Transcript GEOG2021 Environmental Remote Sensing

GEOG2021
Environmental Remote Sensing
Lecture 3
Spectral Information in Remote Sensing
Aim
• Mechanisms variations in reflectance optical/microwave
• Visualisation/analysis
• Enhancements/transforms
Mechanisms
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
Mechanisms
Optical Mechanisms
Reflectance
causes of spectral variation in reflectance
• (bio)chemical & structural properties
– chlorophyll concentration
– soil - minerals/ water/ organic matter
Optical Mechanisms
vegetation
Optical Mechanisms
soil
Optical Mechanisms
soil
RADAR Mechanisms
See: http://southport.jpl.nasa.gov/education.html
RADAR Mechanisms
RADAR Mechanisms
RADAR
Mechanisms
Vegetation amount
consider
• change in canopy cover over time
• varying proportions of soil / vegetation
(canopy cover)
Vegetation amount
Bare soil
Full cover
Senescence
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
Vegetation amount
1986 Rondonia
Vegetation amount
1992 Rondonia
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)
Leaf
Area
Index
See: http://edcdaac.usgs.gov/modis/dataprod.html
See:
http://www.bsrsi.msu.edu/rfrc/stats/seasia7385.html
Forest cover 1973
Forest cover 1985
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
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., FCC)
• learn interpretation
Std FCC - Rondonia
visualisation/analysis
Std FCC - Swanley TM data - TM 4,3,2
visualisation/analysis
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 +0.14
PC2 -0.44
PC3 +1.68
PC4 +0.29
PC5 +0.03
PC6 10.42
PC7 -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)
Enhancements
Vegetation Indices
– reexamine red/nir space features
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)
Enhancements
Vegetation Indices
– function known as ‘vegetation index’
– Main categories:
• ratio indices (angular measure)
• perpendicular indices (parallel lines)
RATIO
INDICES
Enhancements
Vegetation Indices
RATIO
INDICES
Enhancements
Vegetation Indices
– Ratio Vegetation Index
• RVI
– Normalised Difference Vegetation Index
• NDVI
RATIO
INDICES
Enhancements
Vegetation Indices
NDVI
RATIO
INDICES
Enhancements
Vegetation Indices
NDVI
RATIO
INDICES
NDVI over Africa (AVHRR-derived) Tucker et al. - A: April; B: July; C: Sept; D: Dec 1982
PERPENDICULAR
INDICES
Enhancements
Vegetation Indices
PERPENDICULAR
INDICES
Enhancements
Vegetation Indices
– Perpendicular Vegetation Index
• PVI
– Soil Adjusted Vegetation Index
• SAVI
PERPENDICULAR
INDICES
And others ...
Summary
• Scattering/reflectance mechanisms
• monitoring vegetation amount
• visualisation/analysis
– spectral plots, scatter plots, PCA
• enhancement
– VIs