Interferometric TanDEM-X data are used together with high-resolution, airborne lidar-derived digital elevation models (DEMs) to produce digital canopy models (DCMs) for the boreal forests of Remningstorp and Krycklan, situated in southern and northern Sweden, respectively. An overview of interferometric data processing is given. First results showing the potential of TanDEM-X-based forest canopy mapping are presented. It is concluded that baselines giving height-of-ambiguity values in the order of 50-80 meters are preferable, although factors such as angle of incidence and along-track baseline are also of importance. Clear-cuts can easily be detected thanks to the high resolution of TanDEM-X imagery. Seasonal variations of scattering height are most visible for deciduous trees, where the scattering height is significantly lower in the winter, probably due to the lack of leaves. \ua9 2013 IEEE

Soja, Maciej J.

Ulander, Lars M. H.

Soja, Maciej

Ulander, Lars

Chalmers Research

Digital Canopy Model Estimation from TanDEM-X Interferometry using High-Resolution Lidar DEM

Chalmers Publication Library

 DIGITAL CANOPY MODEL ESTIMATION FROM TANDEM-X 
INTERFEROMETRY USING HIGH-RESOLUTION LIDAR DEM 
Maciej J. Soja1) and Lars M. H. Ulander1,2) 
1) Chalmers University of Technology, Gothenburg, Sweden 
2) Swedish Defence Research Agency, Linköping, Sweden 
 
 
ABSTRACT 
 
Interferometric TanDEM-X data are used together with 
high-resolution, airborne lidar-derived digital elevation 
models (DEMs) to produce digital canopy models (DCMs) 
for the boreal forests of Remningstorp and Krycklan, 
situated in southern and northern Sweden, respectively. An 
overview of interferometric data processing is given. First 
results showing the potential of TanDEM-X-based forest 
canopy mapping are presented. It is concluded that baselines 
giving height-of-ambiguity values in the order of 50-80 
meters are preferable, although factors such as angle of 
incidence and along-track baseline are also of importance. 
Clear-cuts can easily be detected thanks to the high 
resolution of TanDEM-X imagery. Seasonal variations of 
scattering height are most visible for deciduous trees, where 
the scattering height is significantly lower in the winter, 
probably due to the lack of leaves. 
 
 Index Terms— synthetic aperture radar (SAR), digital 
canopy model (DCM), forest mapping 
 
1. INTRODUCTION 
 
TanDEM-X (TDX) is a new X-band (9.65 GHz) synthetic 
aperture radar (SAR) satellite from the German Aerospace 
Center (DLR) launched in June 2010. TanDEM-X circulates 
in Earth’s orbit in a tight tandem formation with its almost 
identical twin satellite TerraSAR-X (TSX), launched in June 
2007. Together, the two satellites create the first permanent, 
single-pass SAR interferometer in space (here simply called 
TDM). The main goal of the TDM mission is to obtain the 
first global, high-resolution digital elevation model (DEM) 
[1]. 
 In densely forested regions, X-band signals will 
primarily scatter from tree canopies, thus introducing a 
significant offset from ground level in TDM-based DEM 
measurements. However, if an accurate, high-resolution 
external DEM is available, this offset can be estimated and 
used for forest height mapping. 
 The main scope of this paper is to show that digital 
canopy models (DCMs) can be derived from TDM 
interferometry using an accurate, high-resolution external 
DEM. Two test sites are used: Remningstorp, situated in 
southern Sweden, and Krycklan, situated in northern 
Sweden. TDM-derived DCMs are here compared to lidar 
DCMs acquired during the BioSAR 2008 and 2010 
campaigns. Similar studies were performed in [2], where X-
SAR data from the SRTM InSAR mission were used, and in 
[3], where airborne X-band ESAR data were used to 
measure forest height.  
 This paper is structured as follows. First, the 
experimental and reference data used in this study are 
presented. Next, the processing steps for TDM DCM 
generation are summarized. Thereafter, a comparison of 
TDM DCMs and lidar DCMs is shown, and some 
observations are made. The work is then summarized and 
evaluated in the last section. 
 
2. EXPERIMENTAL DATA 
 
In this study, interferometric TDM image pairs are used to 
create digital canopy models (DCM) for two test sites in 
Sweden: Remningstorp, a hemi-boreal forest situated in 
southern Sweden, and Krycklan, a boreal forest situated in 
northern Sweden. While the topography in Remningstorp is 
fairly flat, Krycklan features a hilly terrain with stand-level 
slopes reaching 20 degrees. Both test sites have been 
extensively used in previous studies (see [4] and references 
therein); large quantities of reference data are thus available. 
Interferometric TDM image pairs provided by the German 
Aerospace Center for both Remningstorp and Krycklan are 
used. 
 As ground level reference, high-resolution lidar DEMs 
are used. They were acquired during an on-going nation-
wide lidar mapping campaign run by Swedish Land Survey. 
The campaign aims at covering the whole of Sweden with a 
2 m x 2 m grid DEM with a mean height error of the order 
of 0.5 meters. The campaign is planned to be finished by the 
year 2015.  
 The reference lidar DCMs used in this study were 
acquired within the BioSAR campaigns (in 2010 for 
Remningstorp and in 2008 for Krycklan). Stand delineations 
are also used. For Remningstorp, a set of 665 delineated 
regions is available, consisting primarily of forest stands, but 
also pastures, marshes, open fields, water, private properties, 
and others. For Krycklan, a set of 1372 delineated regions is 
available, consisting primarily of mature and young forest 
stands, but also bare rock, marshes, private properties, and 
others. For both test sites, meteorological data provided by 
Swedish Hydrological and Meteorological Institute (SMHI) 
including temperature, precipitation, and wind information 
are also available.  
 
3. DCM PROCESSING 
 
TDM data are provided by DLR in CoSSC (Co-registered 
Slant range Single look Complex) format. For the data 
acquired in the bistatic mode, two images are available – one 
monostatic image acquired by the active satellite (master 
image), and one bistatic image acquired by the passive 
satellite (slave image). As the two TDM images are already 
co-registered and filtered to the same Doppler spectrum [5], 
no additional pre-processing is required and interferometric 
processing can be carried out directly. 
 TDM data are processed interferometrically using a 
Matlab algorithm based on [5] and developed specially for 
this project. The processing can be summarized in the 
following five steps: 
1. Georeferencing and DEM interpolation, 
2. DEM interferogram computation, 
3. TDM interferogram flattening, 
4. DCM computation, 
5. DCM geometric correction. 
 During the first step, range (R) and zero-Doppler time 
(ZDT) are computed for each DEM pixel using a Newton-
Rhapson-based algorithm. A look-up table (LUT) mapping 
DEM coordinates to range-ZDT coordinates is created. The 
LUT is then re-sampled to the range-ZDT grid of the master 
image, giving a geocoding LUT containing DEM 
coordinates for each SLC pixel. This inversion is the single 
most computationally demanding step in the whole 
processing chain. When the geocoding LUT is created, 
DEM data can easily be interpolated to radar geometry.  
 In the second step, DEM phase is computed using 
satellite state vectors (orbit position vectors). The quasi-
bistatic acquisition geometry has to be considered in this 
step. In practice, the difference between the bistatic range 
(distance from the master satellite to a DEM pixel and then 
back to the slave satellite) and the monostatic range (two-
way distance between the master satellite and a DEM pixel) 
is computed, and then transformed into a synthetic DEM 
interferogram. Also here, a Newton-Rhapson based 
algorithm is used to compute the exact monostatic and 
bistatic range values. The displacements of the satellites 
during pulse propagation time are also accounted for.  
 During the third step, the DEM phase is removed from 
the TDM interferogram by means of complex multiplication 
of the TDM interferogram with the complex conjugate of the 
synthetic DEM interferogram. The interferogram is then 
multilooked coherently and the phase is unwrapped using an 
optimal 2π-window which minimizes the risk of wrapping. 
In practice, this is done by a multiplication with a complex 
constant with unit magnitude and a phase chosen in such a 
way, that the unwrapped phase histogram has its minima at 0 
and 2π. 
 In the fourth step, interferometric phase is transformed 
into forest height using a linear function. The slope of the 
function is equal to the height-of-ambiguity (HOA) divided 
by 2π, while the intercept is an image-specific height offset. 
HOA is the vertical height shift corresponding to a 2π 
interferogram phase shift, and it is computed geometrically 
from orbit state vectors and DEM, see [1]. The height offset 
is chosen in such a way, that the mean TDM height in a 
sample of non-forested areas is zero. The non-forested areas 
used as reference are chosen from the delineated regions 
classified as open fields and pastures, with an additional 
restriction on lidar DCM being lower than 0.1 m. 
 In the final fifth step, the DCMs are geometrically 
corrected to better match the lidar maps. As SAR images are 
acquired in side-looking geometry, there is a range offset 
present if scatterer position deviates from the DEM. This 
offset is compensated through range resampling and 
interpolation. This step is not mandatory, but it improves the 
geometric agreement between lidar DCM and TDM DCM, 
especially in the borders between regions with different land 
use or forest characteristics. 
 Note that all interferometric processing is performed in 
SLC (radar) geometry rather than in DEM geometry to 
preserve the high resolution of the data, minimize the need 
for oversampling, and preserve the original frequency 
components. Processing in DEM geometry would be simpler 
to implement and would not require the computationally 
demanding LUT inversion, but it is not recommended due to 
the aforementioned problems. Stand-level averages were 
also computed in SLC geometry. Only the final DCM 
products were re-sampled to DEM geometry for better 
viewing and comparison with reference data. All figures 
shown in this paper are in UTM coordinates (zone 33N for 
Remningstorp and zone 34N for Krycklan). Polarization 
dependence is not studied in this paper. All DCMs presented 
in this work were derived from VV-polarized TDM data. 
 
4. RESULTS 
 
A comparison between TDM DCM and lidar DCM is shown 
in Figure 1 for a 1500 m x 1200 m region in Remningstorp. 
The three TDM DCMs were acquired at three different 
baselines corresponding to different HOA values. As it can 
be observed, image quality decreases when HOA increases. 
HOA is inversely proportional to the perpendicular baseline. 
An increased HOA means lower baseline, which leads to a 
decreased height sensitivity. The signal-to-noise ratio in 
phase images becomes lower. Large baselines are therefore 
desirable for interferometric forest imaging. However, too 
  
0
5
10
15
20
25
(a) Lidar: 20100829 (b) TDM: 20110604 (c) TDM: 20120223 (d) TDM: 20111226
H
ei
gh
t [
m
]
 
Figure 1 A comparison between four DCMs for a 1500 m x 1200 m region in Remningstorp is here shown: a) Lidar DCM from 
August 2010, b) TDM DCM from June 2011 with HOA=49 m and θ0=41°, c) TDM DCM from February 2012 with HOA=77 m 
and θ0=41°, and d) TDM DCM from December 2011 with HOA=178 m and θ0=34°. Clearly, noise level in TDM DCMs increases 
with HOA. VV-polarized TDM data were used for TDM DCM estimation. θ0 is the nominal angle of incidence. 
-5 0 5 10 15 20 25
-5
0
5
10
15
20
25
R2=0.98
HOA=49 m, θ0=41
° (20110604)
H
O
A
=7
7 
m
, θ
0=
41
°  (
20
12
02
01
)
(a) HOA comparison: small difference
-5 0 5 10 15 20 25
-5
0
5
10
15
20
25
R2=0.91
HOA=77 m, θ0=41
° (20120201)
H
O
A
=1
78
 m
, θ
0=
34
°  (
20
11
12
26
)
(c) HOA comparison: medium difference
-5 0 5 10 15 20 25
-5
0
5
10
15
20
25
R2=0.89
HOA=49 m, θ0=41
° (20110604)
H
O
A
=1
78
 m
, θ
0=
34
°  (
20
11
12
26
)
(b) HOA comparison: large difference
-5 0 5 10 15 20 25
-5
0
5
10
15
20
25
R2=0.99
HOA=52 m, θ0=41
° (20110809)
H
O
A
=5
4 
m
, θ
0=
41
°  (
20
11
08
20
)
(d) Temporal stability: low HOA
-5 0 5 10 15 20 25
-5
0
5
10
15
20
25
R2=0.99
HOA=80 m, θ0=41
° (20120201)
H
O
A
=7
9 
m
, θ
0=
41
°  (
20
12
02
12
)
(e) Temporal stability: medium HOA
-5 0 5 10 15 20 25
-5
0
5
10
15
20
25
R2=0.9
HOA=172 m, θ0=34
° (20120117)
H
O
A
=1
82
 m
, θ
0=
34
°  (
20
12
01
28
)
(f) Temporal stability: high HOA
 
Figure 2 Scatter plots showing the consistency of stand-level height estimates (in meters) for 429 delineated forest regions in 
Remningstorp in different acquisition scenarios are shown above. Only VV-polarized acquisitions over Remningstorp were used. 
The dates of the acquisitions are shown in parentheses. θ0 is the nominal angle of incidence.  
 
 
0
5
10
15
20
(a) Lidar: 20080806 (b) TDM: 20110811 (c) TDM: 20120808
H
ei
gh
t [
m
]
 
Figure 3 A comparison between three DCMs for a 1600 m x 1000 m region in Krycklan is here shown: a) Lidar DCM from 
August 2008, b) TDM DCM from August 2011 with HOA=56 m, and c) TDM DCM from August 2012 with HOA=38 m. Two 
clear-cuts have been delineated: one clear-cut made between August 2008 and August 2011 is delineated with solid lines, while a 
second clear-cut made between August 2011 and August 2012 is delineated with dashed lines. VV-polarized TDM data acquired 
at the nominal angle of incidence of 41° were used for TDM DCM estimation. 
large baselines lead to strong baseline decorrelation and an 
increased risk of wrapping. It has been observed, that HOA 
values between 50 and 80 meters present a good 
compromise. Note that, although HOA is a good indicator of 
the potential quality of an image, other parameters such as 
the angle of incidence, along-track baseline, and weather 
conditions can be of importance. 
 In Figure 2, scatter plots for stand-level TDM height 
averages are shown. In Figure 2a), a comparison between 
HOA=49 m and HOA=77 m is shown. The correlation is 
very high (R2=0.98), but a bias can be observed. This can 
probably be attributed to seasonal changes as the two images 
are acquired in summer and winter, respectively. In Figure 
2b) and Figure 2c), height estimates from an image with 
HOA=178 m are compared with two previously mentioned 
images with lower HOA values. When comparing HOA=178 
m and HOA=79 m, an offset can be observed in the first 
image. This can be explained by the uncertainty of absolute 
height calibration due to the noisiness of the image. In 
Figure 2d)-f), scatter plots showing temporal stability are 
shown. Two images separated in time with one repeat cycle 
(11 days) are compared for three HOA levels: around 50 m, 
around 80 m, and around 180 m. As it can be observed, 
correlation is excellent for the lower HOA levels. For the 
highest HOA value, the aforementioned offset is clearly 
visible. 
 In Figure 3 a comparison between three DCMs for a 
1600 m x 1000 m region in Krycklan is shown. Lidar DCM 
was acquired in August 2008, while the two other DCMs 
were acquired with TDM in August 2011 and 2012, 
respectively. A few changes can be observed between these 
acquisitions. These changes can be attributed to clear-cutting 
done in the forest.  
 In Figure 4, a comparison between a summer DCM and 
a winter DCM for a 1200 m x 1000 m region in 
Remningstorp is shown. The two DCMs are in general 
consistent with each other. The previously mentioned 
seasonal change can, however, be observed in the delineated 
deciduous (birch) stand. For this stand, TDM height in the 
winter DCM is significantly lower than in the summer DCM. 
This is probably due to the lack of leaves during winter. 
 
5. CONCLUSIONS 
 
The approach presented in this paper shows great potential 
as a future tool for detailed forest mapping. In countries like 
Sweden, where a high-resolution DEM will be available on a 
national scale, this method can easily be extended to larger 
areas. Further work in the project involves derivation of 
biomass retrieval algorithms based on physical models and 
regression, modeling of seasonal variations and topographic 
influence. 
 
ACKNOWLEDGEMENTS 
 
The authors would like to thank Swedish National Space 
Board (SNSB) for the funding, German Aerospace Center 
(DLR) for the TanDEM-X data, Swedish Land Survey for 
the DEM data, and Swedish Meteorological and 
Hydrological Institute (SMHI) for the weather data. 
 
REFERENCES 
 
[1] G. Krieger, A. Moreira, H. Fiedler, I. Hajnsek, M. 
Werner, M. Younis, et al., "TanDEM-X: A Satellite 
Formation for High-Resolution SAR Interferometry," 
IEEE Transactions on Geoscience and Remote Sensing, 
vol. 45, pp. 3317-3341, 2007. 
[2] S. Solberg, R. Astrup, T. Gobakken, E. Naesset, and D. 
J. Weydahl, "Estimating Spruce and Pine Biomass with 
Interferometric X-Band SAR," Remote Sensing of 
Environment, vol. 114, pp. 2353-2360, 2010. 
[3] J. Praks, O. Antropov, and M. T. Hallikainen, "LIDAR-
Aided SAR Interferometry Studies in Boreal Forest: 
Scattering Phase Center and Extinction Coefficient at X- 
and L-Band," Geoscience and Remote Sensing, IEEE 
Transactions on, vol. 50, pp. 3831-3843, 2012. 
[4] M. J. Soja, G. Sandberg, and L. M. H. Ulander, 
"Regression-Based Retrieval of Boreal Forest Biomass in 
Sloping Terrain using P-band SAR Backscatter Intensity 
Data," IEEE Transactions on Geoscience and Remote 
Sensing, vol. 51, pp. 2646-2665, 2013. 
[5] U. Balss, H. Breit, S. Duque, T. Fritz, and C. Rossi, 
"TanDEM-X Payload Ground Segment: CoSSC 
Generation and Interferometric Considerations," German 
Aerospace Center (DLR)2012.
 
 
0 5 10 15 20 25
(a) TDM: 20110604 (HOA=49 m)
(b) TDM: 20120201 (HOA=77 m)
Height [m]
 
Figure 4 A comparison between two TDM DCMs for a 1600 
m x 600 m region in Remningstorp is here shown. The birch 
stand discussed in Section 4 is delineated with solid lines. Also, 
a clear-cut is detected and delineated in dashed lines.  VV-
polarized TDM data acquired at the nominal angle of 
incidence of 41° were used for TDM DCM estimation. 


Digital Canopy Model Estimation from TanDEM-X Interferometry using High-Resolution Lidar DEM

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