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

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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

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L

n r

n r

n

r

n

r

n

r

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n r

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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)

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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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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