1. Two-band vegetation indices Three-band vegetation indices
Class 9
Vegetation Indices
Two-band vegetation indices
Three-band vegetation indices
Leaf area index

2. Structure of a Leaf Red and blue light Cuticle largely absorbed
for use in
photosynthesis
Strong Infrared
reflectivity and
transmittance.
Cuticle
Upper
Epidermis
Palisade
Layer
Spongy
Tissue
Lower Epidermis
and Cuticle
Stomates and
Guard Cells
Campbell 16.3

3. r r Moss Reflectance (%) n r Wavelength (nm) Visible Near Infrared 5040
Visible
Near Infrared 30
Black Spruce Needle
Moss
Reflectance (%) 20 r n 10 r r
GREEN
RED
BLUE
400
500
600
700
800
900
1000
Wavelength (nm)

4. Vegetation Indices
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.
Campbell 16.5

5. Leaf Area Index (LAI)
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.
Campbell 16.6

6. Spectral response to vegetation amount (grass)

7. Response of Red and NIR to LAI changes in crops
Martin and Heiman, 1986,
Photogrammetric Engineering and Remote Sensing

8. croplands, grasslands
Near Infrared
Reflectance
Red
LAI
Campbell 16.5

9. Response of Red and NIR to LAI Changes
Chen, 1996, Canadian Journal of Remote Sensing

10. Forest remote sensing (Hyperspectral) Chen and Leblanc, 2000
Measurements
Simulation
Chen and Leblanc, 2000

11. Forests More trees-foliage means more shadows when the density is low
Because transmittance in near-infrared is high
infrared shadows appear less shaded
than shadows in visible
Near Infrared
Reflectance
Red
LAI
Campbell 16.5

12. r r Moss r - r NDVI = r + r Reflectance (%) n r Wavelength (nm)50 r - r
NDVI = n r r + r n r 40
Visible
Near Infrared 30
Black Spruce Needle
Moss
Reflectance (%) 20 r n 10 r r
GREEN
RED
BLUE
400
500
600
700
800
900
1000
Wavelength (nm)

13. Vegetation Indices Normalized Difference Vegetation Index (NDVI)
Saturation
problems
NDVI
LAI
Simple Ratio (SR) SR
LAI
NIR = reflectance in near-infrared band
RED = reflectance in red band

14. Perpendicular Vegetation Index (PVI) Near Infrared reflectance
S = soil, V = vegetation X
C: dry soil
B: wet soil
X: “pure” vegetation
Y: Partialy vegetated pixel Y C W
Based on
Eucledian
distance
Near Infrared reflectance B A
Red Reflectance
Campbell 16.9

15. Near Infrared reflectance
Simple Ratio (SR)
>
>
SR1
SR2
SR3
>
SR4
SR1
SR2
SR3
Near Infrared reflectance
SR4
Red Reflectance

16. Near Infrared reflectance
Normalized Difference Vegetation Index (NDVI)
>
NDVI1
>
NDVI2
NDVI3
>
NDVI4
NDVI1
NDVI2
NDVI3
Near Infrared reflectance
NDVI4
Red Reflectance

17. Principles of SAVI
Huete, 1988, Remote Sensing of Environment

18. Near Infrared reflectance
Soil Adjusted Vegetation Index (SAVI)
SAVI1
SAVI2
SAVI3
SAVI4
>
SAVI1
SAVI2
SAVI3
Near Infrared reflectance
SAVI4 L
Red Reflectance

19. Two-band Vegetation Indices (1)
Name
Formula
Reference
NDVI
Rouse et al., 1974 SR
Jordan, 1969
MSR
Chen, 1996
RDVI
Roujean and Breon, 1995
WDVI ,
Clevers,1989

20. Two-band Vegetation Indices (2)
Name
Formula
Reference
SAVI ,
Huete, 1988
SAVI1
Qi et al., 1994
SAVI2
GEMI
Pinty & Verstraete, 1992
NLI
Goel & Qin, 1994

21. Two-band Vegetation Indices:References
Chen, J. M., (1996). Evaluation of vegetation indices and a modified simple ratio for boreal applications. Can. J. Remote Sensing. 22:
Clevers, J. G. P. W. (1989). The applications of a weighted infrared-red
vegetation index for estimating leaf area index by correcting for soil
moisture. Remote Sens. Environ. 29:25-37.
Goel, N. S., and Qin, W. (1994). Influences of canopy architecture on
relationships between various vegetation indices and LAI and FPAR: a computer
Simulation, Remote Sens. Rev. 10:
Huete, A.R. (1988). A soil adjusted vegetation index (SAVI), Remote Sens.
Environ. 25:
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:
Jordan, C.F. (1969). Derivation of leaf area index from quality of light on
the forest floor. Ecology 50:
Kaufman, Y. J., and Tanre, D. (1992). Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans. Geosci. Remote Sens. 30:   
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). A
modified soil adjusted vegetation index, Remote Sens. Environ. 48:
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:
Roujean, J.-L. and Breon, F. M. (1995). Estimating PAR absorbed by vegetation
from bidrectional reflectance measurements. Remote Sens. Environ.
51:

22. Some useful features of vegetation indices (1)
1. NDVI, SR, MSR are based on the ratio of
red and NIR bands. They are often preferred
because the ratio can remove much
measurement noise in individual bands
2. SAVI, SAVI1 and SAVI2 have the advantage of
considering the influence of the soil background
Effect, but it is not based on the ratio and much of
Measurement noise is retained
3. Other more complicated indices might have
Advantages in specific applications, but they have
The potential to amplify measurement noise
Chen, 1996, Canadian Journal of Remote Sensing

23. Effectiveness of VIs in retrieving LAI of boreal forests
Note:
The usefulness
of VIs in other
ecosystems
may differ

24. Satellite-based LAI algorithm development Canada-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

25. LAI - Agriculture

26. Three-band Vegetation Indices
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

27. Three-band Vegetation Indices (References)
Brown, L. J., J. M. Chen, S.G. Leblanc, and J. Cihlar “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 “MODIS vegetation index (MOD 13)”. EOS MODIS Algorithm-Theoretical baiss document, NASA Goddard Space Flight Center, Greenbelt, Maryland USA. 115pp.
Kaufman, Y. J., and Tanre, D. (1992). Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans. Geosci. Remote Sens. 30:
Liu, H. Q. and A. R. Huete “A feedback based modification of the NDVI to minimize canopy background and atmospheric noise.” IEEE Trans. Geosci. Remote Sens. 33:
Nemani, R., L. Pierce, S. Running, and L. Band “Forest Ecosystem Processes at the Watershed Scale: Sensitivity to Remotely Sensed Leaf Area Index Estimates,” Intl. J. Remote Sens., 14, Pp

28. Some useful features of vegetation indices (2)
1. ARVI, SARVI, and SARVI2 are able to reduce the
the influence of the atmosphere.
2. MNDVI and RSR are designed to reduce the background
effects.
The best way is to do proper atmospheric
correction and use ratio-based indices

29. The mid-infrared scales the background effect
Reduced Simple Ratio
The mid-infrared scales the background effect
Brown et al, 1999

30. Brown et al, 1999, Remote Sensing of EnvironmentSR
RSR
LAI
LAI
a = aspen m = mixed
s = spruce p = pine
Brown et al, 1999, Remote Sensing of Environment