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:

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.

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:

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

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

28. visualisation/analysis

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

32. 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),

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

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
visualisation/analysis

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

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
red
NIR
See:

39. Scatter Plot reveals relationship between information in two bands
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 :

41. visualisation/analysis
Principal Components Analysis
particularly strong between visible bands
indicates (statistical) redundancy
TM data, Swanley:
correlation coefficients :

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 :
PC1 PC2 PC3 PC4 PC5 PC6 PC7
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 :

45. visualisation/analysis
Principal Components Analysis
display PC 1,2, % 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 PC PC PC PC PC PC PC

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’) PC
PC 2 principally, the difference between band 4 and rest of the bands (NIR minus rest) PC

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. RATIO
INDICES
Enhancements
Vegetation Indices

54. Enhancements Vegetation Indices Ratio Vegetation Index
RVI
Normalised Difference Vegetation Index
NDVI

55. RATIO
INDICES
Enhancements
Vegetation Indices
NDVI

56. RATIO
INDICES
Enhancements
Vegetation Indices
NDVI

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

58. PERPENDICULAR
INDICES
Enhancements
Vegetation Indices

59. Enhancements Vegetation Indices Perpendicular Vegetation Index
PVI
Soil Adjusted Vegetation Index
SAVI

60. PERPENDICULAR
INDICES
And others ...

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