Vegetation

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Vegetation
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Mayer "Nature has put itself the problem of how
to catch in flight light streaming to the Earth
and to store the most elusive of all powers in
rigid form. The plants take in one form of power,
light and produce another power, chemical
difference."
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The Leaf
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Chloroplasts
sieve effect
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Chlorophyll
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Carotenes
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Light Harvesting Complex II

• Chlorophyll A green Chlorophyll B orange
  Carotene red Structural proteins yellow

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Antennae Complex
Wavelength of max absorption
Energy Gradient
light
shorter
High
Carotenoids
?
Chlorophyll b
650 nm
670 nm
Chlorophyll a
longer
low
P 680 Chl a
Reaction Center
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Photosynthesis
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Photosynthesis
The Cornerstone of Life on this planet!
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Vegetation Spectra
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Pigment Absorption
Chlorophyll
b
Absorption Spectra
of Chlorophyll
a
and
b
Chlorophyll
a
Absorption Efficiency
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
a.
v
iolet
b
lue
g
reen
y
ellow
red
Wavelength,
m
m
Phycocyanin
Phycoerythrin
Absorption Efficiency
b
-carotene
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
v
iolet
b
lue
g
reen
y
ellow
red
b.
Wavelength,
m
m
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Cell Wall Constituents
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Protein Constituents
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Cellular Water
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Leaf Biochemistry

• Leaf biochemistry
• pigments chlorophyll a and b, a-carotene, and
  xanthophyll
• absorb in blue ( red for chlorophyll)
• Absorbed radiation converted into
• heat energy, flourescence, or carbohydrates
  through photosynthesis

Stored carbon!
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Chlorophyll and Carbon Assimilation
The big picture
Potential Carbon Assimilation
This is important
Amount of Chlorophyll
(Concentration x phytomass)
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Chlorophyll Concentration
Reduced absorption due to decreasing chlorophyll
concentration
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Chlorophyll Concentrations
Red or blue wavlength radiance,reflectance
chlorophyll concentration
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What about estimating Phytomass?
First lets define phytomass leaf area x leaf
mass per unit area (m2 x kg/m2 kg) Then lets
introduce some surrogates for phytomass Leaf
area Index (LAI)- one sided leaf area per unit
ground area Leaf area density (LAD) leaf area
per unit volume
Why use area instead of mass? Because estimating
the leaf mass per unit area using remote sensing
is very difficult. (How thick and heavy are the
leaves?) Estimating area is more straight forward
(remember areal mixtures)
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Estimating Phytomass Additive Reflectance
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Leaf Area Estimation
Most leaf area
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Vegetation Indices
Both red and NIR bands carry complementary
information. How can we use that info and reduce
noise introduced by other sources?
NIR 75 Red 32 NIR/R 2.34
NIR 119 Red 49 NIR/R 2.38
Shaded area
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Vegetation Indices

• Vegetation indices (VI) are combinations of
  spectral measurements in different wavelengths as
  recorded by a radiometric sensor. They aid in the
  analysis of multispectral image information by
  shrinking multidimensional data into a single
  value. Huete (1994) defined vegetation indices
  as
• dimensonless, radiometric measures usually
  involving a ratio and/or linear combination of
  the red and near-infrared (NIR) portions of the
  spectrum. VI s may be computed from digital
  counts, at satellite radiances, apparent
  reflectances, land-leaving radiances, or surface
  reflectances and require no additional ancillary
  information other than the measurements
  themselvesWhat VI s specifically measure remains
  unclear. They serve as indicators of relative
  growth and/or vigor of green vegetation, and are
  diagnostic of various biophysical vegetation
  parameters.

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Vegetation Indices

• Vegetation indices (VIs) can be broken up into
  two basic categories
• Ratio based indices VIs based on the ratio of
  two or more radiance, reflectance, or DN values
  (or linear combinations thereof).
• Difference indices VIs based on the
  difference between the spectral response of
  vegetation and the soil background.

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Common Ratio Indices
Simple Ratio Index (SR) NIR/R
Normalized Difference Vegetation Index (NDVI)
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Common Difference Indices
Perpendicular Vegetation Index (PVI) for a single
soil background
Where Rsoil and NIRsoil are the red and NIR
reflecatance/radiance for the soil background.
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Common Difference Indices (continued)
Or the PVI for a multiple soil background
Where a and b are the slope and intercept
respectively of the universal soil line for the
area
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Typical Vegetation Index Response
But what about other objects within the field of
view (FOV) of the sensor other than vegetation?
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Leaf area Density
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Composite Canopy Reflectance
100 veg. Cover 1 leaf layer LAI 1
0 veg. Cover LAI 0
1 m2 of leaf area
pixel
50 veg. Cover 2 leaf layers LAI 1
33 veg. cover 3 leaf layers LAI 1
Are the reflectances for these 3 pixels the same?
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Composite Canopy Reflectance
This region of the curve is dominated by a change
in percent vegetation cover
100 vegetation cover
In this region, there is complete vegetion cover
and differences are due to increasing canopy
density-Additive Reflectance (multiple scattering)
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Determining Vine Canopy Density in a Wine Grape
Vineyard A Case Study

• Can Vine Canopy Density Be Remotely Determined?

• Single vine scale canopy density manipulation
  experiment field spectroscopy techniques.

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Case Study 1 Field Spectroscopy
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Case Study 2 Canopy Density Manipulation
Experiment

• 6 canopy density treatments
• 4 single vine replications
• 2 spectrometer configurations

Spectrometer configuration above the target
canopy (shaded area). Circles represent the field
of view (FOV) of the optic at each configuration.
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Case Study 3 Canopy Thinning
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Case Study 4 Composite Canopy Spectra by Density
Class
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Case Study 5 Red and NIR Reflectance by Canopy
Density
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Case Study 6 Ratio Vegetation Index by Canopy
Density
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Leaf area Density
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Case Study 9 Shadow Spectra by Density Class
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Case Study 10 Shadow Red and NIR reflectance by
Canopy Density
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Case Study 11 Shadow SR by Canopy Density
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Case Study 11 Can Vine Canopy Density Be
Remotely Determined?
Yes but. (the plot thickens)
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Case Study 12 How do the scene components change
over time?
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Case Study 13 Due to the discontinuous nature of
grapevine canopies, scene reflectance is dynamic!
It changes over the course of a day.

• It is dependent on
• Vine canopy characteristics
• Solar geometry (position of the sun)
• row orientation
• Field of view of the sensor (image resolution)

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Case Study 14 How variable is the spectral
signature?
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Case Study 15What are the limitations of using
this?
Question Why is the reflectance and VI values
varying over the course of the Day? Answer
Because the suns position is changing resulting
in different proportions of shadow and sunlit
soil within the FOV of the sensor.
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Case Study 16
X (sin ?- ?) (tan ? h)w
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Case Study 17Geometric Optical Model
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Case Study 18
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Case Study 19
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Bi-directional Reflectance Distribution Function
(BRDF)
Review These Equations assume Isotropic incident
and isotropic reflected radiation
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Bi-directional Reflectance Distribution Function
(BRDF)
Light reflecting off of a surface is rarely
isotropic. Most surfaces exhibit anisotropic
reflectance (reflectance amount varies with
direction).
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BRDF
Li(?i,?i,?)
Lr(?i,?i , ? ?r,?r)
BRDF Lr(?i,?, ? ?r,?r)/ Li(?i,?i, ?)
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BRDF
sensor
Forward scatter direction
Back scatter direction
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BRDF
Backscatter direction
Forward scatter direction
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BRDF
Backscatter direction
Forward scatter direction
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BRDF
Backscatter direction
Forward scatter direction
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BRDF a Goniometer
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BRDF
hot spot
Backscatter direction
Forward scatter direction
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BRDF
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