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Services for an Agricultural Application

Services for an Agricultural Application. Shinobu Kawahito JAXA / RESTEC Kengo Aizawa, Satoko Miura JAXA. WGISS-22 @Annapolis. Project Background. Purpose. Prove the usefulness of OGC compliant distributed systems to support an agricultural application

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Services for an Agricultural Application

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

  2. Project Background • Purpose • Prove the usefulness of OGC compliant distributed systems to support an • agricultural application • - Transition to an operational service (more than testbed development) • 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) • MAFFIN (MAFF Information Network) has knowledge of satellite Data, and also holds other data related to agriculture. • Partner (MAFF) has expertise in an application area • User Involvement of multiple types of users in the agricultural domain (Decision makers, Researchers, and indirectly Farmers)

  3. Major Achievements • Major Achievements up to now • Test systems developed for 3 themes. • - Hotspot Monitoring • - Vegetation Monitoring • - Flood Monitoring • Efficiency of WMS based systems has been recognized. • Hotspot Monitoring systems have: - 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

  4. Ideas from MAFF on Using Data for Agriculture • Importance to create information • 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. • Importance of integration of diverse types of data E.g In-situ data, - can be used to evaluate satellite data - can be used to used in combination with satellite data • Support for Decision Makers - areas of interest may change rapidly (depending on circumstances) - the more focused the area of interest, the more detailed information is needed • Present the information in a user-friendly way Make information easy to use, easy to understand, and user interactive

  5. Change Detection and Interpretation • For Agricultural Monitoring (e.g. Drought Monitoring) 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. • To detect change and interpret the change • 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).

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

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

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

  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.

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