vegetation indices and the red-edge index jan clevers centre for geo-information (cgi)
TRANSCRIPT
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Quantitative Remote Sensing: The Classification Signatures: Spectral, Spatial, Temporal, Angular, and
Polarization Statistical 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) Physical Methods
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
mostly utilizing a red (R) and NIR spectral band
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Ratio-based Vegetation Indices
1.0
0.8
0.6
0.4
0.2
0
R
NIR
NDVI
LAI2 à 3
1
0
NIR/R ratio (RVI) NDVI = (NIR-R)/(NIR+R)
(Normalized Difference VI)
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Orthogonal-based Vegetation IndicesNIR
R
soil line
(PVI = 0)
Perpendicular VI (PVI): 1/(a2+1) (NIR – a × R)
Weighted Difference VI (WDVI):
NIR – a × R
Difference VI (DVI): NIR – 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): (1 + L) × (NIR – R)/(NIR +R + L)
L = l1 + l2 0.5
NIR
R
l1
l2
Broge & Leblanc, Remote Sens. Environ. 76 (2000): 156-172
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Enhanced Vegetation Index (EVI)for use with MODIS data
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
LBCRCNIR
RNIREVI
21
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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 stress60
40
20
00.4 0.5 0.6 0.7 0.8
healthy
with stress
1 2
wavelength (µm)
reflectance (%)
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Calculation REIP
Δλ
RR
dλ
dR 1λλ
λ
Red edge inflection point (REIP) =
Red edge position (REP) =
Maximum of the first derivative.
is maximum.
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680
690
700
710
720
730
740
0 10 20 30 40 50 60 70 80
Chlorophyll Content (mg.cm-2)
Re
d E
dg
e P
os
itio
n (
nm
)
LAI = 0.5
LAI = 1.0
LAI = 2.0
LAI = 4.0
LAI = 8.0
PROSPECT – SAIL simulation
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690
700
710
720
730
740
0 5 10 15 20 25 30
Soil Reflectance (%)
Re
d E
dg
e P
os
itio
n (
nm
)
LAI = 0.5
LAI = 1.0
LAI = 2.0
LAI = 4.0
LAI = 8.0
Soil background influence
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690
700
710
720
730
740
0 10 20 30 40 50 60 70 80 90 100
Visibility (km)
Re
d E
dg
e P
os
itio
n (
nm
)
CHL = 5
CHL = 10
CHL = 20
CHL = 40
CHL = 80
Atmospheric influence
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Inverted Gaussian function
2
2o
oss 2σ
λλ exp RRRλR
σλREP o
Rs = shoulder reflectanceRo = minimum reflectanceo = wavelength at Ro
= Gaussian shape parameter
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Linear interpolation method
0
10
20
30
40
50
60
600 650 700 750 800 850 900
Wavelength (nm)
Re
fle
cta
nc
e (
%)
Rre
lre
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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)
970 nm 1200 nm
ASD Fieldspec Pro
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
400 600 800 1000 1200 1400 1600 1800 2000 2200 2400
Wavelength (nm)
Ref
lect
ance
970 nm 1200 nm
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Reflectances Continuum removal: MBD, AUC, ANMB Water band indices: WI, NDWI
WI = R900/R970
NDWI = (R860 – R1240) / (R860 + R1240)
Derivatives
Estimators for Canopy Water Content
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Results: PROSPECT-SAILH simulation CWC
y = -202.24x + 0.0437
R2 = 0.9849
0
5
10
15
20
25
30
35
40
-0.2 -0.15 -0.1 -0.05 0
Derivative @ 942.5 nm
Can
op
y w
ater
co
nte
nt
(to
n/h
a)
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Results: Millingerwaard 2004 - FieldSpec
y = -155.2x + 4.0005
R2 = 0.7211
0
5
10
15
20
25
30
-0.15 -0.13 -0.11 -0.09 -0.07 -0.05 -0.03 -0.01
Derivative @ 936.5 nm
Can
op
y w
ater
co
nte
nt
(to
n/h
a)
0
2
4
6
8
10
12
Dry
wei
gh
t (t
on
/ha)
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PROSPECT-SAILH
FieldSpec2004
HyMap2004
FieldSpec2005
AHS2005
DerivativeLeft slope
0.98@ 942.5 nm
0.72@ 936.5 nm
0.50@ 936 nm
0.55@ 936.5 nm
0.56@ 933 nm
DerivativeRight slope
0.45@ 1030 nm
[email protected] nm --
WI 0.94 0.37 0.38 0.40 0.41
NDWI 0.86 0.50 0.25 0.36 --
Summary
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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.
(Source: ENVI online help)
Convex hull
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Continuum removal (3)
AUC = Area Under CurveANMB = Area Normalized by the Maximum Band depth
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Each endmember has a unique spectrum
IFOV of pixel
A
B
C
A
B
C
A single pixel with three materials A, B and C
Material Fraction
0.25
0.25
0.50
The mixed spectrum is just a weighted average
mix=0.25*A+0.25*B+0.5*C
Spectral unmixing aims at finding the fractions or abundances of end-members, which are spectrally pure by deconvolving them from a mixed spectrum
Reflectance spectra
Spectral unmixing
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Mathematics of linear unmixing
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
Constraining assumptions: and
iij
n
jji fR
Re1
11
n
jjf 10 jf
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Spectral unmixing at Cuprite
Alunite Calcite Kaolinite Silica Zeolite
RMSimage
Geologic mapfrom unmixing
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Problems with unmixing
How to select the end members? Do these describe the data spectrally? Are these of interest? Is mixing a linear process?
Spectrometer
Incidentsolar irradiance
Heterogeneous IFOVfor a single pixel
Spectralunmixing
Questions ?
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