This part of the course presents the specificities of forests in SAR images. Polarimetric and interferometric images of forests are explained.

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FOREST IN SAR IMAGES
A small introduction to forest in remote
sensing by radar
Elise Koeniguer
Elise.koeniguer@onera.fr

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Biomass estimation
Forest studies:
• Civil applications
• Defense applications
Detection of vehicles behind
forest
Applications

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C Thiel et al. Forestry 2006;79:589-597
Earth specific sites for BIOMASS mission preparation

4. System Parameters (Sensor)
Wavelength/Frequency (X, C, L, and P bands)
Polarization (HH, VV, and HV)
Incidence angle
Resolution
Pixel size ( different from resolution !)
Target Parameters (Ground)
Structure (size, orientation, and distribution of scattering surfaces)
Surface roughness (relative to wavelength)
Dielectric constant (moisture content)
Slope angle/orientation
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main parameters

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Radar bands
Low frequency –
P-band
resolution cell
structure and size ⇔ wavelength λ
The longer the wavelength, the greater the
sensitivity to the vertical structure of vegetation

6. 6
one antenna
Measurement:
One complex
Coefficientantenna Image 1
i
j
1
S
Aim : 2 D imaging
Only absolute value
is used

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Amazon deforestation in 10 years as determined using L-band SAR data.
Left: Image of Amazon forest area acquired by JERS-1/SAR in 1996.
Right: Image of same area acquired by ALOS/PALSAR in 2006.
How does forest look like in a SAR image
Forest: high signal

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𝐸 𝑟
ℎ
𝐸 𝑟
𝑣
=
𝐽ℎℎ 𝐽ℎ𝑣
𝐽 𝑣ℎ 𝐽 𝑣𝑣
𝐸ℎ
𝑖
𝐸 𝑣
𝑖
Polarimetric transformation
Polarization ellipse
Polarimetry is sensible to:
• Geometrical information (roughness, branches orientations
• dielectric information (material, wet conditions)
A polarimetric radar image
Radar targets
Determinist / Non-determinist (Number of element per resolution
cell, “random” position)

9. 9







vvvh
hvhh
ss
ss
S
H
V











vv
hv
hh
L
s
s
s
k 2
analysis of the polarimetric properties of electromagnetic
waves and the scatterers of these waves
the complete scattering
properties of a radar scatterer
can be determined
Animals make use of their
polarization-sensitive visual
systems
Degree of polarization: help for navigation
Detection of surfaces: water is an horizontal polarizer
Contrast enhancement: between any object and its
surroundings.
Polarimetry
Image 1
i
j

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Usual representation of a polarimetric image
The Pauli basis
2S
2
1
hV












 vVhH
vVhH
SS
SS
k











0
0
1
k








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0
1
0
k











1
0
0
k

11. 0 230
t/ha
Previous onera campaign: Guyana

12. 12
Previous onera campaign: Nezer

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Monostatic depolarization
for Nezer forest (Landes France)
Forest radar UHF:
Depolarization/entropy presents good contrast over forest stands
© google map
Second order polarimetric parameters for forest SAR images
Example of second order polarimetric parameter image
 
kkT






vvvh
hvhh
ss
ss
S
2S
2
1
hV












 vVhH
vVhH
SS
SS
k

14. • As soon as three different parameters are available, they can be used
for a colored representation
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Alternative representations of a polarimetric image
Example : Freeman decomposition

15. • What do we know about a tree / forest in a POLSAR image ?
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Polarimetric signal for the forest
Range axis
shadow
Strong focused double bounce
Oriented components

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Some real examples
- Where is the range axis ?
- Why the double bounce echo is not exactly red?

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Polarimetric signal for the forest

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Two antennas
Measurement:
two complex
coefficients
h
Antenna 2
Antenna 1
Image 1
i
j
Image 2
i
j
2
S
1
S
Aim : 3 D cartography
Interferometric image : InSAR

19. 19
H
h
iobs
iinc
Combination of two radar measurements of the same
point on the ground, from slightly different angles
differences in phase related
to (R1-R2)
to the altitude at each position



2
2
2
1
*
21
2,1



2
1

 are two stochastic
complex signals
Interferometric coherence:
(Schwarz inequality)10 2,1  
■ The height h of the pixel is deduced from φ=arg()
■ The coherence level is deduced from |  |
Interferometry

φ
||

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Interfermetric image for the forest
||
φ

21. • What do we know about a tree / forest in a InSAR image ?
• Coherence level
• Mainly temporal decorrelation
• Not so high for low frequencies
• More linked to change of meteo conditions than wind
• Scattering phase center:
• somewhere in the forest.
• Linked to attanuation and density
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Interfermetric signal for the forest

22. 22








 11
11
1
vvvh
hvhh
SS
SS
S 







 22
22
2
vvvh
hvhh
SS
SS
S















2212
1211
TT
TT
kkT
k
k
k †
†
2
1






2112
2222
1111
kkT
kkT
kkT







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




hv
vvhh
vvhh
P
s
ss
ss
k
1
11
11
1
2

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
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hv
vvhh
vvhh
P
s
ss
ss
k
2
22
22
2
2
Image 1 Image 2
i
j j
i
Polarimetric Interferometry : POLINSAR

23. 23
hH
vV
hV
Applications :
-coherence optimization for
the estimation of heights
- target analysis: how to get
the maximum information
- separation and
interpretation of different
heights


 


2211
12
)(
TT
T



22221111
2121
21 ),(


 


TT
T









2112
2222
1111
kkT
kkT
kkT
Coherence
matrices
3 x 3
• generalized coherence
POLINSAR – main parameters

24. • What do we know about a tree / forest in a POLInSAR image ?
• Coherence optimization
• Mainly temporal decorrelation
• Not so high for low frequencies
• More linked to change of meteo conditions than wind
• Scattering phase center:
• somewhere in the forest.
• Linked to attanuation and density
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POLINSAR signal for the forest

25. 25
Existing Models:
 analytical models : Random Volume Over Ground model and inversion
[Treuhaft et al., 1996] Radio Science
 exact models
 descriptive models ex COSMO
Problems:
 The Random Volume Over Ground Model description is not suitable at P-
band or X-band
 Exact models : time consuming
 Descriptive modeling : very high numbers of entries for inversion
Modelling forest in POLINSAR

26. 26
Vh
0i
e
veg
i
e 0
inversion
 ,, 0Vh
coherence
set predicted
by the model
0

The classical RVoG model : Random Volume over Ground

27. 27
● attenuation: bad precision of the inversion for high frequencies (>C-band)
● importance of the precision on the estimation of the ground phase
● if differences between polarizations are too small, the linear regression is badly conditioned
)(
opt
),( 21 
opt
● the accuracy depends on the number of
samples used to compute the coherence.
● coherence optimization has to be used
carefully.
Limitations of RvoG

28. 28
Vh
Litteral model (RVoG) descriptive model (Ex:COSMO)
very simple
more realistic
(cylinders at different
position,orientations)
Inversion
 ,, 0Vh
Analysis of
forest trends
not possible, or at least
numerical and very difficult
not possible
possible:
outputs by mechanism
outputs by scatterers
possible
litteral, POL, IN numerical, POL, INoutputs
description
[Treuhaft et al., 1996] Radio Science [Thirion, 2003] phD Thesis
Comparative analysis between two kinds of models

29. 29
Litteral model (RVoG) descriptive model (Ex:COSMO)
Advantages
Drawbacks
attenuation expressed in dB/m is
overestimated by a factor of 2.
bad precision of the inversion for the
attenuation
not adapted to high densities.
description too simple for certain forest,
at certain frequencies
validated for several forest configurationssimple enough for inversion
does not enable the time-frequency
analysis
not adapted to high densities.
too many inputs for inversion
does not take into account group
effects
Comparative analysis of two kinds of models

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Further : tomography