1 / 31

A comparison of remotely sensed imagery with site-specific crop management data

A comparison of remotely sensed imagery with site-specific crop management data. Anjelien Drost Land Resource Science University of Guelph. Outline. Introduction Objectives Data Acquisition Methodology Results Conclusion. Introduction. Technology in Agriculture.

erin-larson
Download Presentation

A comparison of remotely sensed imagery with site-specific crop management data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. A comparison of remotely sensed imagery with site-specific crop management data Anjelien Drost Land Resource Science University of Guelph

  2. Outline • Introduction • Objectives • Data Acquisition • Methodology • Results • Conclusion

  3. Introduction Technology in Agriculture Changes in farming practices due to advent of new technologies: • Global Positioning Systems (GPS) • Yield monitors • Geographic Information Systems (GIS) • Remote Sensing

  4. Introduction Applications These technologies have lead to: • On-the-go yield mapping • GPS soil sampling for nutrient mapping • Variable application of fertilizers • Ability to manage large acreage • GPS field scouting for pests, weeds, disease

  5. Introduction Objectives • to determine the steps involved in analysing CASI imagery for SSCM • to use acquired knowledge of image analysis software (PCI Geomatics) and GIS software (ESRI) to correlate imagery to SSCM data.

  6. Introduction Data Acquisition- Imagery • Compact Airborne Spectragraphic Imager (CASI)

  7. Introduction Data Acquisition- Imagery • False color image

  8. Introduction Data Acquisition- Yield • Grain monitor on a yield combine attached to a differential global positioning system. • Every 1.5m

  9. Introduction Data Acquisition- Soils • 1995 sampling on a 30m grid • Organic matter content, soil texture, pH pH Organic Matter Soil texture

  10. Introduction Data - Standard statistics

  11. Methodology • Image correction • Unsupervised Classification • Normalized Difference Vegetation Index (NDVI) • Classified surface interpolations • Gridded data points

  12. Unsupervised Classification • K-means unsupervised classification • red, and NIR bands • aggregated into four (high, high-medium, medium-low, low) Place classified image here

  13. NDVI • (NIR -RED)/(NIR + RED) • NDVI * 1000 Place classified image here

  14. Interpolations • Inverse Distance Weighted • Reclassified into classes

  15. Interpolations • Inverse Distance Weighted • Reclassified into classes

  16. Grid Points • Converted points to grid • 3 m resolution • Compared these to NIR and NDVI bands • Pixel to pixel analysis

  17. Results • Comparison of yield to image • classified image to classified yield • classified image to grid yield • NDVI to grid yield • NDVI to classified yield • Comparison of soil properties to image • OM • soil texture • pH

  18. Yield • Classified to classified • R2 = 0.71 • y= 0.76x+0.56

  19. Yield • Gridded yield to classified image • High yield values in class 4 (high) • Low yield values in class 1 (low)

  20. Yield • NDVI values to classified yield • High NDVI values in class 4 (high yield) • Lower NDVI values in class 1 (low yield)

  21. Yield • Gridded yield to NDVI values • R2 = 0.59

  22. Problems • GPS from monitor • yield sensor • crop moisture • interpolation • GPS • sensor calibration • image registration • positional errors • interpolation Instrument Errors Product Georeferenced Yield map Yield Monitor Coregistration errors Spectrographic Imager NDVI Global Positioning System Digital Elevation Model Source: M. Wood et al., 1997

  23. Organic matter content • Interpolated organic matter map

  24. Organic matter content • Organic matter values in classified image

  25. Soil Texture • Interpolated soil texture map

  26. Soil Texture • NDVI distribution in soil texture classes • low NDVI values in fine sandy areas • high NDVI values in loamy soils

  27. Soil Texture • Soil texture class distribution in classified image • Fine sands fall into class 1 and 2 • more loamy soils in class 3 and 4

  28. pH • Interpolated pH map

  29. pH • pH distribution in classified image • higher pH in class 4

  30. SSCM Conclusions • One image provides quite accurate insight into crop yield variability • Can use NDVI or classified image for interpretation • Imagery is also an indicator of the variability of soil properties • always remember sources of error • Imagery has the potential to predict yield variability

  31. Questions?

More Related