VTT TECHNICAL RESEARCH CENTRE OF FINLAND LTD

Oil Detection among Ice and Snow –

Lessons learned

Arctic Oil Recovery Exercise “Kemi Arctic 2015”

Jukka Sassi, VTT

Jorma Rytkönen, Finnish Environmental Institute SYKE

24 March, 2015

Photo: J. Sassi, VTT

224 March, 2015 2

Content

Oil detection in arctic conditions

Overview of oil detection sensor technology

Deployment platforms

R&D activities in surveillance technology

Conclusions

Announcement of the field test trials of novel sensor technology in 2016

KEMI ARCTIC 2015

Jukka Sassi, VTT & Jorma Rytkönen, SYKE

324 March, 2015 3

Oil detection in arctic conditions

KEMI ARCTIC 2015

Jukka Sassi, VTT & Jorma Rytkönen, SYKE

(Ref.: Arctic Monitoring and Assessment Programme (AMAP), 1998)

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Recognised oil detection sensor technologies

• Extensive studies conducted by SINTEF in Oil In Ice – JIP and in Arctic Oil Spill

Response Technology Joint Industry Programme (JIP) and recent R&D Finland in sensor

technology

• Passive optical sensors: cameras and multispectral imaging systems, ultraviolet (UV) and

Near-InfraRed (NIR) sensors, hyperspectral sensors

• Passive Thermal InfraRed sensors (TIRs) and MicroWave Radiometer (MWR) systems

• Active radar sensors: Side-Looking Airborne Radar (SLAR) and (Synthetic Aperture radar

(SAR) systems, Marine Radar, Ground Penetrating Radar (GPR)

• Active Laser and fluorosensors: fluorosensors, Tunable Diode Laser Spectroscopy

(TDLS), Laser-Ultrasonic Remote Sensing of Oil Thickness (LURSOT), Light Detection

and Ranging LiDAR

• Experimental sensors: Acoustic Sensors, Nuclear Magnetic Resonance (NMR)

Spectroscopy, trained dogs

KEMI ARCTIC 2015

Jukka Sassi, VTT & Jorma Rytkönen, SYKE

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Recognised oil detection sensor technologies

KEMI ARCTIC 2015

Jukka Sassi, VTT & Jorma Rytkönen, SYKE

Oil among Pack Ice

Oil on ice

Oil under ice / snow Low visibility

Ice concentration or Blowing Darkness Rain or

Technology < 30% 30-60% >60% encapsulated snow fog

VIS, MS, UV,

Hyperspectral Active systems

TIR

MWR

SAR, SLAR

Marine radar

GPR

LFS

TDL

LURSOT

LIDAR

Acoustic

NMR

Trained dogs

Green box: Proven and validated technology, its performance and limitations under current scenario well understood.

Orange box: Technology potentially applicable, partial validation may have taken place but the technology has not been comprehensively validated for

performance under the given scenario.

White box: The likely performance of the technology not known; never been tested under the given scenario.

Red box: The technology is not applicable to the given scenario.

(Ref. Puestow et al. 2013)

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Recognised oil detection sensor technologies

KEMI ARCTIC 2015

Jukka Sassi, VTT & Jorma Rytkönen, SYKE

Platform Ice surface AUV Shipborne Airborne Satellite

Sensor Dogs GPR SonarMarine

radarFLIR GPR Visible UV FLIR SLAR SAR

OIL ON ICE

Exposed on cold ice surface Y N/A N/A N Y Y Y N Y N N

Exposed on spring melt pools Y N/A N/A ? Y N Y ? Y ? N

Buried under snow Y Y N/A N/A N Y N N N N N

OIL UNDER ICE

Smooth fast ice ? Y Y N/A N/A Y N/A N/A N/A N N

Deformed pack ice ? ? Y N/A N/A ? N/A N/A N/A N N

OIL IN ICE

Discrete encapsulated layer ? Y N N/A N/A Y N/A N/A N/A N N

Diffuse vertical saturation ? ? N N/A N/A ? N/A N/A N/A N N

OIL BETWEEN ICE FLOES

1 - 3/10 concentration N/A N/A N Y Y N Y Y Y Y Y

4 - 6/10 concentration N N/A N ? Y N Y ? Y ? ?

7 - 9/10 concentration ? N/A N N Y N Y N Y N N

(Ref. Dickins, 2010)LEGEND

Likely Y

Possible ?

Not likely N

Not applicable N/A

Blocked by dark/cloud/fog/precip.

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Deployment platforms

• Remotes sensing technology can be deployed to

different platforms

• Platforms currently used for remote sensing

technology deployment are satellite systems,

aircraft systems, unmanned aerial vehicles/systems

(UAV/UAS), tethered balloon systems, surface

vessels and Autonomous Underwater Vehicles

(AUVs) & Remote Operated Vehicles (ROVs)

• Depending on size, location and time of incident,

different type of data can be gathered from different

platforms

KEMI ARCTIC 2015

Jukka Sassi, VTT & Jorma Rytkönen, SYKE

Ref. www.sciencedaily.com

Ref. ESA/ATG medialab

Ref. NOAA

Ref. Aerophile SAS

Ref. SYKE

Ref. NOAA

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Satellite Systems

KEMI ARCTIC 2015

Jukka Sassi, VTT & Jorma Rytkönen, SYKE

Advantages Disadvantages

Can potentially cover a large area in a

short period of time

The timing and frequency of overpasses by

satellite systems may not be optimal for the

situation

Data may potentially be transmitted via the

internet almost immediately

Clear skies are needed to perform optical work

Many radar satellites are useful in

detecting large offshore spills and spotting

anomalies

The probability of detecting oil may be low

Some operational commercial satellites

can be tasked to respond to emergencies

within a range of 90 minutes to 4 hours

Developing algorithms to highlight oil slicks is

difficult

Extensive time may be required to convert data

into actionable information

(Image: RADARSAT-2 Data

and Products © MacDONALD,

DETTWILER AND

ASSOCIATES LTD. (2011)

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Aircraft Systems

KEMI ARCTIC 2015

Jukka Sassi, VTT & Jorma Rytkönen, SYKE

Advantages Disadvantages

Large areas can be surveyed in a relatively short

time frame

Weather and daylight/darkness must be suitable for the

type of aircraft and sensors being utilized

Aircraft are usually available on short notice and can

be more cost effective

Safety margins for operation need to be determined and

adhered to. Regional flight rules may dictate operating

conditions

Most types of remote sensors can be deployed on

aircraft

Remote sensing equipment should be in a “universal”

package that can be deployed on any type of aircraft

Multiple sensor types may be deployed on a single

aircraft

Some remote sensing equipment too bulky and can be

used only from the dedicated aircraft

Aircraft usually have multiple navigation aids that can

assist in pinpointing locations

Remote sensing operation must be coordinated with

other aircraft activities (e.g. overflight, dispersant,

observer

Remote sensing package must have the necessary

method of data capture and communications

Ref.: www.raja.fi

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Unmanned Aerial Vehicles/Systems

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Advantages Disadvantages

Can fly lower than aircraft and generate

imagery of high spatial resolution

Payload capacity limits the sensors

available for operations

Can fly below low clouds, removing that

obstruction from the field of view

Local aviation and/or governmental

regulations restrict the use

Cost of some UAVs is significantly lower than

the cost of some other platforms

Slow speeds and short-duration flights

may limit the amount of data collected

Launch and recovery requirements help UAVs

reach some places inaccessible to other aircraft

Can be deployed rapidly

Less noisy, smaller and less disturbing or

annoying than manned aircraft

Ref: http://theuav.com/altair_uav.html

Ref: http://www.unmanned.co.uk/

Ref: http://www.honeywellnow.com/

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Tethered Balloon Systems

KEMI ARCTIC 2015

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Advantages Disadvantages

Relatively low cost Operating the system to its full capability can

require extensive training

Can fly below low clouds High winds can degrade the system’s capabilities

Increases the height of observation, compared to vessel-

based observers

Obtaining adequate volumes of helium for larger

balloons may be difficult in some areas

Can be deployed from a moderate-size ship Experiences in Arctic oil detecting operations?

Transmit pictures wirelessly

Can operate 24 hours per day with few weather

limitations

Less regulation limitations compared to UAVs

Source: SPEC Inc.

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Surface vessels

KEMI ARCTIC 2015

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Advantages Disadvantages

Increased interface with the thickest oil based on

remote sensing data and visual means

Limited to small coverage areas in the immediate

vicinity of the vessel

Versatile and can be manoeuvred to remain in desired

location

Limited usefulness in high seas due to sensor

movement at the high point of the vessel.

Instrumentation and sensors can easily be changed to

meet needs and weather conditions

Some oil combating technologies (e.g. booms, arms)

have limited operational capabilities in high seas and/or

in harsh ice conditions

The probability of detecting oil is very high Heavy ice conditions limit the operational operational

capabilities of smaller vessels

Human presence on manned vessels enables the

presence of oil to be validated by visual means

Can provide a much longer “time on station” (e.g.

hours to days) in the area of intent for observations

grouped with other platforms

Oil recovery vessel Halli. Photo: Syke

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Autonomous Underwater Vehicles (AUVs) &

Remote Operated Vehicles (ROVs)

KEMI ARCTIC 2015

Jukka Sassi, VTT & Jorma Rytkönen, SYKE

Autonomous Underwater Vehicles (AUV):

• Robot which travels underwater without requiring input from

an operator, controlled and piloted by onboard computer

• AUV system consist of the body, sensors, navigators,

propulsion, power supply and remote receiver (e.g. laptop)

• AUVs can be equipped with a wide variety of sensors

Remote Operated Vehicle (ROV):

• Tethered underwater robot that allows the operator to remain

in a comfortable environment while the ROV works

underwater

• ROV system comprises the vehicle, group of cables for signal

and power transfer, handling system to control the cable

dynamics, a launch system and associated power supplies

• ROVs can equipped with video and still camera, lights and

additional equipment e.g. samplers and various sensors

Ref: Kongsberg Maritime

Ref: Lamor Corporation

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Autonomous underwater vehicles (AUVs) and

Remote Operated Vehicle (ROVs)

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Jukka Sassi, VTT & Jorma Rytkönen, SYKE

Advantages Disadvantages

Operational time limited only by operators (ROVs) and

battery power (UAVs)

Limited experiences in ice conditions

Can be used for oil detection under ice when equipped with

e.g. sonar sensors

Cables may cause severe problems when operated

in rough ice conditions

Highly manoeuvrable and can cover wide surveillance areas Equipment quite expensive

Allows detailed examination of target area Requires reliable and robust communication platform

Depth range limited by the length of umbilical cable (ROVs)

and battery capacity (AUVs)

Operation requires trained persons

Various systems available due to oil and gas exploration in

the Arctic

Effectiveness can be limited by water turbidity or

darkness (if adequate illumination not available)

Applications for underwater oil removal in design phase (i.e.

Lamor)

In case of lost equipment or accident, no loss of life

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Jukka Sassi, VTT & Jorma Rytkönen, SYKE

(Ref. Cardno Entrix, 2012)

Total Publications per PlatformAerial Platform Publications per Sensor [%]

Satellite Platform

Publications per Sensor [%]

Multiple Platform

Publications per Sensor [%]

Current R&D and emerging trends in

surveillance technology

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Conclusions – Lessons Learned (1/2)

KEMI ARCTIC 2015

Jukka Sassi, VTT & Jorma Rytkönen, SYKE

• Flexible combination of sensors operating from aircraft, helicopters, vessels,

satellites and the ice surface are recommended for future Arctic oil spill

emergency preparedness.

• Most useful remote sensors and systems for spills in ice are expected to be

aircraft and vessel-based FLIR for oil on the surface in a broad range of ice

concentrations, trained dogs on solid ice, GPR operated from helicopters and

the ice surface for oil under snow or trapped in the ice, and SLAR and satellite-

based SAR for large slicks on the water in very open ice covers.

• Current generation of all-weather SAR satellites can play a valuable support role

in mapping detailed ice conditions and directing marine resources.

• Existing commercial GPR systems can be used from low-flying helicopter to

detect oil trapped under snow on the ice and to detect oil trapped under solid ice.

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Conclusions – Lessons Learned (2/2)

KEMI ARCTIC 2015

Jukka Sassi, VTT & Jorma Rytkönen, SYKE

• Detecting isolated oil patches trapped among closely packed ice floes is major

challenge with any current remote sensing system, especially during periods of

extended darkness, low clouds or fog. The most effective solution is to deploy closely

spaced GPS tracking buoys to follow the ice and the oil.

• Trained dogs can reliably detect very small oil volumes and map oiled boundaries on

solid ice and in sediments on Arctic shorelines under extreme weather conditions.

• New technologies may enhance the ability to detect oil over a broader range of Arctic

spill scenarios in the near future. These include NMR, UAVs, AUVs and next

generation GPR optimised for the oil in ice problem.

• The optimum mix of remote sensing technologies depends heavily on the spill

characteristic and prevailing weather and ice conditions.

• Arctic spill contingency plans need to account for the operational constraints of aircraft

and helicopter endurance, weather and the likelihood of competing demands in limited

remote sensing resources.

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Oil detection in Arctic conditions -

Planned Field Experiments in Finland 2016

KEMI ARCTIC 2015

Jukka Sassi, VTT & Jorma Rytkönen, SYKE

• SYKE had a plan to test UAV’s and sensors in March

2015 in Kotka. Tests were postponed due to too

warm environmental conditions.

• New plan is to perform tests in 2016 and study

sensors and oil early warning systems in ice and

snow

• Deployment Platforms: aircrafts, UAVs, in-situ

measuring units

• Sensor technologies: To be defined

• Location: Northern Finland

• Expected time: January – March 2016

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Abbreviations

KEMI ARCTIC 2015

Jukka Sassi, VTT & Jorma Rytkönen, SYKE

. AUV Autonomous Underwater Vehicle MS Multi-Spectral

DWH DeepWater Horizon MWR Microwave Radiometer

FLIR Forward Looking InfraRed NMR Nuclear Magnetic Resonance

GIS Geographic Information System SAR Synthetic Aperture Radar

GPR Ground Penetrating Radar SLAR Side-Looking Airborne Radar

LFS Laser Fluorosensor TDLS Tunable Diode Laser Spectroscopy

LiDAR Light Detection and Ranging TIR Thermal InfraRed

FLIR Forward-Looking InfraRed UAV Unmanned Aerial Vehicle

LFS Laser Fluorosensor UAS Unmanned Aerial System

LURSOT Laser-Ultrasonic Remote Sensing UV UltraViolet

of Oil Thickness VIS Visible

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Selected References

KEMI ARCTIC 2015

Jukka Sassi, VTT & Jorma Rytkönen, SYKE

American Petroleum Institute (API). 2013. Remote Sensing in Support of Oil Spill Response. Planning Guidance. API Technical Report.

September 2013.

Arctic Monitoring and Assessment Programme (AMAP), 1998. AMAP Assessment Report: Arctic Pollution Issues. Arctic Monitoring and

Assessment Programme (AMAP), Oslo, Norway.

Dickins, D. (editor, DF Dickins Associates LLC). 2010. Project P5: Remote Sensing Summary Report. Oil in Ice – JIP. SINTEF Materials and

Chemistry. 24.05.2010. Report no. 30.

Partington, Kim. 2014. An Assessment of Surface Surveillance Capabilities for Oil Spill Re-sponse using Airborne Remote Sensing. Polar

Imaging Limited. 21 May 2014. PIL-4000-38-TR-1.0

Puestow, T.; Parsons, L; Zakharov, I.; Cater, N.; Bobby, P.; Fuglem, M.; Parr, G.; Jayasiri, A. and Warren, S. (C-CORE), Warbanski, G.

(Emergency Spill and Consulting Inc.). 2013. Oil Spill Detection and Mapping in Low Visibility and Ice: Surface Remote Sensing. Final Report

5.1, 15 October 2013. Arctic Oil Spill Response Technology Joint Industry Programme (JIP).

Sørstrøm, S., Brandvik, J., Buist, I., Daling, P., Dickins, D., Faksness, L-G., Potter, S., Ras-mussen, J., Singsaas, I. 2010. Joint industry

program on oil spill contingency for Arctic and ice-covered waters. Summary report. Oil in Ice – JIP, Report no 32. SINTEF Materials and

Chemistry, Marine Environmental Technology. 10.04.2010.

Limnaios, G. 2014. Current Usage of Unmanned Aircraft Systems (UAS) and Future Chal-lenges: A Mission Oriented Simulator for UAS as a

Tool for Design and Performance Evalua-tion. Journal of Computations & Modelling, vol.4, no.1, 2014, 167-188. ISSN: 1792-7625 (print), 1792-

8850 (online). Scienpress Ltd, 2014.

Cardno Entrix. 2012. Surveillance Technologies for Oil Spill Response. Current Research and Emerging Trends.

Thank You!

Further Information

jukka.sassi@vtt.fi & jorma.rytkonen@ymparisto.fi