1. Software and Tools Overview Dream Ocean Satellite Image Workshop CH2M Hill Alumni Center, Corvallis, Oregon August 18-19, 2011 Ichio ASANUMA The Tokyo University of Information Sciences

2. Software and Tools Overview Tools and operating systems Data levels and manipulation Geophysical parameters Spatial and temporal analysis Models for spatial and temporal estimate Models for decision making

3. Tools and operating systems Operating systems and PCs –Significant improvements of PCs as workstations, desk-top or notebook computers Changes of windows system –Entertainments to business –Sometimes slowing our use by version change Stable dissemination of linux system –Commercial to freeware linux system Improvement of VMware player –Realization of two operating system within one computer

4. Large disk space VMware player Implementation of linux with Vmware player within windows system Windows operating system C drive for Windows / drive for Linux VMware LINUX operating system with VMware player software

5. General image processing software IDL ENVI EARDAS MATLAB Geographic Information System Radiance Geophysical data data Regional Instantaneous Global Time series Value added Mission oriented data processing system IPOPP, SeaDAS

6. Sharing roles in data levels and manipulation Mission oriented data processing system –IPOPP –SeaDAS General image processing software with geophysical parameters –IDL, ENVI, EARDAS, MATLAB, etc Geographic information system –ArcGIS

7. Mission oriented data processing system IPOPP SeaDAS Future requirements –Continuity to the future missions –Share of roles with other processing or GIS systems

8. General image processing software General image processing software works on geophysical parameters and with geolocation data –Inputs: GeoTiff data & others –Processing: Spatial composite and/or time series analysis Modeling –Outputs: GeoTiff data –Future requirements: Cost

9. Example using general image processing software Asanuma, 2006, Depth and Time Resolved Primary Productivity Model Examined for Optical Properties of Water, Elsevier Oceanography Series 73, 91-109. Inputs to IDL under SeaDAS –Chlorophyll-a –Sea Surface Temperature –Photo-synthetically Available Radiation Outputs from IDL –Primary productivity Application with general image processing software

10. 10 SST Chl-a PAR MODTRAN PAR (June) PP eu = ∫ t ∫ z P b (z,PAR(z,noon),T) C(z) PAR M (0,t)/PAR M (0,noon)dz dt Primary productivity model Asanuma 2002

11. 11 Vertical distribution of Chl-a and PAR Vertical distribution of EdPAR for chlorophyll-a concentration for 5.0 mg/m3. Vertical distribution of chlorophyll-a concentration for 5.0 mg/m3. Asanuma 2001

12. 12 Asanuma 2002 Validation of the model

13. Example using general image processing software Zainuddin et al., 2006, Using multi-sensor satellite remote sensing and catch data to detect ocean hot spots for albacore (Thunnus alalunga) in the northwestern North Pacific, Deep-Sea Research Part II 53, 419-431. Inputs to IDL –AVISO mean sea-level anomaly (MSLA) –Chlorophyll-a –Primary productivity –Sea surface temperature Output from IDL –Probability of catch rate of albacore Application with general image processing software

14. Albacore CPUE Albacore/boat-days in 1999-11 on TRMM/TMI SST (contour on 20 deg-C) Albacore/boat-days in 1999-11 on SeaWiFS chlorophyll-a (contour on 0.3 mg m-3) Albacore/boat-days in 1999-11 on an environmental provability map generated from SST and Chl.

15. Geographic information system (GIS) GIS approach –GIS controls layers and provides value added maps –GIS could be useful in real world decision making. Simple approach by GIS –Boolean logic with un-weighted layers Complex decision support by GIS –Multiple criteria, multiple objectives weighted variable layers

16. GIS integration Global, time series and value addition –ArcGIS Inputs: GeoTiff data/Layer components Processing: –Insert additional information –Projection etc. Outputs: GeoTiff data/Layers Future requirements: >> Connecting effort between RS & GIS Primitive use of GIS

17. GIS produces value added maps with GIS components of points, lines, or polygons in conjunction with raster data and control capability of layers.

18. GIS integration Multi-criteria decision tools –ArcGIS Inputs: GeoTiff data/Layer components Processing: –Multi-criteria/Multi-objective decision making –Value added data production Outputs: GeoTiff data/Layers Future requirements: –Algorithm implementation to GIS software Decision making with GIS

19. GIS decision approach Multi-criteria evaluation (MCE) Methodology –Determination of criteria (factors) –Normalization of factors / criterion scores –Determination of weights for each factors Analytical hierarchy (AHP) process to calculate weights –Evaluation using MCE algorithms –Sensitivity analysis of results AHP & MCE are functions of ArcGIS

20. Application of GIS multi-criteria evaluation Radiarta & Saitoh, 2009, Biophysical models for Japanese scallop, Mizuhopecten yessoensis, aquaculture site selection in Funka Bay, Hokkaido, Japan, using remotely sensed data and geographic information system, Aquacult. Int. 17:403-419. Inputs to ArcGIS –Sea surface temperature –Chlorophyll-a –Suspended solid bathymetry –Scoring and weighting parameter Output from ArcGIS –Aerial distribution of suitable aquaculture site

24. Why RS and GIS ? (from JPL-ESRI report, 02, 2011) Characterize and understand complex process using measurements from multiple sources. GIS benefits: –To visualize, analyze, and overlay geo- referenced data –To access to the actual data values –To access to a suite of robust analytic tools.

25. What are the problems that require connecting RS and GIS? (from JPL-ESRI report 02, 2011) Basic questions: –Where is data? –How much does data cost? –What data is necessary? –What knowledge is necessary to have for it? –What HW/SW is necessary? –How can we share the way with clients/users? For data access: –How can I gain access and how can I share? For analysis: –How do I go from data to information?

26. Key gaps or barriers to the use of RS data by GIS application (from JPL-ESRI report 02, 2011) Many RS users do not need GIS to accomplish their work. Difficulties in integrating raster and vector dataset in GIS. Large volume of RS dataset for GIS. Difficulties in integrating GIS software with other applications. Difficulties to deal multi-dimension dataset in GIS.

27. http://support.esri.com/en/downloads/datamodel/detail/21

28. Summary Hardware and operating system provide more possibility in further application of satellite data. Mission oriented data processing system is the important function to support geophysical data to end user. General image processing software opens a possibility to implement new approaches by end users working on geophysical data. GIS provides capabilities to generate value added map and decision making tools.