1 vegetation indices prof. arnon karnieli the remote sensing laboratory jacob blaustein institute...

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1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory cob Blaustein Institute for Desert Research Ben-Gurion University of the Negev Sede-Boker Campus 84990, ISRAEL

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Page 1: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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Vegetation IndicesVegetation Indices

Prof. Arnon Karnieli 

The Remote Sensing LaboratoryJacob Blaustein Institute for Desert Research

Ben-Gurion University of the NegevSede-Boker Campus 84990, ISRAEL

Prof. Arnon Karnieli 

The Remote Sensing LaboratoryJacob Blaustein Institute for Desert Research

Ben-Gurion University of the NegevSede-Boker Campus 84990, ISRAEL

Page 2: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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DefinitionDefinition

Page 3: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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SR = NIR/Red

Simple Ratio (SR)Simple Ratio (SR)

Page 4: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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Vegetation healthVegetation health

Page 5: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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• The SR is based on the difference between the maximum absorption of radiation in the red (due to the chlorophyll pigments) and the maximum reflection of radiation in the NIR (due to the leaf cellular structure), and the fact that soil spectra, lacking these mechanisms, typically do not show such a dramatic spectral difference.

• The values of the SR range from 0 to infinity

• SR uses radiance, surface reflectance (), or apparent reflectance (measured at the top of the atmosphere) rather than digital numbers.

Characteristics of SRCharacteristics of SR

Page 6: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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NIR-Red scatterplotNIR-Red scatterplot

SR = NIR/RedNIR

Page 7: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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DNs for Band A: 62 54 39 43

The same ground cover has different DNs...

DNs for Band B: 71 63 24 31

Spectral Ratioing may alleviate the problem...

ExampleExample

Page 8: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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DNs for Band A: 62 49 19 27 Band B: 71 63 24 31 Ratio A/B: 0.87 0.78 0.79 0.87

The same groundcovers now havesimilar DNs...

Example (Cont.)Example (Cont.)

Page 9: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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NDVI = (NIR- Red)/(NIR+ Red)

The Normilized Difference Vegetation IndexThe Normilized Difference Vegetation Index

Page 10: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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• NDVI has the advantage of varying between -1 and +1, while the SR ranges from 0 to infinity.

• In NDVI it is easier to separate snow, clouds, and water (negative values) from soil and vegetation (positive values).

1

1

SRNDVI

SR

SR vs. NDVISR vs. NDVI

Page 11: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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SR vs. NDVISR vs. NDVI

Page 12: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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High index values dense/health vegetation

Low index values sparse/stress vegetation

Typical NDVI values:Bare soils: 0.08 – 0.1Desert vegetation: 0.1 – 0.3Tropical forest: 0.4 – 0.6Water, snow, clouds: <0

Interpretation of NDVIInterpretation of NDVI

Page 13: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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High Correlation with: • Photosynthetic activity• Vegetation cover• Leaf area index• Green leaf biomass• Carbon fluxes• Foliar loss and damage• Chlorophyll contentAlso used for:• Crop classification• Plant Phenology• Change detection

Applications of NDVIApplications of NDVI

Page 14: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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Leaf Area Index is defined as the total one- side green leaf area per unit ground surface area (m2/ m2).

Leaf Area Index (LAI)Leaf Area Index (LAI)

LAI example

LAI = 6 means 6m2 leaf area per 1m2 ground area.

Page 15: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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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 that is responsible for carbon absorption and exchange with the atmosphere..

Leaf Area Index (LAI)Leaf Area Index (LAI)

Destructive sampling

Radiation measurements

Remote sensing

MethodsDirect: Indirect:

Page 16: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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Non-linear correlationsNon-linear correlations

Page 17: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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Linear correlationsLinear correlations

Page 18: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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NDVI calculationsNDVI calculations

Page 19: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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Red

Near

Infr

are

d

NDVINDVI

NDVI productNDVI product

NIR - R

NIR + R

Page 20: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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NDVI derived from NOAA-AVHRR

NDVI - IsraelNDVI - Israel

Page 21: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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True Color NDVI

NDVI of snowNDVI of snow

Page 22: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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Low NDVI VALUEHighLow NDVI VALUEHigh

NDVI PhenologyNDVI Phenology

Page 23: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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VI optimizationVI optimization

Page 24: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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August Average NDVI, AfricaAugust Average NDVI, Africa

Page 25: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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Maximum Value Composite (MVC)Maximum Value Composite (MVC)

2,400 km

Page 26: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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Day 10.120.200.100.15

Day 20.150.100.500.12

Day 30.090.500.150.11

***************

Day n0.060.250.630.11

Composite0.150.500.630.15

Pixel 1 Pixel 2 Pixel 3 Pixel 4T

ime

Maximum Value Composite (MVC)Maximum Value Composite (MVC)

Page 27: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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MVC - importanceMVC - importance

• Eliminate effects of cloud cover

• Eliminate effects of atmospheric

aerosols

• Eliminate effects of view zenith angle

• Eliminate effects of solar zenith angle

Forward view angle

Backward view angle

Nadir

2330 km SWATH

Page 28: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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residual clouds

Maximum Value Composite (MVC)Maximum Value Composite (MVC)

Page 29: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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NDVI time seriesNDVI time series

Movie

Page 30: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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NDVI global coverageNDVI global coverage

Movie

Page 31: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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Soil and Vegetation Reflectance ScatterplotSoil and Vegetation Reflectance Scatterplot

Red

NIR

Page 32: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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Perpendicular Vegetation Index (PVI)Perpendicular Vegetation Index (PVI)

A

B

C

D

E

Red Reflectance

NIR

Ref

lect

ance

Soil Back

ground L

ine

Greening Line

A, B = Pixels of bare soilC, D = Pixels of partly green vegetation cover.E = Pixel of green vegetation

21 a

baPVI

RNIR

The objective of PVI is to remove the effect of soil brightness and isolate reflectance changes due to vegetation only

Page 33: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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NDVI – soil sensitivityNDVI – soil sensitivity

Page 34: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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(1 )NIR R

NIR R

SAVI LL

L = 1 For low vegetation densityL = 0.5 For intermediate vegetation density;L = 0.25 For high vegetation density.

Soil Adjusted Vegetation Index (SAVI)Soil Adjusted Vegetation Index (SAVI)

Page 35: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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SAVISAVI

Page 36: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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NDVI – soil sensitivity SAVI – soil sensitivity

Soil sensitivitySoil sensitivity

Page 37: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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Surface colorSurface color

Page 38: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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( )

NIR rb

NIR rb

rb red blue red

ARVI

The resistance of the ARVI to atmospheric effects (in comparison to the NDVI) is accomplished by a self-correction process for the atmospheric effect on the red channel, using the difference in the radiance between the blue and the red channels to correct the radiance in the red channel.

= 1.0

Atmospheric Resistant Vegetation Index Atmospheric Resistant Vegetation Index

Page 39: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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NNormalized DDifference

VVegetation IIndex

NIR RED

NIR RED

NDVI

AAtmospheric RResistant

VVegetation IIndex

NIR rb

NIR rb

ARVI

rb RED BLUE RED

Atmospheric

Resistance

SSoil AAdjusted

VVegetation IIndex

NIR RED

NIR RED

SAVI LL

1

Soil Background Correction

EEnhanced VVegetation

IIndex

NIR RED

NIR RED BLUE

EVI GL c c1 2

The ultimate index!

Enhanced Vegetation Index (EVI)Enhanced Vegetation Index (EVI)

Page 40: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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The enhanced vegetation index (EVI) was developed to optimize the vegetation signal with improved sensitivity in high biomass regions and improved vegetation monitoring while correcting for canopy background signals reducing atmosphere influences.

1 2

NIR R

NIR R B

EVI GC C L

where are atmospherically-corrected or partially atmosphere corrected (Rayleigh and ozone absorption) surface reflectances, L is the canopy background adjustment term, and C1, C2 are the coefficients of the aerosol resistance term, which uses the blue band to correct for aerosol influences in the red band. The coefficients adopted in the EVI algorithm are, L=1, C1 = 6, C2 = 7.5, and G (gain factor) = 2.5.

Enhanced Vegetation Index (EVI)Enhanced Vegetation Index (EVI)

Page 41: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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EVI Image of Riparian, Wetland, and Agricultural Areas along the Lower Colorado River and U.S.-Mexico Border

Enhanced Vegetation Index (EVI)Enhanced Vegetation Index (EVI)

Page 42: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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Ndvi vs. EVINdvi vs. EVI

Page 43: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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EVI – global coverageEVI – global coverage

Page 44: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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Red Edge

Blue Shift of the Red edgeBlue Shift of the Red edge

Blue Shift

Page 45: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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Red Edge PositionRed Edge Position

0

10

20

30

40

50

60

600 650 700 750 800 850 900

Wavelength (nm)

Ref

lect

ance

(%

)

RREP

REP

670 780 700 740 700700 40(((( ) / 2) ) /( ))rep

B7

B10

B9

B8

Page 46: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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Red edge positionRed edge position

Page 47: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

700 710 720 730 740 750 760 770 780 790 800

Wavelength (nm)

Ref

lect

ance

0.000

0.005

0.010

0.015

0.020

0.025

700 710 720 730 740 750 760 770 780 790 800

Wavelength (nm)

1s

t D

eri

va

tiv

e

Reflectance

2nd Derivative

1st Derivative

-0.0010

-0.0008

-0.0006

-0.0004

-0.0002

0.0000

0.0002

0.0004

0.0006

0.0008

0.0010

700 710 720 730 740 750 760 770 780 790 800

Wavelength (nm)

2n

d D

eri

va

tiv

e

Red edge positionRed edge position

Page 48: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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False color image

2nd derivative image

Red edge positionRed edge position

Page 49: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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Perpendicular Vegetation Index:

Vegetation Indices – summary (1)Vegetation Indices – summary (1)

21 a

baPVI

RNIR

/NIR RSR

NIR R

NIR R

NDVI

Simple Ratio:

Normalized Difference Vegetation Index:

Page 50: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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(1 )NIR R

NIR R

SAVI LL

( )

NIR rb

NIR rb

rb red blue red

ARVI

1 2

NIR R

NIR R B

EVI GC C L

Vegetation Indices (2)Vegetation Indices (2)

Soil Adjusted Vegetation Index:

Atmospheric Resistance Vegetation Index:

Enhanced Vegetation Index:

Page 51: 1 Vegetation Indices Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev

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Types of vegetation indicesTypes of vegetation indices

Ratio-based (red and NIR spectral bands):

SR, NDVI, ARVI

Orthogonal-based difference of red and NIR spectral bands:

PVI

Hybrid/combination of the two:

SAVI, EVI