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

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Spatial Pattern is Complicated whenHyperspectral Data are <strong>in</strong>volved!
Integration of Imagery <strong>in</strong> GIS
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 19 and 20: Data Interpretation• Classificati
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: Spatial Pattern
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

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

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