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22nd APAN Meeting in Singapore National University of Singapore, July 17-21, 2006

22nd APAN Meeting in Singapore National University of Singapore, July 17-21, 2006. Operational Fisheries Application of RS/GIS in Japan. Sei-Ichi SAITOH *, Hidetada KIYOFUJI*, Daichi TACHIKAWA**, Mihoko ABE**, Kazuhiko TATEYAMA** and Motoki HIRAKI*** * Hokkaido University

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22nd APAN Meeting in Singapore National University of Singapore, July 17-21, 2006

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  1. 22nd APAN Meeting in Singapore National University of Singapore, July 17-21, 2006 Operational Fisheries Application of RS/GIS in Japan Sei-Ichi SAITOH*, Hidetada KIYOFUJI*, Daichi TACHIKAWA**, Mihoko ABE**, Kazuhiko TATEYAMA** and Motoki HIRAKI*** * Hokkaido University ** FUJITSU Hokkaido Systems Limited *** GIS Hokkaido Limited

  2. Contents • Background • Objectives • ubiquitous Information System • Overview of Information Transfer • Hierarchical Product Structure • WebGIS and Onboard GIS • Concluding Remarks

  3. Fisheries as Social Systems ○△魚市場 特売 ウニ 冷 スーパー○△ RS/GIS logistics Fishing Fish Market Retail Stock Release Aquaculture Sustainability Sea Ranching Consumer

  4. Fisheries-GIS • 4-D (x,y,z,t) Geographical Information System • Dynamic Information of Oceanic Features

  5. Objectives • To develop fisheries information system • To produce high value-added fisheries oceanographic information • To provide such information in near real time in anytime and at anywhere (Ubiquitous) For sustainable use and fisheries management…… …..using remote sensing and GIS

  6. Fisheries Information System WebGIS OnboardGIS ArcIMS GeoBase L&X-band Satellite Receiving Module NOAA・Aqua/Terra SST Chl Sea Ice Real time and automatic processing TeraScan Datebase Module Oracle GIS Module Analysis Module • Calculate • Overlay • raster to vector • ERDAS/Imagine • ArcIGIS Internet Clients Satellite Communication

  7. Characteristics For sustainable use and fisheries management…… • WebGIS(ArcIMS) and Onboard GIS (GEOBASE) • Ubiquitous information system using satellite communication (WIDE-STAR and SUPERBIRD) • High value-added fisheries oceanographic information (Hierarchical Structure) • Near real time with automatic data processing (ERDAS/Imagine and ArcGIS) and data base management system (ORACLE) …..using remote sensing and GIS

  8. Overview of Information Transfer Communication satellite MODIS Database Analysis WIDE-STAR(64Kbps) SUPERBIRD(6Mbps) J-SAT(3Mbps) GPS Display Overlay Measure Internet WebGIS(Land) Onboard GIS (Offshore)

  9. Satellite Communication • SUPERBIRD-D 110 E Coverage

  10. Target Species • Japanese common Squid (Todarodes pacificus) • Pacific Saury (Cololabis saira) • Albacore (Thunnus alalunga) • Skipjack (Katsuwonus Pelamis)

  11. Squid Pacific saury Fishing fleets detected by OLS/DMSP October 13, 1998

  12. Distribution and Migration Bering Sea Okhotsk Sea Sea of Japan Northwestern Pacific Squid Skipjack saury Albacore

  13. Service Regions Squid Albacore saury

  14. Hierarchical Structure Level 1 Product Processing Level 2 Product Combination Level 3 Product Integration Level 4 Product Potential Fishing Ground

  15. Brief descriptions of each product level Product level and Product description 1 Raster-image data from the Terascan system 2 Single-image processing such as SST gradient or contour line 3 Integration of Level 1 and Level 2 products 4 Estimated potential fishing ground 5 Predicted one- or two-days-forward fishing ground

  16. How to make potential fishing ground Oyashio Japan Subarctic Front Shatsky Rise Emperor Seamount Chain Kuroshio Extension Kuroshio Subtropical Front Questions of interest: Why albacore – abundant What oceanographic factors – habitat How to determine – proxy indicators Albacore tuna (Thunnus alalunga) (www.ncfisheries.net/fishes/)

  17. How to make potential fishing ground JAFIC DATA BASE NASA,RSS and AVISO Satellite RS Fisheries Monthly 1998-2003 TRMM/TMI-AVHRR SST Classified fishing data High catch data SeaWiFS SSC ArcGIS Simple prediction habitat Enhancement Visualization T/P-ERS Merged SSHA Spatial analyst Probability of environmental map Examination Visualization Statistical model for prediction CPUE GAM-GLM Geostatistical analyst Oceanic features: eddies,fronts Potential albacore habitat

  18. Method 1 : Simple prediction map Input map Combine High catch range (mean ± one SD) SSHA 13.5 ± 19.16 cm Chl-a 0.3 ± 0.11 mg m-3 SST 19.5 ± 1.52 °C ArcGIS: Spatial analyst Output map

  19. Method 2 : Environmental probability map Input map Fishing effort CPUE SSHA + Chl-a + SST + ArcGIS: Spatial analyst Prob. Each interval Prob. Each interval Average 1.Fishing efforts : indices of fish availability (Andrade & Garcia, 1999) 2.CPUEs: indices of fish abundance (Bertrand et al., 2002) Output map

  20. Method 3 : Statistical model for prediction Input map Satellite data Catch data SSHA GAM Piecewise GLM Chl-a • Stepwise GLM • Residual deviance • AIC • F-statistic SST ArcGIS: Geostatistical analyst Significant equation Output map

  21. Space Fish LLP (Limited Liability Partnership) SpaceFish SpaceFish LLP was estabulished on June 20, 2006

  22. TOREDAS: start to fish Traceable Operational Resources Environment Data Acquisition System

  23. Overview of Information Transfer Communication satellite MODIS Database Analysis WIDE-STAR(64Kbps) SUPERBIRD(6Mbps) J-SAT(3Mbps) GPS Display Overlay Measure Internet WebTOREDAS(Land) Onboard TOREDAS (Offshore)

  24. Web TOREDAS

  25. Onboard TOREDAS

  26. Overview of Information Transfer Communication satellite MODIS Database Analysis WIDE-STAR(64Kbps) SUPERBIRD(6Mbps) J-SAT(3Mbps) GPS Display Overlay Measure Internet WebTOREDAS(Land) Onboard TOREDAS (Offshore)

  27. Antenna for Satellite Communication -Diameter:60cm -Ku band

  28. Notebook PC Receiver

  29. Sahara

  30. Panasonic - Tough Book

  31. Panasonic - Tough Book For Fishermen • Hard Duty • Touch Panel • Protect water

  32. A: clear, B: distance measurements, C: trip history, D: search the nearest predicted fishing ground, E: change menu.

  33. A B C D E A: clear B: distance measurements C: trip history D: search the nearest predicted fishing ground E: change menu.

  34. Web TOREDAS

  35. Operational Fisheries Oceanography Research and Development -Models -Technology (Observing/sampling) -Raw data -Statistics -etc • Operational Fisheries Oceanography • Measurements: systematic, long-term, routine • Interpretation of data: fast(near real-time) • Dissemination of results: rapid, e.g. internet -Funding -Data -Application -Products USERS Governments, Regulatory Authorities, Commercial Enterprises, Research Institutes

  36. Concluding Remarks • Support ubiquitous RS(MODIS)/GIS assisted fisheries operation • Contribute to save energy and fuel for optimum fishing activities • Improve understanding fishing ground formation and fish migration • Promote to develop “Operational” Fisheries Oceanography For sustainable use and fisheries management…… This project is supported by Ministry of Economy, Trade and Industry (METI)

  37. Future Satellite MODIS Database Fishing fleet TOREDASnever sleep 24 hours/365 days

  38. Future System • VMS （Vessel Management System）: Operation Management and Resource Management • Safety Operation: Weather and Wave Information • Economic Usage: Fish Market Information • Traceable: Food safety

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