vegetation indices two-band vegetation indices three-band vegetation indices leaf area index class 9
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Vegetation Indices
Two-band vegetation indices
Three-band vegetation indices
Leaf area index
Class 9
Structure of a Leaf
Red and blue lightlargely absorbedfor use in photosynthesis
Strong Infraredreflectivity and transmittance.
Cuticle
Upper Epidermis
PalisadeLayer
SpongyTissue
Lower Epidermisand Cuticle
Stomates andGuard Cells
Campbell 16.3
Wavelength (nm)
400 500 600 700 800 900 1000
Ref
lect
anc
e (
%)
0
10
20
30
40
50
r
n
Visible Near Infrared
Black Spruce Needle
Moss
BLUE GREEN RED
Vegetation Indices
Campbell 16.5
• Quantitative measures for vegetation abundance and vigour.
• Formed from combinations of two to several spectral bands that are added, divided, or multiplied in a manner to yield a single value that indicates the amount or vigour of vegetation within a pixel.
Leaf Area Index (LAI)
Campbell 16.6
LAI is defined as the total one-sided (or one half of the total all-sided) green leaf area per unit ground surface area.
It is an important biological parameter because: it defines the area that interacts with solar radiation and provides the remote sensing signal;
It is the surface responsible for carbon absorption and exchange with the atmosphere.
Spectral response to vegetation amount (grass)
Response of Red and NIR to LAI changes in crops
Martin and Heiman, 1986, Photogrammetric Engineering and Remote Sensing
Re
flec
tan
ce
croplands, grasslands
LAI
Near Infrared
Red
Campbell 16.5
Response of Red and NIR to LAI Changes
Chen, 1996, Canadian Journal of Remote Sensing
Forest remote sensing(Hyperspectral)
Chen and Leblanc, 2000
Measurements
Simulation
ForestsMore trees-foliage means more shadows when
the density is low
Because transmittance in near-infrared is highinfrared shadows appear less shaded
than shadows in visible
Near Infrared
Red
LAI
Re
flec
tan
ce
Campbell 16.5
Wavelength (nm)
400 500 600 700 800 900 1000
Ref
lect
anc
e (
%)
0
10
20
30
40
50
r
n
n - r
n + r
NDVI =
Visible Near Infrared
Black Spruce Needle
Moss
BLUE GREEN RED
Vegetation Indices
Normalized Difference Vegetation Index (NDVI)
REDNIR
REDNIRNDVI
NIR = reflectance in near-infrared bandRED = reflectance in red band
Simple Ratio (SR)
RED
NIRSR
Saturation problems
LAI
ND
VI
LAI
SR
Perpendicular Vegetation Index (PVI)
A
B
C
X
W
Y
Ne
ar
Infr
are
d r
efle
ctan
ce
Red Reflectance
C: dry soilB: wet soilX: “pure” vegetationY: Partialy vegetated pixel
22IRIRRR VSVSPVI S = soil, V = vegetation
Campbell 16.9
Based on Euclediandistance
Ne
ar
Infr
are
d r
efle
ctan
ce
Red Reflectance
SR1
SR2
SR4
SR3
SR1 SR2 SR3 SR4> > >
Simple Ratio (SR)
RED
NIRSR
Ne
ar
Infr
are
d r
efle
ctan
ce
Red Reflectance
NDVI1
NDVI2
NDVI4
NDVI3
NDVI1 NDVI2 NDVI3 NDVI4> > >
Normalized Difference Vegetation Index (NDVI)
REDNIR
REDNIRNDVI
Principles of SAVI
Huete, 1988, Remote Sensing of Environment
Ne
ar
Infr
are
d r
efle
ctan
ce
Red Reflectance
SAVI1
SAVI2
SAVI4
SAVI3
SAVI1 SAVI2 SAVI3 SAVI4> > >
Soil Adjusted Vegetation Index (SAVI)
LREDNIR
REDNIRLSAVI
)1(
L
n r
n r
n
r
n
r
n
r
1
1
n r
n r
n ra an soil
r soil
,
,
Name Formula Reference
NDVI Rouse et al., 1974
SR Jordan, 1969
MSR
Chen, 1996
RDVI Roujean and Breon, 1995
WDVI , Clevers,1989
Two-band Vegetation Indices (1)
n r
n r
L
L
1L 05.
L
L
rn
rn
1
L NDVI WDVI 1 212.
n n n r 0 5 0 5 22
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1 0 25 0125
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. .r
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2 15 0 5
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2 2n r n r
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n r
n r
2
2
Name Formula Reference
SAVI , Huete, 1988
SAVI1 , Qi et al., 1994
SAVI2
GEMI
, Pinty & Verstraete, 1992
NLI Goel & Qin, 1994
Two-band Vegetation Indices (2)
Chen, J. M., (1996). Evaluation of vegetation indices and a modified simple ratio for boreal applications. Can. J. Remote Sensing. 22:229-242
Clevers, J. G. P. W. (1989). The applications of a weighted infrared-redvegetation index for estimating leaf area index by correcting for soilmoisture. Remote Sens. Environ. 29:25-37. Goel, N. S., and Qin, W. (1994). Influences of canopy architecture onrelationships between various vegetation indices and LAI and FPAR: a computerSimulation, Remote Sens. Rev. 10:309-347. Huete, A.R. (1988). A soil adjusted vegetation index (SAVI), Remote Sens.Environ. 25:295-309. Huete, A. R. and Liu, H. Q., (1994). An error and sensistivity anbalysis of the atmospheric- and soul-correcting variants of the NDVI for the MODIS-EOS. IEEE Trans. Geisci. and Remote Sens. 32:897-905. Jordan, C.F. (1969). Derivation of leaf area index from quality of light onthe forest floor. Ecology 50:663-666. Kaufman, Y. J., and Tanre, D. (1992). Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans. Geosci. Remote Sens. 30:261-270. Pinty, B. and Verstrate, M. M. (1992). GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio 101:15-20. Qi, J., Chehbouni, A., Huete, A.R., Kerr, Y.H. and Sorooshian, S. (1994). Amodified soil adjusted vegetation index, Remote Sens. Environ. 48:119-126.Rouse, J. W., Hass, R. H. Shell, J. A., and Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS-1. Third Earth Resources Technology Satellite Symposium 1: 309-317. Roujean, J.-L. and Breon, F. M. (1995). Estimating PAR absorbed by vegetationfrom bidrectional reflectance measurements. Remote Sens. Environ.51:375-384.
Two-band Vegetation Indices:References
Some useful features of vegetation indices (1)
1. NDVI, SR, MSR are based on the ratio ofred and NIR bands. They are often preferredbecause the ratio can remove much measurement noise in individual bands
2. SAVI, SAVI1 and SAVI2 have the advantage ofconsidering the influence of the soil background Effect, but it is not based on the ratio and much ofMeasurement noise is retained
3. Other more complicated indices might have Advantages in specific applications, but they haveThe potential to amplify measurement noise
Chen, 1996, Canadian Journal of Remote Sensing
Effectiveness of VIs in retrieving LAI of boreal forests
Note:The usefulnessof VIs in otherecosystemsmay differ
Satellite-based LAI algorithm developmentCanada-wide LAI map validation involving all five forest research centres
and several universities(satellite: Landsat; ground data: TRAC)
Chen et al. 2001, Remote Sensing of Environment
LAI - Agriculture
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rbn
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ss
r
n
Name Formula* Reference
ARVI
Kaufman and Tanre, 1992
SARVI
,
Liu and Huete, 1995
SARVI2
Huete et al., 1996
MNDVI
Nemani et al., 1993
RSR
Brown et al., 1999
Three-band Vegetation Indices
Brown, L. J., J. M. Chen, S.G. Leblanc, and J. Cihlar. 2000. “Short Wave Infrared Correction to the Simple Ratio: An Image and Model Analysis,” Remote Sens. of Environ, . 71:16-25
Huete, A. R., C. Justice, W. van Leeuwen. 1996. “MODIS vegetation index (MOD 13)”. EOS MODIS Algorithm-Theoretical baiss document, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771. USA. 115pp.
Kaufman, Y. J., and Tanre, D. (1992). Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans. Geosci. Remote Sens. 30:261-270.
Liu, H. Q. and A. R. Huete. 1995 “A feedback based modification of the NDVI to minimize canopy background and atmospheric noise.” IEEE Trans. Geosci. Remote Sens. 33:481-486.
Nemani, R., L. Pierce, S. Running, and L. Band. 1993. “Forest Ecosystem Processes at the Watershed Scale: Sensitivity to Remotely Sensed Leaf Area Index Estimates,” Intl. J. Remote Sens., 14, Pp. 2519-2534.
Three-band Vegetation Indices (References)
Some useful features of vegetation indices (2)
1. ARVI, SARVI, and SARVI2 are able to reduce thethe influence of the atmosphere.
2. MNDVI and RSR are designed to reduce the backgroundeffects.
The best way is to do proper atmospheric correction and use ratio-based indices
Reduced Simple Ratio
MINMAX
MIN
MIRMIR
MIRMIRSRRSR 1
Brown et al, 1999
The mid-infrared scales the background effect
LAI LAIR
SRSR
a = aspen m = mixeds = spruce p = pine
Brown et al, 1999, Remote Sensing of Environment