1. Vegetation indices and the red-edge index
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Vegetation indices and the red-edge index
Jan Clevers
Centre for Geo-Information (CGI)

2. Quantitative Remote Sensing: The Classification
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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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3. 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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4. Ratio-based Vegetation Indices
NIR
1.0
0.8
0.6
0.4
0.2
NIR/R ratio (RVI)
NDVI = (NIR-R)/(NIR+R)
(Normalized Difference VI)
NDVI 1 R
LAI
2 à 3
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5. Orthogonal-based Vegetation Indices
NIR 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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6. 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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7. 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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8. Hybrid Vegetation Indices
NIR R l1 l2
Soil Adjusted VI (SAVI):
(1 + L) × (NIR – R)/(NIR +R + L)
L = l1 + l2  0.5
Broge & Leblanc, Remote Sens. Environ. 76 (2000):
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9. 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]
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10. 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
Centre for Geo-information

11. 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
Centre for Geo-information

12. Red Edge Index
Determining vegetation condition using RS: e.g. blue shift of the red edge as a result of stress
reflectance (%) 1 2 60
healthy
with stress 40 20
0.4
0.5
0.6
0.7
0.8
wavelength (µm)
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13. Calculation REIP Red edge inflection point (REIP) =
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Red edge inflection point (REIP) =
Red edge position (REP) =
Maximum of the first derivative.
is maximum.
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14. PROSPECT – SAIL simulation
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15. Soil background influence
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16. Atmospheric influence
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17. Inverted Gaussian function
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Rs = shoulder reflectance Ro = minimum reflectance o = wavelength at Ro  = Gaussian shape parameter
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18. Linear interpolation method
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19. Linear interpolation method
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20. REP image for MERIS
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Each digital number represents a wavelength value (being the REP)
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21. 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.
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22. 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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23. 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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24. Estimating Canopy Water Content (CWC)
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970 nm nm
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25. Estimators for Canopy Water Content
Reflectances
Continuum removal: MBD, AUC, ANMB
Water band indices: WI, NDWI
WI = R900/R970
NDWI = (R860 – R1240) / (R860 + R1240)
Derivatives
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26. Results: PROSPECT-SAILH simulation CWC
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27. Results: Millingerwaard 2004 - FieldSpec
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28. Summary Centre for Geo-information PROSPECT- SAILH FieldSpec 2004
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PROSPECT-
SAILH
FieldSpec
2004
HyMap
2005
AHS
Derivative
Left slope
0.98
@ nm
0.72
@ nm
0.50
@ 936 nm
0.55
0.56
@ 933 nm
Right slope
0.93
@ nm
0.34
@ nm
0.45
@ 1030 nm
0.43 -- WI
0.94
0.37
0.38
0.40
0.41
NDWI
0.86
0.25
0.36
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29. Continuum removal (1)
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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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30. Continuum removal (2) Centre for Geo-information 16/04/2017
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31. Continuum removal (3)
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32. Continuum removal (3) MBD = Maximum Band Depth
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MBD = Maximum Band Depth
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33. Continuum removal (3) AUC = Area Under Curve
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AUC = Area Under Curve
ANMB = Area Normalized by the Maximum Band depth
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34. 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
Reflectance spectra
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35. 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
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36. Spectral unmixing at Cuprite
Alunite
Calcite
Kaolinite
Silica
Zeolite
RMS
image
Geologic map
from unmixing
Centre for Geo-information

37. Problems with unmixing
How to select the end members?
Do these describe the data spectrally?
Are these of interest?
Is mixing a linear process?
Spectral
unmixing
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38. Spectral field measurements
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39. Questions ? www.scopus.com/home.url www.isiknowledge.com
16/04/2017
Questions ?
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