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Sunny Ng, Columbia University Yifang Yang, Columbia University

Identifying crops in Uruguay at an early stage using remotely sensed landcover changes and crop field geometries. Sunny Ng, Columbia University Yifang Yang, Columbia University Dr. Pietro Ceccato, IRI, Science Advisor. Phase I.

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Sunny Ng, Columbia University Yifang Yang, Columbia University

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  1. Identifying crops in Uruguay at an early stage using remotely sensed landcover changes and crop field geometries Sunny Ng, Columbia University Yifang Yang, Columbia University Dr. Pietro Ceccato, IRI, Science Advisor

  2. Phase I • Depiction of soybean fields using spectral categories 1,2,3,4 (white outline)

  3. Phase I • Depiction soybean fields (white outline) and maize fields (red outline) using spectral categories 1,2,3,4

  4. Earth Observations Landsat 7 NASA

  5. Data Acquisition USGS Earthexplorer Path 224 Row 83

  6. Data Processing • Satellite Imagery Automatic Mapper (SIAM), developed by Andrea Baraldi at the University of Maryland, automatically classifies pixels in 95 spectral categories using physical properties. • Isodata unsupervised classification • k-means unsupervised classification SIAM Classification: 95 spectral categories

  7. Decision Model Nov Dec Jan Feb Mar Apr • A decision model was developed in ArcGIS ModelBuilder to identify soybean and maize crops. • Our model was based on two criteria: • Temporal evolution of spectral categories/classes using knowledge of when crops are planted b) Geometric characteristics of crop fields

  8. Temporal evolution of spectral categories …

  9. Temporal Evolution of Spectral Categories Evolution Pathways of Spectral Categories for Soybean and Maize

  10. Temporal evolution of spectral categories Soybean and maize fields December 2010 to February 2011 Feb 2011 dense vegetation Dec 2010 soil/barren land T+1 Jan 2011 vegetated

  11. Geometric characteristics of crop fields Area > 1 pixel (.0009 km squared) … Length-width ratio of crop fields < 5

  12. Crop Field Geometry • Exclude strips of vegetation along rivers with length-width ratio greater than 5 K-means processed with crop field geometries K-means classification output

  13. Crop Field Geometry • Exclude detected fields 1 pixel or less in size K-means processed with crop field geometries K-means classification output

  14. Results: SIAM, Isodata, K-means Comparison SIAM output SIAM classification Isodata output K-means output

  15. Results : Pixel Count and Statistics Percent Overlap of Detected Crops with GIS field data

  16. Conclusion • This research explores new methodologies to help improve detection of crops • Future work to be done to reduce pick up of noise/ other types of vegetation that happen to follow similar evolution pathways • The decision model can be transferred to INIA for crop yield forecasts

  17. Thank you!

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