1. GEOG2021 Environmental 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

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)

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

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

24. Forest cover 1985

25. 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

26. 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

27. visualisation/analysis

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

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

32. REP correlates with ‘stress’, but no information on type/cause Measure REP e.g. by 1st order derivative 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.

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

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

35. visualisation/analysis Std FCC - Rondonia

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

37. 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

38. visualisation/analysis Principal Components Analysis redNIR See: http://rst.gsfc.nasa.gov/AppC/C1.html

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

40. 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

41. 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

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

43. visualisation/analysis Principal Components Analysis –explore dimensionality of data % pc variance : –PC1PC2PC3PC4PC5PC6PC7 –79.0 11.9 5.2 2.3 1.0 0.5 0.1 96.1% of the total data variance contained within the first 3 PCs

44. 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

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

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

47. 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 PC610.42 +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

48. 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

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

50. Enhancements Vegetation Indices –reexamine red/nir space features

51. Enhancements Vegetation Indices –define function of the two channels to enhance response to vegetation & minimise response to extraneous factors (soil) –maintain (linear?) relationship with desrired quantity (e.g., canopy coverage, LAI)

52. Enhancements Vegetation Indices –function known as ‘vegetation index’ –Main categories: ratio indices (angular measure) perpendicular indices (parallel lines)

53. Enhancements Vegetation Indices RATIO INDICES

54. Enhancements Vegetation Indices –Ratio Vegetation Index RVI –Normalised Difference Vegetation Index NDVI RATIO INDICES

55. Enhancements Vegetation Indices NDVI RATIO INDICES

56. Enhancements Vegetation Indices NDVI RATIO INDICES

57. RATIO INDICES NDVI over Africa (AVHRR-derived) Tucker et al. - A: April; B: July; C: Sept; D: Dec 1982

58. Enhancements Vegetation Indices PERPENDICULAR INDICES

59. Enhancements Vegetation Indices –Perpendicular Vegetation Index PVI –Soil Adjusted Vegetation Index SAVI PERPENDICULAR INDICES

60. PERPENDICULAR INDICES And others...

61. Summary Scattering/reflectance mechanisms monitoring vegetation amount visualisation/analysis –spectral plots, scatter plots, PCA enhancement –VIs