Sea-floor videos and location of bottom video tracklines collected in Nantucket Sound, Massachusetts, in May 2016 and May 2017 by the U.S. Geological Survey during field activities 2016-005-FA and 2017-022-FA (MP4 video files and polyline shapefile)

Step 1: Collected data.
Two marine geological surveys were conducted in Nantucket Sound, Massachusetts, in May 2016 and May 2017. The survey vessel occupied one of the target sites and the MiniSEABOSS was deployed off the vessel's starboard side in 2016 and off the vessel's stern in 2017. The MiniSEABOSS was equipped with a modified Van Veen grab sampler, two GoPro HERO4 Black digital cameras, an oblique downward-looking SeaViewer 6000 HD Sea-Drop video camera with a topside feed, and a dive light to illuminate the sea floor for video and photograph collection. The elements of this particular MiniSEABOSS system were held within a stainless-steel frame that measured ~1 x 1 meter. The frame had a stabilizer fin that oriented the system as it drifted over the seabed. Two red lasers were set 20 centimeters apart (both as they are mounted on the MiniSEABOSS frame and as seen in photographs and video on the seabed) for scale measurements. The red laser dots can usually be seen in the sea-floor photos and videos depending on the bottom type and distance to the sea floor. A third laser is positioned at an angle so that when it intersects the other lasers, the MiniSEABOSS is at the optimum height (approximately 75 centimeters) off the bottom for a photograph. The winch operator lowered the sampler until the sea floor was observed in the topside live video feed. Generally, the vessel and sampler drifted with wind and current for up to a few minutes to ensure a decent photo with a clear view of the sea floor was acquired. Bottom video was also recorded during the drift from the oblique downward-looking SeaViewer 6000 HD Sea-Drop video camera directly to a solid-state drive using an Odyssey7 video recorder. Then, the winch operator lowered the Van Veen sampler until it rested on the sea floor. When the system was raised, the Van Veen sampler closed and collected a sample as it was lifted off the sea floor. The sampler was recovered to the deck of the survey vessel where a subsample was taken for grain-size analysis at the sediment laboratory at the USGS Woods Hole Coastal and Marine Science Center. A total of 76 sites were occupied with the MiniSEABOSS: 55 sites were occupied aboard the R/V Rafael in May 2016 during field activity 2016-005-FA, and 21 sites were occupied aboard the R/V Tioga in May 2017 during field activity 2017-022-FA. Bottom video was collected at all but one site (site 2016-005-043 does not have a video).

Step 2: Acquired and processed navigation.
During field activity 2016-005-FA, DGPS navigation from a Hemisphere DGPS receiver was logged through HYPACK navigation software and a DataBridge data logger. The DGPS was set to receive fixes at a 2-second interval in geographic coordinates (WGS 84). Dates and times were recorded in Coordinated Universal Time (UTC). Log files for each MiniSEABOSS deployment were saved in text format and then combined for each Julian day. An AWK script (awkseth.gprmc.16005.awk) was used to parse the GPRMC navigation string from the log files and create ASCII Comma Separated Values (CSV) text files. The output files were merged and then reformatted using an AWK script (nav_time_reformat.awk), creating a processed navigation CSV text file for the survey (2016-005-FA_OdysseyAudioStamp_nav_ALL.csv). For field activity 2017-022-FA, a GPS log file in text format for the R/V Tioga was provided by the Woods Hole Oceanographic Institution. The navigation was collected at a 1-second interval in geographic coordinates (WGS 84), and dates and times were recorded in UTC. An AWK script (awksethTioga.gprmc.17022.awk) was used to parse the GPRMC navigation string from the log file, creating a processed navigation CSV text file for the survey (2017022FA_TiogaShip_nav.csv).

Step 3: Processed video files.
The original video files were copied from the Odyssey7 solid-state drive to the processing computer. A shell script (do_concat_and_TCBurn.sda) was run on the original video files to join the video clips for each site (the Odyssey7 splits clips into less than 4 GB segments), burn the GPS date and time in UTC on to the upper right corner of the video, and transcode the video from MOV to MP4 format. The script also created a text file for each survey with the date, start time, and duration of each video recording. The videos were then clipped to the duration that the camera was within view of the sea floor as needed. One video (clip 100) was of two deployments at the same site, so it was split to create separate videos for each deployment and clipped to the duration that the camera was within view of the sea floor. The text files with the date, start time, and duration of each video recording for each survey were updated with the start times and durations of the clipped videos. The videos were renamed to include the field activity identifier, camera, and date and start time in the ISO 8601 standard (YYYYMMDD T [time separator] HHMMSS Z [Zulu/UTC time]) in the filename.

Step 4: Interpolated to create 1-second navigation.
The navigation fixes were interpolated to create 1-second navigation. This was done because the application used to geotag the bottom photos would have interpolated between fixes; therefore, the navigation was interpolated so that the sediment and imagery data could be mapped using the same 1-second navigation. To interpolate the 2016 navigation data, first, a column of the original source filename was deleted from the processed navigation CSV text file (2016-005-FA_OdysseyAudioStamp_nav_ALL.csv). Next, a shapefile was created from the CSV file in Esri ArcGIS (version 10.3.1) and projected to WGS 84 UTM Zone 19N so that the distance in meters between the navigation fixes could be calculated. Then, the Generate Near Table tool was run using the planar method with the input and output features set to the UTM navigation shapefile and with the option checked to find only the closest feature. The output table was used to identify erroneous fixes, and two erroneous fixes were deleted from the navigation CSV text file. A Jupyter Notebook Python script (Interp_NAV_Dec2019_for_Nantucket2016-005-FA.ipynb) was run to interpolate and create a CSV text file of 1-second navigation. The original 2-second fixes were logged only when the video was recorded, so the script also interpolated between the end point of a video trackline and the start point of the next trackline. To create a final file of 1-second navigation, the points between the video trackline start and end times were extracted (which removed the interpolated positions during the transits between the video tracklines) and were saved as a CSV text file (out_interp_2016Nantucket_sel_for_gpx.csv). Finally, the CSV file was reorganized and formatted to have fields of the latitude, longitude, hours, minutes, seconds, Julian day, year, field activity ID, and Julian day and time. For the 2017 survey, navigation data were logged for the full Julian day, including when the survey vessel was at the dock and when it was transiting to, from, and between the sampling sites. First, the navigation data from 13:30:00 to 19:14:59 were extracted from the processed navigation CSV text file (2017022FA_TiogaShip_nav.csv) to remove the navigation logged at, departing, and returning to the dock. Next, columns of the original source filename and time (the hours, minutes, and seconds were listed individually in other columns) were deleted from the CSV text file, and leading spaces were deleted as needed. Then, a shapefile was created from the CSV file in Esri ArcGIS (version 10.3.1) and projected to WGS 84 UTM Zone 19N so that the distance in meters between the navigation fixes could be calculated. The Generate Near Table tool was run using the planar method with the input and output features set to the UTM navigation shapefile and with the option checked to find only the closest feature. The output table was used to identify erroneous fixes, but no erroneous fixes were identified. The navigation data, however, were noisy. To smooth the data, the coordinates were rounded from seven to six decimal places and every fourth fix was extracted. A Jupyter Notebook Python script (Interp_NAV_Dec2019_for_Nantucket2017-022-FA.ipynb) was run to interpolate and create a CSV text file of 1-second navigation (2017022FA_TiogaShip_nav_4secint_round6_interp_for_gpx.csv). Finally, the CSV file was reorganized and formatted to have fields of the latitude, longitude, hours, minutes, seconds, Julian day, year, field activity ID, and Julian day and time to create a final CSV file of 1-second navigation for the survey. This process step and the subsequent process steps were performed by the same person, Emily Huntley.

Step 5: Created a CSV file of the bottom video trackline points.
A Jupyter Notebook Python script (Video_trackline_prep_WORKING_v2.ipynb) was run for each survey to create a CSV file of the bottom video trackline points by extracting the navigation data for each video drift using information from the start times/durations text files. The script reads the video start time and duration from the text files, calculates the video end time, extracts the navigation points that fall within those start and end times, and exports the navigation points to a CSV file.

Step 6: Created the final bottom video tracklines shapefile.
Point shapefiles were created for each survey using the bottom video trackline points CSV files in Esri ArcGIS (version 10.3.1). The Points to Line tool was then run for each survey with the video trackline points as the input features and the video filenames as the line field to create a polyline shapefile of the video tracklines. XTools Pro (version 12.0) for Esri ArcGIS was used to rename, reorganize, and add new fields (Table Operations - Table Restructure) to the polyline shapefiles, including an attribute for the site number of the video trackline (FIELD_NO), start time of the bottom video drift in UTC (STARTTIME), end time of the video drift in UTC (ENDTIME), Julian day of collection (JD), date of collection (DATE), year of collection (YEAR), trackline length in meters (LENGTH_M), camera used (CAMERA), survey ID (FA_ID), sampling device used to collect the video (DEVICE_ID), and survey vessel (VEHICLE_ID). XTools Pro changed some of the bottom video trackline features from singlepart to multipart features if they overlapped or intersected themselves. To correct this, the Spatial Join tool was run for each survey with the original bottom video tracklines shapefile as the input features and the updated tracklines shapefile as the join features using the intersect match option to add the updated attributes to the original singlepart features. Unnecessary fields created when running the Spatial Join tool were deleted (i.e., Join_Count and TARGET_FID). Next, the bottom video tracklines shapefiles for each survey were joined with the trackline points shapefiles to add the start and end times of the video drifts (STARTTIME and ENDTIME, respectively), Julian day of collection (JD), and date of collection (DATE). Then, the trackline length (LENGTH_M) was calculated using the Calculate Geometry tool (Property=Length; Use coordinate system of the data frame=WGS 1984 UTM Zone 19N; Unit=Meters). Four tracklines from 2016 (clips 44, 46, 58, and 90) and three tracklines from 2017 (clips 239, 245, and 259) were for calibration videos or videos taken during transits between sites and were removed. The bottom video tracklines shapefiles for each survey were then joined with the survey logs to assign the site number (FIELD_NO) of each video trackline. The trackline shapefiles were then combined using the Merge tool. Finally, the bottom video line names (LINENAME) were updated to match the new video filenames, which include the field activity identifier, camera, and date and start time in the ISO 8601 standard in the filename.