Inventory and monitoring of Aquaculture and the environment: presentation from COFI 2016 side event "Innovative IT solutions to support Data needs for Blue Growth – Google and iMarine examples". José Aguilar-Manjarrez, FAO Aquaculture Branch Xiaowei Zhou, FAO Statistics and Information Branch

1. Inventory and monitoring of
Aquaculture and the environment
Innovative IT solutions to support Data needs for Blue
Growth – Google and iMarine examples
José Aguilar-Manjarrez, Aquaculture Branch
Xiaowei Zhou, Statistics and Information Branch
COFI 32, July 13, 2016

2. Satellite remote sensing provides a large range of
observation data

3.  Decision-makers need important baseline data and
trends on production, area boundaries, size
distribution of farms, environmental conditions and
impacts
 Mapping improves the effectiveness of planning and
management interventions to increase production
improve emergency preparedness (including for
diseases) and reduce risks in general.
Rational

4.  limited awareness of its benefits for decision-makers
and technical personnel;
 limited knowledge on how to conduct inventories and
analysis;
 limited number of innovative mapping applications;
and
 limited human resources, infrastructure and financing
Challenges

6. National Aquaculture Sector Overview (NASO)
map collection
http://www.fao.org/fishery/naso-maps/naso-home/en/

7. Sub-national and individual farm level data
Inventory of shrimp farms, Nicaragua, 2009

8.  For non-reporting producers, estimates could be made using
satellite images of aquaculture facilities or structures
Use of Google imagery information to fill the
gap of missing data in FAO global statistics
Lebanon: Raceways used for rainbow trout culture
along a river
Martinique: Earthen ponds used for tilapia culture

9. Summary of inventory
Fish farms 58
Cages 1
Production 450 tonnes (2013)
305 tonnes (2014)
For reporting producers with certain productions missing in
national report, estimates are made using satellite images of
aquaculture facilities or structures
Angola

10. Spatial planning and area management
Aquaculture zoning
Fish cages before relocation (2007) and after relocation (2008-2009)
Gulluk Bay, Turkey.

11. Spacing between fish farms and other economic, cultural
or ecological assets is of critical concern in determining
where a farm is likely to succeed

12. Satellite remote sensing can support the development of
appropriate disaster risk management interventions
Timely access to
satellite imagery
can help assess
damages in a post
disaster situation

13. Satellite imaging radar

14. Fish cages Fish ponds
Lingayen gulf, The Philippines
Source: Travaglia, Profeti, Aguilar-Manjarrez, and Lopez (2004)

15. Care is needed to keep pace with the rapid rise in
shrimp farms and to preserve the local environments
Keeping pace with the rapid rise in shrimp farms
1996 – 1998, Sri Lanka.
Abandoned shrimp farm in western Sri Lanka
Source: Travaglia, Kapetsky and Profeti (1999).

16. iMarine

17. Technical opportunities
 semi-automate inventory of aquaculture structures
 enhance optical and radar image analysis
 facilitate environmental monitoring
 promote standards

18. Semi-automatic detection of marine fish cages

19. Semi-automatic detection of marine fish cages
(Google earth engine example)
South East China Bay
Time series 2000-2016

20. The iMarine infrastructure is aimed to:
Key benefits
 raise awareness of the benefits and
constraints
 lower skillset required to conduct
inventories and analysis
 facilitate access to innovative
mapping applications
 Increase human resources,
infrastructure and financing thanks to
cross-disciplinary collaboration and
partnerships

21. Gulf of Fonseca, Nicaragua
The choice of aquaculture site should take into consideration
the location of sensitive habitats such as mangroves

22. “Satellite remote sensing
and fish farming are a
strange combination but
one that can contribute to
increasing food security,
alleviating poverty and
safeguarding our
environment”.
Concluding remarks

23. Semi-automatic detection of marine fish cages
(Google earth engine example)

24. Semi-automatic detection of marine fish cages
(Google earth engine example)

25. Semi-automatic detection of marine fish cages
(Google earth engine example)

26. Semi-automatic detection of marine fish cages
(Google earth engine example)

27. Semi-automatic detection of marine fish cages
(Google earth engine example)