DEFORMATION MEASUREMENT USING INTERFEROMETRIC SAR DATA
M. Crosetto
Institute of Geomatics, Campus de Castelldefels, 08860 Castelldefels (Barcelona), Spain - michele.crosetto@ideg.es
Commission II, WG 11/2
KEY WORDS: Remote Sensing, Monitoring, Detection, Modelling, Decision Support.
ABSTRACT:
The paper focuses on the differential interferometric SAR (Synthetic Aperture Radar) technique for the monitoring of terrain surface
deformations. The paper begins with a concise description of the properties of the differential interferometric phase, which represents
the main observation for the estimation of the deformations. Then the paper discusses of the main features of a new interferometric
SAR procedure. In particular, the interferometric SAR processing, the least squares adjustment procedure to estimate the terrain
deformations, and the geometric model that is needed to geocode the SAR products are described. The second part of the paper
illustrates two applications of the proposed procedure. The first one is a screening analysis, whose main goal is the detection of
unknown subsidence phenomena over a large area, based on a limited set of SAR images. The second one is a quantitative analysis
of a urban subsidence of small spatial extent, which was based on two independent ascending and descending SAR datasets.
1. INTRODUCTION
This paper addresses the quantitative measurement of terrain
deformations using the differential interferometric SAR
technique (DInSAR) based on satellite data. For a general
review of SAR interferometry, see Rosen et al. (2000). The
DInSAR technique has demonstrated its capability to measure
deformations in a wide range of applications, which include
landslides (Carnec et al., 1996), earthquakes (Massonnet et al.,
1993), volcanoes (Amelung et al., 2000), glacier dynamics
(Goldstein et al., 1993), and urban subsidences (Amelung et al.,
1999). A general discussion of different DInSAR applications
can be found in Hanssen (2001). There are different factors that
make the DInSAR technique a useful tool for deformation
monitoring. Firstly, it is sensitive to small terrain deformations,
say up to few millimetres in the best measurement conditions
(high image coherence, ete.). Secondly, DInSAR provides a
large area coverage, e.g. 100 by 100 km using ERS scenes,
with a relatively high spatial sampling density (with a typical 5-
look azimuth compression, the ERS images have a 20 by 20 m
pixel footprint). The third important characteristic is the
availability of large time series of SAR images, that for the
ERS satellites cover more than a decade, starting from 1991:
with these images it is possible to study the evolution of
deformation in the last 12 years. This represents an unmatched
capability compared with the traditional geodetic techniques.
An additional characteristic is that DInSAR can (potentially)
provide measurements with a quality that is comparable with
that of the traditional geodetic techniques. However, this can
only be achieved by implementing advanced DInSAR
processing and analysis procedures. In fact, besides the
deformation component, the DInSAR observations contain
different sources of errors: only appropriate modelling and
estimation procedures allow the deformations to be estimated
with high quality standards. Some of these procedures will be
discussed in the following section. In this section we briefly
recall the main components of the DInSAR observations.
The interferometric SAR (InSAR) techniques exploit the
information contained in the phase of two complex SAR
images (hereafter referred to as the master, M, and slave, S,
images). In particular, they exploit the phase difference
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(interferometric phase, AD,, ) of S and M. Let us consider a
point P on the ground, which remains stable in the time interval
between the image acquisitions. A®,, is related to the
distance difference SP — MP , which is the key element for the
InSAR DEM generation. When the point moves from P to P!
between two image acquisitions, besides the topographic phase
component «b AD includes the terrain movement
Topo * Int
contribution, D 4, - In the general case AD, includes:
Int
SPIMP SP.
Ep. o DARMP AMD
: Ë A A
4-7 4-7
+ ect Oh += OD
Atm Noise ?
AD Ini Atm + o Noise 7
= 0
where «*..«c,, are the phases of S and M; D is the
Atm
atmospheric contribution; @ , . 1S the phase noise; SP' is the
‘oise
| © E :
slave-to-P' distance; and A is the radar wavelength. If the
terrain topography is known (i.e. a DEM of the imaged area is
available), ®7,, can be computed ( 7, sim ) and subtracted
from A ,, . obtaining the so-called DInSAR phase Ao, ,, :
Mb, nt = AD, = o;
opo Sim -
=o Mov +O + Res Topo +P Noise ( | )
Atm
where O,., ;,, represents the residual component due to
DEM errors. In order to derive information on the terrain
movement, ® ,, has to be separated from the other phase
components. The best results are achieved when multiple
interferograms of the same scene are available.
In the following sections the strategy implemented at the
Institute of Geomatics to estimate the terrain deformations from
time series of SAR images is described. In the second part of
the paper two examples of DInSAR analysis based on stacks
ERS SAR images are illustrated. The first one is a screening
analysis, which allows unknown subsidence phenomena over
large areas to be detected using a limited set of images. The
second one is a quantitative analysis of a subsidence of small
spatial extent duc to mining activity, which is based on
ascending and descending datasets.
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