Key Advances and Remaining Knowledge Gaps in Remote Sensing ...

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Proximal Remote Sensing• Sensors can be mounted on tractors,spreaders, sprayers or irrigation booms– GreenSeeker– Crop Circle– WeedSeeker– Infrared thermometers• Allows real time site specificmanagement of fertilizer, pesticides orirrigation
Data Interpretation• Classification (Bauer, 2000)– Supervised, unsupervised, multi-temporal temporal data sets,decision tree classifiers, neural network classification,guided clustering, fuzzy sets, contextual classifiers,and use of auxiliary information• Pattern Detection (Civco, 2002)– Image differencing, cross correlation, chemometricanalysis, principal component analysis, neuralnetworks, object oriented classification (Walker, 2003),fuzzy set theory (Viscarra Rossel, 2006)• Assess accuracy (Congalton, 1991; Foody, 2002)Vi l t A i S li t t i– Visual assessment, Area comparisons, Sampling strategies,Kappa statistic, Confusion matrix
Page 1: Key Advances and RemainingKnowledge
Page 4 and 5: Precision ManagementOptimal Resourc
Page 6 and 7: Remote Sensing• Bare soil reflect
Page 8 and 9: Remote Sensing Sugar Beetsg g(Seela
Page 10 and 11: Satellite Remote SensingSatelliteSp
Page 12 and 13: Increasing Spatial Resolution ofIma
Page 14 and 15: Increased Homogeneity of Pixels
Page 16: Aerial RemoteSensing• Airplanes
Page 21 and 22: Fuzzy Classification
Page 23 and 24: Spectral Mixing Analysiswhere ρ is
Page 25 and 26: Artificial Neural NetworksOutput La
Page 27 and 28: Confusion MatrixOmission errors inv
Page 29 and 30: Confusion MatrixDCNIRB Leaf NUser's
Page 31 and 32: Spatial Pattern
Page 33 and 34: Integration of Imagery in GIS
Page 35 and 36: Using Geostatistics toCorrect Error
Page 37 and 38: 400Kriged Phosphorus (ppm)300(m)DIS
Page 39: Targeted Soil Sampling StrategyBase
Page 42 and 43: Crop SensorsYear Innovation Citatio
Page 45 and 46: Visible Absorption Spectrum
Page 47 and 48: Satellite RS-based N Managementof R
Page 49 and 50: Spectral Indices• Canopy reflecta
Page 51 and 52: RGBThermalCourtesy Drs. V. Alchanat
Page 53 and 54: Multi-spectral broad-band vegetatio
Page 55 and 56: Best Reflectance Wavelengths?Thenka
Page 57 and 58: Hyperspectral narrow-bandvegetation
Page 59 and 60: Derivative SpectraNigon, Rosen, Mul
Page 61 and 62: LambdalambdaplotNigon,Rosen,Mulla e
Page 63 and 64: In-Season N Management(From J. Sche
Page 66 and 67: Hyperspectral Imaging forN Sufficie
Page 68 and 69:
Results/discussionLeaf NN stress in
Page 70:
LiDAR• Light Detection and Rangin
Page 74 and 75:
Example: Using Specific Catchment A
Page 76 and 77:
Conclusions• Wavelengths in use r
Page 78 and 79:
Remaining Knowledge Gaps• More em
Page 80 and 81:
Remaining Knowledge Gaps• At pres
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Key Advances and Remaining Knowledge Gaps in Remote Sensing ...

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