use of satellite imagery for the generation of an aquaculture atlas : a case study in greece

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USE OF SATELLITE IMAGERY FOR THE GENERATION OF AN AQUACULTURE ATLAS : A CASE STUDY IN GREECE Nicolas Longépé, CLS 1

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USE OF SATELLITE IMAGERY FOR THE GENERATION OF AN AQUACULTURE ATLAS : A CASE STUDY IN GREECE Nicolas Longépé, CLS

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• Objective • Design an “Aquaculture Atlas Production System” (AAPS). The AAPS takes Earth Observation

products as inputs and generates “Aquaculture Products” (for instance spatial information)

• Implement a prototype to support a demonstration on selected areas (Greece, Indonesia…)

• Approach: • Extract data existing or made available within BlueBRIDGE VRE as ancillary data to support Earth

Observation processing (especially data coming from FAO NASO fact sheets...)

• Explore automatic methods for detection of aquaculture features, in particular regarding EU Copernicus data (Radar Sentinel1 and Optical Sentinel-2)

• Store “Aquaculture products” in the e-infrastructure for collaborative exploitation (statistics, comparison with other DB, integration in FAO DB)

• Collaboration: • FAO on thematic use

• CNR and ENG on VRE integration and IT solution

• Grid-Arendal (especially for Indonesian case study)

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FAO National Aquaculture Sector Overview (NASO) maps

• Content of NASO fact sheet for Greece (http://www.fao.org/fishery/countrysector/naso_greece/en)

• Characteristics, Structure And Resources Of The Sector

• Summary

• History And General Overview

• Human Resources

• Farming Systems Distribution And Characteristics

• Cultured Species

• Practices/Systems Of Culture

• Sector Performance

• Production

• Market And Trade

• Contribution To The Economy

• Promotion And Management Of The Sector

• The Institutional Framework

• The Governing Regulations

• Applied Research, Education And Training

• Trends, Issues And Development

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FAO National Aquaculture Sector Overview (NASO) fact sheets

Earth Observation imagery as input

Sentinel-1 ESA / EC mission (radar) – 2 satellites launched in 2014 and 2016 Independant on light condition (night/day) / through cloud which can be convenient in some regions (tropical…) and for reactivity C-Band Synthetic Aperture Radar data - Eventually for preliminary fish cages (Best resolution 10 m)

Sentinel- 2 ESA / EC mission (optical) - 2 satellites launch in 2015 and end 2016 (tentative) Prime objectives of Land monitoring, but coastal zones (20 km off the shore) and entire Med Sea covered Eventually for preliminary fish cages, shrimp farms or seaweed parcel detection (Best spatial resolution 10 m) Adequate for High Resolution inland and coastal water quality index (Sediment, Chlorophyl…)

Sentinel – 3 ESA / EC mission Prime objective of ocean monitoring, Several sensors and applications: oceanography, water quality (> 300 meters) and SST (500m – 1km)

Japanese ALOS-1 and ALOS-2 Adequate for mangrove assessment as ALOS is L-band (penetration though forest canopy, senstive to biomass)

High resolution basemap image from MICROSOFT Bing, Google Earth or Here WeGo (ex Nokia) No control on data processing, and revisit, but Very High Resolution (< 50 cm) In the case of Bing, derived products (aquaculture contours in our case) shall be disseminated under the same licence as the Open Street Map data (the Open Database License)

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the VRE Infrastructure

Ontology and Outputs from AAPS

• Several fish cages topologies found in Greece

• Basic features will be automatically retrieved from optical image (number of fish cages per structure, shape- circular or square, size – area, perimeter, width, length…)

• However, more thematic information may be of interest for end users

• Fish species: depending on fish cages structure, size, shape, infrastructure around

• Fish growth stage …

Image from Microsoft Bing Aerial – Derived products will be available under the Open Database Licence © Open Street Map 7

Image from Microsoft Bing Aerial – Derived products will be available under the Open Database Licence © Open Street Map 8

Geospatial data analysis toolbox

• @CLS: imagery analysis tools currently used in NRT operational contexts for a wide range of application and users • mostly for the maritime sector, pollution surveillance

at sea and illegal fishing monitoring

• The new toolset in BlueBRIDGE VRE • Refine this set of geospatial tools with the capacity to

detect and characterize aquaculture features • Toolset dedicatedly tuned for the aquaculture sector • Simple to use, efficient and adapted to the sector

needs

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How it works

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• Step 1: An overview of fish farm in Greece • Database from FAO – extract from NASO

How it works

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• Step 2: Region of Interest selection

How it works

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• Step 3: automatic fish cage detection • Dedicated algorithms implemented to detect circular (flexible) and square

(rigid) floating fish cages

How it works

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• Step 3: automatic fish cage detection • Dedicated algorithms implemented to detect circular (flexible) and square

(rigid) floating fish cages, fast - few seconds

How it works

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• Step 4: eventually manual editing • Dedicated tools implemented to handle geospatial vectorial information –

simple to use (compared to SIG soft.), dedicated to aquaculture features

How it works

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• Step 5: fill metadata • Adequate ontology can be proposed depending on context, aquaculture

features, input EO images

• Step 6: redo the operation • Automatic pre-screening envisaged at mid-term

Dissemination of products in the VRE

• All detected fish cages will be available in the VRE BlueBRIDGE workspace (over the whole Greece by end 2016) and where applicable, in a geoserver and Geonetwork • Contour of each fish cage for all farms

• Statistical assessment : number of fish farms, number of cages, production surface, feeding systems…

• Also available via the VRE in the CLS WebGIS (EODA) platform (coming soon as well) • Sentinel-1 data + Sentinel-2 data + metocean information (SST, Wind, wave, …) from model or EO measurements

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Dissemination of products in the VRE

• All detected fish cages will be available in the VRE BlueBRIDGE workspace (over the whole Greece by end 2016) and where applicable, in a geoserver and Geonetwork • Contour of each fish cage for all farms

• Statistical assessment : number of fish farms, number of cages, production surface, feeding systems…

• Also available via the VRE in the CLS WebGIS (EODA) platform (coming soon as well) • Sentinel-1 data + Sentinel-2 data + metocean information (SST, Wind, wave, …) from model or EO measurements

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Dissemination of products in the VRE

• All detected fish cages will be available in the VRE BlueBRIDGE workspace (over the whole Greece by end 2016) and where applicable, in a geoserver and Geonetwork • Contour of each fish cage for all farms

• Statistical assessment : number of fish farms, number of cages, production surface, feeding systems…

• Also available via the VRE in the CLS WebGIS (EODA) platform (coming soon as well) • Sentinel-1 data + Sentinel-2 data + metocean information (SST, Wind, wave, …) from model or EO measurements

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Sentinel-1 radar image (right) compared to optical VHR images (left) BUT: Work day/night even through cloud which can be convenient in some regions (tropical…) and for reactivity

Conclusion

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• What?

• Design an “Aquaculture Atlas Production System” (AAPS). The AAPS takes Earth Observation products as inputs and generates “Aquaculture Products”

• Implement a prototype to support a demonstration on selected areas (Greece, Indonesia…)

• When?

• Integration ongoing -> Data over Greece available for any users of the BlueBRIDGE VRE by end 2016

• At short term:

• All the outputs available for additional use by end –users (vector information in shapefile and/or spatiaLite format)

• Statistical assessment such as number of farms, number of cages per farm, production surface, feeding systems…

• Complete FAO fact sheet

• Who can benefit? Serving private companies, including SMEs, research sector and international organisations

• Efficient identification of strategic locations of interest that meet multifactor selection criteria (spatial planning, correlation with environmental condition…)

• support the collaborative production of scientific knowledge required for aquaculture monitoring, for analyzing socio-economic performance in aquaculture (time series, production assessment…)