Use of Low-Cost, Autonomous, Underwater Camera Systems to Advance Understanding of Fish Habitat Usage and Behavior

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Recent technological advancements have led to a proliferation in alternative sampling methods that can provide refined fisheries and habitat data. Recent advances in camera technology, motion detection software, high-capacity cloud storage, solar systems, and machine learning systems, have provided scientists with a variety of tools to examine fish populations and habitats. Although camera systems can have imperfect detection, they can be deployed continuously and offer a non-invasive alternative to compare with other monitoring approaches. Further, they can be more effective than traditional sampling methods in complex habitats and can be cost-effective. We use three versions of underwater, automated camera systems to passively monitor for fishes in a variety of situations where handling fish is not possible or desirable. These camera systems, which incorporate motion detection software, have high storage capacity and can be deployed in remote locations using solar power. In addition, the systems can be paired with more traditional sampling techniques to maximize results and supplement long-term monitoring data. We highlight our current applications and explore future applications to improve our understanding of key species of interest. Additionally, processing of the videos collected can incorporate machine-learning software to reduce review time and aid in species identification.

Tecnología

AFS / TWS Annual
Meeting Reno, Nevada
Sept 28 – Nov 3, 2019
Presenter: Dana Lee
Co-Presenters: Matt Peterson,
Matt Simenc, Garth Jaehnig,
Andrea Fuller, Doug Demko
Use of Low-Cost, Autonomous,
Underwater Camera Systems to
Advance Understanding of Fish Habitat
Usage and Behavior

Overview
www.FISHBIO.com
• Advantages and limitations of cameras for
fisheries research
• Recent technological advancements and
design improvements
• Current applications
• Future applications
• Recommendations

Advantages
www.FISHBIO.com
• Reliable, low-cost
monitoring
• Diverse and complimentary
applications
• Able to sample in complex
habitats
• Count and identify species
and observe behavior
• No handling of sensitive
fish species

Limitations
www.FISHBIO.com
• Turbid or fast-moving
water
• Review time
• Limited battery life
and storage space
• Power supply
• No handling of fish
for measurements or
tagging

Summit Lake
www.FISHBIO.com
• Monitor cutthroat trout
migrating in Mahogany Creek
• Fish-passage tunnel equipped
with a single digital camera and
infrared lighting
• Motion-activated video clips
accurately record passing fish
as small as 50 mm
• Digital videos sent to a
connected computer and saved
to portable hard drives.

Summit Lake
Installation
• Remote, solar-
powered, and semi-
automated
• Ideal conditions for
video monitoring
• Small channel with
low and laminar flows
• IR light worked well
due to small passage
size.
www.FISHBIO.com

Calaveras River
www.FISHBIO.com
• Operated Oct.–Apr.
annually at the Bellota
Weir to monitor trout and
salmon passage
• Camera system ‘slides’
into the fish ladder
• Equipped with multiple
cameras and IR light
• Motion detection
software reduces review
time to <1 hr per day

Motion Detection
• Speed up review time using motion-detection software
• Adjust sensitivity and detection area
www.FISHBIO.com

Modifications
www.FISHBIO.com
• Moved cameras into
the pool to reduce
turbulence
• Turbidity
• Schooling fish in the
pool
• Beavers
• LED lights to replace
IR
• Camera angle

Floodplain Cameras
www.FISHBIO.com
• Observe behavior and
enumerate Chinook
salmon and predatory
species on floodplains
• Transects across
complex habitats and
difficult sampling
conditions
• Data to pair with other
sampling methods

Floodplain Cameras
www.FISHBIO.com
• Designed to work
remotely and
autonomously to
answer a variety of
questions:
• Detect and enumerate
sensitive fish species
• Observe behavior and
species interactions
• Many potential
applications

Floodplain Cameras
www.FISHBIO.com
• Built-in motion
detection
• External hard-drive for
infrequent downloads
• Solar powered
• 2+ days worth of
battery storage
• Security?
• Review time?

Machine Learning
• Developed an algorithm to automatically
detect fish in a video
• Trained the algorithm with a total of:
• 537 videos
• 10,952 images
• 25,312 boxes
• Currently the model is ~90% accurate,
depending on the size of the fish
www.FISHBIO.com

Machine Learning
• Working on training the algorithm to improve
performance
• Future improvements include
• Species detections
• Sex
• Life histories
• Size?
• Training the algorithm for various applications
www.FISHBIO.com

Recommendations
• Camera placement to minimize blind spots
and avoid false triggers
• Motion detection saves time but good to verify
data with continuous video
• Advantages to each style of camera housing
• IR light works well in shallow water, LED is a
good alternative
• Many future applications as technology
improves!
www.FISHBIO.com

FISHBIO
Oakdale, California
Chico, California
Santa Cruz, California
FISHBIO Laos
Vientiane Capital, Lao PDR
FISHBIO CR
Boca del Rio Sierpe
Costa Rica
Thank You!
Dana Lee
danalee@fishbio.com
www.FISHBIO.com

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