1. Services for an Agricultural Application Shinobu Kawahito JAXA / RESTEC Kengo Aizawa, Satoko Miura JAXA WGISS-22 @Annapolis

2. 2 Project Background - Prove the usefulness of OGC compliant distributed systems to support an agricultural application - Transition to an operational service (more than testbed development)  Purpose  Merit of JAXA/MAFF Collaboration (Ministry of Agriculture, Forestry and Fishery)  Increased use of JAXA satellite data (but the operational system is maintained by MAFF)  User Involvement of multiple types of users in the agricultural domain (Decision makers, Researchers, and indirectly Farmers)  Partner (MAFF) has expertise in an application area  MAFFIN (MAFF Information Network) has knowledge of satellite Data, and also holds other data related to agriculture.

3. 3 Major Achievements  Major Achievements up to now - Successfully been transferred from JAXA to MAFF. - Monitoring Services are being transitioned to operational by MAFF. - New project within MAFF: -Deliver information to Japanese local fire departments via Web Mapping + Email/Fax -Upgrade original software/systems Test systems developed for 3 themes. - Hotspot Monitoring - Vegetation Monitoring - Flood Monitoring Efficiency of WMS based systems has been recognized. Hotspot Monitoring systems have:

4. 4 Ideas from MAFF on Using Data for Agriculture - Simply providing archives of data is not very useful – added value is needed. - Just getting time sequential images is not sufficient to determine the presence of a problem (e.g. drought), quantification is needed.  Present the information in a user-friendly way E.g In-situ data, - can be used to evaluate satellite data - can be used to used in combination with satellite data - areas of interest may change rapidly (depending on circumstances) - the more focused the area of interest, the more detailed information is needed  Importance to create information  Support for Decision Makers  Importance of integration of diverse types of data Make information easy to use, easy to understand, and user interactive

5. 5 Change Detection and Interpretation First, quantify the information: Quantify time sequential change - e.g. statistics per polygon Second, detect the change: Compare current data against predefined criteria to detect change and determine amount of change Third, interpret the meaning of the change: - Try to determine the reason why the change occurred. - Try to determine if the change indicates drought or not. - Estimate the Impact (as if it were a drought). Various things can cause a reduction in vegetation compared to other years. E.g. Non-drought (Delay in planting, Plant types change) vs. Actual drought.  For Agricultural Monitoring (e.g. Drought Monitoring)  To detect change and interpret the change

6. 6 Region A Region B Region C ― Ongoing NDVI ― ＮＤＶＩ Average Period ： Year month day ~ Year month day 2000 1 12 1 31 Quantification and Comparison of Time Sequential Changes per GIS Region Provide quantified information: - Statistics per polygon - With comparative data Graph is not showing actual NDVI.

7. 7 Agricultural Knowledge Required for Higher Interpretation Apr. May. Jun. Jul. Aug. Sep. Oct. Vegetation Stages E.g. rules to interpret reduction in vegetation (drought vs. other cause)  Need to develop a drought model for operational monitoring. To interpret the vegetation data into a drought interpretation (“higher level product”), plant A Growth patterns of major plants Interpretation plant B planting / early stage / growth stage / maturing / harvesting Estimate the Impact Find and Monitor Tendency Decision Flow

8. 8 Work Plan To process observations into higher level information - Select test sites, and create GIS polygons - Use observation data to establish basic knowledge of vegetation at the site - Define and establish Functional Components E.g. GIS Subset, Statistics per region, etc - Design GUI to present information in a user friendly way E.g. Graphs with editable data ranges, etc.

9. 9 Future Ideas for Presenting Information in a User Friendly Way Web Map Images + Observation data. Statistical information. Time Sequential Information. Etc. Web Map Images + Observation data. Statistical information. Time Sequential Information. Etc. NDVI ： 10%down Drought likelihood : NDVI ： 1.5% down Drought likelihood : Place A Place B For example – use of symbols (like a stock chart):

10. 10 Conclusion Involves close work with user agency to focus on usage–oriented investigation and development. Involves an effort to examine and establish methods to use Earth observation satellite data to provide information and products useful in agricultural. Functions should be built in an easy way to reuse. - Similar functions may be needed in flood monitoring. - Reusable components may helpful in building future flexible service systems.