Vegetation indices and
the red-edge index
Jan Clevers
Centre for Geo-Information (CGI)
Quantitative Remote Sensing: The
Classification

Signatures: Spectral, Spatial, Temporal, Angular, and Polarization

Statistical Methods





Physical Methods





Correlation relationships of land surface variables and remotely sensed
data
+ Easy to develop, effective for summarizing local data
- Models are site-specific, no cause-effect relationship
Example: WDVI (Clevers, 1999), GEMI (Pinty and Verstraete, 1992)
Inversion of [snow | canopy | soil] reflectance models
+ Follow a physical law, improvement through iteration
- Long development curve, potentially complex
Example: MODIS LAI (Myneni, 1999)
Hybrid Methods


Combination of Statistical and Physical Models
Example: EO-1 ALI LAI (Liang, 2003)
Source: Liang, S., 2004
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Vegetation Indices

strengthening the spectral contribution of
green vegetation

minimizing disturbing influences of:

soil background
irradiance
solar position
yellow vegetation
atmospheric attenuation



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mostly utilizing a red (R) and NIR spectral
band
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0
0.2
0.4
NIR
1.0
0.8
0.6
Ratio-based Vegetation Indices
NIR/R ratio (RVI)
 NDVI = (NIR-R)/(NIR+R)
(Normalized Difference VI)

NDVI
1
R
0
2à3
LAI
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Orthogonal-based Vegetation Indices
 Perpendicular VI (PVI):
NIR
1/(a2+1) (NIR – a × R)
soil line
 Weighted Difference VI
(PVI = 0)
(WDVI):
NIR – a × R
 Difference VI (DVI):
NIR – R
R
a = slope soil line
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Simplified reflectance model
R = Rv × B + Rs × (1 – B)
R : measured reflectance
Rv : reflectance vegetation
Rs : reflectance soil
B : apparent soil cover
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Calculate WDVI
Red: R = Rv × B + Rs × (1 – B)
NIR: NIR = NIRv × B + NIRs × (1 – B)
Assume: a = NIRs / Rs (slope soil line)
The NIR signal coming from the vegetation only
can be approximated by the WDVI:
WDVI = NIR – a × R
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Hybrid Vegetation Indices
 Soil Adjusted VI (SAVI):
NIR
(1 + L) × (NIR – R)/(NIR +R +
L)
L = l1 + l2  0.5
R
l2
l1
Broge & Leblanc, Remote Sens.
Environ. 76 (2000): 156-172
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Enhanced Vegetation Index (EVI)
for use with MODIS data
NIR  R
EVI 
NIR  C1  R  C2  B  L

C1 = atmospheric resistance red correction coefficient [6.0]
C2 = atmospheric resistance blue correction coefficient [7.5]
 L = canopy background brightness correction factor [1.0]

http://tbrs.arizona.edu/project/MODIS/evi.php
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Use of vegetation Indices
Estimation of:
 Leaf Area Index (LAI)
 Vegetation cover
 Absorbed Photosynthetically Active Radiation
(APAR)
 Chlorophyll or nitrogen content
 Canopy water content
 Biomass
 Carbon
 Structure of the canopy
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Use of vegetation Indices
Estimation of:
 Leaf Area Index (LAI)
 Vegetation cover
 Absorbed Photosynthetically Active Radiation
(APAR)
 Chlorophyll or nitrogen content
 Canopy water content
 Biomass
 Carbon
 Structure of the canopy
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Red Edge Index
Determining vegetation condition using RS:
e.g. blue shift of the red edge as a result of
stress
1 2
reflectance (%)
60
healthy
with stress
40
20
0
0.4
0.5
0.6
0.7
0.8
wavelength (µm)
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Calculation REIP
Red edge inflection point (REIP) =
Red edge position (REP) =
Maximum of the first derivative.
R λ  R λ 1
 dR 

 
Δλ
 dλ  λ
is maximum.
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PROSPECT – SAIL simulation
740
Red Edge Position (nm)
730
720
LAI = 0.5
LAI = 1.0
710
LAI = 2.0
LAI = 4.0
LAI = 8.0
700
690
680
0
10
20
30
40
50
60
70
80
-2
Chlorophyll Content (mg.cm )
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Soil background influence
740
Red Edge Position (nm)
730
720
LAI = 0.5
LAI = 1.0
LAI = 2.0
LAI = 4.0
LAI = 8.0
710
700
690
0
5
10
15
20
25
30
Soil Reflectance (% )
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Atmospheric influence
740
Red Edge Position (nm)
730
720
CHL = 5
CHL = 10
CHL = 20
CHL = 40
CHL = 80
710
700
690
0
10
20
30
40
50
60
70
80
90
100
Visibility (km)
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Inverted Gaussian function
R λ   R s  R s  R o 
  λ o  λ 2 

exp 
2

2σ


Rs = shoulder reflectance
Ro = minimum reflectance
o = wavelength at Ro
 = Gaussian shape parameter
REP  λ o  σ
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Linear interpolation method
60
50
Reflectance (%)
40
30
Rre
20
10
0
600
650
700
 re
750
800
850
900
Wavelength (nm)
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Linear interpolation method
 R 670  R 780 /2  R 700 

REP  700  40 
R 740  R 700


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REP image for MERIS
Each digital
number
represents a
wavelength value
(being the REP)
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Chlorophyll Index (CI)
CIred_edge
= (RNIR / Rred_edge) – 1
= (R780 nm / R710 nm) – 1
As estimator of chlorophyll content
Gitelson et al., Geophysical Research Letters
33 (2006), 5 pp.
http://www.calmit.unl.edu/people/gitelson/
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Photochemical Reflectance Index (PRI)
PRI = (R531 nm – R570 nm) / (R531 nm + R570 nm)
As estimator of photosynthetic activity
Gamon et al., Remote Sensing of
Environment 41 (1992), 35 – 44.
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Use of vegetation Indices
Estimation of:
 Leaf Area Index (LAI)
 Vegetation cover
 Absorbed Photosynthetically Active Radiation
(APAR)
 Chlorophyll or nitrogen content
 Canopy water content
 Biomass
 Carbon
 Structure of the canopy
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Estimating Canopy Water Content (CWC)
ASD Fieldspec Pro
970nm
nm 1200
970
1200nm
nm
0.7
0.6
Reflectance
0.5
0.4
0.3
0.2
0.1
0
400
600
800
1000
1200
1400
1600
1800
2000
2200
2400
Wavelength (nm)
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Estimators for Canopy Water Content
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Reflectances
Continuum removal: MBD, AUC, ANMB
Water band indices: WI, NDWI
WI = R900/R970
NDWI = (R860 – R1240) / (R860 + R1240)

Derivatives
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Results: PROSPECT-SAILH simulation CWC
40
Canopy water content (ton/ha)
35
30
25
20
y = -202.24x + 0.0437
2
R = 0.9849
15
10
5
0
-0.2
-0.15
-0.1
-0.05
0
Derivative @ 942.5 nm
Centre for Geo-information
Results: Millingerwaard 2004 - FieldSpec
30
12
10
20
y = -155.2x + 4.0005
2
R = 0.7211
8
15
6
10
Dry weight (ton/ha)
Canopy water content (ton/ha)
25
4
5
2
0
-0.15
0
-0.13
-0.11
-0.09
-0.07
-0.05
-0.03
-0.01
Derivative @ 936.5 nm
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Summary
PROSPECTSAILH
FieldSpec
2004
HyMap
2004
FieldSpec
2005
AHS
2005
Derivative
Left slope
0.98
@ 942.5 nm
0.72
@ 936.5 nm
0.50
@ 936 nm
0.55
@ 936.5 nm
0.56
@ 933 nm
Derivative
Right slope
0.93
@1032.5 nm
0.34
@1031.5 nm
0.45
@ 1030 nm
0.43
@1031.5 nm
--
WI
0.94
0.37
0.38
0.40
0.41
NDWI
0.86
0.50
0.25
0.36
--
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Continuum removal (1)
Use Continuum Removal to normalize reflectance
spectra to allow comparison of individual
absorption features from a common baseline.
The continuum is a convex hull fit over the top of a
spectrum utilizing straight line segments that
connect local spectra maxima.
The first and last spectral data values are on the
hull and therefore the first and last bands in the
output continuum-removed data file are equal to
1.0.
Convex hull
(Source: ENVI online help)
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Continuum removal (2)
http://speclab.cr.usgs.gov/PAPERS.refl-mrs/
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Continuum removal (3)
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Continuum removal (3)
MBD =
Maximum
Band Depth
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Continuum removal (3)
AUC = Area
Under
Curve
ANMB =
Area
Normalized
by the
Maximum
Band depth
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Spectral unmixing
Spectral unmixing aims at finding the fractions or
abundances of end-members, which are spectrally
pure by deconvolving
them from a mixed
spectrum
A single pixel with three materials A, B and C
Material
IFOV of pixel
Fraction
A
0.25
B
0.25
C
0.50
A
Each endmember
has a unique spectrum
B
C
Reflectance spectra
The mixed spectrum is just
a weighted average
mix=0.25*A+0.25*B+0.5*C
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Mathematics of linear unmixing
n
Ri   f j Re ij   i
j 1
Ri
= reflectance of the mixed spectrum of a pixel
in image band i
j = fraction of end-member j
Reij = reflectance of the end-member spectrum j in band i
i = the residual error
n = number of end-members
n
Constraining assumptions:
 f j 1
and
0  f j 1
j 1
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Spectral unmixing at Cuprite
Alunite
Calcite
Kaolinite
Silica
Zeolite
RMS
image
Geologic map
from unmixing
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Problems with unmixing


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How to select the end members?
Do these describe the data spectrally?
Are these of interest?
Is mixing a linear process?
Spectrometer

Incident
solar irradiance
Spectral
unmixing
Heterogeneous IFOV
for a single pixel
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Spectral field measurements
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Questions ?
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