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The use of Spot imagery in support of crop area estimates in South Africa by Geoterraimage | Spot Image - Agriculture
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The use of Spot imagery in support of crop area estimates in South Africa by Geoterraimage | Spot Image - Agriculture

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Agriculture and satellite imagery: the use of Spot imagery in support of crop area estimates in South Africa

Agriculture and satellite imagery: the use of Spot imagery in support of crop area estimates in South Africa

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  • Throughout presentation will adress issues
  • Kan hier ook na ArcView revert om hele GT te wys
  • Kry foto van GPS en PDA
  • Kan hier ook na ArcView revert om hele GT te wys
  • Transcript

    • 1. Spot Image and its partners add value to satellite imagery in Agriculture The use of Spot imagery in support of crop area estimates in South Africa Fanie FERREIRA An applicative session on
    • 2. The use of Spot imagery in support of crop area estimates in South Africa Fanie FERREIRA GeoTerraImage
    • 3. National Crop Statistics Consortium
      • Agricultural Research Council
        • Institute Soil Climate &Water:
          • Yield modelling research
        • Summer Grain Institute: objective yield – maize
          • Field measurements:
        • Small Grain Institute: objective yield – wheat
          • Field measurements
      • SiQ
        • Aerial surveys & telephonic interviews
        • Statistical processing
      • GeoTerraImage
        • Satellite image processing
        • Crop type classifications
    • 4. Overview
      • Field Boundary Mapping
        • Stratification of Agricultural activity
      • PICES: Producer Independent Crop Estimate Survey
        • Aerial Surveys: crop type area per province
        • Verified during the Gauteng census project:
          • difference < 1.8%
      • Crop Calendar
        • Understanding crop evolution/phenology
      • Crop Type Classification
        • Image processing & classification of satellite imagery
      • Classified Field Boundaries
        • Various applications
    • 5. Use of satellite imagery SPOT4 / LANDSAT Previous seasons 2006/7/8 Area calculation @ field level Field crop boundary In-season 2009 Satellite Analysis PICES survey @ provincial level SPOT5
    • 6. Stratification
      • Rate of interview refusal increased
      • Requirement: Develop new methodology
        • Producer Independent Crop Estimate Survey
        • Stratification: Field Boundary on 2.5m Spot5
    • 7. Field Boundary Mapping
      • Digitising manual to ensure consistency
      • Crop Field Boundaries: every cultivated field
        • Cultivation, Irrigation, Smallholdings
        • Orchards (Horti/Viticulture), Subsistence
      • Irrigation
        • Centre pivots
    • 8. SA coverage: 13 million ha
    • 9. PICES: Sample selection Shortest routing algorithm Points to be surveyed Gauteng Province
    • 10. PICES Infrastructure
    • 11. PICES: aerial survey
        • Additional points used for image training
        • Selected fields with identified crop types
      • Vast improvement: survey efficiency
      • Support image classification
      • Statistical calculated of area
    • 12. Crop Type Development
      • Seasons
        • Temperature: Summer vs Winter
        • Summer rainfall vs Winter rainfall
      • Vegetative growth
        • Annual vs Perennial
        • Annual: germination, growth, senescence, harvest
        • Perennial: deciduous vs evergreen
      • Cultivation Practices
        • Field preparation: fallow / bare soil
        • Planting dates
    • 13. Crop Evolution: Multi Season
    • 14. R/S Process Sequence
      • Selecting cloud free images
      • Ortho-rectification
      • Mapping cloud areas
      • Building image sets: optimal cloud free
      • Indices: NDVI, Tasseled Cap, PCA, BSI
      • Select bands for best discrimination
      • Image Calibration: PICES Crop Types
      • Classification: Supervised – user defined classes
      • Field Boundaries (shp) populate: Zonal Majority
    • 15. Freestate Province
    • 16. Classification Procedure
        • Erdas: Supervised classification
          • Maximium likelihood combined with Parallelepiped
        • Calibration / Training
          • Generate signature file from PICES crop types
          • Buffer field boundaries -60m: remove edge pixel
        • Heterogenity within field cause confusion
        • Evaluate spectral parametres
          • Select spectral bands from profile: bands vs crops
          • Class confusion/conflict – Set Std Dev
        • Field parcel (shp) populated: crop type
            • Zonal majority function & record majority fraction
            • Irrigated maize vs rainfed maize: 12% higher fraction
    • 17. Classification Analysis
    • 18. Maize Dominant Area
    • 19. Wheat Dominant Area
    • 20. Maize Comparison: 2007vs2008
      • Spatial Distribution
      • Cultivated area
        • Crop type classification
      • District level comparison:
        • Maize area / district
    • 21. SoyaBean Comparison: 2007vs2008
      • Spatial Distribution
      • Cultivated area
        • Crop type classification
      • District level comparison:
        • Soya area / district
    • 22. Conclusion
      • Integrated Processing Chain
        • Based on Spot 4 & 5 imagery
      • Spot 5 imagery provided complete coverage
        • Field Boundaries: Improved stratification
          • Large reduction in area to survey: Reduced costs (8X)
          • Accuracy increased
      • Spot 4 imagery regular recordings
        • Complete cloud free coverage
          • Calculation of district level area / field level
          • Similar zones can be calculated
          • Visualise cropping patterns & trends
          • Valuable for agro-industry planning
    • 23. ‘‘ Need the right imagery solutions for your job? Ask for Spot Image.’’ www.spotimage.com