Neville Barrett, Gary Kendrick, Russ Babcock, Craig ...imos.org.au/fileadmin/user_upload/shared/IMOS...

IMOS Integrated Benthic MonitoringNeville Barrett, Gary Kendrick, Russ Babcock, Craig Johnson, Peter Steinberg, Alan Jordan, Tom Bridge, Renae Hovey, Nicole Hill, EzequiellMarzinelli, Will Figueira, Maria Byrne, Vanessa Lucieer, Daniel Ierodiaconou

Stefan B. Williams, Oscar Pizarro, Mitch Bryson, Lachlan Toohey, Christian Lees, Jorja Martin, George Wakeham, Jeremy Randle

AUV Facility

IMOS AUV Facility

• Operation of benthic AUV systems • Sustained observing of benthic

reference sites around Australia• IMOS funds personnel and

logistics• Data integrated into AODN

Sirius

Iver

Integrated Benthic Monitoring Facility Objectives

• National perspective• Long term monitoring of deepwater

(20 – 200 m) reefs• Monitoring of major habitat forming

species around Australia• Interpreting dynamics of benthic reef

systems in the context of biophysical coupling

• Strong engagement with node science, particularly in temperate Australia along the East and West coasts

• Review of program in 2016 to clarify distinction between facility and user group

Integrated Benthic Monitoring Facility 2017

Western Australia

• Surveys sites offshore of Rottnest Island and the AbrolhosIslands in May 2017 • Have detected significant shifts

in Kelp cover from 2014 to 2017. To be confirmed in 2018.• Machine learning applied to

detect change in coral cover at Abrolhos from 2010 to 2013

A. Mahmood, M. Bennamoun, S. An, F. Sohel, F. Boussaid, R. Hovey, G. Kendrick, R. Fisher, ‘Deep Image Representations for Coral Image Classification’, IEEE Journal of Oceanic Engineering, 2018

Coral Maps for the three sites of the Abrolhos Island (2010 – 2013)

Tasmania• Surveys of CMR in Bass Strait and Flinders Island in July 2017• Repeat surveys on Tasman Peninsula• Reports of large aggregation of Port Jackson sharks in Bass

Strait

Tasmania

LC James, MP Marzloff, N Barrett, A Friedman, CR Johnson (2017), ‘Changes in deep reef benthic community composition across a latitudinal and environmental gradient in temperate Eastern Australia’, Marine Ecology Progress Series 565, 35-52NR Perkins, SD Foster, NA Hill, MP Marzloff, NS Barrett (2017), ‘Temporal and spatial variability

in the cover of deep reef species: Implications for monitoring’, Ecological Indicators 77, 337-347

Ningaloo, WA

• Surveys offshore of Ningaloo in Sept. 2017 with CSIRO• Repeated sites first surveyd in

2007 and again in 2012

Gippsland Lakes, Victoria

Next Generation AUV• Next generation AUV designed to replace

Sirius• Pair of 6 Mpx cameras, multibeam, CTD,

fluorometers• Speed: 2-3 knots – halve survey time• Obstacle avoidance sonar, DVL, USBL• 120 kg, 2.7m, 300m depth• More compact shape allows

deployment/recovery on smaller vessels• First deployment in Hawaii in Feb 2018

Australian Antarctic Gateway AUV

• Invited to Hobart and Tasmania to inspect Gateway AUV• 6.5m, 1.5T, 5000m rated, 150km

range, multibeam• Targeted at under ice surveys• High operation cost, uncertain

funding• Some discussions with IMOS

Office about how to support anational fleet of AUVs

Antarctica?(TBC)

Integrated Benthic Monitoring Facility 2018

IMOS AUV Facility Updates

• Have ship time commitments for WA, Tasmania, NSW, Victoria, SA and Queensland

• Proposals for work in Antarctica with NZARI, Investigator for work in the Coral Sea and Lord Howe, Schmidt Ocean Institute

• NSW RAAP• Funding for a postdoc to support AUV facility in machine learning

Papers

International Engagement• Okinawa, Japan

• U. Tokyo/U. Southampton

• Lizard Island, Qld• St. Andrews, U. Macquarie

• Cuba• Scripps, SAGAX

• Maui and Big Island, Hawaii• Schmidt Ocean Institute, WHOI, MIT, URI, U Michigan

• Coconut Island, Hawaii• University of Hawaii

• Schmidt Ocean Institute coral reef workshop

Autonomously & Collaboratively

Coordinate multiple vehicles across multi-day campaigns.

Adapt models and selection of science opportunities as observations are made.

Manage risk, while maximizing resource usage and opportunities.

(Following slides courtesy of Brian Williams, MIT)

Coordinated Robotics

Select sites to maximize information gain

Sparse Prior Data

High Resolution Bathymetry

Coral Cover Prediction Model

Experimental Verification

Selection of Sites with Most Model Impact

Feed New Data into Model

Ocean Currents

Everyday scenario: morning, Captain and Science team meet to elicit constraints and construct plan

7 a.m.

Slocum Glider

Sirius Umich Iver

Planning...5:30 p.m.

Dinner

I lost radio signal...

We’re still waiting... 5:30 p.m.

Dinner1:00 p.m.Execution... A whale is in

the ship’s way...

Everyday scenario: during the day, execution inevitably goes wrong...

7:30 p.m.Delay in missions

5:30 p.m.Negotiation...

Can you shorten your mission from 90 minutes to 1 hour?

Can we push off the mission and recover your vehicle after dinner?

Can we use the emergency boatto recover the glider?

Everyday scenario: towards the end of day, there are many negotiations between the teams...

Risk Aware Planning Tools● Helps elicit each sub-team’s options

and preferences.

● Helps negotiate conflicting goals between teams, and suggests adjustments.

● Monitors mission and environment, and proactively alerts relevant team members, while considering delaysin communication and taking action.

Inputting the problem in scheduler

Feasible schedule in Gantt Chart

WHOI Glider

R/V Falkor ACFR Sirius

GlobalArchive

2) The National Service for Underwater Imagery

Repository>500Tb

Imagery stored here• Still imagery• BRUVs• DOVs• Towed video• Etc.

Annotation done here

Annotations stored hereAnalytics done here

Marine Research Data Cloud 2018

SoE Reporting(Marine Science CloudShiny Notebook)

User Upload

EventMeasure –fish annotation

Squidle+ - Habitat annotation

An example of the value of GlobalArchive

GlobalArchive

- 32 BRUV researchers- 6 Government institutions and 6 Universities- Part funded by Paul G Allen Philanthropies

- generic

The Australian National BRUV synthesisStarted at the week-long workshop in early Feb 2018

Ø Majority of available annotation analyses Ø Represents an investment of ~$10M

Curtin, Deakin, Flinders, JCU, Utas, UWA,AIMS, CSIRO, NSW-DPI_F, SA-DEWNR, WA-DBCA, WA-DPIRD_F

GlobalArchive

- 20,022 BRUV deployments- 1888 species- 2 693 906 individual fish- 660 481 length measurements

The Australian National BRUV synthesis

GlobalArchive

Initial synthesis: information of value to

Tiger shark Grey reef shark Port Jackson shark

• State of Environment reporting• Potential for improved fisheries management• Conservation from broad scale assessments

Opportunity to integrate with other data in the AODN portal

Key features of SQUIDLE+• Flexible data storage:

Sync with existing data storage infrastructure (i.e.: data linked from AODN). Avoids needing to copy and duplicate data. Takes minutes instead of days to import data into the system.

• Flexible, translatable annotation schemes:Users can define their own annotation schemes or select from existing ones, and can translate between them meaning all annotations can be viewed in a unified consistent framework

• Collaborative / automated labelingData can be annotated by different users with different skill levels and automated algorithms can be called upon to speed up the annotation process.

• "Media object" annotationNot limited to still images - system can handle annotation of videos, mosaics, etc all using the same consistent framework.

Facility for Marine Image Annotation and Understanding (MIAU)

• IMOS 5-year extension proposal to

provide quantitative estimates

from benthic imagery collected by

AUVs, divers (RLS), BRUVs and

other platforms

• Need to convert more imagery into

useful numbers. So far annotated:

• IMOS AUV Facility: < 1% of 4.3M+

stereo pairs

• Reef Life Survey: < 25% of ~90k

photoquadrats collected by volunteer

dives

• BRUVs: data collected at sites around

the country

• Proposal to:

• Extend classification scheme

• Annotation of video and large-scale

image mosaics

• Integrate machine learning

algorithms to increase rate of

annotations

• Manage speed, cost and quality by

using annotations from experts,

machine learning algorithms, and

general public

• Need to consider how to fund this

activity going forward to meet user

group needs