LONG-TERM SUBSIDENCE MONITORING OF CITY AREAS AT NORDIC LATITUDES USING ERS SAR DATA

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1 LONG-TERM SUBSIDENCE MONITORING OF CITY AREAS AT NORDIC LATITUDES USING ERS SAR DATA Tom R. Lauknes (1,2), Geir Engen (1), Kjell A. Høgda (1), Inge Lauknes (1), Torbjørn Eltoft (2), Dan J. Weydahl (3) and Knut Eldhuset (3) (1) NORUT Information Technology, Forskningsparken, PO Box 6434, 9294 Norway, (2) University of Tromsø, Norway, (3) Norwegian Defence Research Establishment, PO Box 25, N-2027 Kjeller, Norway, ABSTRACT The interferometric SAR (InSAR) technique may be a valuable tool for subsidence monitoring at different places of the Earth surface. Many results have been reported during the 1990 s where data from the ERS-1 and ERS-2 SAR sensors were used. This paper will present the ongoing long-term subsidence project in Norway, AO-1104, that are making use of ERS SAR data. The test area is located in the Oslo region in Norway. During the 1990 s, this area has undergone a lot of tunnel construction work, but also some smaller earthquakes have been registered in the region. Subsidence effects of several centimeters are connected to these events and documented by land surveillance. The changing topography from sea level to 700 m above sea level, the dominating coniferous forests outside the built-up areas, and the seasonal variations with snow, rain and temperatures below freezing at several months of the year, makes this project a real challenge when it comes to masking out the non-subsidence factors and interpreting the interferometric SAR signal. For this AO-project, a total of 45 ERS-1 and ERS-2 SAR raw data sets were obtained from ESA. The scenes are spanning a time interval from 1992 to Out of these scenes, 13 are from the winter, and 32 from the spring/summer/autumn period. The raw data sets are processed to single-look-complex SAR images using the phase preserving Extended Exact Transfer Function (EETF). Satellite orbit baselines are estimated using precise orbit data from ESA. Multiple differential interferograms are produced, as well as other products: coherence, lay-over and shadow masks and finally a simulated intensity map. 1 INTRODUCTION The interferometric SAR (InSAR) technique has been shown to be a valuable tool for subsidence monitoring in many different places of the Earth using various techniques of multi-temporal InSAR processing [1, 2, 3] The Oslo region in Norway is an interesting area to do subsidence monitoring with satellite SAR data. Between 1994 and 1999 there have been four smaller earthquakes in the Oslo region. Their strength has been from 2.5 to 3.6 on Richter s scale. In addition, there has been a lot of tunnel construction work in Oslo from 1992 to This has caused subsidence of several cm at different places around the city, which are documented by land surveillance techniques carried out by NORSAR, The Geological Survey of Norway, The Norwegian National Rail Administration and The Norwegian Public Roads Administration in conjunction with the tunnel construction work. It is also interesting to monitor the stability of the area north of Oslo where the new Oslo Airport Gardermoen was built in An introductory study was carried out in 2001 where interferometric ERS SAR coherence properties from the Oslo region were investigated [4]. This study concluded that as many as 811 ERS SAR scattering points within a 20 square km area in Oslo city gave high and stable coherence (greater than 0.8) over a time span of 22 months, even if the baselines varied from -670 m to +530 m. This indicated that there should be good possibilities for carrying out a closer investigation of the interferometric phase over a larger time period (i.e. between 1992 and 2000). This is indeed the aim with the work presented here that is supported under the ESA AO-1104 project where Norwegian Defence Research Establishment (FFI) is the principal investigator, and where NORUT-IT is the co-investigator. This paper first describes the test area and data set used. It then goes on to the particular SAR processing algorithm used, satellite baseline estimation and co-registration of the SAR scenes. Description of the interferometric SAR processing software is then given together with some preliminary results. Proc. of FRINGE 2003 Workshop, Frascati, Italy, 1 5 December 2003 (ESA SP-550, June 2004) 41_lauknes

2 2 TEST AREA AND DATA SET The test area is located in the region around Oslo, the capital of Norway. This area have a lot of coniferous forest. The built-up areas consist of city centers having large concrete blocks and houses, residential areas with concrete blocks of flats, residential areas with houses made of wood and with small gardens attached, industrial areas with large concrete/metal warehouses and parking areas, roads/highways and railway lines. Some agricultural areas are located in the northeastern part, and to the south. The elevations are ranging from sea level and up to 700 m. The higher elevations in the Oslo region will normally be covered by snow several months during the winter season. However, for the built-up areas the winter conditions in the 1990 s have varied considerably from snow to sleet and rain. We requested ESA for 41 ERS SAR raw data sets acquired over the Oslo region between May 1992 and January Some of the scenes were not available, and we finally received 20 ERS-1 and 14 ERS-2 SAR scenes from ESA. They were delivered from both the Italian PAF and UK PAF. In addition, FFI had 11 ERS-2 SAR raw data scenes that previously had been ordered from Tromsø Satellite Station (TSS) for another project [4]. The SAR data ordered from TSS were acquired between December 1997 and March All the 45 ERS SAR scenes were ordered using descending satellite track 337 and frame The ERS SAR coverage over the Oslo region is shown in Fig. 1. Out of these scenes, 20 were acquired with ERS-1 and 25 with ERS-2. There are 6 tandem pairs from 1995/1996. There are 13 scenes from the winter season (November to April), and 32 from the spring/summer/autumn. Fig. 1: Approximate ERS SAR coverage over the Oslo region in south Norway. 3 METHODOLOGY The raw scenes are processed to single look complex (SLC) images using the 2nd order Extended Exact Transfer Function (EETF) developed by FFI [5]. This SAR processor is preserving the phase very well. The processor has previously been tested on large volumes of ERS SAR data [6]. The SLC-images are laid out in the zero-doppler coordinate system during the SAR processing. This processing to zero-doppler results in congruent geometry for repeat orbit images and significantly simplifies the interferometric processing. The co-registration and the interferometric processing is performed using software developed by NORUT-IT. Parts of this software is tested extensively through the ESA pilot project [7]. The coarse co-registration of the SLC images is already defined through the zero-doppler coordinate system, the azimuth time to the first range line, and the precise orbit parameters. In the co-registration, one image will be referred to as the master, while the other is the slave. The PRC orbit defines the geometry for the master image, while the slave orbit is modified according to the co-registration process. In this manner it is possible to index every pixel in the two images by referring to the inter-orbit geometry that is now established between the images. ERS precision orbit files (PRC) are

3 downloaded from an ESA server at DLR where the ERS orbit data are given in 30 seconds intervals. The satellite baseline for pairs of images are estimated using the following procedure The master image is defining the scene geometry using the PRC information The slave image is using an estimated orbit based on the geometry established in the co-registration process. The satellite baseline is estimated for the center azimuth line Satellite baselines for other time positions along the azimuth direction are estimated using forward and backward orbit propagation using the ESA-propagator. SAR Raw data FFI EETF SAR Data (Single Look) Master Slave Precise Orbits DEM in map grid Coregistration and Resampling Calibration and Filtering MAP2SAR transform DEMSAR Formation Topographic phase estimation Layover/Shadow Mask Reference phase Removal Reference phase estimation Phase Complex Coherence map Topographic phase Removal Differential Fig. 2: Figure showing the processing flowchart The complex master and slave images are spectral filtered in both range and azimuth to enhance the signal to noise ratio. When producing the interferogram we do a complex multi-looking in both range and azimuth direction before calculating the interferogram phase. This measured phase can be given as a sum of different contributions [1]. φ = φ top + φ def + φ atm + φ n (1) Here, φ top is the phase due to two slightly different viewing angles, φ def is the phase contribution due to a possible motion of the target in the line-of-sight (LOS) direction. φ atm is a phase contribution caused by different path length through the atmosphere due to changing refractive index along the signal path. Finally, φ n is the phase noise. For this project we are interested in φ def, the deformation signal. The only component we can estimate and remove is φ top, and there are at least two ways to do it, one can either use the so-called three pass interferometry [8] or one can use a-priori information about the topography. In our case we have available a N50 Digital Elevation Model (DEM) delivered by the Norwegian Mapping Authority. This national DEM is made from digitizing the 1:50000 topographic maps with 20 m elevation contours. The digital raster version of this DEM has a grid spacing of 25x25 m. Our approach to simulate the topographic phase is to use the external reference DEM, and a backward solution of the range-doppler equations similar to [9]. By using this method the need for regridding in the SAR image domain is reduced. Using the original state vector of the master image, and the radar converted DEM, we simulate a brightness image using the local incidence angle and the terrain slope. We also produce lay-over and shadow masks to facilitate data processing. The simulated brightness image is correlated with the measured one to obtain and correct for a constant offset. The

4 offset in azimuth is mainly due to the uncertainties in the orbital state vectors. Finally, we generate the differential interferogram by doing a complex multiplication with the conjugated simulated topographic interferogram. After generation of the Differential interferogram, the baseline component is adjusted to obtain a flat interferogram without any orbital fringes. Fig. 2 shows the processing flowchart leading to the output products: phase, coherence, differential interferogram, lay-over/shadow mask and a simulated intensity image. The DEM is also available in the radar coordinate system after processing. Fig. 3 and Fig. 4 shows examples of some products. 4 EXPERIMENTAL RESULTS Fig. 3: Upper left: Elevation map of Oslo area. Upper right: Intensity map of ERS-1 image taken August 16, Lower left: Simulated intensity map. Lower right: Estimated coherence, August 16, 1995 June 26, Temporal baseline is 315 days. 5 SUMMARY The processing method will be tested on multiple SAR data for further verification. For the temporal analysis of the long-term Interferometric data set we will investigate the SBAS technique [2]. This technique makes it possible to link different subsets of interferograms separated by a rather short baseline. An estimate of atmospheric and topographic

5 Fig. 4: Left: Differential interferogram, Right:Differential, artifacts is given. Finally a temporal displacement estimation will be done and the results compared with land surveillance measurements. The final results will be geocoded and included in a GIS environment. REFERENCES [1] Ferretti A., Prati C. and Rocca F., Nonlinear Subsidence Rate Estimation Using Permanent Scatterers in Differential SAR Interferometry, IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 5, pp , [2] Berardino P., Fornaro G., Lanari R. and Sansosti E., A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential s, IEEE Transactions on Geoscience and Remote Sensing, Vol. 40, No. 11, pp , [3] Werner C., Wegmüller U., Strozzi T. and Wiesmann A., Interferometric Point Target Analysis for Deformation Mapping, Proc. of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Toulouse, France, July 2003, Vol. VII, pp , [4] Weydahl D. J., Eldhuset K. and Hauge S., Atmospheric effects on advanced modes, WEAG EUCLID CEPA9 RTP9.6 WE4.1.3, FFI-RAPPORT-2001/04826, Norwegian Defence Research Establishment, Kjeller, Norway, [5] Eldhuset K., Andersen P. H., Hauge S., Isaksson E., and Weydahl D. J., ERS tandem InSAR processing for DEM generation, glacier motion estimation and coherence analysis on Svalbard, International Journal of Remote Sensing, Vol. 24, No. 7, pp , [6] Eldhuset K., Aanvik F., Aksnes K., Amlien J., Andersen P. H., Hauge S., Isaksson E., Wahl T. and Weydahl D. J., First results from ERS tandem INSAR processing on Svalbard, ESA Fringe 96 Workshop on ERS SAR Interferometry, Vol. 1, pp , [7] Engen G., Guneriussen T. and Overrein Ø., Delta-K Interferometric SAR Technique for Snow Water Equivalent (SWE) Retrieval, IEEE Transactions on Geoscience and Remote Sensing, in press, [8] Zebker H. A., Rosen P. A., Goldstein R. M., Gabriel A. and Werner C. L., On the derivation of coseismic displacement fields using differential radar interferometry: The Landers Earthquake, Journal of Geophysical Research, Vol. 99, No. B10, pp , [9] Eineder M., Efficient Simulation of SAR s of Large Areas and of Rugged Terrain, IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No. 6, pp , 2003.