Dataset: Zooplankton In Situ Videos
Deployment: UW_SPC-2_Zoocam

In situ zooplankton ZooCam video captured in the Puget Sound during summer 2018

View Data: For data, See Dataset Metadata Page: https://www.bco-dmo.org/dataset/928222

Principal Investigator:

Julie E. Keister (University of Washington, UW)

Co-Principal Investigator:

Daniel Grunbaum (University of Washington, UW)

Scientist:

Paul Roberts (University of California-San Diego, UCSD)

Student:

Amy Wyeth (University of Washington, UW)

Contact:

Amy Wyeth (University of Washington, UW)

BCO-DMO Data Manager:

Sawyer Newman (Woods Hole Oceanographic Institution, WHOI BCO-DMO)

Project:

Causes and consequences of hypoxia and pH impacts on zooplankton: Linking movement behavior to vertical distribution. (Zooplankton Swimming)

Version:

Coordinated Deployments:

RC008

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Description

The primary file of this dataset is a tabular file of data containing an overview of each in situ video included in this analysis, the nearest CTD cast to the recording location of that vide, and selected environemntal data from the nearest CTD cast.

Methods & Sampling

Dataset acquisition description

Videos were recorded only under low-flow (< 1 cm/s) conditions, using infrared lighting to avoid modifying zooplankton behavior. The video camera recorded swimming in the X (left, right) and Z (up, down) directions, observing true vertical motion and projected horizontal motion. Videos were recorded at an average frame rate of 20 frames per second. The frame rate varied slightly during videos due to data throughput limitations of the onboard, single-board computer. However, each video frame was precisely timestamped, and the exact difference in time between frames was used when converting speeds from pixels per frames to mm per second. Each frame was 650 x 876 pixels, and each pixel was 0.088 mm2, corresponding to a 57.2 x 77.1 mm field of view.

Data Processing Description

Dataset Processing Description

Zooplankton and other particles appeared in videos as grayscale images against a dark background. Regions of Interest (ROIs) from each frame were identified using the Python package OpenCV (version 4.7.0) (Bradski 2000). OpenCV-fitted contours and rotated bounding boxes were used to calculate width, height, and area metrics for each ROI. The nominal “length” of each ROI was defined as the longer of its width and height measurements. Position, defined as the optical centroid of the zooplankter outline, and size metrics were saved for each ROI that were potentially zooplankton (areas larger than 30 pixels) within each frame.

Zooplankton positions were assembled into swimming trajectories using the Matlab software Tracker3D (Chan and Grünbaum 2010). Small particles in videos were used to reconstruct a background flow field with Particle Image Velocimetry (PIV), using the Python package OpenPIV (version 0.23.9) (Liberzon et al. 2021).

Each zooplankton ROI was assigned a taxonomic identification using a Machine Learning (ML) image classification algorithm. The algorithm was generated using a pre-trained convolutional neural network 50 layers deep (ResNet-50) with PyTorch, an open-source, Python-based machine learning framework.

More information about this dataset deployment

Funding

Award Number	Funding Source
OCE-1657992	NSF Division of Ocean Sciences

Instruments

Underwater Camera

Supplied Name: SPC-UW-Zoocam

Supplied Description:

The SPC-UW-Zoocam designed and built specifically for this project. The Zoocam was custom-built by Paul Roberts in the Jaffe Imaging Laboratory at the University of California San Diego. It is an underwater camera system with a 500-mL imaged area that captured still images while profiling using lighting in the visible wavelength range (images submitted as a separate dataset) and videos of zooplankton using IR lighting (while stationary and under low-flow conditions.

Instrument Type

Generic Name: Underwater Camera

Community Identifier: http://vocab.nerc.ac.uk/collection/L05/current/180/

Generic Description:

All types of photographic equipment that may be deployed underwater including stills, video, film and digital systems.

Parameters

Supplied Name	Supplied description	Supplied Units	Standard Name
vid_id	Video identifier, also the timestamp of when the recording started in Unix epoch time	unitless	time_begin
datetime_utc	Datetime value derived from the unix timestamp represented in the image filename.	unitless	time_begin
total_frames	The total number of frames in the video, a way to measure the video length	frames	image_analysis
avg_frm_rate	The average frame rate of the video (frame rates varied slightly during videos)	frames per second	image_analysis
nearest_ctd_cast	Date and time of the nearest completed CTD cast to the recorded video	unitless	cast
nearest_ctd_offset	Time difference between the nearest completed CTD cast and the video recording	unitless	cast
depth	The depth the camera was parked when the video was recorded	meters (m)	depth
oxygen	Oxygen concentration from the nearest CTD cast at the depth the video was recorded	milligrams per liter (mg/L)	O2
temperature	Temperature from the nearest CTD cast at the depth the video was recorded	degrees Celcius	temp

Database

Contribute Data

Dataset: Zooplankton In Situ Videos
Deployment: UW_SPC-2_Zoocam

Dataset acquisition description

Dataset Processing Description

Database

Contribute Data

Dataset: Zooplankton In Situ VideosDeployment: UW_SPC-2_Zoocam

Dataset acquisition description

Dataset Processing Description

Dataset: Zooplankton In Situ Videos
Deployment: UW_SPC-2_Zoocam