CSIRO OCEANS AND ATMOSPHERE

Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel-1 SAR satellite

David Blondeau-Patissier, Thomas Schroeder, Foivos Diakogiannis, Paul Irving, Christian Witte, Andy Steven

Pacific Islands GIS & RS User Conference 2019, 25th - 29th November 2019, Suva

In the context of Fiji

2 | Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Regional context

Viti Levu seen from Sentinel-1 SAR

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Sentinel-1A SAR scene

Acquired: 24th November 2019

Overpass time: 05.40am local time

Frequency: every 6 days (ascending and descending pass)

Viti Levu

Note: Satellite imagery from the Sentinel satellites is free

Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Regional context

Content

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1. Synoptic overview of the research Objectives

a) Milestones

b) Key outcomes

2. Methodology and Example cases

3. Project outcomes and future work

Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Outline

• ContextNo regular routine, broad scale monitoring of oil spills

• ObjectiveTo develop a semi-, or fully-automated satellite-imagery-based oil spill detection system.

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This project - Context and objective

Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Context & Objectives

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Synthetic Aperture Radar

Ocean colour,SST

Multispectral

The European Space Agency’s Sentinels

Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | The Sentinels

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Our main challenges – true vs false positives

Biogenic slicks

Oil slicks

Wind shadows

Lucinda jetty coastal observatory

Townsville

Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Key challenges

Key challenges

• Size of the Sentinel-1 SAR scenes

• Complexity of differentiating true-positives (oil spill features) from other features

• Time difference between Sentinel-1 SAR and the other Sentinels, Sentinel-2 / -3

• Processing of the SAR images

• We thought there would be a better coverage from Sentinel-1 SAR i.e., less than 12 day repeat

• Added complexity of the Reef matrix when interpreting the images

… and many more

8 | Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Key challenges

Methodology and

Example cases

9 | Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Methodology and Example cases

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Sentinel-1 SAR coverage – Great Barrier Reef

Sentinel-1ASentinel-1B EEZ

Oct 2014 – Nov 2019

Ntotal: >3,000 scenes

Average: 75/month

Great Barrier Reef Fiji

348,700 km2 18,275 km2

75 scenes/month 10 scenes/month

Comparison Great Barrier Reef - Fiji

Month of October

Day of the month

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Num

ber

of

Sentinel-

1 S

AR

scenes

0

1

2

3

10 scenes/month

Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Acquisitions

Presentation title | Presenter name11 | https://emergency.copernicus.eu

The service can be provided in:• 'rush' mode: for emergency management

activities that require immediate response• 24/7 basis• products are provided ASAP (from a few

hours to a few days after the user request)

• 'non-rush' mode: to support activities that do not require immediate response, i.e. for prevention, preparedness, disaster risk reduction and recovery phases.

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Our database

Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Database

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Our database

MauritiusOil Spill captured by Sentinel-1 on 05 February 2019

Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Database

Dark area

detection

Features

extraction

Classification

1st Step

2nd Step

3rd Step

SAR image

Results

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Sentinel-1 SARImage processing

Sentinel-1 SARImage processing

Rule-based approach ( selected

parameters)

Use of convolutional neural networks (image based)

Semi-empirical approach Parametric approach

METHOD 1 METHOD 2

Methodology – Rule based vs Machine learning

Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Methodology

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METHOD 1 Rule-based approach

Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Rule-based approach

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METHOD 2 Machine Learning (CNN)

• Use of Python and PyTorch

• Computer vision-based approach

• Convolutional neural network architecture

• Dataset of 3,296 labelled “chips” used as input for the training phase

• Work in progress but promising results

Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Convolutional neural networks

Cape York, Northern Australia

ESA; Sentinel-1 SAR scene of07th November 2018(085337UTC)

Reefs

LAND MASS

ship

oil

Inset from left

North West Shelf

ESA; Sentinel-1 SAR scene of09th July 2019(213920UTC)

LAND MASS

Inset from left

Wandoo-B platform(Vermilion Oil and Gas)

oil

oil

Karratha

North West Shelf

ESA; Sentinel-1 SAR scene of09th April 2018(213909UTC)

LAND MASS

oil

Inset from left

oil

Karratha

Wandoo-B platform(Vermilion Oil and Gas)

Outcomes and future workDatabase of past and current events

Processing workflow for Sentinel-1 SAR

Continuously updated archive and on-demand processing

Automated approaches for oil spills detection

Processing workflow can be relocated to .e.g. South Pacific / Fiji

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• Synergy between various satellite sources, models for improved detection

Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Future work

David Blondeau-PatissierResearch ScientistCSIRO Oceans and Atmosphere

e David.Blondeau-Patissier@csiro.au

Thank you

OCEANS AND ATMOSPHERE

Thomas SchroederSenior Research ScientistCSIRO Oceans and Atmosphere

e Thomas.Schroeder@csiro.au

Suva

Aggregation of Sentinel-1 images acquired between January 2018 and November 2019.

Mapping major sea lanes from the Google Earth Engine

Synthetic Aperture Radar workshop – THIS FRIDAY22 |

This Friday

Please contact Amy Parker

Zheng-Shu Zhou