1. Andrey V. Golik, Vitaly K. Fischenko, Stepan G. Antushev V.I.Il’ichev Pacific Oceanological Institute Far-Eastern Branch of Russian Academy of Sciences Space Technology & Geo-Informatics 2006, Pattaya, Thailand, 2006 Development of satellite oceanography methods in FEB RAS corporate oceanographic GIS

2. FEB RAS – Far-Eastern Branch of Russian Academy of Sciences This is: 25 institutes (6 scientific centers), from them 12 institutes specializing in «Earth sciences», from them 5 institutes specializing in «Oceanography»: Pacific Oceanological Institute (300 scientists), total about 1000 scientists Main area of researches: Northwestern Pacific (lithosphere, hydrosphere, atmosphere) Oceanographic researches at FEB RAS

3. Scientific centers and institutes of FEB RAS, which perform researches in Northwestern Pacific Primorsky Scientific Center Pacific Oceanological Institute, Institute of Marine Biology et all (4 institutes) Sakhalin Scientific Center Institute of Marine Geology and Geophysics (1 institute) Kamchatsky Scientific Center Institute of Volcanology and Seismology et all (2 institutes) Northeast Scientific Center ( 2 scientific institutes )

4. Corporative oceanographic GIS of FEB RAS Primary task – “deliver to any scientist workplace: 1.all available data about sea and atmosphere in region 2.obvious tools for joint cartographical and scientific data visualization and analytical data processing 3.possibility of use distributed computing resources of FEB RAS network for solving complex resource- intensive tasks”

5. Typical view of GIS FEB RAS user interface

6. Bottom sediments in Japan sea CTD station locations in 1958 Morphologica l image analysis (oil spill localized and described) Query for satellite images contain oil spills Current status: 54 thematical layers, about 150 Gb of data, 6 software tools for analytical data processing, link to 3 remote data storage in FEB RAS network, monitoring of 5 oceanographic internet resources. Work with different data layers and types

7. Information layer “Satellite oceanography” Supported in GIS FEB RAS since 2002 Purposes of satellite data integration into GIS: provide all interested FEB RAS scientists with online access to new information layer – sea environment satellite observations data; for “satellite” oceanographers – possibility to get various corresponding data on state of the sea environment in order to improve methods of satellite information interpretation; for “traditional” oceanographers – possibility to use results of satellite observations over research area in analysis and interpretation of oceanographic data; provide all interested GIS users with effective software tools for processing, analysis and interpretation of satellite images.

8. Main part is database of SAR-images from ESA received by satellites ERS-1/2. It prepared in POI Satellite oceanology department. Registering device: synthetic aperture radar (frequency: 5.3 GHz, frame size: 100x100 km, resolution: ~25x25 m). Observation regions: Okhotsk, Japan, East and South China, Yellow, Sulawesi and Sulu Seas. Observation period: 1991 – 2005 years. Data volume: ~ 3 Gb, more than 1000 images. Primary tasks which are being solved with this set of SAR images: 1.development of methods for detection and spatial localization of oceanological phenomena on SAR images 2.demonstrate to scientists of FEB RAS possibilities of satellite radar with synthesized aperture for tasks of monitoring of sea state on large areas SAR-images in GIS FEB RAS GIS contains large collection of different data from satellites ERS-1/2, Envisat, NOAA, Terra/Aqua, etc. (about 2000 images, total volume more than 10 Gb).

9. Phenomena on satellite images With every SAR-image linked set of oceanographic and atmospheric phenomena that has visual appearance oceanographic phenomena: coastal front current current front eddy ice internal waves ocean front oil pollution slicks upwelling etc. atmospheric phenomena: atmospheric front atmospheric waves rain wind etc. Total 47 oceanographic and atmospheric phenomena

10. User interface

11. SAR-images with oil pollutions

12. SAR-images with internal waves

13. SAR-images with ice in bay Aniva in March 1999

14. Expert interface – add new SAR-image in GIS

15. Expert interface – select image for description

16. Expert interface – phenomena description Expert use visual analyze and data processing tools from GIS

17. Using GIS analytical tools for satellite image processing GIS users can use a set of image processing tools from analytical support system. These tools allow to: perform various image transformations for visual improvements, noise reduction and restoration of source physical fields using algorithms of linear and non-linear spatial filtration, filtration algorithms based on fast orthogonal transformations; perform wide set of orthogonal image transformations (Fourier, Haar, Hadamard, Hartley, Cos & Sin – transformations, wavelet transformations); perform correlation-spectral image analysis; perform morphological image analysis; analyze any one-dimension sections of image using modern methods of digital signal processing.

18. Usage of GIS analytical tools is very simple Expert can copy image from GIS window to clipboard and paste in desktop program

19. Spatial satellite image filtering Original image and 5 different filtering results

20. Spatial-frequency filtering (SFF) of satellite image using « global » filter Original image, Fourier-spectrum, modified Fourier-spectrum, result

21. «Dynamical» operation – very useful tool for local features analysis “Dynamical spectral analysis” of any satellite image fragment

22. “ Dynamical SFF filtering ” Swell-waves deleted by using local SFF, keep only internal waves

23. Using « dynamical template matching » for mesoscale ocean eddy moving analysis Two satellite image with time difference in half hour (maximum of cross correlation function determine shift of eddy structure)

24. Correlation-spectral analysis of SAR image ISC IFC 1 and it approximation IFC 2 and it approximation Image Fourier- spectrum Correlation ISC – integral spatial characteristics IFC – integral frequency characteristics On this figure presented: original image; 2D Fourier spectrum; 2D correlation function; integral spatial characteristic describing properties of image structure anisotropy; 2 modifications of integral frequency characteristic with results of it ’ s approximation using one of the correlation-spectral models provided by tool.

25. Morphological analysis of SAR image Oil pollution recognition (original, binary, recognized)

26. Joint usage of satellite and non-satellite data Important advantage of conception of union geoinformatics and space technologies is opportunity to organize joint work of specialists in different knowledge fields. Such joint work encourages development of both satellite methods and other scientific methods. During trial use of oceanographic GIS FEB RAS there were outlined some « points of interest intersection » for satellite oceanographers and specialists in different oceanography fields.

27. restored SST fieldrestored SSW field Restoration SST & SSW fields from AMSR-E data task channel 6GHz - Vchannel 6GHz - Hchannel 10.65GHz - Vchannel 10.65GHz - H T = fT(Ch1, Ch2, Ch3, Ch4, … ) W = fW(Ch1, Ch2, Ch3, Ch4, … )

28. Development and research of algorithms of physical field restoration using AMSR-E data and Near-GOOS data Configuration of task POI FEB RAS GIS-server Server contains local copies of AMSR-E data Laboratory of satellite oceanology Client PClient Q Computing resources Client IClient JClient 1Client N Gateway Internet Server contains satellite data AMSR-E (in Japan) Server NEAR-GOOS (in Japan)

29. Validation of the SST field retrieval algorithm

30. Joint using in GIS two methods of remote sensing: 1. Satellite oceanography 2.Seismoacoustic with laser interferometer methods

31. Base idea of seismoacoustic methods on Shultz cape B AB A R R Δ R

32. Support of seismoacoustic researches on Shultz cape SAR-image in same time (internal waves?) Shultz cape signal of Earth ’ s deformationsFourier-spectrum of signal surface waves? internal waves?

33. Analysis tide effects: 7-days record and result wavelet-filtering tide effects, Fourier-spectrum, continuous wavelet transformation. Detected periods– 12 and 24 hours.

34. Analysis hydro acoustic signal response: earth microdeformation signal (1 second), Fourier-spectrum, wavelet transform. Detected base frequency of hydro acoustic signal – 22 Hz.

35. About possibilities of joint use satellite and seismoacoustic data for tsunami detection. 1. At present time discussed different satellite methods for tsunami detection. 2. Seismoacoustic data allow differ «tsunami-alert» and «tsunami-not alert» underwater earthquakes. Tsunami-not alert earthquake in Japan sea.

36. Tsunami-alert earthquake in Japan sea (was not tsunami)

37. Tsunami-alert earthquake in Adaman sea (was tsunami)

38. Conclusion We believe that joint usage of geoinformatics and space technologies by specialists in various fields of science encourages development of both corresponding fields of science and space technologies. As we tried to show in this presentation, it is fair at least for oceanography. Thank you for your attention!