Environmental Remote Sensing GEOG 2021 Lecture 3 Spectral information in remote sensing.
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Transcript of Environmental Remote Sensing GEOG 2021 Lecture 3 Spectral information in remote sensing.
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
REP moves to shorter
wavelengths as chlorophyll decreases
Red Edge Position
point of inflexion on red edge
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.
Consider red / NIR ‘feature space’
Soil line
vegetation
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/analysisStd 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
NIR
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 0.069 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
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
visualisation/analysis
Principal Components Analysis– PCT is a linear transformation– Essentially rotates axes along orthogonal axes of
decreasing variance
red
NIR
PC1
PC2
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
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 0.874 0.927 1.000 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 +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
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)