Digital Surface Models (DSM) from UAS surveys of the upper reservoir delta at Jenkinson Lake, El Dorado County, California

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Frequently anticipated questions:

What does this data set describe?

Title:
Digital Surface Models (DSM) from UAS surveys of the upper reservoir delta at Jenkinson Lake, El Dorado County, California
Abstract:
This portion of the data release presents high-resolution Digital Surface Models (DSM) of the Jenkinson Lake upper reservoir delta in El Dorado County, California. The DSMs have resolutions of 10 centimeters per pixel and were derived from structure-from-motion (SfM) processing of aerial imagery collected during surveys with unoccupied aerial systems (UAS). The surveys were on 2021-10-13, 2021-11-04, 2022-10-25, and 2023-11-13, and were generally timed to coincide with low water level in the reservoir to maximize sub-aerial coverage. The raw imagery used to create the orthomosaics was acquired with a UAS quadcopter 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, from an approximate altitude of 100 meters above ground level (AGL), resulting in a nominal ground-sample-distance (GSD) of 2.6 centimeters per pixel. The raw imagery was geotagged using positions from the UAS onboard single-frequency autonomous GPS. Survey control was established using temporary ground control points (GCPs) consisting of a combination of small square tarps with black-and-white cross patterns and temporary chalk marks placed on the ground. The GCP positions were measured using dual-frequency real-time kinematic (RTK) GPS with corrections referenced to a static base station operating nearby. The images and GCP positions were used for structure-from-motion (SfM) processing to create topographic point clouds, high-resolution orthomosaic images, and DSMs. The DSMs are provided in a cloud optimized GeoTIFF format with internal overviews and masks to facilitate cloud-based queries and display.
Supplemental_Information:
Additional information about the field activities from which these data were derived is available online at: https://cmgds.marine.usgs.gov/fan_info.php?fan=2021-655-FA https://cmgds.marine.usgs.gov/fan_info.php?fan=2021-659-FA https://cmgds.marine.usgs.gov/fan_info.php?fan=2022-666-FA https://cmgds.marine.usgs.gov/fan_info.php?fan=2023-678-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., and East, Amy E., 20240807, Digital Surface Models (DSM) from UAS surveys of the upper reservoir delta at Jenkinson Lake, El Dorado County, California: data release DOI:10.5066/P14QWDYN, 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., and East, Amy E., 2024, Digital Surface Models and orthomosaic images from UAS surveys of Jenkinson Lake, El Dorado County, CA: data release DOI:10.5066/P14QWDYN, U.S. Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA.

    Online Links:

    Other_Citation_Details:
    Suggested Citation: Logan, J.B. and East A.E., 2024, Digital Surface Models and orthomosaic images from UAS surveys of Jenkinson Lake, El Dorado County, CA: U.S. Geological Survey data release, https://doi.org/10.5066/P14QWDYN.
  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -120.54202
    East_Bounding_Coordinate: -120.52718
    North_Bounding_Coordinate: 38.73757
    South_Bounding_Coordinate: 38.73085
  3. What does it look like?
    https://www.sciencebase.gov/catalog/file/get/667c5976d34e828a20a696d5?name=JenkinsonLake_2021-11-04_DSM_browse.jpg&allowOpen=True (JPEG)
    Color-shaded relief image of Digital Surface Model from 2021-11-04 UAS survey.
  4. Does the data set describe conditions during a particular time period?
    Calendar_Date: 13-Oct-2021
    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 0.025
      Ordinates (y-coordinates) are specified to the nearest 0.025
      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 GEOID18
      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)

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
    • Amy E. East
  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 obtained to measure potential sediment inputs into the reservoir following the Caldor Fire in 2021. These data are intended for science researchers, students, policy makers, and the general public. These data can be used with geographic information systems or other software to identify topographic features on the sub-aerially exposed portions of the reservoir delta.

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: 13-Nov-2023 (process 1 of 4)
    Aerial imagery was collected using a Department of Interior-owned quadcopters fitted with Ricoh GR II digital cameras featuring global shutters. The cameras were mounted using fixed mount on the bottom of the UAS and oriented in an approximately nadir orientation. During acquisition the UAS was flown on pre-programmed autonomous flight lines at an approximate altitude of 100 meters above ground level (AGL), resulting in a nominal ground-sample-distance (GSD) of 2.6 centimeters per pixel. The flight lines were spaced to provide approximately 70-80 percent overlap between images from adjacent lines. The camera was triggered at 1 Hz using either a built-in or external intervalometer. Before each flight, the camera’s 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. The images were recorded in raw Adobe DNG format. The flights were conducted on 2021-10-13, 2021-11-04, 2022-10-25, and 2023-11-13. 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: 13-Nov-2023 (process 2 of 4)
    Ground control was established using ground control points (GCPs) consisting of a combination of small square tarps with black-and-white cross patterns and temporary "X" marks placed with chalk on the ground surface throughout the survey area before each flight. The GCP positions were measured using post-processed kinematic (PPK) GPS, using corrections from a GPS base station located on a temporary benchmark ("HZ01") established near the El Dorado Irrigation District Hazel Creek Day Use Area. The coordinates for HZ01 were derived using the average of three National Geodetic Survey OPUS-S solutions from the static GNSS observations collected during the 2021 surveys. For each GCP measurement the GPS receiver was placed on a fixed-height tripod and set to occupy the GCP for a minimum occupation time of one minute. Post-processing was conducted using the Trimble Business Center software package. 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: 2023 (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 either a custom python script which calls the exiftool utility, or by using the exiftool utility via the command line. To improve timestamp accuracy, the image acquisition times were adjusted to true ('corrected') UTC time as necessary by comparing the image timestamps with several images taken on the day of the flights of a smartphone app ('Emerald Time') showing accurate time from Network Time Protocol (NTP) servers. The positions stored in the EXIF are in geographic coordinates referenced to the WGS84(G1150) coordinate reference system (EPSG:4979), with elevation in meters relative to the WGS84 ellipsoid. Additional pertinent metadata were added to the EXIF headers using the command-line 'exiftool' software. 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: 2023 (process 4 of 4)
    Structure-from-motion (SfM) processing techniques were used to create point clouds, DSMs, and orthomosaic images in the Agisoft Photoscan/Metashape software package using the following workflow: 1. Initial image alignment was performed in "4D" whereby the images from all survey dates were aligned together to improve survey-to-survey precision. The following parameters were used: Accuracy: 'high' Pair selection: 'reference', 'generic' Key point limit: 40,000 Tie point limit: 4,000 2. Ground control point (GCP) positions were imported and markers were manually identified and placed in the images. 3. Sparse point cloud error reduction was performed using an automated python script (Logan and others, 2022), to sequentially apply the Reconstruction Uncertainty and Projection Accuracy gradual selection filters to remove 50 percent of the sparse points, followed by camera optimization. This resulted in the following final gradual selection filter values: Final Reconstruction Uncertainty: 20.0 Final Projection Accuracy: 4.0 Lens calibration parameters for optimization: f, cx, cy, k1, k2, k3, p1, and p2 4. Additional sparse point cloud error reduction was performed using the automated python script to iteratively apply the Reprojection Error gradual selection filter and camera optimization such that no more than 10 percent of the remaining sparse points are deleted at a time. Between each iteration of the filter, camera optimization was performed with the following lens calibration parameters: f, cx, cy, k1, k2, k3, p1, and p2. Once Reprojection Error was reduced below 1 pixel, additional lens calibration parameters (k4, b1, b2, p3, and p4) were included during optimization. This process was repeated until the following final Reprojection Error filter levels were achieved: Final Reprojection Error: 0.5 Lens calibration parameters for optimization: f, b1, b2, cx, cy, k1, k2, k3, k4, p1, p2, p3 and p4 Additional remaining 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. After final error reduction, images from each survey date were separated into individual chunks for each date. 6. Dense point clouds were created for each survey-date chunk using the 'high' accuracy setting, with 'aggressive' depth filtering. 7. Digital Surface Models (DSM) for each survey date were created using the dense point clouds and exported to GeoTIFF files with 10-centimeter pixel resolutions and LZW compression. 8. A clipping mask polygon shapefile was derived in QGIS using the 1056 meter elevation contour as an approximate upper bound. The lower bound was hand digitized to exclude areas below the reservoir water surface. Additional areas with standing water or excessive noise were also excluded by the clipping mask. 9. The GDAL "gdalwarp" utility was used to clip the GeoTIFF files using the shapefile polygons to produce GeoTIFFs in cloud-optimized GeoTIFF (COG) format. The "target aligned pixels (-tap)" parameter was used to ensure cell alignment between the DSMs from each survey date. 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?
    Logan, J.B., Wernette, P.A., and Ritchie, A.C., 2022, Agisoft Metashape/Photoscan Automated Image Alignment and Error Reduction version 2.0.

    Online Links:

    Other_Citation_Details:
    Logan, J.B., Wernette, P.A. and Ritchie, A.C., 2022, Agisoft Metashape/Photoscan Automated Image Alignment and Error Reduction version 2.0: U.S. Geological Survey code repository, U.S. Geological Survey software release, python package, Reston, Va., (https://doi.org/10.5066/P9DGS5B9).
    Federal Geographic Data Committee, 1998, Geospatial Positioning Accuracy Standards Part 3: National Standard for Spatial Data Accuracy.

    Online Links:

    Other_Citation_Details: FGDC-STD-007.3-1998

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 precision was estimated by comparing ground control point (GCP) positions measured with survey-grade GNSS to their SfM-estimated positions. 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. To evaluate the horizontal precision of the final SfM alignments, a subset of GCPs for each survey date was disabled one at a time using a python script to create a 'temporary check point'. With the 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 GNSS-measured position were tabulated. After all temporary check point iterations were complete, the root-mean-square error (RMSE) was calculated. Following the Federal Geographic Data Committee (FGDC) National Standard for Spatial Data Accuracy guidelines, we use the RMSE to calculate the positional horizontal accuracy at the 95 percent confidence level. These values represent the relative precision of the SfM image alignment with respect to the GNSS-measured positions of the GCP locations. To derive an estimate of horizontal accuracy, we account for additional uncertainty in the GNSS-measured positions of the GCPs by including the following error estimates through summation in quadrature: 0.020 meters of horizontal uncertainty in the NGS OPUS-derived position of the GNSS base station operating on temporary benchmark “HZ01”; 0.010 meters of estimated horizontal uncertainty in the position of the GNSS rover. Additional sources of error in the GNSS rover measurements resulting from survey rod errors, antenna height measurement errors, and errors resulting from the settling of the survey rod in soft sediment are unknown. The horizontal precision of the orthomosaic shows a spatially variable pattern, based on visual review of stable objects on the ground in the orthomosaic from each survey date. The orthomosaics show good horizontal precision in the open region of the main delta on the Hazel Creek inlet as well as below the confluence of Hazel and Sly Park creeks. In contrast, the region of the narrow Sly Park Creek inlet, east of longitude -120.533935 shows larger than expected variability in the horizontal positions of fixed objects between survey dates. We attribute these apparent horizontal displacements to the abundance of tree canopy along the creek leading to poor SfM image alignment (from fewer high quality image tie points) as well as poor performance of GNSS in this environment. Thus, we provide two estimates of horizontal accuracy for the orthomosaics, one for the main delta region and one for the Sly Park Creek inlet region:
    - Main delta region, west of longitude -120.53394: Horizontal precision, based on one-at-a-time GCP check points, at the 95 percent confidence level (m): 0.049 meters Estimated accuracy of the GNSS-measured positions of GCPs (m): 0.022 meters Total horizontal accuracy estimate at the 95 percent confidence level, including GNSS uncertainty (m): 0.054 meters
    - Sly Park Creek inlet region, east of longitude -120.53394: Horizontal precision, based on one-at-a-time GCP check points, at the 95 percent confidence level (m): 0.380 meters Estimated accuracy of the GNSS-measured positions of GCPs (m): 0.022 meters Total horizontal accuracy estimate at the 95 percent confidence level, including GNSS uncertainty (m): 0.381 meters
    It should be noted that this error estimate is for areas of bare ground where GCPs were placed. Additional sources of error such as poor image-to-image point matching due to vegetation, shallow water, or uniform substrate texture (such as sand with uniform coloration) resulting in poor surface reconstruction may cause localized errors in some portions of orthomosaic to exceed this estimate.
  3. How accurate are the heights or depths?
    Vertical precision was estimated by comparing ground control point (GCP) positions measured with survey-grade GNSS to their SfM-estimated positions. 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. To evaluate the vertical precision of the final SfM alignments, a subset of GCPs for each survey date was disabled one at a time using a python script to create a 'temporary check point'. With the 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 GNSS-measured position were tabulated. After all temporary check point iterations were complete, the root-mean-square error (RMSE) was calculated. Following the Federal Geographic Data Committee (FGDC) National Standard for Spatial Data Accuracy guidelines, we use the RMSE to calculate the positional vertical accuracy at the 95 percent confidence level. These values represent the relative precision of the SfM image alignment with respect to the GNSS-measured positions of the GCP locations. To derive an estimate of vertical accuracy, we account for additional uncertainty in the GNSS-measured positions of the GCPs by including the following error estimates through summation in quadrature: 0.045 meters of vertical uncertainty in the NGS OPUS-derived ellipsoid height of the GNSS base station operating on temporary benchmark “HZ01”; 0.059 meters of uncertainty in the geoid undulation model (NGS GEOID18) at this location, used to transform ellipsoidal heights into orthometric heights; 0.025 meters of estimated vertical uncertainty in the position of the GNSS rover. Additional sources of error in the GNSS rover measurements resulting from survey rod errors, antenna height measurement errors, and errors resulting from the settling of the survey rod in soft sediment are unknown. The vertical precision of the DSM shows a spatially variable pattern, based on queries of elevations on stable objects on the ground in the DSM from each survey date. The DSMs show good vertical precision in the open region of the main delta on the Hazel Creek inlet as well as below the confluence of Hazel and Sly Park creeks. In contrast, the region of the narrow Sly Park Creek inlet, east of longitude -120.533935 shows larger than expected variability in the elevations of fixed objects between survey dates. We attribute these vertical errors to the abundance of tree canopy along the creek leading to poor SfM image alignment (from fewer high quality image tie points) as well as poor performance of GNSS in this environment. Thus, we provide two estimates of vertical accuracy for the orthomosaics, one for the main delta region and one for the Sly Park Creek inlet region:
    - Main delta region, west of longitude -120.53394: Vertical precision, based on one-at-a-time GCP check points, at the 95 percent confidence level (m): 0.104 meters Estimated accuracy of the GNSS-measured positions of GCPs (m): 0.078 meters Total vertical accuracy estimate at the 95 percent confidence level, including GNSS uncertainty (m): 0.130 meters
    - Sly Park Creek inlet region, east of longitude -120.53394: Vertical precision, based on one-at-a-time GCP check points, at the 95 percent confidence level (m): 0.310 meters Estimated accuracy of the GNSS-measured positions of GCPs (m): 0.078 meters Total vertical accuracy estimate at the 95 percent confidence level, including GNSS uncertainty (m): 0.320 meters
    It should be noted that this error estimate is for areas of bare ground where GCPs were placed. Additional sources of error such as poor image-to-image point matching due to vegetation, shallow water, or uniform substrate texture (such as sand with uniform coloration) resulting in poor surface reconstruction may cause localized errors in some portions of the DSMs to exceed this estimate.
  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 No access constraints
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 DSMs 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: 07-Aug-2024
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)
This page is <https://cmgds.marine.usgs.gov/catalog/pcmsc/DataReleases/ScienceBase/DR_P14QWDYN/JenkinsonLake_DSM_metadata.faq.html>
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