Environmental Remote Sensing
GEOG 2021
Lecture 3
Spectral information in remote sensing
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
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
visualisation/analysis
http://envdiag.ceh.ac.uk/iufro_poster2.shtm
Red Edge Position
point of inflexion
on red edge
REP moves to
shorter
wavelengths as
chlorophyll
decreases
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.
Consider red / NIR ‘feature space’
vegetation
Soil
line
visualisation/analysis
• Colour Composites
• choose three channels of information
– not limited to RGB
– use standard composites e.g. false colour
composite (FCC)
• learn interpretation
• Vegetation refl. high in NIR on red channel, so veg red
and soil blue
visualisation/analysis
Std FCC - Rondonia
visualisation/analysis
Std FCC - Swanley TM data - TM 4,3,2
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
visualisation/analysis
Principal Components Analysis
red
NIR
See: http://rst.gsfc.nasa.gov/AppC/C1.html
red
Scatter Plot reveals relationship
between information in two bands
here:
correlation coefficient = 0.137
visualisation/analysis
Principal Components Analysis
– show correlation between all bands
TM data, Swanley:
correlation coefficients :
1.000
0.927 0.874
0.927
1.000
0.954
0.874
0.954
1.000
0.069
0.172 0.137
0.593
0.691 0.740
0.426
0.446 0.433
0.736
0.800 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
visualisation/analysis
Principal Components Analysis
– particularly strong between visible bands
– indicates (statistical) redundancy
TM data, Swanley:
correlation coefficients :
1.000
0.927 0.874
0.927
1.000
0.954
0.874
0.954
1.000
0.069
0.172 0.137
0.593
0.691 0.740
0.426
0.446 0.433
0.736
0.800 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
visualisation/analysis
Principal Components Analysis
– PCT is a linear transformation
– Essentially rotates axes along orthogonal axes of
decreasing variance
PC1
PC2
red
visualisation/analysis
Principal Components Analysis
– explore dimensionality of data
% pc variance :
– PC1
– 79.0
PC2
11.9
PC3
5.2
PC4
2.3
PC5
1.0
PC6
0.5
PC7
0.1
96.1%
of the total data variance contained within the first 3 PCs
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
0.927
1.000
0.874 0.954
0.874
0.954
1.000
visualisation/analysis
Principal Components Analysis
– display PC 1,2,3 - 96.1% of all data variance
Dull -
histogram equalise ...
visualisation/analysis
Principal Components Analysis
– PC1 (79% of variance)
Essentially
‘average brightness’
visualisation/analysis
Principal Components Analysis
stretched sorted eigenvectors
PC1
PC2
PC3
PC4
PC5
PC6
PC7
+0.14
-0.44
+1.68
+0.29
+0.03
10.42
-8.77
+0.13
-0.27
+1.35
+0.10
-0.39
+1.10
28.50
+0.28
-0.60
+2.45
-1.22
-2.81
-6.35
-8.37
+0.13
+2.23
+1.34
+1.90
+0.70
-0.70
-1.43
+0.82
+0.47
-1.49
-1.83
-1.78
+1.64
+1.04
+0.12
-0.49
-0.67
+4.49
-5.12
-0.23
-0.40
+0.43
-0.77
+0.05
+2.30
+6.52
-2.39
-1.75
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
visualisation/analysis
Principal Components Analysis
– Display of PC 2,3,4
Here, shows
‘spectral differences’
(rather than
‘brightness’
differences in PC1)