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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

Services for an Agricultural Application

Shinobu Kawahito JAXA / RESTEC

Kengo Aizawa, Satoko Miura JAXA

WGISS-22 @Annapolis


Project background
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)


Major achievements
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


Ideas from maff on using data for agriculture
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


Change detection and interpretation
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).


Quantification and comparison of time sequential changes per gis region
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.


Agricultural knowledge required for higher interpretation
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.


Work plan
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.


Future ideas for presenting information in a user friendly way
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 :


Conclusion
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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