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

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??