Digital Surface Model (DSM) from UAS survey of the debris flow at South Fork Campground, Sequoia National Park, CA

Frequently anticipated questions:

• What does this data set describe?
  1. How might this data set be cited?
  2. What geographic area does the data set cover?
  3. What does it look like?
  4. Does the data set describe conditions during a particular time period?
  5. What is the general form of this data set?
  6. How does the data set represent geographic features?
  7. How does the data set describe geographic features?
• Who produced the data set?
  1. Who are the originators of the data set?
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?
• Why was the data set created?
• How was the data set created?
  1. From what previous works were the data drawn?
  2. How were the data generated, processed, and modified?
  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?
  2. How accurate are the geographic locations?
  3. How accurate are the heights or depths?
  4. Where are the gaps in the data? What is missing?
  5. How consistent are the relationships among the data, including topology?
• How can someone get a copy of the data set?
  1. Are there legal restrictions on access or use of the data?
  2. Who distributes the data?
  3. What's the catalog number I need to order this data set?
  4. What legal disclaimers am I supposed to read?
  5. How can I download or order the data?
• Who wrote the metadata?

What does this data set describe?

1. How might this data set be cited?
   Logan, Joshua B., and East, Amy E., 20241219, Digital Surface Model (DSM) from UAS survey of the debris flow at South Fork Campground, Sequoia National Park, CA: data release DOI:10.5066/P144KDGN, U.S. Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, California.

   Online Links:

   • https://doi.org/10.5066/P144KDGN
   • https://www.sciencebase.gov/catalog/item/673535e2d34e6fbce7b5d589

   This is part of the following larger work.
   Logan, Joshua B., and East, Amy E., 2024, Aerial imagery and structure-from-motion derived data products from a UAS survey of the debris flow at South Fork Campground, Sequoia National Park, CA: data release DOI:10.5066/P144KDGN, U.S. Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA.

   Online Links:

   • https://doi.org/10.5066/P144KDGN

   Other_Citation_Details:

   Suggested Citation: Logan, J.B., East A.E., 2024, Aerial imagery and structure-from-motion derived data products from a UAS survey of the debris flow at South Fork Campground, Sequoia National Park, CA: U.S. Geological Survey data release, https://doi.org/10.5066/P144KDGN.

2. What geographic area does the data set cover?

   West_Bounding_Coordinate: -118.76724
   East_Bounding_Coordinate: -118.75407
   North_Bounding_Coordinate: 36.35335
   South_Bounding_Coordinate: 36.33801
   

3. What does it look like?

   https://www.sciencebase.gov/catalog/file/get/673535e2d34e6fbce7b5d589?name=SEKI_SouthFork_DebrisFlow_2024-04-30_DSM_10cm_browse.jpg&allowOpen=True (JPEG)

   Color-shaded relief image of Digital Surface Model of the debris flow at South Fork Campground.

4. Does the data set describe conditions during a particular time period?

   Calendar_Date: 30-Apr-2024

   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: 11
      Transverse_Mercator:

      Scale_Factor_at_Central_Meridian: 0.9996
      Longitude_of_Central_Meridian: -117.0
      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 0.1
      Ordinates (y-coordinates) are specified to the nearest 0.1
      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?

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: 30-Apr-2024 (process 1 of 4)

   Aerial imagery was collected using a Department of Interior-owned quadcopter fitted with Ricoh GR II digital cameras featuring global shutters. The camera was 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 110 meters above ground level (AGL), resulting in a nominal ground-sample-distance (GSD) of 2.9 centimeters per pixel. The flight lines were spaced to provide approximately 70-80 percent overlap between images from adjacent lines. Terrain-following was used to maintain a somewhat consistent height above the ground, as the UAS flew over terrain of increasing elevation away from the takeoff location. The camera was triggered at 1 Hz using an 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. 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: 2024 (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 the flight. The GCP positions were measured using real-time kinematic (RTK) GPS, using corrections from a GPS base station located on a temporary benchmark ("SFKW") established on the foot of the debris flow, near the takeoff site. The approximate base station position was initially derived using an autonomous position. The final coordinates for SFKW were derived using a seven-hour static occupation, submitted to the National Geodetic Survey Online User Positioning Service (OPUS-S), conforming to the criteria for a Level II single-base OPUS-S survey according to USGS Techniques and Methods 11-D1. 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. After the survey was completed, the RTK data were updated using the final SFKW position 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: 2024 (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 the exiftool utility via the command line. To improve timestamp accuracy, the camera time was set to coordinated UTC time using a smart phone wireless connection to the camera. 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.
   The Adobe Camera RAW software package was used to adjust the exposure value (EV) of the DNG images. For the image from flight F01, the EV was increased by an EV of +2.0; for flight F02, the EV was increased by an EV of +2.7. The images were exported in TIFF format, and JPG format with a quality setting of 93. 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: 2024 (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. Image alignment was performed using the TIFF images, and the following parameters: 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: 26.7 Final Projection Accuracy: 3.0 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: 0.3 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. A dense point cloud was created using the 'high' accuracy setting, with 'aggressive' depth filtering, and 'confidence' calculated. Points with confidence less than 3 were assigned a classification value of 7 (noise). 6. A Digital Surface Model (DSM) was created using all points in the dense point cloud. The DSM was exported to a GeoTIFF file with 10-centimeter pixel resolution and Lempel-Ziv-Welch (LZW) compression. The raster cell origin was set to an integer value to ensure horizontal raster alignment with other data products. 7. A orthomosaic image was created using the DSM as a rectification surface. 8. A clipping mask was manually digitized to exclude areas outside of the area of interest. 9. Data products were exported, using the clipping mask to exclude areas outside of the area of interest. The point cloud was exported to a cloud-optimized point cloud (COPC LAZ) file. The DSM was exported to a GeoTIFF file with 10-centimeter pixel resolutions and LZW compression. The orthomosaic was exported to a GeoTIFF file with a 3-cm pixel resolution. The raster cell origins of both raster products were set to integer values to ensure horizontal raster alignment with other geospatial data. 10. The GDAL "gdal_translate" utility was used to convert the DSM GeoTIFF file to a cloud-optimized GeoTIFF, with statistics calculated and "DEFLATE" compression applied. 11. The GDAL "gdal_translate" utility was used to convert the orthomosaic GeoTIFF to a cloud-optimized GeoTIFF, with "JPG" compression applied with a quality level of 90. 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:

   • https://doi.org/10.5066/P9DGS5B9

   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).

   American Society for Photogrammetry and Remote Sensing, 2024, ASPRS Positional Accuracy Standards for Digital Geospatial Data, ed. 2, ver. 1.0.

   Online Links:

   • https://publicdocuments.asprs.org/PositionalAccuracyStd-Ed2-V2

   Other_Citation_Details: DOI 10.14358/ASPRS.PAS.2024
   

   Rydlund, P.H. Jr., and Densmore, B.K., 2012, Geospatial Methods of Practice and Guidelines for Using Survey-Grade Global Navigation Satellite Systems (GNSS) to Establish Vertical Datum in the United States Geological Survey..

   Online Links:

   • https://pubs.usgs.gov/tm/11d1/tm11-D1.pdf

   Other_Citation_Details:

   Techniques and Methods 11-D1, Chapter 1 of Section D, Field Survey Methods Book 11, Collection and Delineation of Spatial Data.

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?
   This data set was tested according to techniques recommended by ASPRS Positional Accuracy Standards for Digital Geospatial Data, Edition 2, Version 2 (2024). Although the Standards call for a minimum of thirty (30) checkpoints, this test was performed using only 4 checkpoints. This data set was produced to meet a 5-centimeter Horizontal Positional Accuracy Class (RMSEH). The tested horizontal positional accuracy was found to be RMSEH = 4.0 centimeters using the reduced number of checkpoints. Horizontal accuracy was tested by comparing ground control check point locations measured with survey-grade GNSS to their SfM-estimated positions. A total of 23 temporary ground control points (GCPs) consisting of either tarps with a black and white pattern, or temporary chalk marks on the ground were established prior to the survey. These positions were measured using RTK GPS referenced to a local base station with reference coordinates derived using the National Geodetic Survey Online Positioning User Service (OPUS). Nineteen of the GCPs were used for registration and camera optimization in the SfM processing workflow. Four GCPs were used as check points to estimate the horizontal precision of the SfM-estimated positions relative to the GPS-measured positions. We calculated a horizontal checkpoint fit of RMSEH_1 = 0.028 meters. This value represents the relative precision of the SfM image alignment with respect to the GNSS-measured positions of the GCP locations. To derive an estimate of total horizontal accuracy (RMSEH) we account for additional horizontal error in the survey control and check points (RMSEH_2 = 0.029 meters) by including the following error estimates through summation in quadrature: horizontal accuracy of the NGS OPUS-derived position of the GNSS base station operating on temporary benchmark “SFKW” at the 95 percent confidence level (0.026 meters); and estimated horizontal uncertainty in the position of the GNSS rover (0.013 meters). 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. Thus, the tested total horizontal accuracy (RMSEH) for the resulting data product is 0.040 meters. The equivalent FGDC National Standard for Spatial Data Accuracy (NSSDA) horizontal accuracy at the 95 percent confidence level is 0.069 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 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 DSM to exceed this estimate.
3. How accurate are the heights or depths?
   This data set was tested according to techniques recommended by ASPRS Positional Accuracy Standards for Digital Geospatial Data, Edition 2, Version 2 (2024). Although the Standards call for a minimum of thirty (30) checkpoints, this test was performed using only 4 checkpoints. This data set was produced to meet a 10-centimeter Vertical Positional Accuracy Class (RMSEV). The tested vertical positional accuracy was found to be RMSEV = 8.5 centimeters using the reduced number of checkpoints in the Non-Vegetated Vertical Accuracy (NVA) tested area. Vertical accuracy was tested in two ways. First, we compared ground control check point locations measured with survey-grade GNSS to their SfM-estimated positions. A total of 23 temporary ground control points (GCPs) consisting of either tarps with a black and white pattern, or temporary chalk marks on the ground were established prior to the survey. These positions were measured using RTK GPS referenced to a local base station with reference coordinates derived using the National Geodetic Survey Online Positioning User Service (OPUS). Nineteen of the GCPs were used for registration and camera optimization in the SfM processing workflow. Four GCPs were used as check points to estimate the vertical precision of the SfM-estimated positions relative to the GPS-measured positions. We calculated a vertical check point fit of RMSEV_1 = 0.042 meters. This value represents the relative precision of the SfM image alignment with respect to the GNSS-measured GCP elevations. To derive an estimate of total vertical accuracy (RMSEV) we account for additional vertical error in the survey control and check points (RMSEV_2 = 0.074 meters) by including the following error estimates through summation in quadrature: vertical accuracy of the NGS OPUS-derived position of the GNSS base station operating on temporary benchmark “SFKW” at the 95 percent confidence level (0.069 meters, of which the geoid error (GEOID18) accounts for 0.058 meters); and estimated vertical uncertainty in the position of the GNSS rover (0.026 meters). 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. Thus, the total vertical accuracy (RMSEV) for the resulting data product is 0.085 meters. The equivalent FGDC National Standard for Spatial Data Accuracy (NSSDA) vertical accuracy at the 95 percent confidence level is 0.167 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 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 DSM to exceed this estimate. In order to test the DSM accuracy in areas away from the GCP check points, we also compared the DSM elevations to topographic measurements concurrently collected using a backpack-mounted RTK GNSS receiver. The GNSS was referenced to the same base station (SFKW) that was used to survey the GCP locations and was used to survey in distributed locations across the DSM. Using 949 points, we found an RMSEV of 0.082 meters, with a mean offset of -0.027 meters (showing that the backpack measurements were, on average, lower than the DSM elevation, likely resulting from antenna-height errors having to do with surveyor movement over steep terrain or soft sediment). We provide this additional accuracy analysis to give the end user a better understanding of the vertical errors that may be present in the DSM in areas away from the GCP check points.
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?

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 DSM is available as a cloud optimized GeoTIFF file.
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?
   • Availability in digital form:

     Data format:	Cloud Optimized GeoTIFF file contains a Digital Surface Model (DSM) with single-precision floating-point values compressed using the DEFLATE lossless compression method. No data value is -32767. in format GeoTIFF (version 1.0) Size: 80.25
     Network links:	https://www.sciencebase.gov/catalog/file/get/673535e2d34e6fbce7b5d589?name=SEKI_SouthFork_DebrisFlow_2024-04-30_DSM_10cm.tif https://prod-is-usgs-sb-prod-publish.s3.amazonaws.com/673535e2d34e6fbce7b5d589/SEKI_SouthFork_DebrisFlow_2024-04-30_DSM_10cm.tif https://www.sciencebase.gov/catalog/item/673535e2d34e6fbce7b5d589 https://doi.org/10.5066/P144KDGN

   • Cost to order the data: None.

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: 19-Dec-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)