1. Image Pre-Processing Continuation… Spectral Enhancement Brightness – Greenness – Wetness BGW Image Pre-Processing Continuation… Spectral Enhancement Brightness – Greenness – Wetness BGW

2. Image Pre-Processing Radiometric Enhancement:Radiometric Enhancement: Image RestorationImage Restoration Atmospheric CorrectionAtmospheric Correction Contrast EnhancementContrast Enhancement Solar Angle AdjustmentSolar Angle Adjustment Conv. to Exo-Atmos. ReflectanceConv. to Exo-Atmos. Reflectance Spectral Enhancement:Spectral Enhancement: Spectral IndicesSpectral Indices PCA, IHS, Color TransformsPCA, IHS, Color Transforms T-Cap, BGWT-Cap, BGW Radiometric Enhancement:Radiometric Enhancement: Image RestorationImage Restoration Atmospheric CorrectionAtmospheric Correction Contrast EnhancementContrast Enhancement Solar Angle AdjustmentSolar Angle Adjustment Conv. to Exo-Atmos. ReflectanceConv. to Exo-Atmos. Reflectance Spectral Enhancement:Spectral Enhancement: Spectral IndicesSpectral Indices PCA, IHS, Color TransformsPCA, IHS, Color Transforms T-Cap, BGWT-Cap, BGW Consists of processes aimed at the geometric and radiometric correction, enhancement or standardization of imagery to improve our ability to interpret qualitatively and quantitatively image components. Spatial Enhancement:Spatial Enhancement: Focal AnalysisFocal Analysis Edge-DetectionEdge-Detection High/Low Pass FiltersHigh/Low Pass Filters Resolution MergesResolution Merges Statistical FilteringStatistical Filtering Adaptive FilteringAdaptive Filtering Texture FiltersTexture Filters Geometric CorrectionGeometric Correction Polynomial TransformationPolynomial Transformation Ground Control PointsGround Control Points ReprojectionsReprojections Spatial Enhancement:Spatial Enhancement: Focal AnalysisFocal Analysis Edge-DetectionEdge-Detection High/Low Pass FiltersHigh/Low Pass Filters Resolution MergesResolution Merges Statistical FilteringStatistical Filtering Adaptive FilteringAdaptive Filtering Texture FiltersTexture Filters Geometric CorrectionGeometric Correction Polynomial TransformationPolynomial Transformation Ground Control PointsGround Control Points ReprojectionsReprojections

3. Brightness – Greenness - Wetness The Brightness, Greenness, Wetness transform was first developed for use with the Landsat MSS system and called the “Tasseled Cap” transformation. The transform is based on a set of constants applied to the image in the form of a linear algebraic formula. The transform developed for the MSS consisted of coefficients that extracted brightness and greenness. This was due to the spectral resolution of the MSS that focused primarily in the visible and near infrared. The Brightness, Greenness, Wetness transform was first developed for use with the Landsat MSS system and called the “Tasseled Cap” transformation. The transform is based on a set of constants applied to the image in the form of a linear algebraic formula. The transform developed for the MSS consisted of coefficients that extracted brightness and greenness. This was due to the spectral resolution of the MSS that focused primarily in the visible and near infrared. B = 0.332MSS1 + 0.603MSS2 + 0.675MSS3 + 0.262MSS4 G = -0.283MSS1 – 0.660MSS2 + 0.577MSS3 + 0.388MSS4 B = 0.332MSS1 + 0.603MSS2 + 0.675MSS3 + 0.262MSS4 G = -0.283MSS1 – 0.660MSS2 + 0.577MSS3 + 0.388MSS4

4. Brightness – Greenness - Wetness Following the launch of Landsat 4 and the inclusion of the Thematic Mapper, these coefficients were recalculated to take advantage of the increased spectral resolution of the TM. This allowed for the extraction of an additional component called wetness due to the inclusion of the MIR channels that are sensitive to moisture absorption. B = 0.2909TM1 + 0.2493TM2 + 0.4806TM3 + 0.5568TM4 + 0.4438TM5 +0.1706TM7 G = -0.2728TM1 – 0.2174TM2 - 0.55508TM3 + 0.7221TM4 + 0.0733TM5 – 0.1648TM7 W = 0.1446TM1 + 0.1761TM2 + 0.3322TM3 + 0.3396TM4 - 0.6210TM5 – 0.4186TM7 B = 0.2909TM1 + 0.2493TM2 + 0.4806TM3 + 0.5568TM4 + 0.4438TM5 +0.1706TM7 G = -0.2728TM1 – 0.2174TM2 - 0.55508TM3 + 0.7221TM4 + 0.0733TM5 – 0.1648TM7 W = 0.1446TM1 + 0.1761TM2 + 0.3322TM3 + 0.3396TM4 - 0.6210TM5 – 0.4186TM7

5. Brightness  Brightness  defined in the direction of soil reflectance variation. Obtained from a weighted sum of all bands. i.e. urbanized and bare soil areas are evident in this image. Greenness  Greenness  defined in the direction of vegetation reflectance variation. Obtained from the contrast of the visible bands (high absorption) with the infrared bands (high reflectance). i.e. the greater the biomass, the brighter the pixel value in this image. Wetness  Wetness  information concerning the moisture status of the environment (soil & plant moisture). Obtained from the contrast of the sum of visible and near-infrared with the sum of longer-infrared bands. i.e. water bodies are very bright – greater the moisture content = brighter response. Brightness – Greenness - Wetness

6. Brightness Greenness Concrete Bare soil Healthy – dense vegetation Water Clear Turbid Brightness Third Water Clear Turbid Wet soil Dry soil Location for different land cover classes in the B-G spectral space Jensen - 2005 Concrete, Bare soil Brightness – Greenness - Wetness

7. What happens when atmospheric correction is not feasible? Brightness – Greenness - Wetness

8. Landsat TM Band 3 Landsat TM Band 4 Greenness Brightness These feature space plots depict the relationship between the red and NIR reflectance as recorded by the Thematic Mapper and the relationship between Brightness and Greenness from the same data set. Brightness – Greenness - Wetness

9. Brightness Greeness Wetness Brightness – Greenness - Wetness

10. Applications: Example

11. Brightness – Greenness - Wetness Applications: Example

12. Brightness – Greenness - Wetness Applications: Example