Elevation point cloud from low-altitude aerial imagery from UAS flights over Black Beach, Falmouth, Massachusetts on 18 March 2017 (LAZ file)

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Frequently anticipated questions:


What does this data set describe?

Title:
Elevation point cloud from low-altitude aerial imagery from UAS flights over Black Beach, Falmouth, Massachusetts on 18 March 2017 (LAZ file)
Abstract:
Imagery acquired with unmanned aerial systems (UAS) and coupled with structure from motion (SfM) photogrammetry can produce high-resolution topographic and visual reflectance datasets that rival or exceed lidar and orthoimagery. These new techniques are particularly useful for data collection of coastal systems, which requires high temporal and spatial resolution datasets. The U.S. Geological Survey worked in collaboration with members of the Marine Biological Laboratory and Woods Hole Analytics at Black Beach, in Falmouth, Massachusetts to explore scientific research demands on UAS technology for topographic and habitat mapping applications. This project explored the application of consumer-grade UAS platforms as a cost-effective alternative to lidar and aerial/satellite imagery to support coastal studies requiring high-resolution elevation or remote sensing data. A small UAS was used to capture low-altitude photographs and GPS devices were used to survey reference points. These data were processed in an SfM workflow to create an elevation point cloud, an orthomosaic image, and a digital elevation model.
Supplemental_Information:
For more information regarding field activity 2016-010-FA, see https://cmgds.marine.usgs.gov/fan_info.php?fan=2016-010-FA.
  1. How might this data set be cited?
    Sturdivant, Emily J., Lentz, Erika E., Thieler, E. Robert, Remsen, David P., and Miner, Simon, 2017, Elevation point cloud from low-altitude aerial imagery from UAS flights over Black Beach, Falmouth, Massachusetts on 18 March 2017 (LAZ file): data release DOI:10.5066/F7KW5F04, U.S. Geological Survey, Coastal and Marine Geology Program, Woods Hole Coastal and Marine Science Center, Woods Hole, MA.

    Online Links:

    Other_Citation_Details:
    The first link is to the publication landing page. The second link is to the page containing the data.
    This is part of the following larger work.

    Sturdivant, Emily J., Thieler, E. Robert, Lentz, Erika E., Remsen, David P., and Miner, Simon, 2017, Topographic, imagery, and raw data associated with unmanned aerial systems (UAS) flights over Black Beach, Falmouth, Massachusetts on 18 March 2016: data release DOI:10.5066/F7KW5F04, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    Suggested citation: Sturdivant, E.J., Thieler, E.R., Lentz, E.E., Remsen, D.P., and Miner, Simon, 2017, Topographic, imagery, and raw data associated with unmanned aerial systems (UAS) flights over Black Beach, Falmouth, Massachusetts on 18 March 2016: U.S. Geological Survey data release, https://doi.org/10.5066/F7KW5F04.
  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -70.643977
    East_Bounding_Coordinate: -70.639736
    North_Bounding_Coordinate: 41.587071
    South_Bounding_Coordinate: 41.582607
  3. What does it look like?
    https://www.sciencebase.gov/catalog/file/get/59b00efde4b020cdf7d4db64/?name=bb20160318_sfm_points_hq_browse.png (PNG)
    Image of a portion of the point cloud generated from photogrammetry and low-altitude aerial images obtained with unmanned aerial systems (UAS) flights over Black Beach, Falmouth, Massachusetts on 18 March 2016.
  4. Does the data set describe conditions during a particular time period?
    Calendar_Date: 18-Mar-2016
    Currentness_Reference:
    ground condition
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: LAZ binary data
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      This is a Point data set. It contains the following vector data types (SDTS terminology):
      • Point (4,345,647)
    2. What coordinate system is used to represent geographic features?
      The map projection used is Universal Transverse Mercator.
      Projection parameters:
      Scale_Factor_at_Central_Meridian: 0.999600
      Longitude_of_Central_Meridian: -69.00000
      Latitude_of_Projection_Origin: 0.000
      False_Easting: 500000.0000
      False_Northing: 0.0000
      Planar coordinates are encoded using coordinate pair
      Abscissae (x-coordinates) are specified to the nearest 0.001
      Ordinates (y-coordinates) are specified to the nearest 0.001
      Planar coordinates are specified in meters
      The horizontal datum used is North American Datum of 1983 (NAD 83).
      The ellipsoid used is Geodetic Reference System 80.
      The semi-major axis of the ellipsoid used is 6378137.
      The flattening of the ellipsoid used is 1/298.257222.
      Vertical_Coordinate_System_Definition:
      Altitude_System_Definition:
      Altitude_Datum_Name: North American Vertical Datum of 1988 (NAVD88)
      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?
    Entity_and_Attribute_Overview:
    The attribute information associated with points in the LAZ file is standard, as described in ASPRS (2013). Attributes include location (northing, easting, and elevation in UTM Zone 19 North meters in the NAD83 and NAVD88 coordinate systems), color (red, blue, and green components), intensity, and classification. All points are classified as 0 (unclassified). The LAZ file format is described in Isenburg (2013).
    Entity_and_Attribute_Detail_Citation:
    See cross-references for complete citations of ASPRS (2013) and Isenburg (2013).

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Emily J. Sturdivant
    • Erika E. Lentz
    • E. Robert Thieler
    • David P. Remsen
    • Simon Miner
  2. Who also contributed to the data set?
    Acknowledgment of the USGS Coastal and Marine Geology Program as a data source would be appreciated in products developed from these data, and such acknowledgment as is standard for citation and legal practices for data source is expected.
  3. To whom should users address questions about the data?
    U.S. Geological Survey
    Attn: Emily J. Sturdivant
    Geographer
    384 Woods Hole Road
    Woods Hole, Massachusetts

    508-548-8700 x2230 (voice)
    508-457-2310 (FAX)
    esturdivant@usgs.gov

Why was the data set created?

These points provide the calculated XYZ (horizontal and vertical) coordinates and RGB (red-green-blue) values of the land surface during a mid-day low-tide on 18 March, 2016. The product was created to demonstrate the use of structure-from-motion for coastal research and may be used to develop further datasets, such as the digital elevation models, geomorphic feature mapping, and land cover classification.

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-Mar-2016 (process 1 of 1)
    The following process was used to generate the dense point cloud. The images used were acquired from a DJI Phantom 3 Professional flown over Black Beach, Falmouth on 18 March 2016. Eighteen ground control points (GCPs) were incorporated in the photogrammetric processing. Details and locations of the images and GCPs are provided by additional datasets in the larger work citation. The processing was performed using Agisoft Photoscan Professional v. 1.2.6 build 2834 (64 bit) software. The computer was a Mac Pro running OS X 10.11.6 with 8-Core Intel Xeon E5 CPUs running at 3 GHz with 128 GB RAM.
    Add photos
    1) Using the “Add photos…” tool, all 250 images in bb_20160318_UAS_images.zip (larger work) were added to a single “chunk” (Agisoft terminology). Image quality for the photos was estimated using “Estimate image quality…”. The resulting image quality metrics (which are relative non-dimensional measures in which a value of 1 indicates that the image does not have obvious blurring) ranged from 0.69 to 1.26.
    2) Using ”Convert”, the coordinate system of the images (called “cameras” in Photoscan) was converted from native GPS geographic units (latitude/longitude, assumed to be in the WGS84 coordinate system) to meters in NAD83/UTM zone 19N (EPSG:26919) using the toWGS84 transformation in the NAD83 well-known text (WKT). Camera location accuracy was left at the default 10 m (found in Reference Settings on the Reference Pane).
    Ground control points
    3) “Detect Markers” was used to automatically identify targets in the photos, with settings “Cross (non-coded)” and a tolerance of “50” (on a scale of zero to 100, with 100 being the least discriminating). All 18 of the 4-ft square black and white targets deployed were automatically detected. The automatically-generated marker labels were added to the GCP location file (see larger work) by viewing the target label in the images and with reference to a map of the labeled GCPs.
    3) “Import markers” was used to load the GCP location file (see larger work), which assigned precise GCP coordinates (northing, easting, and elevation in UTM Zone 19 North meters in NAD83 and NAVD88 coordinate systems) to the detected markers, and placed new markers for the GCPs that had not been auto-detected.
    4) The locations of all markers were established and verified in all of the images in which they appeared, except when the image of the target was so poor that the reference point on the target could not be precisely determined. This was a manual process aided by the ability of the software to identify images in which each marker appeared and to maintain a centered view at constant zoom level across all of those images. Each of those images was inspected to verify and adjust the precise marker placement. Manual placement was a painstaking and somewhat subjective process that introduced slight uncertainties into the GCP location in the images. However, our experience indicates that addition of GCPs and pinpointing targets in as many images as possible improves the final alignment of the point cloud. The tie-point accuracy was set to 1 pixel in “Reference Settings”.
    Initial alignment
    5) ”Align Photos” was selected to align all of the cameras using the following settings: Accuracy: “High” (which did not downsample the images); Pair selection: “Reference” (which used GPS information identify nearby images when searching for tie points); Key point limit: 5,000; Tie point limit; 0 (unlimited). Adaptive camera model fitting option was selected. 211 cameras (images with varying viewpoints) were initially aligned.
    6) ”Optimize Cameras” was used to perform initial lens calibration and camera alignment. Lens-calibration parameters f, cx, cy, k1, k2, k3 were included; higher-order parameters k4, b1, b2, p1, p2, p3, and p4 were not. These parameters define focal length (f), pixel coordinates of the principal point (cx, cy), and certain radial distortion coefficients (k1, k2, k3, k4, p1, p2, p3, and p4). The software generates a metric for assessing model fit called the standard unit weight error (SUWE). Values close to 1.0 are optimal. The initial SUWE was 0.165 and the overall alignment error for the cameras was 35.26 m.
    Refinement of the sparse point cloud
    The sparse point cloud representing tie points among the images consisted of approximately 690,000 points. An iterative method developed by Tommy Noble and used by Sherwood (2017) was used to identify and remove lower-quality tie points with the “Gradual Selection” tool. Iterations were performed with the following criteria and target values.
    * Reconstruction uncertainty – Quality based on the geometry of the reconstruction. A dimensionless ratio of the maximum/minimum axes of the three-dimensional ellipse describing reconstruction uncertainty based on ray triangulation (target was 10)
    * Projection accuracy – Quality of pixel matching among images. A weighted ranking (1 is best, larger numbers worse) based on the size and sharpness of tie-points (target was 3)
    * Reprojection error – Estimate of residual error in tie-point location. A measure (pixels) of the precision of calculated tie-point locations based on the geometry (target was 0.3 pixels).
    “Gradual Selection” was used and the target value was set, but if more than about 20% of the points were flagged at that setting, the threshold was adjusted to select only about 10% of the points. (The total number of points and the number of flagged points was shown on screen as selections were made). Selected points were deleted, and camera settings were optimized before the next iteration. After each iteration, the improvement in accuracy was assessed by checking the marker error for ground control points. This procedure was repeated three times for each criterion listed above (in order; i.e. points were selected based on reconstruction uncertainty three times before next selecting by projection accuracy).
    When complete, approximately 26 percent of the points had been removed, leaving 581,000 tie points, and the following values for the target metrics were obtained:
    * Reconstruction uncertainty - 10 (no units)
    * Projection accuracy - 6 (no units)
    * Reprojection error - 0.45  (pixels)
    
    The marker error for the ground control points was reduced to 0.083 m (0.43 pixels) from 0.094 m (0.58 pixels).
    As a final alignment optimization/tie point refinement step, we manually deleted outlying tie points, such as those that were clearly above or below ground. We did so conservatively. These points were identified as those that did not follow the known structure of the landscape, and were usually caused by photos in which water was moving and/or inconsistently obstructed the view of submerged areas. With these changes, the updated (and final) optimization produced an estimated marker error of 6 cm for about 500,000 tie points remaining in the sparse point cloud. From beginning to end, optimization removed about 190,000 tie points, 27 percent of the original sparse cloud. This reduced the total error by 90 percent, from 62 to 6 cm.

    Dense point cloud
    “Build Dense Cloud” was invoked with “High” quality and “Mild” depth filtering to generate a dense point cloud. "Export points" was used to export the point cloud in .LAZ format with coordinates referenced to NAD 83/UTM Zone 19N. The resulting dense point cloud containing 4,345,647 points is the associated data product.
    Estimate uncertainty of point cloud
    Uncertainty in the location of points in the dense point cloud is, in general, the quadrature sum of a) uncertainty in the locations of the ground control points (GCPs) to which the point cloud is referenced; b) uncertainty in the geometric reconstruction represented by the sparse point cloud, which includes uncertainty in the location of tie points, camera locations, camera look angles, and camera lens calibrations, assuming the GCP locations are exact; and c) interpolation errors associated with placing the dense-cloud points in the geometric reconstruction, which arise when the locations of dense-cloud points between sparse-cloud points differ from the real-world locations. The horizontal and vertical precision of the surveyed GCP locations (a), was estimated as the average of the two uncertainties reported for every GCP, which were +/- 0.011 m horizontally and +/- 0.014 m vertically. Our replacements for (b) were the horizontal and vertical RMS errors associated with reconstruction of the GCP marker locations reported by Photoscan (+/- 0.06 m and +/- 0.024 m, respectively). In lieu of values for (c), we combined the reported unscaled reprojection error reported by Photoscan (0.45 pixels) with the resolution of the images (1 pixel equaled approximately 0.025 m in nadir views) to derive a reprojection error of 0.01 m. These combined uncertainty estimates (root sums of squares of the uncertainty terms) are +/- 0.057 m horizontal and +/- 0.03 m vertical. Person who carried out this activity:
    U. S. Geological Survey, Woods Hole Coastal and Marine Science Center
    Attn: Emily J. Sturdivant
    Geographer
    384 Woods Hole Road
    Woods Hole, Massachusetts
    U.S.A.

    508-548-8700 x2230 (voice)
    508-457-2310 (FAX)
    esturdivant@usgs.gov
  3. What similar or related data should the user be aware of?
    Isenburg, Martin, 2013, LASzip: Lossless compression of LiDAR data: Phtogrammetric Engineering & Remote Sensing Vol. 79, No. 2, February 2013, pp. 209-217, American Society for Photogrammetry & Remote Sensing, Bethesda, MD.

    Online Links:

    American Society for Photogrammetry & Remote Sensing (ASPRS), 2013, LAS SPECIFICATION VERSION 1.4 – R13: American Society for Photogrammetry & Remote Sensing, Bethesda, MD.

    Online Links:

    Agisoft, 2015, Agisoft Photoscan User Manual: Professional Edition: manual Version 1.2.

    Online Links:

    Sherwood, Christopher R., 2016, Low-altitude aerial imagery and related field observations associated with unmanned aerial systems (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts on 1 March 2016: U.S. Geological Survey, Reston, VA.

    Online Links:

    Sherwood, Christopher R., 2017, Point cloud from low-altitude aerial imagery from unmanned aerial system (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts on 1 March 2016 (LAZ file): U.S. Geological Survey, Reston, VA.

    Online Links:


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?
    Uncertainty in the location of points in the dense point cloud was estimated as the quadrature sum of three error terms. The horizontal precision of the surveyed GCP locations was estimated as the average error reported for every GCP. The horizontal uncertainty for the GCPs was +/- 0.011 m. The horizontal RMS error associated with reconstruction of the GCP marker locations reported by Photoscan was +/- 0.06 m. We combined the reported unscaled reprojection error reported by Photoscan (0.45 pixels) with the resolution of the images (1 pixel equaled approximately 0.025 m in nadir views) to derive a reprojection error of 0.01 m. We combine these uncertainties using root sum of squares to establish minimum estimates of the horizontal uncertainty in real-world coordinates of the reconstructed points as +/- 0.057.
  3. How accurate are the heights or depths?
    A digital elevation model interpolated from the point cloud differs from independently measured ground points with an RMS vertical error of approximately 9 cm. However, biases in some portions of the map area may cause greater errors (up to 50 cm horizontal and 1 m vertical), and some individual points may have gross errors.
    Uncertainty in the location of points in the dense point cloud was estimated as the quadrature sum of three error terms. The estimate of vertical uncertainty for the GCPs was +/- 0.014 m. The vertical RMSE associated with reconstruction of the GCP marker locations was +/- 0.024 m. The reprojection error was 0.01 m (combination of the reported unscaled reprojection error reported by Photoscan (0.45 pixels) with the resolution of the images). We combine these uncertainties using root sum of squares to establish minimum estimate of the vertical uncertainty in real-world coordinates of the reconstructed points as +/- 0.03 m.
  4. Where are the gaps in the data? What is missing?
    The point cloud was constructed from 250 images. Points that were clearly far removed from the ground surface were manually removed during SfM processing. However, there are still many points that likely do not represent ground features, but are instead artifacts generated by moving water surfaces and or erroneous tie points. These points have been retained to allow experimentation with point classification methods.
  5. How consistent are the relationships among the observations, including topology?
    Coordinates recorded for each point describe discrete positions in space and the visual reflectance at the time of capture. No formal logical accuracy tests were conducted. Although some outlying points were manually eliminated, there are still many points that likely do not represent ground features, but are instead artifacts generated by moving water surfaces and or erroneous tie points.

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:
Public domain data from the U.S. Government are freely redistributable with proper metadata and source attribution. Please recognize the U.S. Geological Survey as the originator of the dataset.
  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

    1-888-275-8747 (voice)
    sciencebase@usgs.gov
  2. What's the catalog number I need to order this data set? There is one LAZ file containing the points. A browse graphic and the associated CSDGM FGDC metadata in XML format is also available for download from the page containing the data.
  3. What legal disclaimers am I supposed to read?
    Neither the U.S. Government, the Department of the Interior, the USGS, the Marine Biological Laboratory, Woods Hole Analytics, nor any of their employees, contractors, or subcontractors, make any warranty, express or implied, nor assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, nor represent that its use would not infringe on privately owned rights. The act of distribution shall not constitute any such warranty, and no responsibility is assumed by the USGS in the use of these data or related materials. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
  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 files require software capable of opening binary LAZ files.

Who wrote the metadata?

Dates:
Last modified: 12-Sep-2017
Metadata author:
U. S. Geological Survey, Woods Hole Coastal and Marine Science Center
Attn: Emily J. Sturdivant
Geographer
384 Woods Hole Road
Woods Hole, Massachusetts
U.S.A.

508-548-8700 x2230 (voice)
508-457-2310 (FAX)
esturdivant@usgs.gov
Metadata standard:
FGDC Content Standards for Digital Geospatial Metadata (FGDC-STD-001-1998)

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