Andrew C. Ritchie Jonathan A. Warrick Joshua B. Logan 2019 point cloud digital data data release DOI:10.5066/P973FQ3M Pacific Coastal and Marine Science Center, Santa Cruz, California U.S. Geological Survey https://doi.org/10.5066/P973FQ3M https://www.sciencebase.gov/catalog/file/get/5c6488c7e4b0fe48cb373f54 Andrew C. Ritchie Jonathan A. Warrick Joshua B. Logan 2019 data release DOI:10.5066/P973FQ3M Pacific Coastal and Marine Science Center, Santa Cruz, California U.S. Geological Survey https://doi.org/10.5066/P973FQ3M Presented here is a point cloud produced by the U.S. Geological Survey (USGS) from historical U.S. Air Force vertical aerial imagery, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was downloaded from USGS Eros Data Center and processed using structure-from-motion photogrammetry with Agisoft PhotoScan version 1.2.8 through 1.3.2. Point clouds were clipped to an AOI using LASTools. The AOI was created from a KMZ in Google Earth and transformed to a shapefile using ArcMap 10.5. Data were produced from previously collected mapping products to better understand long-term evolution of an unstable area of the Big Sur coastline presenting a natural hazard to life, property, and navigation, as part of the USGS Remote Sensing Coastal Change (RSCC) Project. The area failed catastrophically on 20 May 2017, and subsequent data collection continued to monitor both the natural and human-modified evolution of the slide area. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. 19671018 ground condition Complete None planned -121.436958 -121.423702 35.870567 35.860814 USGS Metadata Identifier USGS:5c6488c7e4b0fe48cb373f54 ISO 19115 Topic Category geoscientificInformation elevation imageryBaseMapsEarthCover transportation Marine Realms Information Bank (MRIB) keywords geomorphology coastal change hazards topics coastal landsliding aerial and satellite photography agents of coastal change anthropogenic agents of coastal change coastal landslides coastal processes coastal sediment budget coastal zone management effects of coastal change human responses to coastal change hazards and disasters USGS Thesaurus landslides coastal processes hazards topography remote sensing aerial photography image analysis image collections Data Categories for Marine Planning Bathymetry and Elevation None U.S. Geological Survey USGS Coastal and Marine Hazards and Resources Program CMHRP Pacific Coastal and Marine Science Center PCMSC Common geographic areas United States California Central California Coastal Big Sur Villa Creek Cape San Martin Monterey None. 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. U.S. Geological Survey, Pacific Coastal and Marine Science Center PCMSC Science Data Coordinator mailing and physical

2885 Mission Street

Santa Cruz CA 95060 United States 831-427-4747 pcmsc_data@usgs.gov Original survey data collected by United States Air Force Microsoft Windows versions 7 and 10, Agisoft PhotoScan version 1.2.8 through 1.3.2, LASTools 170327 through 180322 were used to process data for this release. Jonathan A. Warrick Andrew C. Ritchie Kevin M. Schmidt Mark E. Reid Joshua B. Logan 2019 Warrick, J.A., Ritchie, A.C., Schmidt, K.M., Reid, M.E., and Logan, J.B., 2019, Characterizing the catastrophic 2017 Mud Creek landslide, California, using repeat structure-from-motion (SfM) photogrammetry: Landslides, doi:10.1007/s10346-019-01160-4 https://doi.org/10.1007/s10346-019-01160-4 No formal attribute accuracy tests were conducted Coordinates recorded for each point in the point cloud describe discrete positions in space and the visual reflectance (in RGB values) at the time of capture. This final point cloud was checked for accuracy by rotating the point cloud to view from multiple angles to ensure that obvious spurious points do not cause artifacts in measurements or derivative products (DEM and Orthomosaic). Although some outlying points were eliminated during processing, there may still be points that do not represent ground features, but are instead artifacts generated by erroneous tie points or spurious matches in the photogrammetric process. Data set 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. Ground control was derived from six photo-identifiable, stable, and prominent rocks that were located around the perimeter of the landslide area. Positions for three of these rocks were derived from survey-grade global navigation satellite system (GNSS) measurements collected by USGS field crews during August 17-20, 2017. Positions for the remaining three rocks were derived from the 2016 airborne lidar point data. The vertical and horizontal accuracies for these positions were 0.02 and 0.03 m for the GNSS measurements and were assumed to be 0.25 m for the lidar-derived data. The lidar-derived data include the additional uncertainty of accurately matching the irregularly spaced lidar points to the photo-identifiable rocks, which is difficult to measure, but estimated to be an additional 0.25 m, resulting in a total estimated 3D uncertainty of 0.5 m. Ground control was derived from six photo-identifiable, stable, and prominent rocks that were located around the perimeter of the landslide area. Positions for three of these rocks were derived from survey-grade global navigation satellite system (GNSS) measurements collected by USGS field crews during August 17-20, 2017. Positions for the remaining three rocks were derived from the 2016 airborne lidar point data. The vertical and horizontal accuracies for these positions were 0.02 and 0.03 m for the GNSS measurements and were assumed to be 0.25 m for the lidar-derived data. The lidar-derived data include the additional uncertainty of accurately matching the irregularly spaced lidar points to the photo-identifiable rocks, which is difficult to measure, but estimated to be an additional 0.25 m, resulting in a total estimated 3D uncertainty of 0.5 m. U.S. Geological Survey (USGS) 19770330 tabular digital data Single Frame Aerial Photography Aerial Photography Sioux Falls, South Dakota U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center Eros Data Center project code 68008, retrieved via spatial search engine. https://earthexplorer.usgs.gov/ 25000 Digital scanned copy of original film 19671018 ground condition 1967 USAF imagery source material for point cloud generation Dewberry 20170412 tabular digital data USGS Contract: G10PC00020 Task Order: G16PD00366 available at ftp://ftp.coast.noaa.gov/pub/DigitalCoast/lidar2_z/geoid12b/data/6259/west_coast2016_usgs_el_nino_m6259_metadata.xml https://coast.noaa.gov/htdata/lidar2_z/geoid12b/data/6259/ ftp://ftp.coast.noaa.gov/pub/DigitalCoast/lidar2_z/geoid12b/data/6259 Digital and/or Hardcopy 2016 ground condition 2016 USGS West Coast El-Nino lidar (WA, OR, CA) ground control and error analysis Digital Mapping, Inc 2011 tabular digital data https://coast.noaa.gov/htdata/lidar1_z/geoid12a/data/4843/ca2013_arra_centralcoast_m4843_metadata.xml https://coast.noaa.gov/htdata/lidar1_z/geoid12a/data/4843/ ftp://ftp.coast.noaa.gov/pub/DigitalCoast/lidar1_z/geoid12a/data/4843/ Digital and/or Hardcopy 2010 ground condition 2010 ARRA lidar: California Coastal Project (Zone 4) ground control and error analysis 1. Aerial imagery was downloaded from archived historical aerial photo single frames on USGS Earth Explorer (https://earthexplorer.usgs.gov/). A total of 31 images were downloaded. The project code in the Earth Explorer database is 68008, but the spatial query tools were used to identify and download the relevant imagery. Online linkage for that data set, per metadata, is https://lta.cr.usgs.gov/Single_Frame_Records 2.Images from the survey area were first aligned using Hugin by rotating and shifting scanned imagery (affine transformation) so that fiducial marks were at the same pixel locations, and all images were cropped to the same center pixel. Images were then imported into Agisoft Photoscan Professional v. 1.4.2 software using the 'Add photos' tool. 3. The photos were processed through an initial alignment and optimization procedure using the following settings: Alignment - Accuracy: 'High'; Pair selection: 'Reference, generic'; Key point limit: 0 (unlimited); Tie point limit; 0 (unlimited). Optimization - Lens-calibration parameters f, cx, cy, k1, k2, k3, p1, and p2 were included; b1, b2, and higher-order parameters k4, p3, and p4 were not. 4. The sparse point cloud (tie points; created as a result of photo alignment and optimization) was edited using an iterative error-reduction procedure to filter the data. This was done in several iterations of a process called "Gradual Selection" to first reduce reconstruction uncertainty (to a unitless value of 10) and then projection accuracy (to a weighted value of 3). 5. Ground control points (GCPs) were identified in the lidar data and the 1967 aerial images, primarily along the highway. Latitude, longitude, and elevation were extracted from lidar and added to the PhotoScan project. 6. Another round of "Gradual Selection" was done to reduce the reprojection error (to a value of 0.4 pixels) 7. A dense point cloud was then created with the parameters set to "High" quality and "moderate" depth filtering. 8. The dense cloud was exported as a .las file and imported to CloudCompare, where it was aligned to the 2010 lidar point cloud using a rigid transformation (rotate, translate) derived from a least-squares adjustment using Horn's least-squares method for rigid-body transformation between two coordinate systems (Horn, B.K., 1987, Closed-form solution of absolute orientation using unit quaternions: Journal of the Optical Society of America A, vol. 4, p.629-642) The transformation matrix using the 2010 lidar as the source data is: 0.999999523163 -0.000057821395 -0.001067430479 4.551651000977 0.000056641620 0.999999344349 -0.001105255098 -2.024248600006 0.001067493809 0.001105194795 0.999998807907 -122.032318115234 0.000000000000 0.000000000000 0.000000000000 1.000000000000 9. The dense point cloud was exported from cloud compare in LAZ format and clipped to a shapefile of the survey area using lastools with the following command: lasclip -i [input_file.laz] -poly [clip_shape.shp] -odir clipped -olaz 2018 Edited metadata to add keywords section with USGS persistent identifier as theme keyword. No data were changed. 20201019 U.S. Geological Survey VeeAnn A. Cross Marine Geologist Mailing and Physical

384 Woods Hole Road

Woods Hole MA 02543-1598 508-548-8700 x2251 508-457-2310 vatnipp@usgs.gov Mud Creek, Monterey County, California. Located on California State Route 1 (SR1), 2 km (1 mi) south of Gorda, CA, and 57 km (35 mi) north of Cambria CA. Point Universal Transverse Mercator 10 0.9996 -123.0 0.0 500000.0 0.0 coordinate pair 0.1 0.1 meters NAD83 GRS 1980 6378137.0 298.257222101 North American Vertical Datum of 1988 0.01 meters Explicit elevation coordinate included with horizontal coordinates The attribute information associated with the point cloud follows the LAZ file standard. Attributes include location (northing, easting, and elevation in the NAD83/UTM zone 10N (EPSG:26910) horizontal and NAVD88 vertical coordinate systems), color (red, blue, and green components), intensity, and classification. All points are classified as 0 (unclassified). American Society for Photogrammetry and Remote Sensing (ASPRS; 2013, https://www.asprs.org/committee-general/laser-las-file-format-exchange-activities.html) and Isenburg (2013, https://doi.org/10.14358/PERS.79.2.209) U.S. Geological Survey GS ScienceBase mailing address

Denver Federal Center, Building 810, Mail Stop 302

Denver CO 80225 United States 1-888-275-8747 sciencebase@usgs.gov 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. LAZ LAS 1.2 This file contains point cloud data in LAZ format (LAS 1.2 specification). No compression applied. 13.35 https://www.sciencebase.gov/catalog/file/get/5c6488c7e4b0fe48cb373f54 https://www.sciencebase.gov/catalog/item/5c6488c7e4b0fe48cb373f54 https://doi.org/10.5066/P973FQ3M Data can be downloaded using the Network_Resource_Name links. The first link is a direct link to download the zipped file of data and metadata. The second link points to a landing page with metadata and data. The third link takes you to a landing page for the entire data release. None. This zip file contains point cloud data in LAZ format (LAS 1.2 specification). The user must have software capable of uncompressing the .zip compressed file and displaying or processing the .laz format file 20201019 U.S. Geological Survey, Pacific Coastal and Marine Science Center PCMSC Science Data Coordinator mailing and physical

2885 Mission Street

Santa Cruz CA 95060 United States 831-427-4747 pcmsc_data@usgs.gov Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998