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GEOG2021 Environmental Remote Sensing

GEOG2021 Environmental Remote Sensing. Lecture 3 Spectral Information in Remote Sensing. Aim. Mechanisms variations in reflectance - optical/microwave Visualisation/analysis Enhancements/transforms. Mechanisms. Reflectance. Reflectance = output / input (radiance)

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GEOG2021 Environmental Remote Sensing

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  1. GEOG2021Environmental Remote Sensing Lecture 3 Spectral Information in Remote Sensing

  2. Aim • Mechanisms variations in reflectance - optical/microwave • Visualisation/analysis • Enhancements/transforms

  3. Mechanisms

  4. Reflectance • Reflectance = output / input • (radiance) • measurement of land complicated by atmosphere • input solar radiation for passive optical • input from spacecraft for active systems • RADAR • Strictly NOT reflectance - use related term backscatter

  5. Mechanisms

  6. Optical Mechanisms

  7. Reflectance causes of spectral variation in reflectance • (bio)chemical & structural properties • chlorophyll concentration • soil - minerals/ water/ organic matter

  8. Optical Mechanisms vegetation

  9. Optical Mechanisms soil

  10. Optical Mechanisms soil

  11. RADAR Mechanisms See: http://southport.jpl.nasa.gov/education.html

  12. RADAR Mechanisms

  13. RADAR Mechanisms

  14. RADAR Mechanisms

  15. Vegetation amount consider • change in canopy cover over time • varying proportions of soil / vegetation (canopy cover)

  16. Vegetation amount Bare soil Full cover Senescence

  17. Vegetation amount 1975 Rondonia See: e.g. http://earth.jsc.nasa.gov/lores.cgi?PHOTO=STS046-078-026 http://www.yale.edu/ceo/DataArchive/brazil.html

  18. Vegetation amount 1986 Rondonia

  19. Vegetation amount 1992 Rondonia

  20. Uses of (spectral) information consider properties as continuous • e.g. mapping leaf area index or canopy cover or discrete variable • e.g. spectrum representative of cover type (classification)

  21. Leaf Area Index See: http://edcdaac.usgs.gov/modis/dataprod.html

  22. See: http://www.bsrsi.msu.edu/rfrc/stats/seasia7385.html Forest cover 1973

  23. Forest cover 1985

  24. visualisation/analysis • spectral curves • spectral features, e.g., 'red edge’ • scatter plot • two (/three) channels of information • colour composites • three channels of information • principal components analysis • enhancements • e.g. NDVI

  25. visualisation/analysis • spectral curves • reflectance (absorptance) features • information on type and concentration of absorbing materials (minerals, pigments) • e.g., 'red edge': increase Chlorophyll concentration leads to increase in spectral location of 'feature' e.g., tracking of red edge through model fitting or differentiation

  26. visualisation/analysis

  27. visualisation/analysis

  28. http://envdiag.ceh.ac.uk/iufro_poster2.shtm

  29. Red Edge Position point of inflexion on red edge REP moves to shorter wavelengths as chlorophyll decreases

  30. Measure REP e.g. by 1st order derivative REP correlates with ‘stress’, but no information on type/cause See also: Dawson, T. P. and Curran, P. J., "A new technique for interpolating the reflectance of red edge position." Int. J. Remote Sensing, 19, (1998), 2133-2139.

  31. vegetation Soil line Consider red / NIR ‘feature space’

  32. visualisation/analysis • Colour Composites • choose three channels of information • not limited to RGB • use standard composites (e.g., FCC) • learn interpretation

  33. Std FCC - Rondonia visualisation/analysis

  34. Std FCC - Swanley TM data - TM 4,3,2 visualisation/analysis

  35. visualisation/analysis Principal Components Analysis • PCA (PCT - transform) • may have many channels of information • wish to display (distinguish) • wish to summarise information • Typically large amount of (statistical) redundancy in data

  36. visualisation/analysis Principal Components Analysis red NIR See: http://rst.gsfc.nasa.gov/AppC/C1.html

  37. NIR red • Scatter Plot reveals relationship between information in two bands • here: • correlation coefficient = 0.137

  38. visualisation/analysis Principal Components Analysis • show correlation between all bands TM data, Swanley: correlation coefficients : 1.000 0.927 0.874 0.069 0.593 0.426 0.736 0.927 1.000 0.954 0.172 0.691 0.446 0.800 0.874 0.954 1.000 0.137 0.740 0.433 0.812 0.069 0.172 0.137 1.000 0.369 -0.084 0.119 0.593 0.691 0.740 0.369 1.000 0.534 0.891 0.426 0.446 0.433 -0.084 0.534 1.000 0.671 0.736 0.800 0.812 0.119 0.891 0.671 1.000

  39. visualisation/analysis Principal Components Analysis • particularly strong between visible bands • indicates (statistical) redundancy TM data, Swanley: correlation coefficients : 1.000 0.927 0.874 0.069 0.593 0.426 0.736 0.927 1.000 0.954 0.172 0.691 0.446 0.800 0.874 0.954 1.000 0.137 0.740 0.433 0.812 0.069 0.172 0.137 1.000 0.369 -0.084 0.119 0.593 0.691 0.740 0.369 1.000 0.534 0.891 0.426 0.446 0.433 -0.084 0.534 1.000 0.671 0.736 0.800 0.812 0.119 0.891 0.671 1.000

  40. PC1 PC2 NIR red visualisation/analysis Principal Components Analysis • PCT is a linear transformation • Essentially rotates axes along orthogonal axes of decreasing variance

  41. 96.1% of the total data variance contained within the first 3 PCs visualisation/analysis Principal Components Analysis • explore dimensionality of data % pc variance : • PC1 PC2 PC3 PC4 PC5 PC6 PC7 • 79.0 11.9 5.2 2.3 1.0 0.5 0.1

  42. visualisation/analysis Principal Components Analysis • e.g. cut-off at 2% variance • Swanley TM data 4-dimensional • first 4 PCs = 98.4% • great deal of redundancy TM bands 1, 2 & 3 correlation coefficients : 1.000 0.927 0.874 0.927 1.000 0.954 0.874 0.954 1.000

  43. Dull - histogram equalise ... visualisation/analysis Principal Components Analysis • display PC 1,2,3 - 96.1% of all data variance

  44. visualisation/analysis Principal Components Analysis • PC1 (79% of variance) Essentially ‘average brightness’

  45. visualisation/analysis Principal Components Analysis stretched sorted eigenvectors PC1 +0.14 +0.13 +0.28 +0.13 +0.82 +0.12 +0.43 PC2 -0.44 -0.27 -0.60 +2.23 +0.47 -0.49 -0.77 PC3 +1.68 +1.35 +2.45 +1.34 -1.49 -0.67 +0.05 PC4 +0.29 +0.10 -1.22 +1.90 -1.83 +4.49 +2.30 PC5 +0.03 -0.39 -2.81 +0.70 -1.78 -5.12 +6.52 PC6 10.42 +1.10 -6.35 -0.70 +1.64 -0.23 -2.39 PC7 -8.77 28.50 -8.37 -1.43 +1.04 -0.40 -1.75

  46. visualisation/analysis Principal Components Analysis • shows contribution of each band to the different PCs. • For example, PC1 (the top line) almost equal (positive) contributions (‘mean’) PC1 +0.14 +0.13 +0.28 +0.13 +0.82 +0.12 +0.43 • PC 2 principally, the difference between band 4 and rest of the bands (NIR minus rest) PC2 -0.44 -0.27 -0.60 +2.23 +0.47 -0.49 -0.77

  47. visualisation/analysis Principal Components Analysis • Display of PC 2,3,4 Here, shows ‘spectral differences’ (rather than ‘brightness’ differences in PC1)

  48. Enhancements Vegetation Indices • reexamine red/nir space features

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