Transcript Measuring Vegetation (NDVI, EVI, and Ocean Color

Land Color
May 2, 1996
North of Denver, CO
August 16, 1995
Central Brazil
violet -
blue - green-yellow-orange
- red -
near IR
Measuring Vegetation
•By carefully measuring the wavelengths and intensity of visible and
near-infrared light reflected by the land surface back up into space a
"Vegetation Index" may be formulated to quantify the concentrations
of green leaf vegetation around the globe.
Normalized Difference Vegetation Index (NDVI)
•Distinct colors (wavelengths) of visible and near-infrared sunlight
reflected by the plants determine the density of green on a patch of
land and ocean.
•The pigment in plant leaves, chlorophyll, strongly absorbs visible light
(from 0.4-0.5 and from to 0.6-0.7 μm) for use in photosynthesis.
The cell structure of the leaves, on the other hand, strongly reflects
near-infrared light (from 0.7 to 1.1 μm).
•The more leaves a plant has or the more phytoplankton there is in the
column, the more these wavelengths of light are affected, respectively.
What colors do we need to observe?
Ocean
Plants
Soils
Attenuation in the Visible Wavelengths
Blue light scattered
Grant Petty, 2004
Daytime Visibility
Distant Dark Objects
Appear Brighter
“Clear” Day
Hazy Day
Daytime Visibility
consider scattering by aerosols
White Sunlight
Top of Atmosphere
Color and Intensity
Distance to the Dark Object
Daytime Visibility
White Sunlight
Top of Atmosphere
Increased contribution of
white light
Object appears lighter
with distance
Longer Distance to the Dark Object
Daytime Visibility
Distant Dark Objects
Appear Brighter
“Clear” Day
Hazy Day
What the satellite sees
White Sunlight
Top of Atmosphere
molecular and aerosol
scattering 400→ 500 nm
near IR
transparent
plants 500-600 nm
ocean water 450-480 nm
Atmospheric Aerosol Correction Procedure
Cloudy
Upwelling
Radiance
at Satellite
due to
molecular
and aerosol
scattering
Cloudless-Polluted
Blue
Green
Red
Near-IR
Atmospheric Aerosol Correction Procedure
Cloudy
Upwelling
Radiance
at Satellite
due to
molecular
and aerosol
scattering
More Polluted
Blue
Green
Red
Near-IR
Angstrom Exponent
 A (1 )  1 
 
 A (2 )  2 

 A (765 nm)  765 nm 

 A (865 nm )  865 nm 

 A (865 nm )
ln
 A (765 nm)
 765 nm 
 865 nm 



  0 clouds
  increases with increases in aerosol load
Sky Imaging
500 nm
AMF
RV Ron Brown
Central Pacific
Sea of Japan
Niamey, Niger
AOT=0.08
AOT=0.98
AOT=2.5-3
Miller, Bartholomew, Reynolds
Atmospheric Correction Methods
• Develop Theoretical Atmosphere including:
• Rayleigh Scattering - (Strongest in Blue region)
• Ozone
• Aerosols - (Absorption and Scattering Characteristics)
• Use Data from Infrared (IR) band and assume
that all of this signal comes from the
atmosphere to get knowledge of aerosols.
• Solve Radiative Transfer Equation
• Geometry
• Location (types of aerosols possible)
NDVI
• NDVI is calculated from the visible and nearinfrared light reflected by vegetation.
• Healthy vegetation (left) absorbs most of the
visible light that hits it, and reflects a large
portion of the near-infrared light.
• Unhealthy or sparse vegetation reflects more
visible light and less near-infrared light.
• Real vegetation is highly variable.
NDVI
NDVI = (NIR — VIS)/(NIR + VIS)
Calculations of NDVI for a given
pixel always result in a number
that ranges from minus one (-1) to
plus one (+1)
--no green leaves gives a value
close to zero.
--zero means no vegetation
--close to +1 (0.8 - 0.9) indicates
the highest possible density of
green leaves.
NASA Earth Observatory (Illustration by Robert Simmon)
Satellite
NDVI
data
sources
NOAA 7
AVHRR
NOAA 9
AVHRR
NOAA-16
NOAA 14
MODISes
AVHRR
NOAA 11
AVHRR
SPOT
C. Tucker
NOAA-18
SeaWiFS
NOAA 9
1980
NPP
1985
1990
1995
NOAA-17
2000
2005
2010
• In December 1999, NASA launched the Terra
spacecraft, the flagship in the agency’s Earth
Observing System (EOS) program. Aboard Terra
flies a sensor called the Moderate-resolution
Imaging Spectroradiometer, or MODIS, that
greatly improves scientists’ ability to measure
plant growth on a global scale. Briefly, MODIS
provides much higher spatial resolution (up to
250-meter resolution), while also matching
AVHRR’s almost-daily global cover and exceeding
its spectral resolution.
Average NDVI 1981-2006
~40,000 orbits of
satellite data
NDVI = (ir- red)
(ir+red)
C. Tucker
Marked contrasts between the dry and
wet seasons
C. Tucker
(~300 mm/yr @ Senegal)
Beltsville USA winter wheat biomass
C. Tucker
S NDVI
vs. total dry biomass
Explained 80% of
biomass
accumulation
C. Tucker
Species mapping with physiological
indices
Meg Andrew
Spectral Indices: NDVI
NDVI 
RNIR  Rred
RNIR  Rred
Creosote
Ag
NDVI = 0.922
NDVI = 0.356
Meg Andrew, UC Davis
Global Vegetation Mapping
SeaWiFS Ocean Chlorophyll Land
NDVI
Ocean Color
• Locates and enables monitoring of regions of
high and low bio-activity.
– Food (phytoplankton associated with chlorophyll)
– Climate (phytoplankton possible CO2 sink)
• Reveals ocean current structure and behavior.
– Seasonal influences
– River and Estuary influences
– Boundary currents
• Reveals Anthropogenic influences (pollution)
• Remote sensing reveals large and small scale
structures that are very difficult to observe
from the surface.
5 SeaWiFS land bands
a) The light path of the water-leaving radiance. b) Shows the attenuation of the water-leaving radiance. c)
Scattering of the water-leaving radiance out of the sensor's FOV. d) Sun glint (reflection from the water
surface). e) Sky glint (scattered light reflecting from the surface). f) Scattering of reflected light out of the
sensor's FOV. g) Reflected light is also attenuated towards the sensor. h) Scattered light from the sun which is
directed toward the sensor. i) Light which has already been scattered by the atmosphere which is then
scattered toward the sensor. j) Water-leaving radiance originating out of the sensor FOV, but scattered
toward the sensor. k) Surface reflection out of the sensor FOV which is then scattered toward the sensor. Lw
Total water-leaving radiance. Lr Radiance above the sea surface due to all surface reflection effects within
the IFOV. Lp Atmospheric path radiance. (Gordan and Wang)
Tasmanian Sea
A break in the clouds over the Barents Sea on August 1, 2007 revealed a large, dense
phytoplankton bloom to the orbiting MODIS aboard the Terra satellite. The bright
aquamarine hues suggest that this is likely a coccolithophore bloom. The visible portion of
this bloom covers about 150,000 square kilometers (57,000 square miles) or roughly the
area of Wisconsin.
Supplements
Nighttime Visibility
Distant Bright
Objects
are dimmer
Attenuation in the Visible Wavelengths
Grant Petty, 2004
Aerosol Hygroscopic Growth
• Deliquescence
– Dry crystal to solution
droplet
• Hygroscopic
– Water-attracting
• Efflorescence
– Solution droplet to
crystal (requires
‘nucleation’)
• Hysteresis
– Particle size and
phase depends on
humidity history
ENVI-1200 Atmospheric Physics
Atmospheric Correction Methods
• Develop Theoretical Atmosphere. Include:
• Rayleigh Scattering - (Strongest in Blue region)
• Ozone
• Aerosols - (Absorption and Scattering Characteristics)
• Use Data from Infrared (IR) band and assume that all of this signal
comes from the atmosphere to get knowledge of aerosols.
• Solve Radiative Transfer Equation
• Geometry
• Location (types of aerosols possible)
• Other considerations:
– Sun Glint. Avoid - Use wind speed to estimate surface roughness.
– White Caps. Measure - Use wind speed to estimate coverage.
Atmospheric Aerosol Correction
Procedure
Cloudy
Clear H2O
Upwelling
Radiance
At Satellite
Cloudless-Polluted
Biological
Blue
Green
Red
Near-IR
History of the NDVI
& Vegetation Indices
Compton Tucker
NASA/UMD/CCSPO
Vegetation Indices from Susan Ustin
Index
Simple Ratio
Normalized
Difference
Vegetation Index
Formula
Details
R NIR
RR
Green vegetation cover.
Various wavelengths,
depending on sensor. (e.g.
NIR = 845nm, R=665nm)
Pearson, 1972
RNIR  RR
RNIR  RR
Green vegetation cover.
Various wavelengths,
depending on sensor. (e.g.
NIR = 845nm, R=665nm)
Tucker 1979
C1 =6; C2=7; L=1; G=2,5
Enhanced
Vegetation Index
Perpendicular
Vegetation Index
Soil Adjusted
Vegetation Index
Modif ied Soil
Adjusted
Vegetation Index
Transformed Soil
Adjusted
Vegetation Index
Soil and
Atmospherically
Resistant
Vegetation Index
C. Tucker
Citation
Hu ete 1997
Rs Rv2  (NIRs NIRv)2
NIR R
1 L
NIR R  L
a NIR  aR  b 
R  a ( NIR  b)  0.08(1  a 2 )
NIR R


2.5  
1 NIR 6R  7.B
Perpendicular distance from
the pixels to the soil line.
L = soil adjusted factor
L = (1-2a x(NIR-aR) x NDVI )
Self ad justing L:f on to
optimize for soil effects.
Higher dy namic range.
Richardson
and Wiegand
1977
Hu ete 1988
Qi et al 1994
a=slope of soil line
b=intercept of soil line
Baret and
Gu yot 1991
More independent of surface
brightness
Hu ete et al
1997
Winter wheat biomass “harvest”
C. Tucker
This figure shows four typically observed wavelength bands and the water leaving
radiance in high (dotted) and low (solid) chlorophyll waters without the atmospheric
signal (lower curves) and with the atmospheric signal (upper curves). The satellite
observes the water leaving radiance with the signal due to the atmosphere (upper
curves). [Gordon and Wang]