Presented at Geomatics Atlantic 2014, 3 to 5 November 2014

1. Challenges in Surveying the Deep Sea using Acoustic
Remote Sensing and Remotely Operated Vehicles
Vincent Lecours1,2, Rodolphe Devillers1,2, and Evan N. Edinger2,3
1 – Marine Geomatics Research Lab, Department of Geography, Memorial University
2 – Marine Habitat Mapping Research Group, Department of Geography, Memorial University
3 – Department of Biology, Memorial University

2. A Vast and Complex Ocean…
Introduction Objective/Problem Approach Challenges Conclusions
What about what
is underneath the
surface…?
Phytoplankton bloom
South Newfounland
July 1999
SeaWiFS
Latitude
Longitude

3. Objective and Problem
Introduction Objective/Problem Approach Challenges Conclusions
• Understanding and mapping deep-sea habitats
-Cold-water corals and sponges
• Sample the environment to identify which characteristics
drive species distribution à Predictions

4. A Diverse Ocean to Sample…
Introduction Objective/Problem Approach Challenges Conclusions
Understanding underwater environments involve knowledge of…
Physical
Environment
Oceanographic
Environment
Biological
Environment
Jones et al. (2008)

5. A Diverse Ocean to Sample…
Introduction Objective/Problem Approach Challenges Conclusions
Traditional sampling methods often collect data at a scale that is
not meaningful for some purposes, especially in the deep sea
Physical
Environment
Oceanographic
Environment
Biological
Environment

6. A Scale Issue…
Introduction Objective/Problem Approach Challenges Conclusions
Acoustic Systems:
Large footprint
Low sounding density
Relatively low resolution data

7. Study Areas
Introduction Objective/Problem Approach Challenges Conclusions

8. Preliminary Results
Introduction Objective/Problem Approach Challenges Conclusions
• Preliminary results show that coarse-scale data do not
always significantly explain coral and sponge distributions
• For instance, many of the seafloor features known to
support cold-water coral and sponge habitats are too small
to be captured using broad-scale bathymetric data

9. A Scale Issue…
Introduction Objective/Problem Approach Challenges Conclusions
Smaller footprint
Higher sounding density
Finer spatial resolution

10. A Scale Issue…
Introduction Objective/Problem Approach Challenges Conclusions
Need higher resolution data to
understand real processes taking place

11. A Scale Issue…
Introduction Objective/Problem Approach Challenges Conclusions
Scale of analysis should match the scale
of natural process under investigation
vs
Metres

12. Multiple Scale Surveys
Introduction Objective/Problem Approach Challenges Conclusions

13. Multiple Scale Surveys
Introduction Objective/Problem Approach Challenges Conclusions

14. Multiple Scale Surveys
Introduction Objective/Problem Approach Challenges Conclusions
World
Coverage
cm Spatial Resolution km
Local
ROV-based
multibeam
Ship-based
multibeam
Physical Environment

15. Multiple Scale Surveys
Introduction Objective/Problem Approach Challenges Conclusions
World
Coverage
cm Spatial Resolution km
Local
ROV-based
Video
Scientific
Trawl Surveys
Biological Environment

16. Multiple Scale Surveys
Introduction Objective/Problem Approach Challenges Conclusions
World
Coverage
cm Spatial Resolution km
Local
ROV-based
CTD
Satellite-based
models
Ship-based
CTD casts
Oceanographic Environment

17. Multiple Scale Surveys
Introduction Objective/Problem Approach Challenges Conclusions
Position Geospatial Data
Accurately
From Different Sources
And Different Instruments

18. Multiple Scale Surveys
Introduction Objective/Problem Approach Challenges Conclusions
Position Geospatial Data
Accurately
From Different Sources
And Different Instruments
Fine-scale observations
-ROV-Based Multibeam Bathymetric Data
-ROV-Based Video Data

19. Multiple Scale Surveys
Introduction Objective/Problem Approach Challenges Conclusions
Position Geospatial Data
Accurately
From Different Sources
And Different Instruments
“To use these data sets at their highest resolution,
there is a need to accurately co-register individual
video observations with corresponding seafloor
Fine-scale observations
-ROV-Based Multibeam Bathymetric Data
-ROV-Based Video Data
data.” (Rattray et al. 2014)

20. Multiple Scale Surveys
Introduction Objective/Problem Approach Challenges Conclusions
Positional Accuracy of each dataset is KEY!
Position Geospatial Data
Accurately
From Different Sources
And Different Instruments
“To use these data sets at their highest resolution,
there is a need to accurately co-register individual
video observations with corresponding seafloor
Fine-scale observations
-ROV-Based Multibeam Bathymetric Data
-ROV-Based Video Data
data.” (Rattray et al. 2014)
What affects it?

21. Sources of Positioning Error
Introduction Objective/Problem Approach Challenges Conclusions
Fine-scale observations
-ROV-Based Multibeam Bathymetric Data
-ROV-Based Video Data

22. Sources of Positioning Error - ROV
Introduction Objective/Problem Approach Challenges Conclusions

23. Sources of Positioning Error - ROV
Introduction Objective/Problem Approach Challenges Conclusions
Ultra-Short Baseline (USBL)
Underwater Acoustic Positioning
Accurate but imprecise
Distance
Angle
Motion

24. Sources of Positioning Error - ROV
Introduction Objective/Problem Approach Challenges Conclusions
Ultra-Short Baseline (USBL)
Underwater Acoustic Positioning
Accurate but imprecise
Doppler Velocity Log (DVL)
Drift over time (or distance traveled)
Distance
Angle
Motion
Bottom Tracking

25. Sources of Positioning Error - ROV
Introduction Objective/Problem Approach Challenges Conclusions
1. GPS location of vessel
2. USBL and DVL
Fine-scale observations
-ROV-Based Multibeam Bathymetric Data
-ROV-Based Video Data

26. Sources of Positioning Error - Multibeam
Introduction Objective/Problem Approach Challenges Conclusions
1. GPS location of vessel
2. USBL and DVL
Fine-scale observations
-ROV-Based Multibeam Bathymetric Data
-ROV-Based Video Data

27. Sources of Positioning Error - Multibeam
Introduction Objective/Problem Approach Challenges Conclusions
Multibeam Transducer
Navigation (Relay Transponder)
Motion Reference Unit (MRU)
Vehicle Telemetry

28. Sources of Positioning Error - Multibeam
Introduction Objective/Problem Approach Challenges Conclusions
To correct for the multibeam
data, we need accurate
relative configuration of the
ROV
Multibeam Transducer
Navigation (Relay Transponder)
Motion Reference Unit (MRU)
Vehicle Telemetry

29. Sources of Positioning Error - Multibeam
Introduction Objective/Problem Approach Challenges Conclusions
1. GPS location of vessel
2. USBL and DVL
3. ROV configuration
Fine-scale observations
-ROV-Based Multibeam Bathymetric Data
-ROV-Based Video Data

30. Sources of Positioning Error - Multibeam
Introduction Objective/Problem Approach Challenges Conclusions
Motion

31. Sources of Positioning Error - Multibeam
Introduction Objective/Problem Approach Challenges Conclusions
Motion
Yaw

32. Sources of Positioning Error - Multibeam
Introduction Objective/Problem Approach Challenges Conclusions
Motion
• Telemetry
• Error on the instrument

33. Sources of Positioning Error - Multibeam
Introduction Objective/Problem Approach Challenges Conclusions
1. GPS location of vessel
2. USBL and DVL
3. ROV configuration
4. Motion of the platform
Fine-scale observations
-ROV-Based Multibeam Bathymetric Data
-ROV-Based Video Data

34. Sources of Positioning Error
Introduction Objective/Problem Approach Challenges Conclusions
Cumulative contribution of each component of relative positioning to total
Rattray et al. (2014)
propagated error with depth
±1.5m ±3.6m ±5.7m

35. Sources of Positioning Error - Multibeam
Introduction Objective/Problem Approach Challenges Conclusions
Motion
• Telemetry
• Error on the instrument
• Frequency of data log
• Time synchronization

36. Sources of Positioning Error - Multibeam
Introduction Objective/Problem Approach Challenges Conclusions
Frequency of data log
Corrected Multibeam
Motion Recording
Corrected Multibeam
Multibeam Recording
Time synchronization

37. Sources of Positioning Error - Multibeam
Introduction Objective/Problem Approach Challenges Conclusions

38. Sources of Positioning Error - Multibeam
Introduction Objective/Problem Approach Challenges Conclusions
1. GPS location of vessel
2. USBL and DVL
3. ROV configuration
4. Motion of the platform
5. Time synchronization
6. Frequency of data log
Fine-scale observations
-ROV-Based Multibeam Bathymetric Data
-ROV-Based Video Data

39. Sources of Positioning Error - Video
Introduction Objective/Problem Approach Challenges Conclusions
Fine-scale observations
-ROV-Based Multibeam Bathymetric Data
-ROV-Based Video Data
Mapping the distribution of
species/surficial geology at a
fine-scale
1. GPS location of vessel
2. USBL and DVL

40. Sources of Positioning Error - Video
Introduction Objective/Problem Approach Challenges Conclusions
• Issues with georeferencing of observations
Clement (2007)
• Positioning at the centre of the track
• Multiple countings
How can we get an accurate fine-scale location of observations?

41. Photomosaicking and Orthorectification
Introduction Objective/Problem Approach Challenges Conclusions
Regular Simplified Procedure
1. Extract image frames
2. Match features from adjacent frames
3. Mosaic
4. Orthorectify with a digital surface

42. Photomosaicking and Orthorectification
Introduction Objective/Problem Approach Challenges Conclusions
2. Match features Issues: from adjacent frames
• Individual features (e.g. sponge) can be surrounded by similar
organisms of the same size
• Seabed sediment structure in the background of images
• Variation in light source and motion
Sameoto et al. (2008)
à Result in non-distinctive features across the images
à Lead to incorrectly matched features/inaccurate image matching

43. Photomosaicking and Orthorectification
Introduction Objective/Problem Approach Challenges Conclusions
Issues:
4. Orthorectify with a digital surface
• Need a surface at a corresponding spatial resolution
• Multibeam Data

44. Solution
Introduction Objective/Problem Approach Challenges Conclusions
• Better Algorithms
Error of
perspective:
Deformation of
the images as
distance
increases
Bagheri & Lecours (2012)

45. Solution
Introduction Objective/Problem Approach Challenges Conclusions
Doing fieldwork on
the seafloor:
Photogrammetric
techniques to yield
3D visual models
from ROV video
Kwasnitschka et al. (2013)

46. Sources of Positioning Error
Introduction Objective/Problem Approach Challenges Conclusions
Position Geospatial Data
Accurately
From Different Sources
And Different Instruments
“To use these data sets at their highest resolution,
there is a need to accurately co-register individual
video observations with corresponding seafloor
Fine-scale observations
-ROV-Based Multibeam Bathymetric Data
-ROV-Based Video Data
data.” (Rattray et al. 2014)
Positional Accuracy is KEY!

47. Sources of Positioning Error
Introduction Objective/Problem Approach Challenges Conclusions
Position Geospatial Data
Accurately
From Different Sources
And Different Instruments
“To use these data sets at their highest resolution,
there is a need to accurately co-register individual
video observations with corresponding seafloor
Fine-scale observations
-ROV-Based Multibeam Bathymetric Data
-ROV-Based Video Data
data.” (Rattray et al. 2014)
Positional Accuracy is KEY!

48. Summary
Introduction Objective/Problem Approach Challenges Conclusions
• Each instrument onboard the ROV and the supporting vessel has its
own error that contributes to the total propagated error
• Time synchronization between all the instruments and equivalent
frequency of data log is crucial
• Limits the positional accuracy of all the data, the spatial resolution of
the multibeam bathymetric data, and the ability to accurately mosaic the
seafloor

49. Potential Solutions
Introduction Objective/Problem Approach Challenges Conclusions
• By splitting the total propagated error into its components,
we can take action to mitigate the effects of positioning
error throughout the process
• By knowing this prior to surveying, we can adapt the survey
plans and instruments
• Ultimately improve both
multibeam data and
georeferencing of species
through video data

50. Recommendation
Introduction Objective/Problem Approach Challenges Conclusions
The deeper the survey, the higher the spatial resolution
but the bigger the propagated error gets
Spatial
Resolution
Using ROV
Propagated
Error
(0,0)
Depth
Know your limit
and stay within it!
The spatial resolution of the data should be larger than their positional
accuracy to ensure spatial matching of relevant information

51. Conclusion
Introduction Objective/Problem Approach Challenges Conclusions
Aim What we get…

52. Conclusion
Introduction Objective/Problem Approach Challenges Conclusions
Aim What we need to do…

53. Conclusion
Introduction Objective/Problem Approach Challenges Conclusions
Aim What we would get…
Fiction
or
Really Possible?
Be aware of your systems’ limitations and
do not let your expectations exceed the limits of your data

54. Acknowledgements
Jean-Guy Nistad
HafenCity University (HCU) Hamburg, Germany and
Interdisciplinary Center for the Development of Ocean Mapping (CIDCO)
Vincent Auger
Canadian Scientific Submersible Facility
Karen Douglas
NEPTUNE Canada, University of Victoria

55. Acknowledgements
Natural Sciences and Engineering Research Council of
Canada (NSERC)
Department of Fisheries and Oceans Canada (DFO)
Videos from
NASA/Goddard Space Flight Center
Scientific Visualization Studio
and
NOAA

56. Questions or comments?
Vincent Lecours
Email: vlecours@mun.ca
Marine Geomatics Research Lab
www.marinegis.com