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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. - PowerPoint PPT Presentation
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A comparison of remotely A comparison of remotely sensed imagery with site-sensed imagery with site-specific crop management specific crop management
datadata
A comparison of remotely A comparison of remotely sensed imagery with site-sensed imagery with site-specific crop management specific crop management
datadata
Anjelien DrostAnjelien Drost
Land Resource ScienceLand Resource Science
University of GuelphUniversity of Guelph
• IntroductionIntroduction• ObjectivesObjectives• Data AcquisitionData Acquisition• MethodologyMethodology• ResultsResults• ConclusionConclusion
OutlineOutline
IntroductionIntroduction
Technology in AgricultureTechnology in Agriculture
Changes in farming practices due to Changes in farming practices due to
advent of new technologies:advent of new technologies:
• Global Positioning Systems (GPS)Global Positioning Systems (GPS)• Yield monitorsYield monitors• Geographic Information Systems (GIS)Geographic Information Systems (GIS)• Remote SensingRemote Sensing
IntroductionIntroduction
ApplicationsApplications
These technologies have lead to:These technologies have lead to:
• On-the-go yield mappingOn-the-go yield mapping• GPS soil sampling for nutrient mappingGPS soil sampling for nutrient mapping• Variable application of fertilizersVariable application of fertilizers• Ability to manage large acreageAbility to manage large acreage• GPS field scouting for pests, weeds, GPS field scouting for pests, weeds,
diseasedisease
IntroductionIntroduction
ObjectivesObjectives
• to determine the steps involved in to determine the steps involved in analysing CASI imagery for SSCManalysing CASI imagery for SSCM
• to use acquired knowledge of image to use acquired knowledge of image analysis software (PCI Geomatics) and GIS analysis software (PCI Geomatics) and GIS software (ESRI) to correlate imagery to software (ESRI) to correlate imagery to SSCM data.SSCM data.
IntroductionIntroduction
Data Acquisition- ImageryData Acquisition- Imagery• Compact Airborne Spectragraphic Imager Compact Airborne Spectragraphic Imager
(CASI)(CASI)
Band Wavelength (nm) Color spectrum3 432.20-466.30 Blue
4 526.94-552.42 Green
5 628.55-654.27 Red
6 736.84-762.73 Near Infrared
7 838.12-864.09 Near Infrared
8 931.93-948.33 Near Infrared
IntroductionIntroduction
Data Acquisition- ImageryData Acquisition- Imagery• False color imageFalse color image
IntroductionIntroduction
Data Acquisition- YieldData Acquisition- Yield
• Grain monitor on a yield combine attached to a Grain monitor on a yield combine attached to a differential global positioning system.differential global positioning system.
• Every 1.5mEvery 1.5m
IntroductionIntroduction
Data Acquisition- SoilsData 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
IntroductionIntroduction
Data - Standard statisticsData - Standard statistics
StatisticYield
(kg/ha)OrganicMatter
pH
Sum 57152963.0256 796.2 1522.6Count 21216 242 250Mean 2693.861 3.3 6.1Minimum 0 0.4 3.8Maximum 5946.315 6.9 7.7Range 5946.315 6.5 3.9Median 3016.83 3.3 6.2StandardDeviation
1358.88 1.228 0.8708
CV (%) 50.4 37.3 14.3Skewness -0.76 0.21 -0.5Kurtosis -0.48 -0.15 -0.46
MethodologyMethodology
• 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 Unsupervised ClassificationClassification
• K-means unsupervised classificationK-means unsupervised classification• red, and NIR bands red, 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
NDVINDVI• (NIR -RED)/(NIR + RED)• NDVI * 1000
Place classified image here
InterpolationsInterpolations• Inverse Distance Weighted • Reclassified into classes
InterpolationsInterpolations• Inverse Distance Weighted • Reclassified into classes
Grid PointsGrid Points
• Converted points to grid • 3 m resolution• Compared these to NIR and NDVI bands• Pixel to pixel analysis
ResultsResults• 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
YieldYield
• Classified to classified • R2 = 0.71• y= 0.76x+0.56
YieldYield• Gridded yield to classified image • High yield values in class 4 (high)• Low yield values in class 1 (low)
YieldYield
• NDVI values to classified yield • High NDVI values in class 4 (high yield)• Lower NDVI values in class 1 (low yield)
YieldYield• Gridded yield to NDVI values• R2 = 0.59
ProblemsProblemsInstrument
Errors
Product
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
Core
gistra
tion
erro
rs
Source: M. Wood et al., 1997
Organic matter contentOrganic matter content• Interpolated organic matter map
Organic matter contentOrganic matter content• Organic matter values in classified image
Soil TextureSoil Texture
• Interpolated soil texture map
Soil TextureSoil Texture• NDVI distribution in soil texture classes• low NDVI values in fine sandy areas• high NDVI values in loamy soils
Soil TextureSoil Texture• Soil texture class distribution in classified image• Fine sands fall into class 1 and 2• more loamy soils in class 3 and 4
pHpH
• Interpolated pH map
pHpH• pH distribution in classified image• higher pH in class 4
SSCMSSCM
Conclusions Conclusions
• One image provides quite accurate insight into One image provides quite accurate insight into crop yield variabilitycrop 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 Imagery is also an indicator of the variability of soil
properties properties • always remember sources of erroralways remember sources of error• Imagery has the potential to predict yield Imagery has the potential to predict yield
variabilityvariability
QuestionsQuestions??