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

Introduction Introduction Objectives Objectives Data Acquisition Data Acquisition Methodology Methodology Results Results Conclusion Conclusion Outline

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

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

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

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

Introduction Introduction Data Acquisition- Imagery False color imageFalse color image

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

Introduction Introduction Data Acquisition- Soils 1995 sampling on a 30m grid1995 sampling on a 30m grid Organic matter content, soil texture, pHOrganic matter content, soil texture, pH Organic Matter pH Soil texture

Introduction Introduction Data - Standard statistics

Methodology Image correctionImage correction Unsupervised ClassificationUnsupervised Classification Normalized Difference Vegetation Index (NDVI)Normalized Difference Vegetation Index (NDVI) Classified surface interpolationsClassified surface interpolations Gridded data pointsGridded data points

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

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

Interpolations Inverse Distance Weighted Reclassified into classes

Interpolations Inverse Distance Weighted Reclassified into classes

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

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

Yield Classified to classified R 2 = 0.71 y= 0.76x+0.56

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

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

Yield Gridded yield to NDVI values R 2 = 0.59

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

Organic matter content Interpolated organic matter map

Organic matter content Organic matter values in classified image

Soil Texture Interpolated soil texture map

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

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

pH Interpolated pH map

pH pH distribution in classified image higher pH in class 4

SSCM SSCMConclusions One image provides quite accurate insight into crop yield variabilityOne image provides quite accurate insight into crop yield variability Can use NDVI or classified image for interpretationCan use NDVI or classified image for interpretation Imagery is also an indicator of the variability of soil propertiesImagery is also an indicator of the variability of soil properties always remember sources of erroralways remember sources of error Imagery has the potential to predict yield variabilityImagery has the potential to predict yield variability

Questions?