CONTINUATION PROPOSAL OF NASA GRANT NAGW4555
DIGITAL TOPOGRAPHY FROM SAR
INTERFEROMETRY: DETERMINATION OF AND
CORRECTION FOR VEGETATION HEIGHT
Submitted to:
Code YS/ NRA97MTPE08
Office of Mission to Planet Earth
NASA Headquarters
300 E. Street SW
Washington D.C. 20546
Attention: Dr. Diane Wickland
Submitted by:
Kamal Sarabandi (PI),
Craig Dobson (CoI),
Radiation Laboratory
Department of Electrical Engineering and Computer Science
The University of Michigan
Ann Arbor, Ml 481092122
Tel: (313) 7640500, Fax: (313) 7472106
Robert Treuhaft and Jakob J. van Zyl (CoIs)
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, CA 91109
and
David Harding (Co1)
NASA Goddard Space Flight Center
Geodynamics Branch
Mail Code 921
Greenbelt, MD 20720
Project Duration: March 15, 1998 to March 14, 2001
332731T = RL2458
1
DIGITAL TOPOGRAPHY FROM SAR
INTERFEROMETRY: DETERMINATION OF AND
CORRECTION FOR VEGETATION HEIGHT
Abstract
In this proposed investigation theoretical and experimental studies will be carried out to demonstrate the potential of SAR interferometry and polarimetry in determining the spatial organization
and retrieving the physical parameters of vegetation canopies. During Phase I of this investigation
(March 1995 present), we have focused our efforts on the development of basic understanding
of the problem which includes: 1) development of simple theoretical models capable of relating
vegetation parameters to the interferogram phase and correlation coefficient, 2) conducting field
experiments using JPL TOPSAR over more than 30 forest stands (physical parameters of these
stands and their ground surface topography are measured very accurately), 3) establishing a fundamental relationship between spatial and frequency interferometry (relationship between INSAR
and Akeradar) which is of great importance in characterizing the scattering phase center using
numerical simulations or conducting experiments using wideband scatterometers, 4) development
and verification of a high fidelity coherent scattering model capable of predicting the interferometric and polarimetric responses of tree canopies over a wide frequency range (P to Xband).
Simulation and experimental results show that the location of scattering phase center (canopy
height measured by an INSAR) is a strong function of tree type and its structure. Extremely
encouraged by the outcome of our research activities over the past two years, we propose to
extend the goal of this study by incorporating radar polarimetry and radar interferometry and/or
multifrequency radar interferometry data to extract important structural and physical parameters of forest canopies. The proposed research plan for Phase II consists of three major activities.
The first activity pertains to the development and validation of semiempirical models for tree
structure of interests derived from the Monte Carlo coherent scattering model. These models are
amenable to inversion processes which require efficient calculation of backscattering coefficients
and the scattering phase center height. Validation will be done using existing TOSAR, AIRSAR,
and SIRC data over our two wellcharacterized sites: the Raco Supersite and the NSF Long
Term Ecological Research Site at the Kellogg Biological Station near Kalamazoo, Michigan. The
second activity involves the development of a general inversion algorithm based on a Genetic
Algorithm (a stochastic optimization technique) for estimation of canopy parameters from an
arbitrary set of polarimetric and interferometric data. This algorithm is specifically useful for the
problem at hand as it searches for the global minimum and provides a set of optimum solutions.
The third activity is to implement the forest stands of BOREAS sites for which extensive groundtruth data and polarimetric and interferometric SAR data exist. In this effort we are also planning
to incorporate the laser altimeter canopy height data taken by SLICER. Activities related to lidar
data fusion is very much in concert with the upcoming Vegetation Canopy Lidar (VCL) mission.
2
Contents
1 Background and Objectives 1
2 Summary of PhaseI Accomplishments 2
2.1 Theoretical Model Development...................................... 2
2.1.1 Ak Radar Equivalence of an INSAR.............................. 2
2.1.2 Statistical Analysis................................... 3
2.1.3 Vegetation M odel............................. 3
2.2 Development of a Monte Carlo Coherent Scattering Model for Tree Canopies
Based on Fractal Theory.............................. 6
2.3 Experimental Activities............................... 8
3 PROPOSED PLAN 11
3.1 Enhancement of The Coherent Monte Carlo Scattering Model................... 11
3.2 INSAR Response To Short vegetation....................... 11
3.3 Investigation on the Utility of MultiPolarization INSAR............. 13
3.4 Inversion Algorithm Based on Multiincidence Angle and/or Multifrequency SAR/INSAR 15
3.5 Investigation on the Utility of MultiBaseline INSAR............... 16
3.6 INSAR Lidar Data Fusion............................. 17
4 Management and Cost Plan 20
4.1 Schedule........................................ 20
4.2 Person nel...................................... 20
4.3 Facilities and Required Resources......................... 21
5 Budget 21
3
1 Background and Objectives
Accurate estimation of gross forest parameters such as total vegetation biomass, total leaf area
index, and tree height in global scale has long been an important goal within the remote sensing
community. Over the past two decades much efforts have been devoted to the development
of scattering models [1, 2, 3], for understanding of interaction of electromagnetic waves with
vegetation, and to the construction and development of advanced imaging radars for acquiring
test data and and examining the feasibility of the remote sensing problem [4]. In most practical
situations the number of vegetation parameters influencing the radar response usually exceeds
the number of radar observation parameters. For this reason the application of multifrequency
and multipolarization radar systems was proposed and such system was flown aboard the Shuttle
Endeavor in April and October 1994 [4]. Preliminary results indicate that the classification and
retrieval of vegetation biophysical parameters indeed require many simultaneous radar channels,
however, freeflight of such systems is not practical due to the exorbitant power requirements.
Characterization of the spatial organization of particles in a vegetation canopy is of great
importance determining many ecosystem processes including energy and chemical exchanges.
Traditional remote sensing instruments provide twodimensional spatial information of the target
which may contain, depending on the instrument, some information vertical particle arrangement in a convoluted fashion. Recent advancements in the field of radar interferometry have
opened a new door to the radar remote sensing of vegetation. In addition to the backscattering
coefficient of a distributed target, radar. interferometers provide two additional parameters that
contain information about the target. These parameters are the correlation coefficient and the
interferogram phase [5, 6]. To interpret these parameters and to characterize their dependency
to the physical parameters of the target, a thorough understanding of coherent interaction of
electromagnetic waves with vegetation particles is required. The premise of this investigation
with regard to retrieving vegetation parameters from INSAR data stems from the fact that the
location of scattering phase center of a target is a strong function of the target structure. For
example the scattering phase centers of nonvegetated terrain are located at or slightly below
the surface depending upon the wavelength and the dielectric properties of the surface media.
Whereas for vegetated terrain, these scattering phase centers lie at or above the surface depending upon the wavelength of the SAR and the vegetation attributes. It also must be recognized
that the vegetation cover in many interferometric SAR applications where the vegetation itself
is not the primary target, such as geological field mapping or surface change monitoring, acts
as an interference. In these cases it is also important to identify and characterize the effect of
vegetation on the topographic information obtained from the interferometric SAR.
The overall objectives of the proposed study are to:
1. Quantify the role of vegetation attributes in determining the location of the scattering
phase centers as measured by SAR interferometry using theoretical and Monte Carlo based
coherent electromagnetic scattering model for vegetation.
2. Examine the utility of the combination of SAR interferometry and polarimetry for estimating
the vegetation and surfaces parameters.
3. Determine the significance of polarimetric SAR interferometry, using the Monte Carlo model
and existing SIRC repeatpass data.
1
4. Map vegetation height and crown layer vegetation attributes, including vegetation structure,
through the combined use of multiincidence angle and/or multifrequency SAR interferometry in conjunction with available radar backscatter coefficients.
5. Correct SAR interferometry for vegetation effects through use of an inversion algorithm
based upon vegetation type and biomass. The end product is surface elevation.
6. Examine the application of ancillary data such as canopy laser altimeter for enhancement
and/or for verification of vegetation parameter estimation.
7. Integrate the products derived from SAR interferometry into ecophysiological classifications
and forest biophysical parameter estimations.
This study proposes to meet these objectives using a methodology that treats the problem
both theoretically and experimentally. Monte Carlo simulations of the forward problem, that
includes multiple scattering between vegetation elements up to second order, will be used to
understand the roles of both sensor parameters (wavelength, polarization and angle of incidence)
and vegetation attributes (type, quantity and dielectric properties) in determining the location
of the scattering phase centers. Experimental efforts will be mounted at three wellcharacterized
sites: the Raco Supersite used by SIRC/XSAR, the NSF Long Term Ecological Research Site
at the Kellogg Biological Station near Kalamazoo, Michigan, and BOREAS sites. These sites
represent a wide range of vegetation conditions. The Raco and BOREAS sites are largely forested
and KBS is mostly agricultural. These studies utilize TOPSAR, AIRSAR, SIRC, and SLICER
data both to verify theoretical efforts and to provide for application development and testing.
2 Summary of PhaseI Accomplishments
In March, 1995, the University of Michigan, in collaboration with the Radar Science Group of
Jet Propulsion Laboratory, was awarded a threeyear grant by the Terrestrial Ecology Program at
NASA Headquarters to characterize and quantify the role of vegetation attributes in determining
the scattering phase centers as observed by interferometric SARs. For this purpose analytical,
numerical, and experimental aspects of electromagnetic scattering from forest canopies have been
under investigation over the past two years. We shall refer to this segment of the overall program
as Phase I and to the proposed contribution as Phase II. A summary of accomplishments realized
to date during phase I is given next.
2.1 Theoretical Model Development
2.1.1 Sk Radar Equivalence of an INSAR
A fundamental relationship between INSAR and Ak radar is established. This relationship is
the cornerstone of analytical and numerical analysis of the problem at hand. Understanding
the relationship between the tree height and the corresponding location of the scattering phase
centers requires numerical simulations (Monte Carlo simulation of a fractal generated forest
stand) or controlled experiments using scatterometers. The scattering phase center of a target
can also be obtained using a SAkradar assuming that the incidence angle is known. Evaluation
2
of the scattering phase centers using frequency shift can easily be accomplished in a numerical
simulation or in a controlled experiment using a wideband scatterometer. Basically by requiring
the backscatter phase differences, once obtained from a small change in the aspect angle and
the other one obtained from a small change in the frequency of operation, be identical for both
approaches we established that
B
Af = o sin(Oo) (1)
where Af is the frequency shift of the equivalent Deltak radar, fo is the operating frequency,
B and 0o are, respectively, the baseline distance and angle, r is the slant range, and 0 is the
look angle. It is mathematically proven that this equivalence relationship is valid for multiple
scattering among particles and the scattering interaction between particles and the ground plane.
The details are reported in reference [7].
2.1.2 Statistical Analysis
In estimating the height of the scattering phase center of a distributed targets, random fluctuations
of the calculated/measured phase due to fading was investigated. An analytical form for the p.d.f.
of the interferogram phase was obtained in terms of two independent parameters: (1)Q: mean
phase and (2)a: degree of correlation, which is given by
12
()  27r[la2cos2(o)]
{+  tan acos I (2)
[ 2 l 2cos2(o, 2 21 acos2( C)J J
C is proportional to the mean scattering phase center height and a is inversely proportional to the
uncertainty with which t can be estimated. It is shown that a is directly related to the frequency
correlation function (FCF) of the distributed target given by
I < El E* >I
~ <.> = (3)
Using this pdf the uncertainty in estimation of (, or equivalently the mean height, from a single
pixel can be evaluated. Figure 1 shows the phase uncertainty range for 80% and 90% confidence
criteria [7]. Statistical analysis shows that the uncertainty in the height estimation of a distributed
target is a function of equivalent frequency decorrelation bandwidth and is independent of the
baseline distance.
2.1.3 Vegetation Model
Theoretical vegetation models capable of predicting backscattering coefficients and location of
scattering phase center for simple canopy structures (homogeneous particle distribution) were
developed [7, 10]. It is also shown that for a uniform closed canopy the extinction and the
physical height of the canopy top can be estimated provided that the correlation coefficient (a)
can be measured very accurately. For example for a dense canopy it is found that the extinction
3
40.
30.   
20.
80%
 90% ^  \ *
0.
0.990 0.992 0.994 0.996 0.998 1.000
a
Figure 1: The phase uncertainty for 80% and 90% error probability criteria as a function of a.
coefficient can be directly obtained from a. Also the location of scattering phase center (from
the canopy top) is given by the following simple relationship:
Ad = cos 0 (4)
z^. i (4)
However, for finite canopies, estimation of extinction and scattering phase center is not straitforward. Using the model developed in [10], the estimation of tree height and surface topography
was attempted. It was shown that measurements of interferometric phase and amplitude were
not enough to estimate the three relevant parameters, which are the tree height, groundsurface
altitude, and extinction coefficient, if only volume scattering (from the leafbranchtrunk canopy)
is considered. The first demonstration was therefore supplemented with in situ extinction coefficient measurements and the dualbaseline estimates were based on INSAR data alone [26].
The results of the dualbaseline demonstration are shown in Figures 2 and 3. Figure 2 shows
the tree heights derived from dualbaseline INSAR alone versus groundtruth tree height. While
there are some outliers, there is generally good agreement within the error bars. Figure 3 shows
the topographic altitudes derived from the INSAR phase in the absence of the modeling which
produced Fig. 2. The actual topography of the region has been largely removed, so the trend
with tree height should be flat. Because trees cause an error of the order of their heights, there
is an upward trend for the uncorrected altitudes as a function of tree height. When the altitudes
derived from phase are corrected by modeling the multibaseline data to determine tree height,
the scatter about zero (rms in the figure) drops from 12.6 m to 6.3 m. Thus the modeling
approach to multibaseline INSAR data has dramatically improved the accuracy of the surface
topographic measurement. For the singlebaseline demonstration in [10], slightly worse results
were achieved with biases in tree height at about the 5m level.
4
L _
Figure 2: Tree heights derived from dualbaseline INSAR alone versus groundtruth tree height.
Q)
(9:3
D
UC
u
3
a)
c'LJ
<r
L/3
— 7 
w s T 1 s s w s w  r T r T l W X W W l f v T T W T t s T
30 F
20
10
0
10
* Uncorrected for Vegetation
+ Corrected for Vegetation Using Model
rms uncorrected=12.6 m
rms corrected=6.3 m
(Actual surface topography=0; flat) *
+ (+
* +
+ +
+. +
i I I... I 1.,.
5
0
5 10 15
GroundTruth Tree Height (m)
20
25
Figure 3: The topographic altitudes derived from the INSAR phase.
5
2.2 Development of a Monte Carlo Coherent Scattering Model for Tree
Canopies Based on Fractal Theory
Although there are a number of EM scattering models for vegetation canopies [1, 2], they are of
little use with regard to INSAR applications due to the models inability to predict the absolute
phase of the scattered field. The absolute phase of the scattered field is the fundamental quantity
from which the interferogram images are constructed. As mentioned earlier in order to simulate
the response of an INSAR system a coherent scattering model capable of preserving the absolute
phase of the scattered field is needed. Traditional scattering models for forest canopy such as
radiative transfer and distorted Born approximation are incapable of providing the phase of the
backscatter and do not preserve the effect of coherence caused by the relative position of scatterers
within a tree. We have completed the task of developing a coherent scattering model for forest
canopies. This model is based on a Monte Carlo scattering simulation which preserves the exact
structure of desired trees [11, 12]. In this model first random generation of tree architectures is
implemented by employing the Lindenmayer systems (Lsystems). The Lsystems is a convenient
tool for creating fractal patterns of botanical structures. After generating a tree structure, the
electromagnetic scattering problem is then solved by invoking the single scattering theory. In
this solution scattering from individual tree components when illuminated by the mean field is
computed and then added coherently. This model was examined thoroughly and its validity
was tested using SIRC data. We used our test site (Hiawatha National Forest) in Michigan's
Upper peninsula for which we collected extensive groundtruth data during SIRC overflight.
Figure 4 shows a photo of a red maple stand, computer simulated tree structure of the same
stand, and the exact extinction profile derived from the Monte Carlo simulation. Figures 5a and
5b show the comparison between the model prediction and SIRC polarimetric backscattering
coefficients at L and Cband respectively. The three angular measurement points correspond
to three different orbits of the October 94 mission. To our knowledge this model is the most
accurate and sophisticated scattering model for forest canopies to date. The model preserves
the exact structure of the trees, it can simulate a forest over a hilly terrain, it can simulate both
coniferous and deciduous trees, it can also incorporate radially inhomogeneous dielectric profile
for branches and tree trunk. The details of this model is reported in [12].
We have also used the Monte Carlo coherent model in simulating the location of scattering
phase center of different forest stands. As mentioned in the summary of the theoretical activities,
the equivalence relationship can be invoked to find the location of the scattering phase center of
a tree. This is basically done by evaluating the backscatter from a forest stand at two slightly
different frequencies and calculating the phase difference of the backscattered. The difference in
frequency is directly proportional to the baseline distance and is also a function of the center
frequency and the incidence angle. In April 1995 JPL TOPSAR flew over one of'our test sites in
the Michigan's Upper peninsula. For this site extensive ground truth data for vegetation including
tree heights, type, number density, dielectric constant and for the ground surface including soil
moisture and surface elevation were collected. We have recently received the processed data
from JPL and were able to compare the result of our model with the actual measurement of
TOPSAR at Cband. Figure 6 shows a photo of a red pine stand, and a computer generated red
pine. Figures 7 and 8 respectively show the TOPSAR image of the test stand and the measured
(at two incidence angles) and estimated height of the scattering phase centers of this stand.
Finally Fig. 9 shows the measured and calculated backscattering coefficients. In Figs. 8 and 9
6
16
14
12
10.

V

la
U)
s..4
Q.
0. 0.1 I. 2 iJ.3
Extinction Coeff. ilNp/l;
(c) Extinction Profile
(a) Stand 31
(b) Fractal Tree
Figure 4: The generated
extinction profile (c).
fractal tree (b), based on the forest Stand 31 (a), and the calculated
Lband
m
o::
Ic3
0,
rU
5^
e:
Z;4..),,
'3
e
Cj
Z;
u^....
I^
ID
0
5 1
I I I I I 
hh
A   I
vh
vh 3 — X
0
10 
0
5
10
15
20
Cband
  vv
*
hh
Q Q
 vh. ._.. .... I...,....,. I..... 1......,....
15 
20 I
25......................
10 20 30 40 50
Incidence Angle (degrees)
(a)
70 10 20 30 40 50
Incidence Angle (degrees)
60 70
(b)
Figure 5: Comparison between the model predictions (lines) and SIRC data (symbols) at (a)
Lband and (b) Cband.
7
v4A,',
(a) Stand 22 (b) Fractal Tree
Figure 6: The red pine forest stand (a), the generated fractal tree (b).
excellent agreements between the measured and calculated results are shown. The details of this
simulation and some sensitivity analysis can be found in [13].
2.3 Experimental Activities
Our experimental activities so far have been focused over two wellcharacterized sites: 1) Hiawatha
National Forest (HNF) in Michigan's Upper peninsula, and 2) the Kellogg Biological Station
(KBS) near Kalamazoo, Michigan. Nearly 25 different forest stands were chosen in the NHF
test site which included varieties of tree types, tree height and density, and surface topography. For these stands, extensive ground truth data were collected. The ground truth for vegetation
includes tree heights, type and structure, number density, and dielectric constant and for the
ground surface includes soil moisture and surface elevation. In April 1995 JPL TOPSAR flew
over this site and interferometric images were collected at two incidence angles. Figure 2.3 shows
the map of HNF site and the location of some of the forest test stands. The grey level indicates
the surface elevation as measured by TOPSAR at incidence angle 31~. An important and most
difficulttocharacterized ground truth parameter was the forest floor surface elevation data which
is required to extract the scattering phase center height from INSAR images. To accomplish this,
differential GPS technique was used to characterize the elevation map of the forest floor of each
stand with a resolution of the order of ~5cmn. Figure 10 shows a typical surface elevation map
of a stand generated from the differential GPS measurements.
8
Runway,/
r Stand 22
Figure 7: Cband image (acO) of Stand 22 in Raco, Michigan.
10
N
m.ct
C.CZ
8
6
4
2
0
10 20 30 40 50 60
Incidence Angle Oi (Degrees)
Figure 8: The estimated height of scattering phase center of Stand
interferometric data from JPL TOPSAR.
70
22, compared with the
9
0.... I  I I I.... I I I I I I....
m
C3
QQ
O
u
0
u
~0:.OJ,u
ct
CZ
m
10 H
  ;' *
20 
30 
0 —~ —
0 —~  6 
TOPSAR
Model (total)
Component o'
Component ob
Component..s.
\
\
\
\
\
\
\
\
I!
I
An.... I A... L. I.... I... 
tv
10 20 30 40 50 60 70
Incidence Angle Oi (Degrees)
Figure 9: The simulated backscattering coefficient of Stand 22, compared with the measured
data from JPL TOPSAR.
GPS Height for Stand 67
280.5
280.279.5
E
E 279
278.5
278
277.5
46.391
84.809 46.3905
84.8085' 46.39
Longitude() > East Sou < Latitude > North
Figure 10: Surface elevation map of Stand 67 at HNF as measured by differential GPS.
10
We also conducted an experiment at the KBS site mainly to characterize the role of short
vegetation on the phase and amplitude of interferograms. TOPSAR and polarimetric L and Cband AIRSAR data were collected for this site. Different test fields with different vegetation type
including wheat, alfalfa, corn, and native grass were considered. Ground truth data for each test
field were also collected. We have also conducted an extensive polarimetric wideband backscatter
measurements of these fields using The University of Michigan L and Cband scatterometers.
The intent of this experiment was to simulate the response of INSARs according to the procedure
outlined in [7, 9]. Basically by invoking the equivalence relationship the location of the scattering
phase center and correlation coefficient can be computed directly from a scatterometer. For
example Fig. 11 shows the measured and modeled (developed in [9]) frequency correlation
function (FCF) of a native grass with physical height 1.2 m at incidence angle 20~. Using the
phase of FCF the location of scattering phase center, from the vegetation top, is computed from
[7]
1
h = = 0.461 m (5)
2cos(O) Ak
Currently we are post processing the rest of data which will be compared with INSAR measurements in Phase II of this investigation.
3 PROPOSED PLAN
3.1 Enhancement of The Coherent Monte Carlo Scattering Model
The coherent Monte Carlo model, as it stands now, is capable of predicting the backscattering
coefficients and the location of scattering phase center as well as the correlation coefficient,
fully polarimetricly over a frequency range extending from Pband to Xband. The calculations
are based on single scattering theory. We are planing to enhance the model by including the
multiple scattering among vegetation particles. Theoretical models that can evaluate scattering
up to second order have already been developed [14, 15, 16]. We will incorporate these into the
coherent model in an efficient manner by examining the significance of the second order terms
prior to their numerical calculations. It is expected that the second order scattering terms would
improve the crosspolarized response.
Another enhancement to the existing model is the inclusion of forest understory. Most forest
stands have underlying layers of vegetation including smaller trees and short vegetation. Depending on the frequency, the underlying layer influences the SAR/INSAR responses to some extent.
In order to examine the effect and importance of the forest understories, an relatively unstructured
layer of vegetation will be added to the existing forest model. The scattering and attenuation
caused by this layer will be taken in to account in a coherent fashion.
3.2 INSAR Response To Short vegetation
As part of our Phase I activities, we will complete the experimental aspects of studying the
effects of short vegetation on the phase and magnitude of interferogram. We have conducted
experiments with TOSAR and polarimetric wideband scatterometers at KBS site. Analysis and
data interpretation will be performed during the Phase II of this project. We will also develop
11
z
I
22
tfl
3i~" c~
401
\i
36
34
w
 o
<j.
566coon 1. 68
..0o —ttcs  )J
I.J
  .. 
6740C0o 3
2 3
  
2
4 5
  
Figure 11: The surface elevation map (measured by TOPSAR) and road map
of the HNF site and the location of some of the forest test stands.
180  ..
1.0 Y  120
~8  rgt 7 60
< 0.6.. mdl O7
0.4 60
0.0 — 180
0 50 100 150 200 0 50 100 150 200
Af (MHz) Af (MHz)
Figure 11: The measured and predicted magnitude and phase of the frequency correlation function
of native grass from which the location of scattering phase center can be evaluated.
analytical models to explain the data. Of particular interest in this study is the behavior of correlation coefficient. Ideally, the measured correlation coefficient is a function of two independent
components: 1) system parameters such as incidence angle and system point spread function,
and 2) target attributes [7]. If the system dependent component of the correlation coefficient
can be estimated accurately, the target dependent component can be evaluated which can be
used in inversion algorithms. One way of estimating the system component is a direct measurements of correlation coefficient of clearcut areas. Clearcut areas are usually covered with short
vegetation, and therefore it is important to investigate their effect on the correlation coefficient.
3.3 Investigation on the Utility of MultiPolarization INSAR
As mentioned earlier, the number of parameters of a forest canopy that influence its backscatter
response is large and therefore parameter retrieval using a singlefrequency, singlepolarization
INSAR is practically very difficult, if not impossible. In this study we propose to investigate
the enhancement achievable by utilizing the polarimetric interferometry for a canopy retrieval
algorithm. Initially the coherent Monte Carlo scattering model will be used to examine the
response of a polarimetric SAR interferometer to a variety different forest structures. This can
be accomplished as the coherent Monte Carlo model is fully polarimetric which preserves the
absolute phase of the backscatter.
To demonstrate the significance of such approach we carried out a simulation for a red maple
stand, denoted by Stand 31, at HNF. A fractal generated red maple tree and a picture of the
stand are shown in Figure??. The simulations for estimating the scattering phase center height
are performed fullypolarimetricly at Lband and Cband. Figure 12 shows the variation of the
apparent height of Stand 31 as a function of the incidence angles for co and crosspolarized
L and Cband INSAR configurations. Simulation results at Cband show that except at very
low angles of incidence, the scattering phase center is near the top of the canopy. In this case
the backscatter in all three polarizations is dominated by the direct backscatter components of
particles near the canopy top. The same is true for LV and Lsh configurations; however, since
13
20.. I I. 15   '   . K.: 
. 1 5
L.',A —  Lh
"0 I 0 J
0... I..
I 5*  Cvh
/ — e  — C!
10 20 30 40 50 60 70
Incidence Angle Oi (Degrees)
Figure 12: The estimated scattering phase center height of Stand 31 as a function of incidence
angle, with fullypolarimetric L and Cand response.
penetration depth at Lband is higher than Cband, the location of the scattering phase center
appears about 13 m below the apparent height at Cband. The scattering phase center height
for Lhh configuration, on the other hand, is a strong function of the incidence angle where it
appears near the ground surface at low incidence angles and increases to a saturation point near
grazing angles. Close examination of this figure indicates that a pair of C,, (or Lv) and Lhh
INSAR data at low incidence angles can be used to estimate the tree height of deciduous
forest stands with closed canopies.
At Cband foliated canopy behaves as a semiinfinite medium and as shown in [9] the knowledge of extinction would reveal the distance between the location of the scattering phase center
and the canopy top (Ad) using Ad = cosO/(2K). If an average extinction coefficient (tc) of
0.2Np/m is used in the above equation, a distance Ad = 1.77m is obtained at 0 = 45~. However, a simple relation for evaluating the apparent height for Lhh does not exist yet. Using the
coherent model, empirical relationships for relating the location of scattering phase center (for
each polarization and frequency) to the canopy parameters for different types of canopy will be
established.
Multipolarization interferometric data will be generated using the existing SIRC data. Following the procedures outlined in the literature repeatpass polarimetric interferograms will be
generated [17, 18, 19]. We have requested repeatpass SIRC data over HNF site which were
acquired towards the end of SIRC mission in October 1995. In order to estimate the baseline
distance and angle, very accurate coordinates of ground control points (GCP) are required. We
have already acquired coordinates of numerous GCPs within the test area of Raco, Michigan using
the differential GPS method. We will focus on Lband interferometry as the temporal decorrelation will not allow meaningful Cband interferometry. Polarimetric backscattering coefficients
together with the location of scattering phase centers at different polarizations will be used for
estimation of height and other canopy parameters. Similar procedure will be extended to the
BOREAS stands.
14
3.4 Inversion Algorithm Based on Multiincidence Angle and/or Multifrequency SAR/INSAR
The overall goal of this investigation is to obtain canopy parameters and structure from an
available set of SAR and INSAR data. The inversion algorithm has to be versatile enough so that
any combination of multifrequency, multiincidence angle, and/or multipolarization SAR and/or
INSAR data set can be used as the input to the algorithm. Sensitivity analysis will be carried out
for determining the most influential canopy parameters on the SAR/INSAR responses. The result
of this analysis would also reveal the most sensitive SAR/INSAR channels to the changes in the
canopy parameters. These sensitive channels will be recommended for the inversion process.
Since the Monte Carlo coherent model is computationally intensive, its direct application
would significantly slowdown the inversion process. To rectify this deficiency while maintaining
the high fidelity of the model, simple empirical models based on the Monte Carlo model for
different tree types will be developed first. Since the quantities of interest are ensemble average
quantities, such as backscattering coefficients and the location of scattering phase center, it
is expected that the dependence of these quantiles on the canopy parameters be very gentle.
Therefore it is possible to obtain simple algebraic expressions for these quantities in terms of
canopy parameters. For example for a given frequency and polarization, Taylor series expansion
can be used to relate radar measured quantities to the canopy parameters at a specific incidence
angle. Then by repeating this process for many incidence angles, the Taylor expansion coefficients
will be fitted to an algebraic equation in terms of incidence angle. To demonstrate feasibility of
such process we have developed an empirical model for red pine stands. Figure 13 shows a
comparison between the empirical model and the Monte Carlo model at Cband over a wide
range parameters including the incidence angular range 250  70~, and 40% variation on truck
diameter (dbh), tree height, tree density, branch angle, branch moisture, and soil moisture. The
top tree graphs show the height of the scattering phase center at the three principal polarizations
and the lower three graphs show the backscattering coefficients.
Once comprehensive (multifrequency and multipolarization) easily calculable scattering and
interferometric models for all tree types of interest are developed, inversion for any available
combination of INSAR and/or SAR data can be attempted by searching for an optimum set
of canopy parameters which would minimize the difference between the model prediction and
measured quantities. It is expected that the objective function be highly nonlinear and complex
containing many local minima. In these situations gradientbased optimization methods usually converge to a weak local minimum. Stochastic algorithms such as simulated annealing [20]
and genetic algorithms [21, 22] offer an alternative for the traditional gradientbased optimization methods where the dimension of parameter space is large and/or the objective function is
nondifferentiable. In recent years, applications of genetic algorithms to a variety of optimization
problems in electromagnetics have been successfully demonstrated [23, 24, 25]. The fundamental
concept of genetic algorithms (GA) is based on natural selection in the evolution process which
is accomplished by genetic recombination and mutation. In this approach the entire parameter
space is discretized and using a Monte Carlo simulation of the evolution process on a randomly
selected subset of the discretized parameter space, the desired objective function is optimized.
Genetic algorithms offer certain advantages over the traditional gradientbased (TGB) optimization algorithms. The most important feature of GAs is that the optimization is accomplished
globally, that is, the probability of converging to a weak local minimum is very low unlike the
15
Htvvw
Htvh
Hthh
C 'I. / c C 5 /
7c 7
24 0 0
0 0 0 1
o
0 ____________ 1 ___Z_______ 1/ __
0 2 4 6 8 2 4 6 0 2 4 6
Simplified Model Simplified Model Simplified Model
sigvv sigvh sighh
8 — 2
C 114 g 4
0 S2/ 14 7/
20 15 10 22 20 18 16 14 15 10 5
Simplified Model Simplified Model Simplified Model
Figure 13: A comparison between an empirical scattering model and the Coherent Monte Carlo
model for a red pine stand.
TGB algorithms. This is particularly the case when the objective function is highly nonlinear and
the dimension of the parameter space is large. GAs perform equally well independent of objective
function's smoothness condition and after convergence provide a list of high quality solutions
which can further be assessed according to criteria not included in the objective function. On the
other hand there are certain disadvantages associated with the GAs. A major drawback of GAs is
their lack of computational efficiency. Basically, far more calculation of the objective function is
required to achieve a convergence when compared with TGBs. Another shortcoming of the GAs
is that they do not provide any insight as to the character of the objective function during the
course of the optimization process. It should also be noted that after convergence the solution
may not necessarily be the true extremum of the objective function. The algorithm is based on
a number of ad hoc steps including: 1) discretization of the parameter space, 2) development of
an arbitrary encoding algorithm to establish a onetoone relationship between each code and the
discrete points of the parameter space, 3) random generation of a trial set known as initial population, 4) selection of high performance parameters according to the objective function known
as natural selection, 5) mating and mutation, 6) recursion of steps 4 and 5 until a convergence
is reached. The population size is provided by the user and a population of the given size is
generated randomly.
3 3 6
E 12 E 16E
3.5 Investigation on the Utility of MultiBaseline INSAR
As mentioned earlier, our priliminary investigation shows that application of multibaseline INSAR
data drastically improves the accuracy of the surface topographic measurement.The proposed ac14 02
o 10
e160
E c12
o 0 0
2 18 220
14
20 __ _ _ _ _ _ _ _ _ ___ _ _ _ _ _ _ _ _ _
22 16
20 15 10 22 20 18 16 14 15 10 5
Simplified Model Simpliffed Model Simplifie Model
Figure 13: A comparison between an empirical scattering model and the Coherent Monte Carlo
model for a red pine stand.
TGB algorithms. This is particularly the case when the objective function is highly nonlinear and
the dimension of the parameter space is large. GAs perform equally well independent of objective
function's smoothness condition and after convergence provide a list of high quality solutions
which can further be assessed according to criteria not included in the objective function. On the
other hand there are certain disadvantages associated with the GAs. A major drawback of GAs is
their lack of computational efficiency. Basically, far more calculation of the objective function is
required to achieve a convergence when compared with TGBs. Another shortcoming of the GAs
is that they do not provide any insight as to the character of the objective function during the
course of the optimization process. It should also be noted that after convergence the solution
may ot ecesarly be the true extremum of the objective function. The algorithm is based on
a number of ad hoc steps including: 1) discretization of the parameter space, 2) development of
an arbitrary encoding algorithm to establish a onetoone relationship between each code and the
discrete points of the parameter space, 3) random generation of a trial set known as initial population, 4) selection of high performance parameters according to the objective function known
as natural selection, 5) mating and mutation, 6) recursion of steps 4 and 5 until a convergence
is reached. The population size is provided by the user and a population of the given size Is
generated randomly.
3.5 Investigation on the Utility of MultiBaseline INSAR
As mentioned earlier, our priliminary investigation shows that application of multibaseline INSAR
data drastically improves the accuracy of the surface topographic measurement.The proposed ac
16
tivity is 1) complete the Boreas demonstration by investigating a wide variety of scenes (the
current demonstration represented only about 20% of the data available) 2) determine the utility
of the combination of multibaseline INSAR and polarimetry (POLSAR), and 3) demonstrate
the estimation of vegetation and surface characteristics from the INSAR/POLSAR combination.
There were many features of the current Boreas demonstration, e.g. anomalously low extinction coefficients, that were not thoroughly studied with the full data set. A sufficiently large
dualbaseline INSAR data set is available for establishing the robustness of the algorithms and
approaches developed in [10]. The material in two presentations this year [27] suggest that the
combination of multibaseline INSAR with POLSAR will enable the estimation of vertical profile
details of vegetation in the presence of groundtrunk/volume returns. The groundtrunk/volume
returns were not considered in [10] but are treated in [27], and based on that and [28], it appears
that the combination POLSAR and multibaseline INSAR will be a powerful tool for understanding
forest vegetation vertical profiles and underlying topography. We therefore propose to continue
the physical modeling and algorithm development which combines POLSAR and multibaseline
INSAR and demonstrate the combination with Boreas and Kellogg data. Although simultaneous
INSAR and POLSAR were not acquired with TOPSAR, INSAR and POLSAR at different epochs
should be a useful first step, coupled with repeatpass interferometry, for which POLSAR is simultaneously acquired. The repeatpass interferometry will, however, pose the additional challenge of
understanding the temporal decorrelation to vegetation movement and chemical change between
passes.
3.6 INSAR Lidar Data Fusion
Data fusion from independent remote sensing instruments can drastically improve the success
of inversion processes. A laser altimetry system that can provide highresolution, geolocated
measurements of vegetation vertical structure and ground elevations beneath canopies is of great
value to the overall goal of PHASE II investigation. The principle of operation is rather simple and
is based on precise timing of the roundtrip travel time of shortduration pulses of a nearinfrared
(1.06 microns) laser illuminating a forest canopy. Digitization of the backscattered return energy,
or laser echo, as a function of time yields a waveform which is a measure of the vertical distribution
of intercepted, nadirprojected surface area. The waveform is composed of both canopy elements
(foliage, needles, stems, branches) and the underlying ground's height distribution introduced by
surface slope and roughness [29, 30, 31]. The lidar elevation data will be used as an independent
set of measurements in the inversion process as well as evaluating the inversion process in the
absence of laser data.
A laser altimeter known as Scanning Lidar Imager of Canopies by Echo Recovery (SLICER)
was piggybacked on the ASAS C130 deployment during the BOREAS Summer 1996 Intensive
Field Campaign in July (ASAS is a highres, multiangle hyperspectral imaging system). During
acquisition of ASAS images at the Southern and Northern Study Area flux tower sites, SLICER acquired nadir transects of canopy vertical structure and subcanopy ground topography. Typically,
the transects extend outward from the tower sites for a distance of approximately 10 km. The
transects consist of narrow swaths nominally composed of five crosstrack footprints, each circular
and 8 m in diameter. There are also two long transects acquired during the transit between the
southern and northern study areas, which were acquired in SLICER singlebeam profiling mode.
The lidar backscatter is digitized at 11 cm vertical sampling, in order to obtain the complete
17
timevarying distribution of return pulse energy from multiple targets at varying heights within a
large footprint. By using large diameter footprints on the order of one to two times the typical
crown widths, each waveform includes returns from the highest elements of the canopy and from
the ground. Ground returns occur where there are sufficient intra or intercrown gaps of any size
extending at nadir to the canopy floor, which is usually the case in all but the densest canopies.
The laser footprints are geolocated, at footprint scale absolute horizontal accuracy, by combining
the laser ranging data with aircraft position, obtained from a differential kinematic GPS trajectory,
and laser pointing knowledge, obtained from an Inertial Navigation System. By scanning the laser
footprint across the track of the aircraft flight line, a narrow swath of threedimensional laser
waveform data is acquired.
LFrom each laser backscatter echo the ground elevation and canopy height are readily derived
at meterlevel absolute vertical accuracy, and from adjacent laser footprints the slope and azimuth
of the canopy top and underlying ground surfaces can be determined. By accounting for extinction
of the laser light with depth through the canopy, the raw echoes can also be converted to canopy
height profiles which are a normalized measure of the vertical distribution of canopy surface area
[32]. Height profiles of absolute, nadirprojected canopy surface area and closure can be derived
where ancillary information on ground and canopy reflectance at 1 micron are available, as for
example from ground measurements or pixel unmixing of hyperspectral imaging radiometer data.
A comprehensive set of figures characterizing the SLICER results at all the BOREAS Southern 
Study Area flux towers is already completed, and analysis of the Northern Study area flux towers
is in progress. For each tower site the pfots include detailed map views of the ground tracks, 3D
perspective views of canopy top and ground elevations along the transects, contour plots of the
vertical distribution of normalized canopy area and closure, and average canopy height profiles.
Figure 14 shows average canopy height profiles for the Southern Study Area flux tower sites.
The stand structures are differentiated by total height, the thickness of the upper story, and the
presence or absence of an understory.
Initial work on INSARlidar fusion will focus on constraining analysis of single wavelength,
baseline length, and polarization INSAR data using lidar profiles, thus providing methodologies
appropriate for the integration of Shuttle Radar Topography Mission (SRTM) and Vegetation
Canopy Lidar (VCL) data. SRTM will provide nearly complete INSAR global coverage at Lband in late 1999, and VCL will provide globally distributed surface lidar profiles at 1.06 microns
starting in early 2000. The model development of [7, 10, 13] provide a basis for this INSARlidar
fusion. For example in the simplified model [10], three surface parameters (the height of the
vegetation layer, the vegetation extinction coefficient, and the elevation of the ground surface)
are expressed in terms of two INSAR parameters (amplitude and phase of the normalized cross
section). This under determined system was evaluated for vegetation height and ground elevation
by applying independent measurements of extinction coefficient. However, it was difficult to
differentiate sources of ambiguity in the height and elevation results between instrumental, model,
and extinction coefficient errors. For the BOREAS region, we will use the SLICER data to provide
independent measures of vegetation height and ground elevation, and thus in the presence of the
SLICER data the model of [10] will be over determined and we can solve for the extinction
coefficient. Instrumental errors can also be assessed, particularly the necessary conversion of
the measured normalized crosscorrelation amplitude to an absolute vegetation crosscorrelation
based on range correlation and noise corrections. The vegetation height profile, closure, and
ground slope measurements provided by the lidar can be used to assess extensions of the simple
18
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5
10 
I5 
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301
25
20
150
10
0.00 0.10
0.20
0.30
0.40
0.50
2 CHP within 100 m of SSAOBS Flux Tower
0.20 0.30 0.40
0.50
3003 CHP within 160 m of SSA_YJP Flux Tower
25
20
5 
10
0.00
25
20
15
10..........................
0.10
0.20
0.30
0.40
0.50
3010 CHP within 100 m of SSAYA Flux Tower
5i ____
W   
0.00
0.10 0.20 0.30 0.40
Fraction of Canopy Surface Area per 66 cm Interval
0.50
Figure 14: Average canopy height profiles (CHP) for SLICER lidar footprints near five BOREAS
Southern Study Area flux towers (OA = old aspen, OJP = old jack pine, OBS = old black spruce,
YJP = young jack pine, YA = young aspen), expressed as normalized distributions (fraction of
total nadirprojected canopy surface area per 66 cm measurement interval) above the ground
surface.
19
models of [7, 10] (dense, homogeneous vegetation layer extending to the ground, a flat ground
surface, and no ground scattering interactions) to account for layered vegetation, sloped ground,
and a ground scattering contribution. Additionally, the lidar data can be used as input to the
more sophisticated model of [13].
The limited spatial coverage of the SLICER lidar data for BOREAS, and ultimately globally
from VCL, will require methods to extend the lidar constraints to areas of INSAR coverage in the
absence of the lidar data. We will evaluate the following methodology for the BOREAS region:
1. define vegetation cover types based on the amplitude and texture of crosscorrelation and
backscatter INSAR images,
2. for cover regions crossed by a subset of lidar profiles, apply the model of [10] constrained by
lidar vegetation height and ground elevation in order to determine instrumental correction
factors and each cover types vegetation extinction coefficient,
3. apply the derived extinction coefficients, based on cover type, throughout the INSAR images
using the model of [10] in order to determine vegetation height and ground elevation in the
absence of lidar data,
4. use a subset of the lidar profiles, withheld from step 2, to assess the accuracy the resulting;vegetation height and ground elevation images.
This approach should provide a means to utilize nearterm INSAR and lidar assets, without relying
on access to multiwavelength, baseline length, or polarization INSAR data.
4 Management and Cost Plan
4.1 Schedule
The proposed investigation will require a three year period of performance. The EM model
enhancement, and the development of inversion algorithm will be conducted during the first year
of the study. Validation of the inverse model using TOPSAR and INSAR data taken over HNF
and KBS will be performed in the second year. The KBS data analysis and interpretation of
TOSAR and polarimetric scatterometer will be done during the first two years. Activities related
to polarimetric interferomtry using SIRC will start from the onset of the Phase II project and
should last about two years. Research activities concerning dual baseline SAR interferomtry over
BOREAS will be conducted throughout the project. SLICER data preparation anid analysis and
comparison with the existing TOSAR data will be performed during the first two years. Data
fusion, implementation of inversion algorithm for BOREAS stands will be completed in the third
year.
4.2 Personnel
The proposed research will be performed under the direction of professor Kamal Sarabandi. He
will be principally assisted by four CoInvestigators: Mr. M. Craig Dobson (UM), Dr. David
Harding(GSFC), Dr. Robert Treuhaft (JPL), and Dr. Jakob van Zyl (JPL). The UM team will be
20
responsible for the development, validation, and implementation of the forward and inverse model.
They will also be responsible for the activities related to polarimetric SAR interferometry using
SIRC data as well as the KBS data analysis and interpretation. JPL team will be responsible for
the dual baseline INSAR activities and TOPSAR data extraction and analysis of BOREAS site.
The JPL team will also be continuing work on forward and inverse models for the INSARPOLSAR
combination. Dr. Harding who has extensive experience in lidar research will be responsible for
the data interpretation of SLICER and will collaborate in the data fusion activity. All will be
engaged in the development and validation of the proposed algorithms. Two graduate student
research assistants will be supported by this project.
4.3 Facilities and Required Resources
All necessary equipment and facilities required are available within the Radiation Laboratory at
the University of Michigan, Jet Propulsion Laboratory, and NASA Goddard Space Flight Center;
no additional equipment will be purchased with the requested funding.
5 Budget
The total cost of the proposed threeyear project is $466,146, of which $454,977 is requested
from NASA and the balance of $11,169 will be provided by The University of Michigan in the
form of cost sharing. Out of total costs of $454,977 to NASA, $295,177 is requested by the
University of Michigan, $70,000 by GSFC, and the remainder of $89,800is requested by JPL.
21
References
[1] F. T. Ulaby, K. Sarabandi, K. MacDonald, M. Whitt, and M. C. Dobson, " Michigan
Microwave Canopy Scattering Model", Int. J. Remote Sensing, Vol. 11, No. 7, pp. 1223 1253, 1990.
[2] M. A. Karam, A. K. Fung, R. H. Lang, and N. H. Chauhan, "A microwave scattering model
for layered vegetation," IEEE Trans. Geosci. Remote Sensing., vol. 30, no. 4, pp. 767784,
July 1992.
[3] K. Sarabandi, Electromagnetic Scattering from Vegetation Canopies, Ph.D. Dissertation,
University of Michigan, 1989.
[4] R. L. Jordan, B. L. Huneycutt, and M. Werner, "The SIRC/XSAR synthetic aperture radar
system," IEEE Trans. Geosci. Remote Sensing., vol. 33, pp. 829839, July 1996.
[5] Zebker, H/A., S. N. Madsen, J. Martin, K. B. Wheeler, T. Miller, Y. Lou, G. Alberti, S.
Vetrella, and A. Cucci, " The TOPSAR interferometric radar topographic mapping instrument," IEEE Trans. Geosci. Remote Sensing., vol.30, No.5, pp.933940, 1992.
[6] Rodriguez, E., and J.M. Martin, "Theory and design of interferometric synthetic aperture
radars," IEE Proceedings, vol. F139, no. 2, pp. 147159, 1992.
[7] Sarabandi,K., "AkRadar equivalent of Interferometric SARs: A Theoretical Study for determination of vegetation height," IEEE Trans. Geosci. Remote Sensing., accepted for publication (Dec. 96).
[8] Sarabandi,K., A. Nashashibi, "Analysis and applications of backscattered frequency correlation function," IEEE Trans. Geosci. Remote Sensing., submitted for publication (April 97).
[9] R. N. Treuhaft, M. Moghaddam, and J. J. van Zyl, "Inteferometric Remote Sensing of
Vegetation and Surface Topography," Radio Science, vol. 31, pp. 14491485.
[10] Lin, Y.C., and K. Sarabandi, "Electromagnetic scattering model for a tree trunk above a
tilted ground plane," IEEE Trans. Geosci. Remote Sensing., vol. 33, no. 4, pp. 10631070,
July 1995.
[11] Lin, Y.C., and K. Sarabandi, "A Monte Carlo Coherent Scattering Model For Forest Canopies
Using Fractal Generated Trees," IEEE Trans. Geosci. Remote Sensing., submitted for publication (Sept. 96).
[12] Sarabandi, K., and Y.C. Lin, "Simulation of Interferometric SAR Response for Characterization of Scattering Phase Center Statistics of Forest Canopies," IEEE Trans. Geosci. Remote
Sensing., submitted for publication (Feb. 97).
[13] Chiu, T.C., and K. Sarabandi, "Electromagnetic scattering interaction between a dielectric cylinder and a slightly rough surface," IEEE Trans. Antennas Propagat., submitted for
publication (June 1997).
22
[14] Sarabandi, K., and P.F. Polatin, "Electromagnetic scattering from two adjacent objects,"
IEEE Trans. Antennas Propagat., vol. 42, no. 4, pp. 510517, April 1994.
[15] Polatin, P.F., K. Sarabandi, and F.T. Ulaby,"Monte Carlo simulation of electromagnetic
scattering from a heterogeneous twocomponent medium," IEEE Trans. Antennas Propagat.,
vol. 43, no. 10, pp. 10481057, Oct. 1995.
[16] Goldstein, R.M., H.A. Zebker, and C.L.Werner, "Satellite radar interferometry: Twodemensional phase unwrapping," Radio Sci., vol.23, no.4, pp. 713720, JulyAugust 1988.
[17] Gens, R., and J.L. van Genderen, "Review article: SAR interferometry issues,techniques,applications," Inter. J. of Remote Sensing, vol. 17, no. 10,pp. 18031835,
1996.
[18] Zebker, H.A., C. L. Werner, P.A. Rosen, S. Hensley, "Accuracy of Topographic Maps Derived
from ERS1 Interferometric Radar," IEEE Trans. Geosci. Remote Sensing., vol. 32,no.4, pp.
823836, July 1994.
[19] Kirkpatrick, S., J.C.D. Gelatt, and M.P. Vecchi, "Optimization by simulated annealing,"
Sci., vol. 220, pp. 671680, 1983.
[20] Holland, J.H., "Genetic Algorithms," Scientific American, pp. 6672, July 1992.
[21] DeJong, K.A.,"An Analysis of the behavior of a class of genetic adaptive systems," Ph.D.
dissertation, The University of Michigan, Ann Arbor, 1975.
[22] Michielssen, E., and R. Mittra, "RCS reduction of dielectric cylinders using a simulated
annealing approach," IEEE Trans. Microwave Theory Tech., vol. 41, no.6/7, pp. 10241031,
1993.
[23] Haupt, R.L., "Thinned arrays using genetic algorithms," IEEE Trans. Antennas Propagat.,
vol. 42, no. 7, pp. 993999, 1994.
[24] Sarabandi, K., and E. Li, "Characterization of Optimum Polarization for Multiple Target
Discrimination Using Genetic Algorithms," IEEE Trans. Antennas Propagat., submitted for
publication (August 1996).
[25] Treuhaft, R.N., E. Rodriguez, M. Moghaddam, J.J. van Zyl, and K. Sarabandi, "Estimating vegetation and surface characteristics with multifrequency SAR interferometry," URSI
General Assembly, Lille, France, August 1996 (invited).
[26] Treuhaft, R.N., M. Moghaddam, and J. J. van Zyl, "Combining Radar Interferometry and
Polarimetry to Estimate Forest Vegetation and Surface Parameters," PIERS97, Cambridge,
Massachusetts, July 1997 (invited).
[27] Cloude, S.R., and K. Papathanassiou, "Polarimetric Effects in Radar Interferometry,"
PIERS97, Cambridge, Massachusetts, July 1997.
23
[28] Blair, J.B., D.B. Coyle, J.L. Bufton, and D.J. Harding, 1994, Optimization of an Airborne Laser Altimeter for Remote Sensing of Vegetation and Tree Canopies, Proceedings of
IGARSS'94, Vol. II, 939941.
[29] Harding, D.J., J.B. Blair, J.B. Garvin, W.T. Lawrence, 1994, Laser Altimetry Waveform
Measurement of Vegetation Canopy Structure, Proceedings of IGARSS'94, Vol 11, 1251 1253.
[30] Harding, D.J.,, J.B. Blair, E. Rodriguez, T. Michel, 1995, Airborne Laser Altimetry and
Interferometric SAR Measurements of Canopy Structure and SubCanopy Topography in
the Pacific Northwest, Proc. Second Topical Symposium on Combined Optical  Microwave
Earth and Atmosphere Sensing (COMEAS'95), 2224.
[31] Lefsky, M.J., 1997, Application of Lidar Remote Sensing to the Estimation of Forest Canopy
Structure, Univ. of Virginia, Ph.D. Dissertation, 185 pp.
24
Digital Topography From SAR Interferometry: Determination of and Correction
for Vegetation
Budget Summary
From March 15, 1998 to March 14, 1999
YEAR ONE
NASA USE ONLY
B C
A. Diret Labor (salaries, wages, and
fringe benefits) $57,721
2. Other Direct Costs:
a. Subcontracts o
b. Consultants o
c. Equipment 0
d. Supplies 1,500
e. Travel 4,000
f. Other(Graduate Student tuition) 4,884
3. IndirC Costs 33,191
4. Other Applicable Costs
5. SubtalEstimat Costs 101,296
6. Less Proposed Cost Sharing (if any) 3,578
7. Carryover Funds (if any)
a. Anticipated amount
b. Amount used to reduce budget
8. Total Estimated Costs $97,718
APPROVED BUDGET XXXXXXX
Budget detail and breakdowns are included following

I
—.II
XXXXXXX
the NASA Budgexxxxt Summary pages.
the NASA Budget Summary pages.
Instructions
1. Provide a separate budget summary sheet for each year of the proposed research.
2. Grantee estimated costs should be entered in Column A. Columns B and C are for NASA
use only. Column C rpresents the approved grant budget.
3. Provide in attachments to the budget summary the detailed computations of estimates in
each cost category, along with any narrative explanation required to fully explain proposed
costs.
— ADDITONAL INSTRUCTIONS ON REVERSE  
Digital Topography From SAR Interferometry: Determination of and Correction
for Vegetation
Budget Summar
From March 15, 1999 to March 14, 2000
YEAR TWO
NASA USE ONLY
B C
A
1. Direct Labor (salaries, wages, and
fringe benefits)$ 58,158
2. Other Direct Costs:
a. Subcontracts
b. Consultants o
c. Equipmet
d. Supplies 1,500
e. Travel 4,000
f. Other (Graduate Student tuition) 5.078
3. ndirt Costs 33,420
4. Other Applicable Costs
5. SubtotalEstimated Costs 102,156
6. Less Proposed Cost Saring (f any) 3.721
7. Carryover Funds (if any)
a. Anticipated amount
b. Amount used to reduce budget
8. Total Estimated Costs $98,435
APPROVED BUDGE XXXXXX
Budget detail and breakdowns are included following
Instructions
 
1.
I, i I I
the NASA Budget Summary pages.
1. Provide a separate budget summary sheet for each year of the proposed earch.
2. Grantee estimated costs should be entered in Column A. Columns B and C are for NASA
use only. Column C represents the approved grant budget.
3. Provide in attachments to the budget summary the detailed computations of estimates in
each cost category, along with any narrative explanation required to fully explain proposed
costs...  — ADDITIONAL INSTRUCTIONS ON REVERSE  
Digital Topography From SAR Interferometry: Determination of and Correction
for Vegetation
Budeet Summary
From March 15, 200C
to March 14, 2001
YEAR THREE
NASA USE ONLY
B C
A
1. Direct Labor (salaries, wages, and
fringe benefits)
2. Other Direct Costs:
a. Subcontat
b. Consultants
c. Equipment
d. Supplies
e. Travel
f. Other(Graduate Student tuition)
3. Inire Costs
4. Other Applicable Costs
5. SubtotalEtimated Costs
6. Less Proposed Cost Sharing (if any)
7. Carryover Funds (if any)
a. Anticipated amount
b. Amount used to reduce budget
8. Total Estimated Costs
APPROVED BUDGET
$ 58,508
0
1.500
4,000
5,282
33,604
102,894
3.870
$ 99,024
XXXXXXX


 
XXXXXXX
Budget detail and breakdowns are included following the NASA Budget Summary pages.
Insarctions
1. Provide a separate budget summary sheet for each year of the proposed research.
2. Grantee estimated costs should be entered in Column A. Columns B and C are for NASA
use only. Column C rresents the approved grant budget.
3. Provide in attachments to the budget summary the detailed computations of estimates in
each cost category, along with any narrative explanation required to fully explain proposed
costs.
— ADDITIONAL INSTRUCTIONS ON REVERSE  —
Digital Topography From SAR Interferometry: Determination of and Correction
for Vegetation
udget Summary
From March 15, 1998 to March 14, 2001
SUMMARY  YEARS One  Three
NASA USE ONLY
B C
A
1. Direct Labor (salaries, wages, and
fringe benefits) $ 174,387
2. Other Direct Costs:
a. Subcontracts
b. Consultants 0
c. Equipment
d. Supplies 4,500
e. Travel 12 000
f. Other (Graduate Student Tuition) 15,244
3. ndirt Costs 100,215
4. Other Applicable Costs 
5. SubtotalEstimated Costs 306,346
6. ess Proposed Cost Sharing (if any) 11.16
7. Carryover Funds (if any)
a. Anticipated amount
b. Amount used to reduce budget _
8. Total Esimated Costs $295,177
APPROVED BUDGET XXXXXXX
Budget detail and breakdowns are included following
Instruciols,11
the NASA Budget Summary pages.
1. Provide a separate budget summary sheet for each year of the proposed research.
2. Grantee estimatd costs should be entered in Column A. Columns B and C are for NASA
use only. Column C resents the approved grant budget.
3. Provide in attachments to the budget summary the detailed computations of estimates in
each cost category, along with any narraive explanation required to fully explain proposed
costs.
ADDITONAL INSTRUCTIONS ON REVERSE 
Digital Topography From SAR Interferometry:
Determination of and Correction for Vegetation  March 15, 1998  March 14, 2001
YEAR ONE  March 15, 1998  March 14, 1999
UM NASA TOTAL
DIRECT COSTS
Prof. Kamal Sarabandi, P.D. 1,833 5,906 7,739
5% x 9 months, 0.5 summer month (8147/month)
M. Craig Dobson, CoP.I. 10%, 12 mos. (6760/month) 8,112 8,112
Adm. Assistant, 5%, 12 mos. (3963/month) 2,378 2,378
Graduate Student Research Assistant
2 @ 1414/month each, 1@12 months, 1@7 months 26,866 26,866
Total Salaries and Wages 1,833 43,262 45,095
Fringe Benefits@ 28% 513 12,113 12,627
Other Direct Costs
Tuition for two graduate students* 4,884 4,884
(Two terms per student per year)
Travel
Working Group Meeting (1 person, 3 days) 1,400 1,400
Field Experiments (3 persons, 5 days) 2,600 2,600
Lab and Consumable Supplies 1000 1 000
Communications (FAX, Xerox, Postage) 500 500
TOTAL DIRECT COSTS 2,346 65,759 68,1 05
Total Indirect Costs @ 52.5% TDC 1,232 31,959 33,191
TOTAL COSTS 3,578 97,718 101,296
*The College will pay the portion of GSRA tuition not paid by the sponsor.
YEAR TWO  March 15, 1999  March 14, 2000
I1! II
_UM NASA TOTAL
DIRECT COSTS
Prof. Kamal Sarabandi, P.D. 1,906 6,143 8,049
5% x 9 months, 0..5 summer month (8473/month)
M. Craig Dobson, CoP.I. 10%, 12 mos. (7030/month) 8,436 8,436
Adm. Assistant, 5%, 12 mos. (4122/month) 2,473 2,473
Graduate Student Research Assistant
2 @ 1471/month each, 1@12 months, 1@6 months 26,478 26,478
Total Salaries and Wages* 1,906 43,530 45,436
Fringe Benefits@ 28% 534 12,188 12,722
Other Direct Costs
Tuition for two graduate students** 5,078 5,078
(Two terms per student per year)
Travel
Working Group Meeting (1 person, 3 days) 1,400 1,400
Field Experiments (3 persons, 5 days) 2,600 2,600
Lab and Consumable Supplies 1000 1000
Communications (FAX, Xerox, Postage) 500 500
TOTAL DIRECT COSTS 2,440 66,296 68,736
Total Indirect Costs @ 52.5% TDC 1,281 32,139 33,420
TOTAL COSTS 3,721 98,435 102,156
*An annual increment of 4% is included in Year Two for Salaries and Tuition.
*The College will pay the portion of GSRA tuition not paid by the sponsor.
YEAR THREE  March 15, 2000  March 14, 2001
r s
UM NASA TOTAL
DIRECT COSTS
Prof. Kamal Sarabandi, P.D. 1,983 6,388 8,371
5% x 9 months, 0.5 summer month (8811/month)
M. Craig Dobson, CoP.I. 10%, 12 mos. (7311/month) 8,773 8,773
Adm. Assistant, 5%, 12 mos. (4287/month) 2,572 2,572
Graduate Student Research Assistant
2 @ 1529/month each, 1@12 months, 1@5 months 25,993 25,993
Total Salaries and Wages* 1,983 43,727 45,709
Fringe Benefits@ 28% 555 12,243 12,799
Other Direct Costs
Tuition for two graduate students** 5,282 5,282
(Two terms per student per year)
Travel
Working Group Meeting (1 person, 3 days) 1,400 1,400
Field Experiments (3 persons, 5 days) 2,600 2,600
Lab and Consumable Supplies 1 000 1 000
Communications (FAX, Xerox, Postage) 500 500
TOTAL DIRECT COSTS 2,538 66,752 69,290
Total Indirect Costs @ 52.5% TDC 1,332 32,272 33,604
TOTAL COSTS 3,870 99,024 102,894
*An annual increment of 4% is included in Year Three for Salaries and Tuition.
*The College will pay the portion of GSRA tuition not paid by the sponsor.
Digital Topography From SAR Interferometry:
Determination of and Correction for Vegetation  March 15, 1998  March 14, 2001
SUMMARY  YEARS ONE  THREE
I I
IIM
NASA I
TOTAL
I# VI I _ _ _ _ II _`
DIRECT COSTS
Prof. Kamal Sarabandi, P.D.
M. Craig Dobson, CoP.I.
Adm. Assistant
Graduate Student Research Assistants
Total Salaries and Wages*
Fringe Benefits@ 28%
Other Direct Costs
Tuition for two graduate students**
(Two terms per student per year)
Travel
Working Group Meeting (1 person, 3 days each year)
Field Experiments (3 persons, 5 days per year)
Lab and Consumable Supplies
Communications (FAX, Xerox, Postage)
I I I
5,722
5,722
1,602
18,437
25,321
7,423
79,337
130,518
36,545
15,244
4,200
7,800
3000
1,500
24,159
25,321
7,423
79,337
136,240
38,147
15,244
4,200
7,800
3000
1,500
I II
TOTAL DIRECT COSTS
Total Indirect Costs @ 52.5% TDC
7,324
3.845
198,807
96.370
206,131
100.215
~_  , W....  
TOTAL COSTS
11,169 295,177 306,346
*An annual increment of 4% is included in Years Two and Three for Salaries and Tuition.
**The College will pay the portion of GSRA tuition not paid by the sponsor.
JET PROPULSION LABORATORY
JPL California institute of TechnologY
4800 Oak Grove Drive
Pasade~na. C2irfornia 01 109
NASA PROPOSAL COST PLAN
Proposal TItIc DIGITAL TOPOGRAPHY FROM SAI9 INTERFEROMETRY:
RTOP No. (if appjlcable) Determination of and Correction for Vegetation Height DatR Prepared Jun97
A. Direct Compensation Product DETERMINATION OF AND Annual Total, $K
(By Classification) Workhours Workyears Salary FY9iIiIY99 IFY'OZ
Fq FY' FY' FY' FY' FY' FY' Rate Forward Pricnan Index
Year 98 99 0 98 99 0 $K 1.000 1.050 1.125
Engineering/Scientist E 274.8 274.8 274.8 0.2 0.2 0.2 88 13.2 13.9 14.9
Administrative A 0.0 0.0 O.O 0.0 0.01 0.0
office/Clerical 0 0.0 0.0 0.0 0.0 0.0 0.0U
Technician/S..ervice T/S 0.0 0.01 0.0 ___ 0.0 0.0 0.0
Subcontractor (S) LOiIIZLL 0.L021.2
Total Workhours A. 1 274.8 274.8 274.~]S
Labor Subtotal A.P, 13.2 13.9 14.Y
A.3 Applied Paid Latve 71 7.5t 1 7.51 1 T. I A.3 2.1i 2.2. 2.3
(%*A. 2) (A.2*A.3)=A.4, 15.3 16.1i 17.2
A.5 Benefits (%' A.4) F22.01 42.2.T123.21 A.5 LII AI 3.151 4.0
Total JPL Direct Cornpensation (A.4+A.5) A. I 18.71 19. 21.1
S. Travel Destination iftrip[Cost ea. TravelSubtotal
I Field work 21 7 o.oj 0.0
2 Conterences 21 2 0.0] 0.0
C.SrieLIst by tpe eq, computing, documentation, etc.I Rale ServiceSubtotal
C.Sr Ie Pubicaiions I~B 00 0.0 0.0
2 7 7 1__ _ 00 0.01 ____
D. Procurement List by Prcreetputoa
EFaiiisLsaseither new construction or modification ___IFacilitiesSubtotal
F. Total Direct Cost Totali$K F_1.7 19.7 1 21.1
G. Indirect CostS FI FY'98 I FY'99 J F'Ooj1
3. 1. Laboatory Burden (% * A.a5) V 1.V0.71 10.7T
Percent of Total Direct Compensation Gi I i.9 zL 2.1
G.2. Project Staff Burden 1 11.21 ii1iL 11.31_________
Percent of Total Direct Compensation G.2L172.0I 2.11, 2.2
G.3. Technical Division Burden [9. 151 9.901 10.441
W/hour A.11 G.3 2.6 2.7 2.9
iH. Subtotal Indirect Costs (G.1~C3.2G.C3).53L 
J. pr udnM'1 — I 5.61 6.4 7.6 J 1.4 1.7 2.0
K. ubotl Cst Bfoe GnealBuden(IJ)7K 26.6 28.2 30.4
L. General Burden (% K) 1 5.2 6o.3 6.21 L 1.41 1.8 1.4.M.ToalJ l Q~SL+QM. 9 28.0 30.0 3 1.
Terrestrial Ecology Program: NRA97MTPE08
Digital Topography from SAR Interferometry:
Determination of and Correction for Vegetation Height
GSFC Budget Component
YR1 YR2 YR3
Manpower (Full Time Equivalents  FTE)
GSFC Civil Servant (D. Harding) 0.2 0.2 0.2
GSFC Onsite Contractor 0.2 0.2 0.2
Budget ($k)
Contractor Salary 15 16 17
Publication Page Charges 0 2 2
GSFC MPS Assessment (per FTE) 5 5 5
GSFC Branch Assessment 1 1 1
TOTAL ($k) 21 24 25
Requested Start Date: March 15, 1998.
Requested Duration: 36 months
CURRICULUM VITAE
February 1997
PERSONAL
NAME
Kamal Sarabandi
Associate Proferssor
ADDRESS
HOME ADDRESS
RESEARCH
INTERESTS
3225 EECS Building,
Radiation Laboratory
Ann Arbor, Ml 481092221.
email: saraband@engin.umich.edu
Office Phone 3139361575
2780 Emberway,
Ann Arbor, MI 48104.
Home Phone 3139951031
Development of scattering models for natural targets (for remote sensing purposes), scattering and propagation of electromagnetic waves in random media,
inverse scattering problem, applied computational electromagnetics, and microwave circuits and antenna.
The University of Michigan, Ann Arbor
Ph.D. in Electrical Engineering.
Thesis Advisers: Prof. F.T. Ulaby and Prof. T.B.A. Senior.
Dissertation topic: Electromagnetic scattering from vegetation canopies.
During five years of graduate studies G.P.A.= 8.41 (A=8.0).
The University of Michigan, Ann Arbor
M.S. in Mathematics (May 1989), concentration in Applied Math.
The University of Michigan, Ann Arbor
M.S.E. in Electrical Engineering, fundamental courses in electromagnetics, optics, and communications.
Sharif University of Technology, Tehran, Iran
B.Sc. in Electrical Engineering.
EDUCATION
1986  1989
1987  1989
1984  1986
1975  1980
FUNDED RESEARCH PROJECTS
1. "Development of an advanced wireless and microwave experimental facilities," Sponsor: HewlettPackard Company, submission date: January 1997, $279,967, PI: K. Sarabandi, L. Katehi, and
B. Gilchrist.
2. "Components and systems for communications: An undergraduate design Laboratory," Sponsor:
National Science Foundation, Submission date: Nov. 1996, $100,000. CoPls: L. Katehi, K.
Sarabandi, B. Gilchrist, G. Rebeiz.
3. "A novel millimeterwave, lowloss, electronically controlled phase shifter for monolithic, beamsteering phased array antenna applications," 8/9612/96, $25,054, Sponsor ONR, PI:K. Sarabandi.
4. "Low Energy Electronic Design for Mobile Platforms," 9/969/01, $5,000,000/5 Yrs. Sponsor:
Army Research Office, CoPls: J Coffey, J. East, A. Hero, L. Katehi, S. Lafortune, P. Mazumder,
D. Neuhoff, K. Sarabandi, D. Teneketzis, and K. Wasserman.
5. "ARL Federated Laboratories: MMW radar phenomenology," 1/9612/95, $500,000/Yr. Sponsor:
ARL, PI: F.T. Ulaby, L. Katehi, G. Rebeiz, and K. Sarabandi ($86,685/Yr.).
6. "Lane Detection for Automotive Sensors," 5/965/97, $167,374/1 Yr., Sponsor:TACOM, CoPls:K. Kluge, S. Lakshmanan, and K. Sarabandi.
7. "Millimeterwave radars as advanced vehicle control and warning systems: A feasibility study,"
5/965/98, $139,167/2 Yrs. Sponsor: General Motors, PI: K. Sarabandi, CoI Adib Nashashibi.
8. "Evaluation of radar techniques for assessing snowcover conditions and their effect on the detection of hard targets," 4/958/96, $40,000/ 1 Yr. Sponsor: Office of Naval Research, PI: K.
Sarabandi, CoI: F.T. Ulaby.
9. "Digital topography from SAR interferometry: Determination of and correlation for vegetation
height," 3/953/98, $375,417/3 Yrs. Funded by NASA. PI: K. Sarabandi, Cols: M.C. Dobson
and J.J. van Zyl.
10. "Development and construction of a 77 GHz dualpolarized planar antenna array and associated reflector," 10/9510/97, $276,000/2 Yrs. Funded by DaimlerBenz Company, CoPIs: G.M.
Rebeiz, K. Sarabandi, and L.P. Katehi.
11. "Development of SAR algorithm for mapping soil moisture and vegetation biomass," 10/9410/97,
$482,716/3 Yrs. Funded by NASA. PI: F.T. Ulaby, Cols: K. Sarabandi and C. Dobson.
12. "MultiFrequency, multipolarization external calibration of the SIRC/XSAR," 10/9310/96,
$259,217/3 Yrs. Funded by: JPL. PI: K.Sarabandi, Col: F.T. Ulaby.
13. "Construct and Deliver an Xband bistatic radar system," 9/935/94, $102,000. Funded by U.S.
Army Engineer Waterways. PI: F.T. Ulaby, Col: K. Sarabandi.
14. "Retrieval of soil moisture and roughness from the polarimetric radar response," 5/9310/96,
$404,505/3 Yrs. Funded by NASA. PI: K. Sarabandi, Col: F.T. Ulaby.
15. "Statistical Behavior of Polarimetric Radar Response of Terrain with Emphasis on the Millimeterwave Region," 1/921/95 $472,000/3 Yrs. Funded by Army Research Office. PI: F.T. Ulaby, Col:
K. Sarabandi.
16. "Investigation of Polarimetric Radar Response to Soil Moisture and Surface Roughness," 4/90 
4/93 $300,000/3 Yrs. Funded by NASA. PI: F.T. Ulaby, Col: K. Sarabandi.
17. "Hewlett Packard University Equipment Grants," June 1993, $42,140. Granted by: HP Company.
PI: K. Sarabandi.
PENDING PROPOSALS
1. "Multifunction, compact vehicular antennas (MCA): New generation of antenna structures,"
Sponsor:National Science Foundation, Submission date: January 1997, $482,432. CoPls: L.
Katehi and K. Sarabandi.
2. "Nearfield polarimetric bistatic scattering: System design and measurements,"' Sponsor: Air
Force, submission date: January 1997, $124,000/3yrs. PI: K. Sarabandi.
3. "Wire Obstacle Detection Radar Sensor For Ground Vehicles," Sponsor: DARPA, submission
date: October 1996, $375,104/2 Yrs. PI: K.Sarabandi, CoPls: S. Lakshmanan, and K.C. Kluge.
4. "Characterization of Bistatic Scattering Coefficient of Some Distributed Targets at Microwave
and Millimeterwave Frequencies," Sponsor: U.S. Army Missile Command, submission date: Oct.
1996, $96,775/yr., PI: K. Sarabandi.
5. "Construction of a 77 GHz Polarimetric Radar FrontEnd for Assessment of Radar Sensors for
Automotive Applications," submission date: Feb. 1997, $60,000/lyr. Sponsor: General Motors
Corporation, PI: K. Sarabandi.
6. "Retrieval Algorithms for Active Remote Sensing," Sponsor: Synoptics (a Subcontract from
European Space Agnecy), Submission date: June 96, $25,000/lYr. PI: K.Sarabandi.
7. "Target Classification and Estimation of Biophysical Parameters Using The Correlation Function
of the Radar Backscatter," Sponsor: Joint Reseach Center of European Commission, submission
date: July 1996, $147,611/lyr. PI: K. Sarabandi.
8. "Initial Testbed Study of Automotive Radar," submission date: Feb. 1996, $50,000/lyr. Sponsor:
TRW Company, CoPIs: Robert Ervin, Gabriel Rebeiz, and Kamal Sarabandi.
PATENTS
1. Sarabandi, K., and R. Hartikka, "Microwave and millimeter wave polarization controller," Disclosure submitted to University of Michigan Intellectual Property Office, May 1994.
BOOK CHAPTERS
1. Kendra, J.R., F.T. Ulaby, and K. Sarabandi, "Snow probe for in situ determination
of wetness and density," in Microwave Aquametry: Electromagnetic Wave Interaction with
WaterContaining Materials, A. Kraszewski editor, IEEE Press, New York, 1996.
2. Ulaby, F.T., and K. Sarabandi, "Characterization of Antenna Polarization and Brightness
Temperature Stokes Vector," in AIAA Space Based Microwave Systems Calibration Manual, J.P.
Hollinger, editor, 1993.
3. Senior, T.B.A., and K. Sarabandi,
"Scattering Models for Point Targets," in Radar Polarimetry for Geoscience Applications, F.T.
Ulaby and C. Elachi, eds., Artech House, Dedham MA, 1990.
4. Whitt, M.W., F.T. Ulaby, and K. Sarabandi, "Polarimetric Scatterometer Systems and Measurements," in Radar Polarimetry for Geoscience Applications, F.T. Ulaby and C. Elachi, eds., Artech
House, Dedham MA, 1990.
JOURNAL PUBLICATIONS
1. Chiu, T.C., and K. Sarabandi, "Electromagnetic scattering interaction between a dielectric cylinder and a slightly rough surface," IEEE Trans. Antennas Propagat., submitted for publication
(June 1997).
2. Ulaby, F.T., A. Nashashibi, A. EIRouby, E. Li, R. Deroo, K. Sarabandi, R. Wellman, and B.
Wallace, "Millimeterwave scattering by terrain at near grazing incidence," IEEE Trans. Antennas
Propagat., submitted for publication (May 1997).
3. Sarabandi,K., A. Nashashibi,"Analysis and applications of backscattered frequency correlation
function,"/IEEE Trans. Geosci. Remote Sensing., submitted for publication (April 97).
4. Sarabandi, K., Eric S. Li, A. Nashashibi, and B. Litkouhi, "Modelling and Measurements of
Scattering from Road Surfaces at Millimeterwave Frequencies," IEEE Trans. Antennas Propagat.,
submitted for publication (Feb. 1997).
5. Sarabandi, K., and Y.C. Lin, "Simulation of Interferometric SAR Response for Characterization
of Scattering Phase Center Statistics of Forest Canopies," IEEE Trans. Geosci. Remote Sensing.,
submitted for publication (Jan. 97).
6. Lin, Y.C., and K. Sarabandi, "A Monte Carlo Coherent Scattering Model For Forest Canopies
Using Fractal Generated Trees," IEEE Trans. Geosci. Remote Sensing., submitted for publication
(Sept. 96).
7. Sarabandi, K., and E. Li, "Characterization of Optimum Polarization for Multiple Target Discrimination Using Genetic Algorithms,"lEEE Trans. Antennas Propagat., submitted for publication
(August 1996).
8. Sarabandi,K., and T.C. Chiu "Electromagnetic scattering from slightly rough surfaces with inhomogeneous dielectric profile," IEEE Trans. Antennas Propagat., submitted for publication (April
1996).
9. Sarabandi,K., "AkRadar equivalent of Interferometric SARs: A Theoretical Study for determination of vegetation height," IEEE Trans. Geosci. Remote Sensing., accepted for publication (Dec.
96).
10. Ulaby, F.T., P.R. Sequeira, A. Nashashibi, and K. Sarabandi, "Semiempirical model for radar
backscatter from snow at 35 and 95 GHz," IEEE Trans. Geosci. Remote Sensing., vol. 34, no.
5, pp. 10591065, Sept. 96.
11. Kendra, J. R., and K. Sarabandi, "A hybrid experimental/theoretical scattering model for dense
random media," IEEE Trans. Geosci. Remote Sensing., submitted for publication (Sept. 95).
12. Kendra, J. R., K. Sarabandi, F.T. Ulaby, "Radar measurements of snow: Experiment and analysis,"IEEE Trans. Geosci. Remote Sensing., submitted for publication (Sept. 95).
13. Nashshibi, A., and K. Sarabandi, "Experimental characterization of the effectrve propagation
constant of dense random media," IEEE Trans. Antennas Propagat., submitted for publication
(August 1995).
14. Siqueira, P.R., and K. Sarabandi, "Method of moments evaluation of the twodimensional quasicrystalline approximation," IEEE Trans. Antennas Propagat., vol. 44, no. 8, pp. 10671077, Aug.
1996.
15. Sarabandi, K., and E. S. Li, "A microstrip ring resonator for noninvasive dielectric measurements," IEEE Trans. Geosci. Remote Sensing., accepted for publication (May 95).
16. Dobson, M.C., F.T. Ulaby, L.E. Pirece, T.L. Sharik, K.M. Bergen, J. Kellndorfer, J.R. Kendra,
E. Li, Y.C. Lin, A. Nashashibi, K. Sarabandi, and P. Siqueira, "Estimation of Forest Biomass,"
IEEE Trans. Geosci. Remote Sensing., vol. 33, no. 4, pp. 887895, July 1995.
17. Sarabandi, K., and P. Siqueira, "Numerical scattering analysis for two dimensional dense random
media: characterization of effective permittivity," IEEE Trans. Antennas Propagat., vol. 45, no.
5, May 1997.
18. Freeman, A., M. Alves, B. Chapman, J. Cruz,, Y. Kim, S. Shaffer, J. Sun, E. Turner, and K.
Sarabandi, "SIRC Calibration Results," IEEE Trans. Geosci. Remote Sensing., vol. 33, no. 4,
pp. 848857, July 1995.
19. Stiles, J.M., and K. Sarabandi, "A scattering model for thin dielectric cylinders of arbitrary crosssection and electrical length," IEEE Trans. Antennas Propagat., vol. 44, no.2,260266, Feb.
1996.
20. Sarabandi, K., and A. Nashashibi, "A novel bistatic scattering matrix measurement technique
using a monostatic radar," IEEE Trans. Antennas Propagat., vol. 44, no. 1, 4150, Jan. 1996.
21. Sarabandi, K., L. Pierce, M.C. Dobson, F.T. Ulaby, J. Stiles, T.C. Chiu, R. De Roo, R. Hartikka,
A. Zambetti, and A. Freeman, "Polarimetric calibration of SIRC using point and distributed
targets," IEEE Trans. Antennas Propagat., vol. 33, no. 4, pp. 858866, July 1995.
22. Sarabandi, K., and T.C. Chiu," An optimum corner reflector for calibration of imaging radars,"
IEEE Trans. Antennas Propagat., vol. 44, no. 10, Oct. 1996.
23. Lin, Y.C., and K. Sarabandi, "Electromagnetic scattering model for a tree trunk above a ground
plane," IEEE Trans. Geosci. Remote Sensing., vol. 33, no. 4, pp. 10631070, July 1995.
24. Oh, Y., and K. Sarabandi, "An improved numerical simulation of electromagnetic scattering from
perfectly conducting random surfaces," IEE Proceedings Microwave, Antennas and Propagat.,
accepted for publication.
25. Nashashibi, A., K. Sarabandi, F.T. Ulaby, "A calibration technique for polarimetric coherentonreceive radar system," IEEE Trans. Antennas Propagat., vol. 43, no. 4, pp. 396404, April
1995.
26. Siqueira, P., K. Sarabandi, and F.T. Ulaby, "Numerical simulation of scatterer positions in a very
dense medium with an application to the twodimensional Born approximation," Radio Sci., vol.
30, no. 5, pp 13251339, 1995.
27. Sarabandi, K., "A technique for dielectric measurement of cylindrical objects in a rectangular
waveguide," IEEE Trans. Instrum. Meas., vol. 43, no. 6, pp. 793798, Dec. 1994.
28. Polatin, P.F., K. Sarabandi, and F.T. Ulaby, "Monte Carlo simulation of electromagnetic scattering
from a heterogeneous twocomponent medium," IEEE Trans. Antennas Propagat., vol. 43, no.
10, pp. 10481057, Oct. 1995.
29. Pierce, L.E., F.T. Ulaby, K. Sarabandi, and M.C. Dobson,"Knowledgebased classification of
polarimetric SAR images," IEEE Trans. Geosci. Remote Sensing., vol. 32, no. 5, pp. 10811086,
Sept. 94.
30. Nashashibi, A., F.T. Ulaby, and K. Sarabandi, "Measurement and modeling the millimeterwave
backscatter response of soil surfaces," IEEE Trans. Antennas Propagat., vol. 34, no. 2, 561572,
March 1996.
31. Kendra, J.R., F.T. Ulaby, and K. Sarabandi, "Snow probe for in situ determination of wetness
and density," IEEE Trans. Geosci. Remote Sensing., vol. 32, no. 6, 11521159, Nov. 1994.
32. Sarabandi, K., "A waveguide polarization controller," IEEE Trans. MTT, vol. 42, no. 11,
21712174, Nov. 1994.
33. Sarabandi, K., and P.F. Polatin, "Electromagnetic scattering from two adjacent objects," IEEE
Trans. Antennas Propagat., vol. 42, no. 4, pp. 510517, April 1994.
34. Sarabandi, K., Y. Oh, and F.T. Ulaby, "A numerical simulation of scattering from inhomogeneous
dielectric random surfaces," IEEE Trans. Geosci. Remote Sensing, vol. 34, no.2, 425432, March
1996.
35. Sarabandi, K., L. Pierce, Y. Oh, and F.T. Ulaby, "Power lines: Radar measurements and detection
algorithm for polarimetric SAR images," IEEE Trans. Aerospace and Electronic Sys., vol. 30, no.
2, 632648, April 1994.
36. Sarabandi, K., L.E. Pierce, Y. Oh, M.C. Dobson, A. Freeman, and P. Dubois, "Cross calibration
experiment using JPL AIRSAR and truckmounted polarimetric scatterometers," IEEE Trans.
Geosci. Remote Sensing, vol. 32, no. 5, 975985, Sept. 1994.
37. Stiles, J.M., K. Sarabandi, and F.T. Ulaby, "Microwave scattering model for grass blade structures," IEEE Trans. Geosci. Remote Sensing, vol. 31, no. 5, 10511059, Sept. 1993.
38. Sarabandi, K., "Calibration of a polarimetric synthetic aperture radar using a known distributed
target" IEEE Trans. Geosci. Remote Sensing, vol. 32, no. 3, 575582, May 1994.
39. Pierce, L.E., K. Sarabandi, and F.T. Ulaby, "Application of an artificial neural network in a canopy
scattering model inversion," Int. J. Remote Sensing, vol. 15, no. 16, 32633270, 1994.
40. Polatin, P.F., K. Sarabandi, and F.T. Ulaby, "An iterative inversion algorithm with application to
the polarimetric radar response of vegetation canopies," IEEE Trans. Geosci. Remote Sensing,
vol. 32, no. 1, 6271, Jan. 1994.
41. Sarabandi, K., P.F. Polatin, and F.T. Ulaby, "Monte carlo simulation of scattering from a layer
of vertical cylinders," IEEE Trans. Antennas Propagat., vol. 41, no. 4, 465475, April 1993.
42. Sarabandi, K., Y. Oh, and F.T. Ulaby, "Measurement and calibration of differential Mueller matrix
of distributed targets," IEEE Trans. Antennas Propagat., vol. 40, no. 12, 15241532, Dec. 1992.
43. Dobson, M.C., L.E. Pierce, K. Sarabandi, F.T. Ulaby, and T. Shark, "Preliminary analysis of
ERS1 SAR for forest ecosystem studies," IEEE Trans. Geosci. Remote Sensing., vol. 30, no. 2,
203211, March 1992.
44. Oh, Y., K. Sarabandi, and F.T. Ulaby,"An empirical model and an inversion technique for radar
scattering from bare soil surfaces," IEEE Trans. Geosci. Remote Sensing., vol. 30, no. 2,
370381, March 1992.
45. Sarabandi, K., A. Tavakoli, and F.T. Ulaby, "Propagation in a twodimensional periodic random
medium with inhomogeneous particle distribution," IEEE Trans. Antennas Propagat., vol. 40,
no. 10, 11751186, Oct. 1992.
46. Sarabandi, K., "Derivation of phase statistics of distributed targets from the Mueller matrix,"
Radio Sci., vol. 27, no. 5, pp 553560, 1992.
47. Ulaby, F.T., K. Sarabandi, and A. Nashashibi,"Statistical properties of the Mueller matrix of
distributed targets," IEE ProceedingsF: "Remote Sensing Radars", vol. 139, no. 2, 136146,
1992.
48. Sarabandi, K., "Scattering from dielectric structures above impedance surfaces and resistive
sheets," IEEE Trans. Antennas Propagat., vol. 40, no. 1, 6778, Jan. 1992.
49. Tavakoli, A., K. Sarabandi, and F.T. Ulaby, " Microwave propagation constant for a vegetation
canopy at Xband", Radio Sci., vol.28, no.4, 549588, JulyAug. 1993.
50. Sarabandi, K., L.E. Pierce, and F.T. Ulaby, "Calibration of a polarimetric imaging SAR", IEEE
Trans. Geosci. Remote Sensing., vol. 30, no. 3, 540549, May 1992.
51. Sarabandi, K., Y. Oh, and F.T. Ulaby, " Application and performance characterization of polarimetric active radar calibrator", IEEE Trans. Antennas Propagat., vol. 40, no. 10, 11471154,
Oct. 1992.
52. Tavakoli, A., K. Sarabandi, and F.T. Ulaby, " Horizontal propagation through periodic vegetation
canopies", IEEE Trans. Antennas Propagat., vol. 39, no. 7, 10141023, July 1991.
53. Sarabandi, K., and F.T. Ulaby, " High frequency scattering from corrugated stratified cylinders",
IEEE Trans. Antennas Propagat., vol. 39, no. 4, 512520, April 1991.
54. Sarabandi, K.," Simulation of a periodic dielectric corrugation with an equivalent anisotropic
layer", Int. J. Infrared and Millimeter Waves, vol. 11, no. 11, 13031321, Nov. 1990.
55. Ulaby, F.T., M.W. Whitt, and K. Sarabandi, "AVNABased polarimetric scatterometers", IEEE
Antennas Propagat. Magazine, vol. 32, no. 5, Oct. 1990.
56. Sarabandi, K., and F.T. Ulaby, " A convenient technique for polarimetric calibration of radar
systems", IEEE Trans. Geosci. Remote Sensing, vol. 28, no. 6, 10221033, Nov. 1990.
57. Sarabandi, K., "Scattering from variable resistive and impedance sheets", J. Electromag. Waves
and Applics., vol. 4, no. 9, 865891, 1990.
58. Sarabandi, K., and T.B.A. Senior, "Low frequency scattering from cylindrical structures at oblique
incidence", IEEE Trans. Geosci. Remote Sensing, vol. 28, no. 5, 879885, Sept. 1990.
59. Senior, T.B.A., K. Sarabandi, J.Natzke, "Scattering by a narrow gap", IEEE Trans. Antennas
Propagat., vol. 38, no. 7, 11021110, July 1990.
60. Ulaby, F.T., K. Sarabandi, K. McDonald, M. Whitt, M.C. Dobson, "Michigan Microwave Canopy
Scattering Model", Int. J. Remote Sensing, vol. 11,no. 7, 12231253, July 1990.
61. Sarabandi, K., F.T. Ulaby, and T.B.A. Senior, "Millimeter wave scattering model for a leaf",
Radio Sci., 25, 918, Jan. 1990.
62. Sarabandi, K., F.T. Ulaby, and M.A. Tassoudji, "Calibration of polarimetric radar systems with
good polarization isolation", IEEE Trans. Geosci. Remote Sensing, vol. 28, no. 1, 7075, Jan.
1990.
63. Sarabandi, K., T.B.A. Senior, and F.T. Ulaby, "Effect of curvature on the backscattering from a
leaf", J. Electromag. Waves and Applics., 2, 653670,1988.
64. Sarabandi, K., and F.T. Ulaby, "Technique for measuring the dielectric constant of thin materials",
IEEE Trans. Instrum. Meas., vol 37, no. 4, 631636, 1988.
65. Senior, T.B.A., K. Sarabandi, and F.T. Ulaby, "Measuring and modeling the backscattering cross
section of a leaf", Radio Sci., 22, 11091116,1987.
CONFERENCE PAPERS
More than 110 papers and presentations in national and international conferences and symposia on electromagnetic scattering, random media modeling, microwave measurement techniques, radar calibration,
application of neural networks in inverse scattering problems, and microwave sensors. model for radar
backscatter from snow at 35 and 94 GHZ,"Proc. IEEE Trans. Geosci. Remote Sensing Symp.,
HONORS, AWARDS, AND PROFESSIONAL ACTIVITIES
* HP Equipment Award, April 1997.
* Henry Russel Award, The Regent of The University of Michigan, January 1997. (The higest
award granted at The University of Michigan).
* Teaching Excellence Award, The University of Michigan, March 1996.
* Second prize, IEEE APS'95 paper contest with Adib Nashashibi.
* HP Equipment Award, June 1993.
* Chairman of Geoscience and Remote Sensing Society Southeastern Michigan chapter.
* Member of the Electromagnetics Academy.
* Member of USNC/URSI Commission F.
* Member of review panel for NASA's Earth Science and Applications Division.
* Listed in Who's Who in Electromagnetics.
* Member of steering committee for IEEE AP/URSI symposium, Ann Arbor, June 1993.
* Member of steering committee for IEEE National Radar Conference, Ann Arbor, May 1996.
* Technical chairman and organizer of CEOS (Committee on Earth Observing Satellites) SAR
Calibration Workshop, Ann Arbor Sept. 1994.
* Chairman of radar science group in "Remote Sensing Science Workshop," Feb. 27  March
1 1995, NASA Goddard Space Flight Center.
* Chairman and organizer of numerous technical sessions in IEEEIGARSS and IEEEAP/URSI symposia.
* Senior member IEEE, Antennas and Propagation Society, Geoscience and Remote Sensing
Society.
* Journal Reviewer
 IEEE transactions on Geoscience and Remote Sensing.
 IEEE transactions on Antennas and Propagation.
 Journal of Electromagnetic Waves and Applications.
 Radio Science.
M. CRAIG DOBSON
Associate Research Scientist
Radiation Laboratory
Department of Electrical Engineering and Computer Science
The University of Michigan
Ann Arbor, MI 481092122
Date of Birth  October 25, 1951
B.A. Geology
B.A. Anthropology
M.A. Geography
September 1996  present
September 1989  September 1996
September 1984  August 1989
July 1983  September 1984
January 1981  July 1983
May 1979  January 1981
September 1973  January 1975
Rochester, New York, USA
University of Pennsylvania, Philadelphia, PA, 1973
University of Pennsylvania, Philadelphia, PA, 1973
University of Kansas, Lawrence, KS, 1981
Assoc. Research Scientist, Radiation Laboratory
Assistant Research Scientist, Radiation Laboratory
Sr. Assoc. Research Engineer, Radiation Lab
Assoc. Research Scientist, University of Kansas,
Center for Research, Remote Sensing Laboratory
Project Manager, Microwave Soil Moisture Project,
RSL, University of Kansas Center for Research
Project Scientist, Remote Sensing Laboratory,
University of Kansas Center for Research
Geologist, Location and Design Concepts Team,
Kansas Department of Transportation
SYNOPSIS OF PUBLICATIONS
Books:
Published (2), Chapt. (1)
Papers in Refereed Journals:
Published (39)
Papers Published in Conference Proceedings and Digests (77)
Technical Reports (46)
Papers Presented at Symposia and Workshops (127)
AWARDS
Prize Paper Award (1994)  IEEE Trans. Geoscience and Remote Sensing
Research Excellence Award (1996)  University of Michigan College of Engineering
BOOKS
Ulaby, F. T. and M. C. Dobson, Handbook of Radar Scattering Statistics for Terrain.
Artech House, Inc., Dedham, MA, 1989, 350 pages.
Ulaby, F. T. and M. C. Dobson, Radar Scattering Statistics Software and User's Manual,
Artech House, Inc., Dedham, MA, 1989, 100 pages.
Dobson, M.C. and F.T. Ulaby, Manual of Remote Sensing. 3rd Ed. in press, Volume on
Radar, chap. on Radar Remote Sensing of Soil, Am. Soc. of Photogrammetry, 1997.
SELECTED RECENT PAPERS
Dobson, M.C., F.T. Ulaby, T. LeToan, A. Beaudoin, E.S. Kasischke, "Dependence of
Radar Backscatter on Conifer Forest Biomass," IEEE Trans. Geosci. Rem. Sens.
30:2:412415,1992.
Dobson, M.C., L. Pierce, K. Sarabandi, F.T. Ulaby, T.L. Sharik, "Preliminary Analysis
of ERS1 SAR for Forest Ecosystem Studies," IEEE Trans. Geosci. Rem. Sens,
30:2:203211,1992.
Way, J.B., E. Rignot, K. McDonald, R. Oren, R. Kwok, G. Bonan, C. Dobson, L.
Viereck, "Evaluating the Type and State of Alaska Taiga Forests with Imaging
Radar for Use in Ecosystem Flux Models," IEEE Trans. Geosci. Rem. Sens,
32:2:353370,1994.
Pierce, L.E., K. Sarabandi, F.T. Ulaby, M.C. Dobson, "KnowledgeBased Classification
of SAR Images," IEEE Trans. Geosci. Rem. Sens, 32:5:10811086,1994.
Dobson, M.C., L.E. Pierce, F.T. Ulaby, "KnowledgeBased LandCover Classification
Using ERS1/JERS1 SAR Composites," IEEE Trans. Geosci. Rem. Sens,
34:1:83  99,1996.
Dobson, M.C., F.T. Ulaby, L.E. Pierce, "LandCover Classification and Estimation of
Terrain Attributes Using Synthetic Aperture Radar," Rem. Sens. Env., 51:1:199 214, 1995.
Peplinski, N.R., F.T. Ulaby, M.C. Dobson, "Dielectric Properties of Soils in the 0.31.3
GHz Range," IEEE Trans. Geosci. Rem. Sens., 33:3:803807,1995.
Dobson, M.C., F.T. Ulaby, L. E. Pierce, T.L. Sharik, K.M. Bergen, J. Kellndorfer, J.R.
Kendra, E. Li, Y.C. Lin, A. Nashashibi, K. Sarabandi, P. Siqueira, "Estimation
of Forest Biophysical Characteristics in Northern Michigan with SIRC/XSAR,"
IEEE Trans. Geosci. Rem. Sens., 33:4:877895,1995.
Kasischke, E.S., J.M. Melack, M.C. Dobson, "The Use of Imaging Radars for Ecological
Applications  A Review," Rem. Sens. Env., 59:141156, 1997.
Pierce, L.E., K.M. Bergen, M.C. Dobson, F.T. Ulaby, "Classification of Northern
Forests Using SIRC/XSAR," IEEE Trans. Geosci. Rem. Sens, 1997, sub.
Bergen, K.M., M.C. Dobson, L.E. Pierce, F.T. Ulaby, "Characterizing Carbon Dynamics
in a Northern Forest Using SIRC/XSAR Imagery," Rem. Sens. Env., in press.
Kellndorfer, J.M., M.C. Dobson, F.T. Ulaby, "A MultiEcoregion Classifier Based on
Existing Orbital Imaging Radar," Proc. 13th William T. Pecora Symp., August
20  22, 1996, Sioux Falls, SD.
Dobson, M.C. and J.M. Kellndorfer, "Spatial and Temporal Stability of a RegionalScale
LandCover Classification from Orbital SAR," 1996 Ecol. Soc. Am. Symp.,
Aug. 1115, 1996, Providence, RI., Sup. Bull. Ecol. Soc. Am., 77:3:115,
Kellndorfer, J.M., M.C. Dobson, F.T. Ulaby, "Toward Consistent Global Physiognomic
Vegetation Mapping Using ERS/JERS SAR Classification", IGARSS'97 Digest,
August 48, 1997, Singapore. 1996.
Robert N. Treuhaft
Radar Science and Engineering Section
Mail Stop 300235
Jet Propulsion Laboratory
4800 Oak Grove Drive
Pasadena, CA 91109
Bob_Treuhaft@ radaremail.jpl.nasa.gov
8183546216
Education
Ph.D. Physics, University of California, Berkeley, 1982, thesis topic in high energy nuclear
physics: "A (proton, 2proton) Study of High Momentum Components at 2.1
GeV".
M. S. Physics, University of California, Berkeley, 1978.
B. S. Physics, Magna cum laude, Departmental Honors, Yale University, 1976.
Employment
19931997: Member technical staff, Radar Science and Engineering, Jet Propulsion Lab.
19861993: Technical Group Supervisor, Astrometric Techniques Group, JPL.
19831986: Project Manager, Radio Metric Technology Development, JPL.
Refereed Publications
R. N. Treuhaft, S. N. Madsen, M. Moghaddam, and J. J. van Zyl, "Vegetation Characteristics
and Surface Topography from Interferometric Radar," Radio Science, 31, p. 14491485, 1996.
R. P. Linfield, S. J. Keihm, M. J. Mahoney, L. P. Teitelbaum, R. N. Treuhaft, and S. J. Walter,
"A Test of WVRBased Troposphere Calibration Using VLBI Observations on a 21km
Baseline," Radio Science, 31, p. 129146, 1996.
R. N. Treuhaft, S. T. Lowe, M. Bester, W. C. Danchi, and C. H. Townes, "Vertical Scales of
Turbulence at the Mt. Wilson Observatory," Astrophysical Journal, 453, p. 522531, November
1995.
R. N. Treuhaft and S. T. Lowe, "A Measurement of Planetary Relativistic Deflection,"
Astronomical Journal, 102, p. 18791888, November 1991. (Reviewed in Science News,
November 9, 1991 and in Nature, November 21, 1991).
0. J. Sovers, C. D. Edwards, C. S. Jacobs, G. E. Lanyi, K. M. Liewer, and R. N. Treuhaft,
"Astrometric Results of 19781985 Deep Space Network Radio Interferometry: The JPL 19871
Extragalactic Source Catalog," Astronomical Journal, 95, p. 16471658, 1988.
R. N. Treuhaft and G. E. Lanyi, "The Effect of the Dynamic Wet Troposphere on Radio
Interferometric Measurements," Radio Science, 22, p. 251265, 1987.
Presentations and Conference Proceedings
R. N. Treuhaft, M. Moghaddam, and B. J. Yoder, "Forest Vertical Structure from Multibaseline
Interferometric Radar for Studying Growth and Productivity" IGARSS97, Singapore, August 1997 (invitec
R. N. Treuhaft, M. Moghaddam, and J. J. van Zyl, "Combining Radar Interferometry and
Polarimetry to Estimate Forest Vegetation and Surface Parameters," PIERS97, Cambridge,
Massachusetts, July 1997 (invited).
R. N. Treuhaft, E. Rodriguez, M. Moghaddam, K. Sarabandi, and J. J. van Zyl., "Multibaseline,
Multifrequency Interferometric SAR for Vegetation and Surface Topographic Parameter
Estimation," URSI 25th General Assembly, Lille, France, August 1996 (invited).
R. N. Treuhaft, M. Moghaddam, K. Sarabandi, and J. J. van Zyl, "Extracting Vegetation and
Surface Characteristics from Multibaseline Interferometric SAR," IGARSS'96, Lincoln,
Nebraska, May 1996.
R. N. Treuhaft, "The Information Content of Interferometric Synthetic Aperture Radar: Vegetation
and Underlying Surface Topography," 6th Annual JPL Airborne Earth Science Workshop, March
1996 (invited).
R. N. Treuhaft, J. J. van Zyl, and K. Sarabandi, "Extracting Vegetation and Surface
Characteristics from Multibaseline Interferometric SAR," EOS Transactions, American
Geophysical Union, 76, November 1995.
R. N. Treuhaft and M. Moghaddam, "The Accuracy of Vegetation Characteristics Extracted from
Interferometric SAR Data," Proceedings of Progress in Electromagnetics Research Symposium,
Seattle Washington, p. 905, July 1995.
R. N. Treuhaft, M. Moghaddam, E. Rignot, S. S. Saatchi, and J. J. van Zyl, "Extracting
Vegetation Topographic and Scattering Characteristics from Interferometric SAR," National Radio
Science Meeting, Boulder, Colorado, January 6, 1995.
L. P. Teitelbaum, S. J. Keihm, M. J. Mahoney, R. P. Linfield, G. M. Resch, and R. N.
Treuhaft, "A Test of WVRBased Troposphere Delay Calibration Using VLBI Observations on a
20km Baseline," National Radio Science Meeting, Boulder, Colorado, January 5, 1995.
R. N. Treuhaft, M. Bester, W. C. Danchi, S. T. Lowe, and C. H. Townes, "Toward 10 Milliarcsecond Infrared Astrometry," Proceedings of SPIE Symposium on Astronomical
Telescopes and Instrumentation for the 21st Century, Kona, Hawaii, March 1994.
R. N. Treuhaft, B. L. Gary, S. J. Keihm, R. P. Linfield, M. J. Mahoney, L. P. Teitelbaum, S. J.
Walter, and J. Z. Wilcox, "Minimizing Tropospheric Path Delay Effects in Astrometric and
Geodetic VLBI," URSI 24th General Assembly, Kyoto, Japan, September 1993 (invited).
M. Bester, W. C. Danchi, C. H. Townes, and R. N. Treuhaft, "Atmospheric Seeing at Infrared
Wavelengths," 182nd Meeting of the American Astronomical Society, Berkeley, California,
June 1993.
R. N. Treuhaft, "Subnanoradian, GroundBased Tracking of Spaceborne Lasers," NASA/DOD
Workshop on Advanced Technologies for Planetary Instruments, Fairfax, Virginia, April, 1993.
R. N. Treuhaft, "Astrometry for Deep Space Tracking," National Radio Science Meeting.
Boulder, Colorado (invited), January 7, 1993.
CURRICULUM VITA
April 1994
JAKOB JOHANNES VAN ZYL
ADDRESS:
Radar Science and Engineering Section,
M.S. 300243, Jet Propulsion Laboratory,
California Institute of Technology,
4800 Oak Grove Drive,
Pasadena, CA 91109
(818) 3541365.
RESEARCH INTERESTS:
EM wave propagation and scattering, development of remote sensing techniques,
radar polarimetry and interferometry, antenna theory.
EDUCATION:
Ph.D., Electrical Engineering, 1986.
California Institute of Technology, Pasadena, CA.
Thesis Title: On the Importance of Polarization in Radar Scattering Problems.
M.S., Electrical Engineering, 1983.
California Institute of Technology, Pasadena, CA.
Hons. B.Eng., (Cum Laude) Electrical Engineering, 1979.
University of Stellenbosch, Stellenbosch, South Africa.
MEMBERSHIPS AND HONORS:
1977 Philips prize for best performing junior in electrical engineering, University
of Stellenbosch.
1979 Siemens prize for best achievement in graduating class, electrical engineering,
University of Stellenbosch.
1
1988 JPL Director's Research Achievement award.
1988 IEEE Geoscience and Remote Sensing Transactions Paper Prize.
Patent: Data volume reduction for imaging radar polarimetry.
Patent: Method for providing a polarization filter for processing synthetic aperture
radar image data
NASA Certificates of Recognition:
* Data volume reduction for imaging radar polarimetry.
* Unsupervised classification of scattering mechanisms using radar polarimetry
data.
* Imaging Radar Polarimetry.
* Polarization filtering of SAR data.
* Data volume reduction for singlelook polarimetric imaging radar data: 8bit
and 4bit quantization.
* Calibration of polarimetric radar images using only image parameters and
trihedral corner reflectors.
* Incorporation of polarimetric radar images into multisensor data sets.
* Classification of earth terrain using polarimetric synthetic aperture radar
images.
* Approaches to modeling polarization characteristics of surfaces for radar polarimetry.
* Calibration of NASA/JPL DC8 SAR data.
* Calibration of Stokes and scattering matrix format polarimetric SAR data
* Unsupervised segmentation of polarimetric SAR data using the covariance
matrix
* POLCAL Version 4.0
* Iterative Bayesian classification in polarimetric SAR
2
* Direction angle sensitivity of agricultural field backscatter with AIRSAR
data
* Software for calibration of polarimetric SAR data
Memberships:
* The Electromagnetics Academy: Institute for Electromagnetic Modeling and
Applications
* IEEE
* Technical Chairman of the 1993 Progress in Electromagnetics Research Symposium
WORK EXPERIENCE:
1980  1982:
1983  1985:
1986  01/1990:
02/1990  present:
Institute for Electronics, University of Stellenbosch, South
Africa. Member of research staff. Responsible for design and
assembly of a microprocessor based radar measurement system
for use in study of ocean waves.
Research Assistant, California
Institute of Technology, Pasadena, CA. Advisor: Prof. C. H.
Papas. Also served as Teaching Assistant for a course on the
physics of remote sensing (Teacher: C. Elachi).
Member of Radar Sciences group of Geology and Planetology
Section at the Jet Propulsion Laboratory, Pasadena, CA.
Group supervisor, Aircraft SAR group in the Radar Science
and Engineering Section at the Jet Propulsion Laboratory,
Pasadena, CA. Overall technical and line management responsibility for the NASA/JPL multifrequency AIRSAR system
3
CURRICULUM VITAE
David J. Harding
ADDRESS:
NASA Goddard Space Flight Center
Mail Code 921
Greenbelt, MD 20771
3012864849 (voice), 1616 (fax)
harding @denali.gsfc.nasa.gov
Ph. D., Cornell University, 1988
major: geological sciences, minor:
B. Sc., Cornell University, 1980
major: geological sciences
EDUCATION:
remote sensing
POSITIONS HELD:
1991current: Staff Scientist, Laboratory for Terrestrial Physics, NASA GSFC
19901991: Research Faculty, Department of Geology, University of Maryland
19881990: NRC Postdoctoral Research Assoc., Lab for Terrestrial Physics, NASA GSFC
CURRENT FUNDING:
19961998:
19951997:
19941997:
Laser Altimeter Processing Facility (PI, NASA MTPE, $200K in FY97)
ThreeDimensional Canopy Structure: Measurement by Laser Altimetry and Input
to Ecology Models (PI, NASA Ecological Processes and Modelling Program,
$118K in FY97, final year)
MultiBeam Laser Altimeter Science Studies (PI, NASA Solid Earth Processes
Branch, $20K in FY97)
PENDING PROPOSALS:
19982000:
19981999:
19982000:
19981999:
this submission
Surface Lidar and Optical Image Fusion for Improved Boreal Forest Canopy
Structure Parameterizations (PI, NASA NRA97MTPE08, proposal in
preparation)
Northern Forest Biophysical Properties for MODIS Land Product Validation (CoI,
NASA NRA87MTPE03, 0.1 MY per year)
Tropical Forest Canopy Structure from Multisensor Remote Sensing (CoI, NASA
NRA97MTPE02, 0.2 MY per year)
PROFESSIONAL SERVICE:
1995:
1994:
1994:
1994:
1993:
19901993:
Remote Sensing Sciences Workshop, NASA Ecological Processes and Modelling
Program
Peer Review Panel, Topography and Surface Change Program, NASA Solid Earth
Processes Branch
Workshop on the Use of Satellites in Natural Disaster Reduction, NASA Solid
Earth Processes Branch
SAR Interferometry and Surface Change Detection Workshop, NASA Solid Earth
Processes Branch
Airborne Geophysics Workshop, Committee on Geodesy, National Academy of Sci.
Joint NASA  Italian Space Agency Topographic Mission Concept Working Group
SELECTED PUBLICATIONS:
Interferometric SAR and Laser Altimeter Measurement of Canopy Height Characteristics for
Coniferous Forests, E. Rodriguez, T. Michel, D. J. Harding, submitted, Radio Science.
Characterization of vertical canopy structure derived from laser altimeter waveforms of Gifford
Pinchot National Forest, J.F. Weishampel, D. J. Harding, and J. B. Blair, accepted, Remote
Sensing of Environment.
Remote Sensing of Forest Canopies, J.F. Weishampel, K.J. Ranson, D.J. Harding, 1996, Selbyana,
17:614.
The Global Topography Mission, T. Farr, D. Evans, H. Zebker, D. Harding, J. Bufton, T. Dixon, S.
Vetrella, and D. Gesch, 1995, EOS, Trans. Amer. Geophys. Union, 76(21):213&218 &
76(22):225&228229.
Airborne Laser Altimetry and Interferometric SAR Measurements of Canopy Structure and SubCanopy Topography in the Pacific Northwest, D.J. Harding, J.B. Blair, E. Rodriguez, T.
Michel, 1995, Proc. Second Topical Symposium on Combined Optical  Microwave Earth and
Atmosphere Sensing (COMEAS'95), 2224.
Laser Altimetry Waveform Measurement of Vegetation Canopy Structure, D.J. Harding, J.B. Blair,
J.B. Garvin, W.T. Lawrence, 1994, Proceedings of IGARSS'94, Vol II, 12511253.
Optimization of an Airborne Laser Altimeter for Remote Sensing of Vegetation and Tree
Canopies, J.B. Blair, D.B. Coyle, J.L. Bufton, and D.J. Harding, 1994, Proceedings of
IGARSS'94, Vol. II, 939941.
Laser Altimetry of Terrestrial Topography: Vertical Accuracy as a Function of Surface Slope,
Roughness, and Cloud Cover, D.J. Harding, J.L. Bufton, and J.J. Frawley, 1994, IEEE Trans.
Geoscience and Remote Sensing, 32:329339.
Erosion Dynamics and Patterns on the Ethiopian Plateau of Northeast Africa: a Fractal Process, J.
Weissel, A. Malinverno, D. Harding, and G. Karer, 1995, in Fractals in Petroleum Geology
and Earth Processes, C. Barton and P. La Pointe, eds., Plenum Press, 127142.