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Online Analysis of Remote Sensing Data for Agricultural Applications - - PowerPoint PPT Presentation

Online Analysis of Remote Sensing Data for Agricultural Applications Athanasios Karmas * Konstantinos Karantzalos Spiros Athanasiou Institute for the Remote Sensing Laboratory Institute for the Management of National Technical University


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Online Analysis of Remote Sensing Data for Agricultural Applications

Athanasios Karmas* Konstantinos Karantzalos Spiros Athanasiou

Institute for the Remote Sensing Laboratory Institute for the Management of National Technical University Management of Information Systems

  • f Athens

Information Systems ”Athena” Research karank@central.ntua.gr ”Athena” Research Center Center karmas@imis.athena- spathan@imis.athena- innovation.gr innovation.gr July 16, 2014

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Motivation

Exploit Big Earth Observation (EO) Data → Various Sensors, Various Platforms → Various Spatial, Spectral, Temporal properties Make EO data a mainstream → Numerous (new) users → Easy, ready-to-use geospatial products Goal: Geospatial Information, Create Accurate Maps

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Problem to Solve

Easy access to EO data archives Process Multimodal data from various sensors Develop efficient Services Offer validated Products → Direct processing and analysis of data, online wherever needed → Efficient spatiotemporal modelling and monitoring (agriculture, urban environment, natural disasters, crisis management and assessment)

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Problem to Solve

Agricultural Applications Crop monitoring Precision farming Creation of accurate agricultural maps Validated products and agricultural maps → Site-specific decisions → In time → Regardless of the areal extent or the ease of physical access

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Technologies

Rasdaman Array DBMS for data storage OGC WCPS interface standard GeoExt/OpenLayers javascript libraries

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Developed Platform (I)

RemoteAgri Web GIS System → Visualization Services → Analysis Services Utilizes the Landsat 8 dataset → Open Data → Multispectral, multitemporal satellite imagery → Fairly good spatial resolution (30m/pixel) Landsat 8 raw data are downloaded, stored and pre-processed automatically

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Developed Platform (II)

Core functionality → Rasdaman Array DBMS → OGC WCPS interface standard Key features → Vegetation Detection → Canopy Estimation → Water Stress Estimation Fully covers Greek territory with Landsat 8 imagery → New dataset every - apprx. - 16 days → 40 scenes per dataset, averaging apprx. 80GB uncompressed

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RemoteAgri WebGIS System

Automated col- lection of newly acquired datasets Extract com- pressed and archive raw data Radiometric corrections (ToA) Rasdaman PetaScope WCPS queries Vegetation Detection, Canopy & Water Stress Estimation GeoExt/OpenLayers Pre-processing Web Client

Figure: The components of the RemoteAgri WebGIS system.

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Implementation Details (I)

Automated Collection & Preprocessing subsystems Automated acquisition through Web Harvesting Archive and extract compressed data Preprocessing to convert to ToA reflectance Ingestion in rasdaman

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Implementation Details (II)

Rasdaman Storage of Landsat 8 multispectral data Suitable data types definition Array types defined with open bounds

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Implementation Details (III)

Web Client OpenLayers library GeoExt library Client side scripts → User interaction → Metadata search → Construction of WCPS queries → Communication with the Server

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Implementation Details (IV)

Developed Agricultural Queries → WCPS interface standard Vegetation Detection Canopy Estimation Water Stress Estimation

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Vegetation Detection

Calculates NDVI Index Creates binary map that distinguishes vegetation from soil and urban environment

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Canopy Estimation

Further classification based on NDVI Zoning the different canopy levels Monitor vegetation health and growth

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Water Stress Estimation

At satellite temperature values Converted to Celsius Degrees Color map that distinguishes different temperature levels The higher the temperature the higher the probability of water stress in irrigated croplands Must be interpreted in close correlation with the Canopy Estimation query

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Use Case Scenario

An agricultural association → Overall state of crops → Ability to provide site-specific information Irrigated croplands in Axios Delta area in Central Macedonia → Rice summer crops (70%) → Cotton and corn crops follow

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Results (I)

  • a. 24/6/2013
  • b. 10/7/2013
  • c. 26/7/2013
  • d. 11/8/2013

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Results (II)

  • a. 24/6/2013
  • b. 10/7/2013
  • c. 26/7/2013
  • d. 11/8/2013

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Use Case Scenario(II)

Canopy Estimation → Crop vigour and state → Site-specific decisions → Vegetation life cycle monitor

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Results (III)

  • a. 24/6/2013
  • b. 10/7/2013
  • c. 26/7/2013
  • d. 11/8/2013

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Results (IV)

  • a. 24/6/2013
  • b. 10/7/2013
  • c. 26/7/2013
  • d. 11/8/2013

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Use Case Scenario (III)

Water Stress Estimation → Temperature Map → Information about irrigation failures → Examine if other factors are responsible for high temperature

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Results (V)

  • a. 24/6/2013
  • b. 10/7/2013
  • c. 26/7/2013
  • d. 11/8/2013

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Conclusion & Future Perspectives

Demonstrated the combination of various FOSS technologies Presented a robust framework with real time analysis potential → Bulk ingestion of geodata from various sensors → Further development of the Web Client → Incorporation of other OGC interface standards → Location based services

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Thank You!

Online Analysis of Remote Sensing Data for Agricultural Applications

Athanasios Karmas* Konstantinos Karantzalos Spiros Athanasiou

Institute for the Remote Sensing Laboratory Institute for the Management of National Technical University Management of Information Systems

  • f Athens

Information Systems ”Athena” Research karank@central.ntua.gr ”Athena” Research Center Center karmas@imis.athena- spathan@imis.athena- innovation.gr innovation.gr

July 16, 2014

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Questions

Questions ?

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Demo

RemoteAgri WebGIS ikaros.survey.ntua.gr/remoteagri Demonstration purposes RemoteAgri Walkthrough

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Demo

RGB

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Demo

RGB 543

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Demo

RGB 654

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Demo

Vegetation Detection

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Demo

Canopy Estimation

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Demo

Water Stress Estimation

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The End !

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