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)