Vegetation Indices

1
Vegetation Indices

• Lecture 7
• prepared by R. Lathrop 10/99
• Modified 3/06

2
Remote Sensing of the Earth Clues to a Living
Planet

• Remote sensing scientists measure the amount of
  energy in different spectral wavelengths
  reflected from the earths surface as one means
  of monitoring the earths biosphere.
• Where there are a lot of plants on the earths
  surface, less red and blue light is measured by
  the satellite sensor. Likewise, the more green
  plants, the more near infrared energy that is
  measured.
• By combining measurements in the red and
  near-infrared wavelengths, scientists have
  devised a remotely sensed vegetation index or
  what is sometimes referred to as a greenness
  index. The more plants, the greener the earth,
  the higher the index.

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Vegetation Indices

• Linear combination of image bands used to extract
  information about vegetation biomass, leaf area,
  productivity
• Most vegetation indices (VIs) based on the
  differential reflectances of healthy green
  vegetation, dead/senescent vegetation and soil in
  visible vs. near IR wavelengths

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Photosynthetically Active Radiation

• PAR 0.40-0.70 um, portion of EMR absorbed by
  plant pigments and used in photosynthesis
• APAR PAR energy actually absorbed by a plant
  canopy
• IPAR intercepted PAR, probability that photons
  are intercepted by plant elements

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How plant leaves reflect light
Graphics from http//landsat7.usgs.gov/resources/r
emote_sensing/radiation.php
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How plant leaves reflect light
Blue red light strongly absorbed by chlorophyll
Sunlight
B G R NIR
NIR
Incoming light
Cross-section of leaf
Reflected light
NIR light scattered within leaf some reflected
back, some transmitted through
Leaf
Transmitted light
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Reflectance from green plant leaves

• Chlorophyll absorbs large of red and blue for
  photosynthesis- and strongly reflects in green
  (.55um)
• Peak reflectance in leaves in near infrared
  (.7-1.2um) up to 60 of infrared energy per leaf
  is scattered up or down due to cell wall size,
  shape, leaf condition (age, stress, disease),
  etc.
• Reflectance in Mid IR (2-4um) influenced by water
  content-water absorbs IR energy, so live leaves
  reduce mid IR return

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Sub-pixel Estimation
N I R Re f l e c tance
Spectral Feature Space
Increasing Vegetation
Example Pixel X proportions IS 50 Grass
30 Trees 20
Soil Line
As green leaf area increases NIR increases red
decreases
Red Reflectance
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Bidirectional Reflectance Distribution Function
BRDF

• Lambertian surface reflected energy is scattered
  equally in all directions no direction bias
  isotropic
• Vegetation canopies are not Lambertian surfaces
  but rather demonstrate definite directional bias
  anistropic
• BRDF is the hemispherical distribution of
  reflectances for a feature as a function of
  illumination geometry
• Bottom line viewing and illumination angle are
  important a nadir view of the same feature may
  record a different reflectance than a side view
  or look differently under different sun angles
• Some sensors designed to provide different look
  angles at the same feature e.g., a forward,
  nadir and backwards view

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Measuring the BRDF example
Aircraft-mounted radiometer used to fly a closed
circle and record reflectance of a site
Note that the reflectance is not equally
distributed across all directions
Graphics from http//car.gsfc.nasa.gov/application
_brdf.html
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Vegetation indices

• Simple ratio nir/red
• Normalized Difference VI (NDVI) nir -
  red nir red NDVI ranges from -1 to
  1
• Transformed VI to eliminate negative values
• TVI /NDVI 0.5

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Vegetation Indices Issues

• VI is a BW image positively correlated with
  greeness, as NIR increases and red decreases,
  VI increases

AVHRR
Landsat TM
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Vegetation Indices Issues

• Soil brightness variations complicating the VI
  response
• Asymptotic relationship leading to loss in
  sensitivity at high vegetation amounts
• Atmospheric interference, especially in the Red
  band.
• Best practice is to convert the original DN
  values to radiance (preferably atmospherically
  corrected) or reflectance before computing the
  vegetation index

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Vegetation Indices Issues

• Scaling ratio of averages (NDVI of larger
  pixels e.g., AVHRR pixels) is not the same as
  the average of the ratios (average NDVI of
  smaller pixels e.g., Landsat TM)

Example (sample area from Landsat TM 30 m pixels
vs. km2 composite) Ratio of averages Mean red
28 3 Mean NIR 73 NDVI
(73-28)/(7328) 45/101 0.446 Average of
ratios NDVI 0.416
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Vegetation indicesPVI

• Perpendicular VI determines a pixels orthogonal
  distance from the soil line in image feature
  space (X axis red Y axis NIR)
• The objective is to remove the effect of soil
  brightness and isolate reflectance changes due to
  vegetation only

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Vegetation IndicesSAVI

• Soil Adjusted Vegetation Index (SAVI) is a
  technique to minimize soil brightness influences.
  Involves shifting the origin of the nir-red
  feature space to account for 1st order
  soil-vegetation interactions and differential red
  nir extinction through vegetation canopies
• SAVI (1L) (nir-red) / (nir red L)
• Where L 0 to 1.
• L 1 for low veg density,
• L 0.5 for intermed veg density
• L 0.25 for high veg density

From Huete, A.R., 1988. A soil-adjusted
vegetation index (SAVI), Rem. Sens. Environ.
25295-309.
17
From Huete, A.R., 1988. A soil-adjusted
vegetation index (SAVI), Rem. Sens. Environ.
25295-309.
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Vegetation indicesMSAVI

• Modified Soil Vegetation Index (MSAVI) employs a
  correction factor to reduce sensitivity to soil
  variation across a scene

                                                
                            
MSAVI0 ((NIR RED) (1 L0)) / (NIR RED
L0) MSAVI1 ((NIR RED) (2 - MSAVI0)) /
NIR RED 1 MSAVI0 Must empirically
determine L MSAVI2 (2NIR 1 ((2NIR 1)2
8(NIR RED)) -1/2 / 2
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Vegetation indices ARVI

• Atmospherically Resistant Vegetation Index (ARVI)
  incorporates the blue channel to account for
  atmospheric scattering in the red channel by
  using the difference between the radiance in the
  red and blue channel
• ARVI (NIR RB) / (NIR RB)
• Where RB Red g (Blue Red)
• Where Blue is Landsat TM band 1 ,visible blue
  wavelengths
• g 1, unless the aerosol model is known a
  priori

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Vegetation indices EVI
Enhanced Vegetation Index (EVI) developed to
optimize the vegetation signal with improved
sensitivity in high biomass regions, a reduction
of sensitivity to the canopy background signal
and a reduction in atmosphere influences.
EVI G (NIR RED) / (NIR C1RED
C2BLUE L) Where C1 atmosphere resistance
red correction coefficient 6 C2 atmosphere
resistance blue correction coefficient 7.5 L
Canopy background brightness correction factor
1 G Gain factor 2.5 Note Example
coefficients, may vary depending on sensor/
situation
Miura, T., Huete, A.R., Yoshioka, H., and Holben,
B.N., 2001, An error and sensitivity analysis of
atmospheric resistant vegetation indices derived
from dark target-based atmospheric correction,
Remote Sens. Environ., 78284-298. Miura, T.,
Huete, A.R., van Leeuwen, W.J.D., and Didan, K.,
1998, Vegetation detection through smoke-filled
AVIRIS images an assessment using MODIS band
passes, J. Geophys. Res. 10332,001-32,011.
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Graphic taken from http//tbrs.arizona.edu/project
s/modis/figures/Slide5.GIF
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MODIS EVI vs. NDVI
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Wide Dynamic Range VI (WDRVI)

• NDVI suffers from a decrease in sensitivity at
  medium to high leaf areas because the NIR
  reflectance continues to increase with increasing
  LAI while red absorption tends to stabilize at
  lower levels
• WDVRI (a NIR RED) / (a NIR RED)
• where a is a weighting coeff , 0 lt a lt 1
• a lt 1, the contribution from the NIR attenuated
• a between 0.05 and 0.2 have been found effective
  in row crops

For more info Gitelson. 2004. J Plant Physiology
161165-173.
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Vegetation indices

• Numerous studies have explored the relationship
  between remotely sensed vegetation indices and
  field measured estimates of vegetation amount
  above-ground biomass, leaf area
• Goal is to be able to estimate and map these key
  variables of ecosystem state
• Best relationships obtained in closed canopy
  crops. Woody material complicates but does not
  invalidate the relationship

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For good review of VI

• NASA Remote Sensing Tutorial http//rst.gsfc.nasa.
  gov/Homepage/Homepage.html
• For specifics on Vegetation Indices
• http//rst.gsfc.nasa.gov/Sect3/Sect3_4.html

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Global Biosphere Vegetation Monitoring

• One of the main satellite systems that have been
  used to measure the vegetation index of the earth
  over long periods of time is the AVHRR satellite.
• AVHRR stands for Advanced Very High Resolution
  Radiometer
• This system has been largely replaced by the
  MODIS AQUA and TERRA systems

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Global AVHRR composite

• 1 band in the Red .58-.6 um
• 1 band in the NIR .72-1.1 um
• Vegetation Index to map vegetation amount and
  productivity

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Global Biosphere Vegetation Monitoring
NOAA AVHRR used to create global greenness maps
based on NDVI. Composited over biweekly to
monthly intervals.
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Integrated NDVI summed over the growing season
to provide index of vegetation productivity
modified from Goward et al. 1985 Vegetation
643-14.
Temperate broadleaf forest Boreal forest Desert
Int NDVI
Apr May June Jul Aug
Sept Oct
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Global NDVI summed over an entire year
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Integrated Growing Season NDVI modified from
Goward et al. 1985 Vegetation 643-14.
1400
NPP g/m2/yr
Moist conifer Dec. broadleaf Boreal
conifer Grassland Tundra desert
0
0 1 2 3 4 5
Integrated NDVI
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Global NDVI converted to LAI (leaf area index
m2/m2)
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Remote Sensing of the Earth Clues to a Living
Planet

• You can access these images over the INTERNET
• You can either browse through individual images
  or watch an animation
• http//svs.gsfc.nasa.gov/search/Keyword/NDVI.html

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Remote Sensing of the Earth Clues to a Living
Planet

• First, click on the Hologlobe Vegetation Index
  for 1991 on a Flat Earth animation. Open it, and
  click on the gt button.
• Watch closely, can you observe the Green Wave in
  the northern hemisphere?
• What about the Brown Wave?
• Now look at the southern hemisphere. What do you
  observe?

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Can you see the Green Wave?
NASA/Goddard Space Flight CenterScientific
Visualization Studiohttp//svs.gsfc.nasa.gov/vis/a
000000/a001300/a001308/index.html
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Remote Sensing of the Earth Clues to a Living
Planet

• Now take a look at the Northern hemisphere in
  greater detail.
• Click on the NDVI Animation over continental
  United States.
• Can you find where you live? How long does it
  stay green?
• Compare Florida with Maine or Minnesota.

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North America Close-up
NASA/Goddard Space Flight CenterScientific
Visualization Studio http//svs.gsfc.nasa.gov/vis/
a000000/a002500/a002568/index.html
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Remote Sensing of the Earth Clues to a Living
Planet

• To access more recently acquired AVHRR imagery go
  to the National Oceanographic Atmospheric
  Administration (NOAA) Satellite Active Archive
  http//www.saa.noaa.gov/

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• 36 discrete bands between 0.4 and 14.5 µm
• spatial resolutions of 250, 500, or 1,000 m at
  nadir.
• Signal-to-noise ratios are greater than 500 at
  1-km resolution (at a solar zenith angle of 70),
  and absolute irradiance accuracies are lt 5 from
  0.4 to 3 µm (2 relative to the sun) and 1
  percent or better in the thermal infrared (3.7 to
  14.5 µm).
• MODIS instruments will provide daylight
  reflection and day/night emission spectral
  imaging of any point on the Earth at least every
  2 days, operating continuously.
• For more info http//eospso.gsfc.nasa.gov/eos_hom
  epage/mission_profiles/instruments/MODIS.php

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Aqua, Latin for water, is a NASA Earth
Science satellite mission named for the large
amount of information that the mission will be
collecting about the Earths water cycle,
including evaporation from the oceans, water
vapor in the atmosphere, clouds, precipitation,
soil moisture, sea ice, land ice, and snow cover
on the land and ice.
Additional variables also being measured by Aqua
include radiative energy fluxes, aerosols,
vegetation cover on the land, phytoplankton and
dissolved organic matter in the oceans, and air,
land, and water temperatures.The AQUA Platform
includes the MODIS, CERES and AMSR_E instruments.
Aqua was formerly named EOS PM, signifying its
afternoon equatorial crossing time. AQUA was
launched May 2002. For more info
http//aqua.nasa.gov/
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Earth Observing 1

• NASAs New Millennium Program
• Multispectral instrument that is a significant
  improvement over the Landsat 7 ETM instrument
  Advanced Line Imager (ALI)
• Hyperspectral land imaging instrument Hyperion
• Low-spatial/high-spectral resolution imager that
  can correct systematic errors in the apparent
  surface reflectances caused by atmospheric
  effects, primarily water vapor - Linear Etalon
  Imaging Spectrometer Array (LEISA) Atmospheric
  Corrector (LAC)

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EO-1 Advanced Line Imager (ALI)

• The EO-1 ALI operates in a pushbroom fashion at
  an orbit of 705 km, 16 day repeat cycle. Launched
  in Nov 2000.
• ALI provides Landsat type panchromatic and
  multispectral bands. These bands have been
  designed to mimic six Landsat bands with three
  additional bands covering 0.433-0.453,
  0.845-0.890, and 1.20-1.30 µm.
• The ALI has 30M resolution multi-spectral 10m
  panchromatic. 37km swath width.
• More info http//eo1.usgs.gov/ali.php

Mt. Fuji Japan ALI Bands 6,5,4.
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ENVISAT

• In March 2002, the European Space Agency launched
  Envisat, an advanced polar-orbiting Earth
  observation satellite which provides measurements
  of the atmosphere, ocean, land,
  and ice.
• http//envisat.esa.int/

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ENVISAT primary instruments for land/sea surface
remote sensing

• ASAR - Advanced Synthetic Aperture Radar,
  operating at C-band,
• MERIS - is a 68.5 o field-of-view pushbroom
  imaging spectrometer that measures the solar
  radiation reflected by the Earth, at a ground
  spatial resolution of 300m, in 15 spectral bands,
  programmable in width and position, in the
  visible and near infra-red. MERIS allows global
  coverage of the Earth in 3 days.

http//envisat.esa.int/
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SPOT Vegetation

• Earth observation sensor on board of the SPOT
  satellite in blue, red, NIR SWIR
• Daily coverage of the entire earth at a spatial
  resolution of 1 km
• The first VEGETATION instrument is part of the
  SPOT 4 satellite and a second payload, VEGETATION
  2, is now operationally operated onboard SPOT 5.
• http//www.spot-vegetation.com/

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SPOT Vegetation Spectral Bands
http//www.spot-vegetation.com/
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http//www.spot-vegetation.com/
Global Annual Changes of Vegetation Productivity
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SPOT Vegetation

• Free products are
• extracts from ten day global syntheses.
• available 3 months after insertion in the
  VEGETATION archive.
• in full resolution (1km).
• in plate carrée projection.
• available on 10 predefined regions of interest.
• in the standard VEGETATION product format.
• http//free.vgt.vito.be/

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• 3 visible/NIR(VNIR 0.5 and 0.9 µm) with 15-m
  resolution
• 3 mid IR (SWIR 1.6 and 2.43 µm) with 30-m res.
• 5 TIR (8 and 12 µm) with 90-m resolution
• 60- km swath whose center is pointable
  cross-track 8.55 in the SWIR and TIR, with the
  VNIR pointable out to 24. An additional VNIR
  telescope (aft pointing) covers the wavelength
  range of Channel 3. By combining these data with
  those for Channel 3, stereo views can be created,
  with a base-to-height ratio of 0.6.
• Overpass every 16 days in all 14 bands and once
  every 5 days in the three VNIR channels. For
  more info http//eospso.gsfc.nasa.gov/eos_homepag
  e/mission_profiles/instruments/ASTER.php

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Vegetation Water Stress Indices

• Moisture Stress Index (MSI) contrast water
  absorption in the MIR with vegetation reflectance
  (leaf internal structure) in the NIR
• MSI MIR / NIR or R1600/R820
• Normalized Difference MSI
  NDMIS (NIR MIR) / (NIR
  MIR)
• Normalized Difference Water Index
  NDWI (R860-R1240) / (R860R1240)

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Other Vegetation Indices NBR

• Normalized Burn Ratio (NBR) contrasts NIR(TM4)
  which decreased after fire and MIR(TM7) which
  increased after fire
• NBR (TM4 TM7) / (TM4 TM7)
• Differencing of pre- vs. post-fire NBR images has
  been found to be an effective measure of burn
  severity

For more info http//nrmsc.usgs.gov/research/nd
br.htm
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Subpixel Analysis Unmixing mixed pixels
Spectral endmembers signature of pureland
cover class
endmember1
Unknown pixel represents some proportion of
endmembers based on a linear weighting of
spectral distance. For example 60 endmember
2 20 endmember 1 20 endmember 1
Band j
unknown pixel
endmember3
endmember2
Band i
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Subpixel Analysis Unmixing mixed pixels
Linear mixture modeling assumes that a pixels
spectral signature is the results of a linear
mixture of the spectra from the component
classes. X Mf e where X mixed pixel
spectral signature M n x c matrix of endmember
spectra f c x 1 vector of land cover class
proportions N number of bands C number of
classes, c must be lt n 1 E noise term
Simple least-squares approach can then be used to
unmix and calculate the proportions of land
cover in each pixel.
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Sub-pixel analysis linear mixture modeling
Least squares approach selecting f that
minimizes the following (x Mf)T(x-Mf) Can be
unconstrained or constrained such that
0 lt f lt 1 With no constraints can be simplified

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Subpixel analysis of urban land cover
Landsat TM pixel boundaries on IKONOS image
backdrop
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Subpixel analysis of urban land cover
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Sub-pixel analysis of urban land cover
Study Area 1 Study Area 2
IKONOS for reference
Landsat TM output Blue IS Green lawn Red
tree