915, Digital surface model (DSM) for the intertidal zone at Lone Tree Point, Kiket Bay, WA, 2019-06-05: data release DOI:10.5066/P9TO24SR, U.S. Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, California.

    Online Links:

    This is part of the following larger work.

    Logan, Joshua B., Grossman, Eric E., and VanArendonk, Nathan R., 2021, Aerial imagery and structure-from-motion data products from UAS survey of the intertidal zone at Lone Tree Point, Kiket Bay, WA, June 2019: data release DOI:10.5066/P9TO24SR, U.S. Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA.

    Online Links:

  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -122.55652
    East_Bounding_Coordinate: -122.55186
    North_Bounding_Coordinate: 48.41205
    South_Bounding_Coordinate: 48.40420
  3. What does it look like?
    https://www.sciencebase.gov/catalog/file/get/5ef5225782ced62aaae6a00e?name=LoneTreePoint_2019-06-05_DSM_browse.jpg&allow=openTrue (JPEG)
    Color-shaded relief DSM for Lone Tree Point Kiket Bay, WA.
  4. Does the data set describe conditions during a particular time period?
    Calendar_Date: 05-Jun-2019
    Currentness_Reference:
    ground condition at time data were collected
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: GeoTIFF
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      This is a Raster data set. It contains the following raster data types:
      • Dimensions, type Grid Cell
    2. What coordinate system is used to represent geographic features?
      Grid_Coordinate_System_Name: Universal Transverse Mercator
      Universal_Transverse_Mercator:
      UTM_Zone_Number: 10
      Transverse_Mercator:
      Scale_Factor_at_Central_Meridian: 0.9996
      Longitude_of_Central_Meridian: -123.0
      Latitude_of_Projection_Origin: 0.0
      False_Easting: 500000.0
      False_Northing: 0.0
      Planar coordinates are encoded using row and column
      Abscissae (x-coordinates) are specified to the nearest 0.040
      Ordinates (y-coordinates) are specified to the nearest 0.040
      Planar coordinates are specified in meters
      The horizontal datum used is NAD83 (National Spatial Reference System 2011) (EPSG:1116).
      The ellipsoid used is GRS 1980 (EPSG:7019).
      The semi-major axis of the ellipsoid used is 6378137.0.
      The flattening of the ellipsoid used is 1/298.257222101.
      Vertical_Coordinate_System_Definition:
      Altitude_System_Definition:
      Altitude_Datum_Name:
      North American Vertical Datum of 1988 (EPSG:5703), derived using GEOID12B
      Altitude_Resolution: 0.001
      Altitude_Distance_Units: meters
      Altitude_Encoding_Method:
      Explicit elevation coordinate included with horizontal coordinates
  7. How does the data set describe geographic features?
    GeoTIFF
    GeoTIFF containing elevation values. (Source: Producer defined)
    N/A
    Elevation relative to the North American vertical datum of 1988 (NAVD88), derived using GEOID12B (Source: Producer defined)
    Range of values
    Minimum:-1.459
    Maximum:35.033
    Units:meters
    Resolution:0.001

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Joshua B. Logan
    • Eric E. Grossman
    • Nathan R. VanArendonk
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?
    U.S. Geological Survey, Pacific Coastal and Marine Science Center
    Attn: PCMSC Science Data Coordinator
    2885 Mission Street
    Santa Cruz, CA

    831-427-4747 (voice)
    pcmsc_data@usgs.gov

Why was the data set created?

These data were collected to characterize the morphology, substrate composition and roughness of intertidal areas to support modeling of coastal storm and wave impacts with sea-level rise as part of the USGS Puget Sound Coastal Storm Modeling System (PS-CoSMoS). The data are also intended to be used to model and evaluate sediment transport and its effects on coastal habitats, a focus of the USGS Coastal Habitats in Puget Sound Project (CHIPS) and its partners to inform resource management and adaptive planning for our Nation's coasts. The DSM can be used with geographic information systems (GIS) software for research purposes.

How was the data set created?

  1. From what previous works were the data drawn?
  2. How were the data generated, processed, and modified?
    Date: 05-Jun-2019 (process 1 of 4)
    Aerial imagery was collected using a Department of Interior-owned 3DR Solo quadcopter fitted with a Ricoh GR II digital camera featuring a global shutter. The camera was mounted using a fixed mount on the bottom of the UAS and oriented in an approximately nadir orientation. For flights F01, F02, F03, and F04, the UAS was flown on pre-programmed autonomous flight lines at an approximate altitude of 70 meters above ground level (AGL), resulting in a nominal ground-sample-distance (GSD) of 1.8 centimeters per pixel. The flight lines were oriented roughly shore-parallel and were spaced to provide approximately 70 percent overlap between images from adjacent lines. For flight F05, the UAS was flown manually to acquire imagery over areas not mapped in the previous flights. The cameras were triggered at 1 Hz using a built-in intervalometer and were programmed to simultaneously acquire imagery in both JPG and camera raw (Adobe DNG) formats. Before each flight, the camera digital ISO, aperture and shutter speed were manually set to adjust for ambient light conditions. Although these settings were changed between flights, they were not permitted to change during a flight; thus, the images from each flight were acquired with consistent camera settings. Person who carried out this activity:
    Joshua Logan
    U.S. Geological Survey, Pacific Coastal and Marine Science Center
    Physical Scientist
    2885 Mission Street
    Santa Cruz, CA
    US

    831-460-7519 (voice)
    831-427-4748 (FAX)
    jlogan@usgs.gov
    Date: 05-Jun-2019 (process 2 of 4)
    Ground control was established using ground control points (GCPs) consisting of small square tarps with black-and-white cross patterns and temporary chalk 'X' marks placed on the ground surface throughout the survey area. The GCP positions were measured using survey-grade GPS receivers operating in post-processed-kinematic (PPK) mode. The GPS receivers were placed on short fixed-height tripods and set to occupy each GCP for a minimum occupation time of one minute. The PPK corrections were referenced to a Continuously Operating Reference (CORS) GPS base station ('SKGT') located approximately 16 kilometers from the study area operated by the Washington State Reference Network (WSRN). Person who carried out this activity:
    Joshua Logan
    U.S. Geological Survey, Pacific Coastal and Marine Science Center
    2885 Mission Street
    Santa Cruz, CA

    831-460-7519 (voice)
    jlogan@usgs.gov
    Date: 2019 (process 3 of 4)
    The image files were renamed using a custom python script. The file names were formed using the following pattern Fx-YYYYMMDDThhmmssZ_Ryz.*, where: - Fx = Flight number - YYYYMMDDThhmmssZ = date and time in the ISO 8601 standard, where 'T' separates the date from the time, and 'Z' denotes UTC ('Zulu') time. - Ry = RA or RB to distinguish camera 'RicohA' from 'RicohB' - z = original image name assigned by camera during acquisition - * = file extension (JPG or DNG)
    The approximate image acquisition coordinates were added to the image metadata (EXIF) ('geotagged') using the image timestamp and the telemetry logs from the UAS onboard single-frequency 1-Hz autonomous GPS. The geotagging process was done using a custom Python script which processes the GPS data from the UAS telemetry log and calls the command-line 'exiftool' software. To improve timestamp accuracy, the image acquisition times were adjusted to true ('corrected') UTC time by comparing the image timestamps with several images taken of a smartphone app ('Emerald Time') showing accurate time from Network Time Protocol (NTP) servers. For this survey, + 00:00:01 (1 second) was added to the image timestamp to synchronize with corrected UTC time. The positions stored in the EXIF are in geographic coordinates referenced to the WGS84(G1150) coordinate reference system (EPSG:7660), with elevation in meters relative to the WGS84 ellipsoid.
    Additional information was added to the EXIF using the command-line 'exiftool' software with the following command: exiftool ^ -P ^ -Copyright="Public Domain. Please credit U.S. Geological Survey." ^ -CopyrightNotice="Public Domain. Please credit U.S. Geological Survey." ^ -ImageDescription="Low-altitude aerial image of the intertidal zone at Lone Tree Point, Kiket Bay, Washington, USA, from USGS survey 2019-623-FA." ^ -Caption-Abstract="Intertidal zone at Lone Tree Point, Kiket Bay, Washington, USA, from USGS survey 2019-623-FA." ^ -Caption="Aerial image of the intertidal zone at Lone Tree Point, Kiket Bay, Washington, USA, from USGS survey 2019-623-FA." ^ -sep ", " ^ -keywords="Marine Nearshore Intertidal, Lone Tree Point, Swinomish Reservation, Kiket Bay, Washington, 2019-623-FA, Unmanned Aircraft System, UAS, drone, aerial imagery, U.S. Geological Survey, USGS, Pacific Coastal and Marine Science Center" ^ -comment="Low-altitude aerial image from USGS Unmanned Aircraft System (UAS) survey 2019-623-FA." ^ -Credit="U.S. Geological Survey" ^ -Contact="pcmsc_data@usgs.gov" ^ -Artist="U.S. Geological Survey, Pacific Coastal and Marine Science Center" Person who carried out this activity:
    Joshua Logan
    U.S. Geological Survey, Pacific Coastal and Marine Science Center
    2885 Mission Street
    Santa Cruz, CA

    831-460-7519 (voice)
    jlogan@usgs.gov
    Date: 2019 (process 4 of 4)
    Structure-from-motion (SfM) processing techniques were used to create the point clouds in the Agisoft Photoscan/Metashape software package using the following workflow: 1. Initial image alignment was performed with the following parameters - Accuracy: 'high'; Pair selection: 'reference', 'generic'; Key point limit: 0 (unlimited); Tie point limit: 0 (unlimited). 2. Sparse point cloud error reduction was performed using an iterative gradual selection and camera optimization process with the following parameters: Reconstruction Uncertainty: 10; Projection Accuracy: 3. Lens calibration parameters f, cx, cy, k1, k2, k3, p1, and p2 were included in the optimization. Additional sparse points obviously above or below the true surface were manually deleted after the last error reduction iteration. 3. Ground control points (GCPs) were automatically detected using the 'Cross (non-coded)' option. False matches were manually removed, and all markers were visually checked and manually placed or adjusted if needed. Markers were manually placed for GCPs that consisted of chalk 'X' marks. 4. Additional sparse point cloud error reduction was performed using an iterative gradual selection and camera optimization process with the following parameters: Reconstruction Error: 0.3. Lens calibration parameters f, cx, cy, k1, k2, k3, p1, and p2 were initially included in the optimization, but additional parameters k4, b1, b2, p3, and p4 were included once Reconstruction Error was reduced below 1 pixel. Additional sparse points obviously above or below the true surface were manually deleted after the last error reduction iteration, and a final optimization was performed. 5. A dense point cloud was created using the 'high' accuracy setting, with 'aggressive' depth filtering. 6. A Digital Surface Model (DSM) with a native resolution of 3.7 centimeters per pixel was created using all points in the dense point cloud, and was exported to a GeoTIFF format with a 4-centimeter pixel resolution. 7. An RGB orthomosaic with a native resolution of 1.84 centimeters per pixel was created using the DSM as the orthorectification surface, and was exported to a GeoTIFF format with a 2-centimeter pixel resolution. 8. An exterior boundary was digitized using the orthomosaic as a reference and was used as a clipping mask to exclude areas of water, obvious edge artifacts, and large areas of interpolation. 9. The DSM was converted to cloud optimized GeoTIFF format for compatibility with cloud storage services using the GDAL software package. The DSM was compressed using the lossless LZW compression method, and 'NoData' value set to -32767. A hillshade of the DSM was created in cloud optimized GeoTIFF format using GDAL. Person who carried out this activity:
    Joshua Logan
    U.S. Geological Survey, Pacific Coastal and Marine Science Center
    Physical Scientist
    2885 Mission Street
    Santa Cruz, CA
    US

    831-460-7519 (voice)
    831-427-4748 (FAX)
    jlogan@usgs.gov
  3. What similar or related data should the user be aware of?

How reliable are the data; what problems remain in the data set?

  1. How well have the observations been checked?
    No formal attribute accuracy tests were conducted.
  2. How accurate are the geographic locations?
    Horizontal accuracy was estimated by comparing SfM-derived ground control point (GCP) positions to PPK GPS measurements. Due to the time-intensive process of placing GCPs in the field, all available GCPs were used for registration and camera optimization in the SfM processing workflow during the creation of the final point clouds. To evaluate the horizontal positional accuracy of the point cloud after processing was completed, each GCPs was disabled one-at-a-time using a python script to create a 'temporary check point'. With a single GCP temporarily disabled, camera optimization was performed with all lens parameters fixed, and all other GCPs enabled. The residual errors of the check point relative to its GPS-measured position were recorded. After all temporary check point iterations were complete, the root-mean-square error (RMSE) and mean-absolute error (MAE) were calculated. The resulting horizontal RMSE was 0.019 meters (MAE 0.016 meters). The addition of the estimated horizontal GPS uncertainty (0.020 meters) in quadrature results in a total horizontal accuracy estimate of 0.028 meters for the point cloud. It should be noted that this error estimate is for areas of bare ground or low vegetation where GCPs were placed. Additional sources of error such as poor image-to-image point matching due to vegetation or uniform substrate texture (such as sand) resulting in poor surface reconstruction may cause localized errors in some portions of the point clouds to exceed this estimate.
  3. How accurate are the heights or depths?
    Vertical accuracy was estimated using two methods to compare the DSM vertical elevations to concurrently collected post-processed kinematic (PPK) GPS measurements. The first method used a comparison of SfM-derived ground control point (GCP) positions to PPK GPS measurements at those points. Due to the time-intensive process of placing GCPs in the field, all available GCPs were used for registration and camera optimization in the SfM processing workflow during the creation of the final DSM. To evaluate the vertical positional accuracy of the DSM after processing was completed, a subset of GCPs was disabled one-at-a-time using a python script to create 'temporary check points'. With a single GCP temporarily disabled, camera optimization was performed with all lens parameters fixed, and all other GCPs enabled. The residual errors of the check point relative to its GPS-measured position were recorded. After all temporary check point iterations were complete, the root-mean-square error (RMSE) and mean-absolute error (MAE) were calculated. The resulting vertical RMSE was 0.037 meters (MAE 0.028 meters). The addition of the estimated vertical GPS uncertainty (0.025 meters) in quadrature results in a total vertical accuracy estimate of 0.045 meters for the point cloud. It should be noted that this error estimate is for areas of bare ground or low vegetation where GCPs were placed. Additional sources of error such as poor image-to-image point matching due to vegetation or uniform substrate texture (such as sand) resulting in poor surface reconstruction may cause localized errors in some portions of the point clouds to exceed this estimate. A second method was used to attempt to quantify the vertical errors in areas away from the GCPs. During field data collection, topographic measurements on unvegetated areas were collected with backpack-mounted PPK GPS. These measurements were compared to the DSM elevations using bilinear interpolation at each GPS point to derive additional accuracy estimates for the DSM. The vertical RMSE of 1,717 backpack-mounted PPK GPS measurements compared to the DSM elevations was 0.064 meters (MAE 0.049 meters). The mean-error (vertical bias) of the GPS measurements relative to DSM elevations was -0.023 meters, meaning the DSM was, on average, higher than the GPS measurements. The addition of the estimated vertical uncertainty of the backpack-mounted GPS (0.035 meters) in quadrature results in a total vertical accuracy estimate of 0.073 meters for the DSM using this method. These slightly higher errors are due in part to the less precise nature of the backpack-mounted GPS measurements. However, these backpack-mounted GPS comparisons do provide a more conservative, and more spatially distributed estimate of the true accuracy of the DSM than the GCP check point method. We present both estimates here to provide the end-user with a more complete understanding of the accuracy of the final data product.
  4. Where are the gaps in the data? What is missing?
    Dataset is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details.
  5. How consistent are the relationships among the observations, including topology?
    No formal logical accuracy tests were conducted.

How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?
Access_Constraints: None
Use_Constraints:
USGS-authored or produced data and information are in the public domain from the U.S. Government and are freely redistributable with proper metadata and source attribution. Please recognize and acknowledge the U.S. Geological Survey as the originator(s) of the dataset and in products derived from these data. This information is not intended for navigation purposes.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey - ScienceBase
    Denver Federal Center, Building 810, Mail Stop 302
    Denver, CO
    United States

    1-888-275-8747 (voice)
    sciencebase@usgs.gov
  2. What's the catalog number I need to order this data set? The Lone Tree Point DSM and hillshade (LoneTreePoint_2019-06-05_DSM_4cm.tif, and LoneTreePoint_2019-06-05_DSM_4cm_hll.tif) are available as Cloud Optimized GeoTIFF files.
  3. What legal disclaimers am I supposed to read?
    Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty.
  4. How can I download or order the data?
  5. What hardware or software do I need in order to use the data set?
    These data can be viewed with GIS software or other software capable of displaying geospatial raster data.

Who wrote the metadata?

Dates:
Last modified: 15-Sep-2021
Metadata author:
U.S. Geological Survey, Pacific Coastal and Marine Science Center
Attn: PCMSC Science Data Coordinator
2885 Mission Street
Santa Cruz, CA

831-427-4747 (voice)
pcmsc_data@usgs.gov
Metadata standard:
Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)

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