Coastal bluff point clouds derived from SfM near Elwha River mouth, Washington from 2016-04-18 to 2020-05-08
Data files
Apr 23, 2024 version files 48.10 GB
Abstract
Point Clouds of an approximately 2.0 km alongshore reach of seaward-facing coastal bluff faces on the Strait of Juan de Fuca, Washington State, were derived using structure-from-motion (SfM) photogrammetry from digital photos collected at least quarterly between 2016 and 2022. The point clouds were derived to assess spatial and temporal patterns of erosion on the bluff face and deposition at the base of the bluff. Photos from Miller, et al. (2022) were aligned using a modified USGS published workflow (Over, et al., 2022) with Agisoft Metashape Professional 1.8.5. Photos were aligned within a single chunk in a 4D approach described by Wernette, et al. (2022), and the sparse point cloud was filtered by reconstruction uncertainty (Ru) and projection accuracy (Pa). Dense point clouds were generated independently for each survey date by disabling all cameras except for a single photo date and then generating the dense cloud. This was repeated for each of the 30 photo survey dates, resulting in 30 dense point clouds (one point cloud per photo survey date).
README: Coastal bluff point clouds derived from SfM near Elwha River mouth, Washington
https://doi.org/10.5061/dryad.8pk0p2nww
These point clouds may be useful for evaluating patterns of erosion on the bluff face and deposition at the base of the bluff. However, because of the extreme obliquity of the photos relative to the beach, reconstruction of the beach was generally poor, and caution should be used if using the beach points for research or analysis.
Description of the data and file structure
Point clouds and their associated metadata plain text file are zipped by survey date with the following format: YYYYMMDD_
Zip archives contain two files: (1) a compressed LAZ format (version 1.2) with RGB colors and point confidence values, and (2) a plain text file with the same name containing metadata about the creation of the point cloud in Agisoft Metashape Professional 1.8.5. RGB color depth is 16-bit (i.e., values range from 0 to 65,535). All point clouds are classified using ASPRS standard classifications. Classes present are 0 (unclassified/never classified) and 10 (high noise), where high noise points have a point confidence less than 3. The projected coordinate system is EPSG:34737, corresponding to WGS84 UTM Zone 10N.
Sharing/Access information
Point clouds were derived from photos published here:
Miller, I.M., P.A. Wernette, A.R. Ritchie, and J.A. Warrick. (2022) Crowd-sourced SfM: Best practices for high resolution monitoring of coastal cliffs and bluffs [Dataset]. Dryad. (https://doi.org/10.5061/dryad.63xsj3v4s).
The associated publication exploring the utility of crowd-sourced photogrammetry and SfM for coastal bluff monitoring can be found here:
Wernette, P.A., I.M. Miller, A.W. Ritchie, and J.A. Warrick. (2022) Crowd-Sourced SfM: Best Practices for High Resolution Monitoring of Coastal Cliffs and Bluffs. Continental Shelf Research 245: 104799. (https://doi.org/10.1016/j.csr.2022.104799).
Code/Software
Processing within Agisoft Metashape Professional 1.8.5 used the following Python script:
Logan, J.B., P.A. Wernette, and A.C. Ritchie. (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).
which is a Python representation of the U.S. Geological Survey SfM processing workflow:
Over, J.R., A.C. Ritchie, C. Kranenburg, J.A. Brown, D. Buscombe, T. Noble, C.R. Sherwood, J.A. Warrick, and P.A. Wernette. (2021) Processing Coastal Imagery with Agisoft Metashape Professional Edition, Version 1.6—Structure from Motion Workflow Documentation: U.S. Geological Survey Open-File Report 2021-1039. U.S. Geological Survey Open-File Report. U.S. Geological Survey. (https://doi.org/10.3133/ofr20211039).
Methods
Photos of the bluff were taken from a boat or while walking along the beach (Miller, et al., 2022). Where possible, photos were geotagged with information from a stand-alone RTK GNSS, although not all survey dates had GNSS geolocation information. Photos from Miller, et al. (2022) were aligned using the USGS published workflow (Over, et al., 2022) with Agisoft Metashape Professional 1.8.5. All photos from all 30 survey dates were aligned within a single chunk in a 4D approach described by Wernette, et al. (2022) with the default parameters of Over, et al., (2022). A two-step filtering process was applied to the aligned sparse point clouds, with a reconstruction uncertainty (Re) value of 10 and a projection accuracy (Pa) of 3. Dense point clouds were then generated with ultra-high quality and aggressive filtering. Finally, point clouds were filtered by their point confidence to eliminate points with a confidence of 2 or lower.
No ground-truth dataset was available for any of the point clouds, nor were ground-control points (GCPs) available. Wernette, et al. (2022) found that the 4D with differential GNSS geotagged photos has a median accuracy of 0.38 m from an independent LIDAR point cloud approximately 2 years prior to the point cloud survey date. However, given the likelihood of bluff change within the two years between the first SfM point cloud and the LIDAR point cloud, the true accuracy of the SfM point clouds is likely better than 0.38 m. The relative accuracy of all point clouds to each other is significantly greater, although this is challenging to quantify without ground-truth observations. In this way, the point clouds presented here may be used with greater confidence to track relative changes from one survey to another, but caution should be taken when comparing these point clouds against other independent point cloud dataset.