DIFFERENTIAL INSAR STUDIES IN THE BOREAL FOREST ZONE IN FINLAND

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1 DIFFERENTIAL INSAR STUDIES IN THE BOREAL FOREST ZONE IN FINLAND Kirsi Karila (1,2), Mika Karjalainen (1), Juha Hyyppä (1) (1) Finnish Geodetic Institute, P.O. Box 15, FIN Masala, Finland, (2) Helsinki University of Technology, P.O. Box 1200, FIN HUT, Finland ABSTRACT In this paper we present preliminary results on the applicability of differential SAR interferometry to detect and measure land movement in the vegetated areas where only few targets or small areas maintain coherence. Strong temporal decorrelation in the vegetated areas makes traditional DINSAR impracticable and, therefore, techniques that are based on phase values of single pixels and long time series should be applied. The study area is located on the southwest coast of Finland (22 35 E, N). The area consists mostly of forest and fields but it also comprises cities of Turku, Salo and Hanko and several small settlements. Related to the INSAR research there occur two interesting phenomena within the study area. Firstly, postglacial land uplift of a few millimeters per year is known to take place in the area. And secondly, since the center of Turku has been built on clay soil there occurs subsidence of many buildings. The data set consists of 24 ERS SAR images from Weather data provided by Finnish Meteorological Institute were used in the selection of SAR acquisitions. A new precise leveling, to be completed in 2004, will be used as a reference data for the land uplift study. Altogether 23 differential interferograms were generated and the analysis on the amount of coherent targets was carried out. Coherent targets or permanent scatterers can be found throughout the study area: buildings, cliffs and rocky islands in Turku archipelago. Preliminary analysis of the phase values has shown that there is remarkable phase noise present in the data. The phase data should be corrected for possible atmospheric effects and orbital errors in order to get deformation data. First we must investigate further whether the coherent target density is high enough for permanent scatterers analysis. And secondly more data will be acquired to get more reliable results. 1 INTRODUCTION SAR interferometry (INSAR) consists of groups of methods that can be applied for instance to measure land movement from synthetic aperture radar (SAR) images. Displacement detection is based on phase information of two SAR images. In order to measure the displacement reliably, the two signals must be coherent or the backscattering must come from the same sources. Often changes in the target area corrupt the coherence especially in areas covered by vegetation. In optimum conditions yearly deformation rates of a few millimeters have been detected [1,2]. Finland is sparsely inhabited and heavily vegetated, thus, the use of INSAR-techniques is difficult. In addition, varying weather conditions cause loss of coherence and also delay the signal as it passes through the atmosphere, and that also causes problems in the interpretation of results. The Southwest coast of Finland was selected as test area. Related to the INSAR, there are two interesting phenomena that occur in the study area: post glacial rebound and urban subsidence. The area consists mostly of forest and fields but it also comprises cities of Turku, Salo and Hanko and several small settlements. The rate of post-glacial rebound decreases 1-2 mm/year from northwest to southeast over 100 km distance (Fig. 1) [3]. DINSAR could offer spatially extensive information on postglacial rebound that is usually measured by leveling and GPS, and therefore possible local anomalies could be detected. Subsidence of buildings has been detected in the city center of Turku. The ground water level subsides, old wooden poles decompose and center sinks with rate of 1cm/year [4]. Selection of the ERS 1/2 SAR images was based on the weather data, normal baselines, and the temporal distribution of the images. Weather data from the Finnish Meteorological Institute contained information about snow cover, Proc. of FRINGE 2003 Workshop, Frascati, Italy, 1 5 December 2003 (ESA SP-550, June 2004) 56_karila

2 Fig. 1. Post glacial land uplift in Finland [3]. Table 1. Acquisitions selected and weather conditions in Turku [5] for each image. Mission Date Orbit _I_ Baseline Snow (cm) Rain (mm/d) Temperature ( C) E E E E E E E E E E ET ET 21 1 E E E E E E E E E E E E E E E Cloudiness (0-8)

3 precipitation, cloudiness, and temperature for 4 weather stations in the test area. SAR scenes that probably contained snow (especially melting snow) and clouds were avoided in the selection of the images. A total of 25 ERS 1/2 SLCI images from were requested. Table 1 presents the weather conditions for selected images. The National Land Survey (NLS) DEM with 25-meter cell size was used as reference DEM. Since it is evident that traditional DINSAR methods are practically useless for long-term deformation detection, we decided to concentrate on new techniques that make deformation measurement possible also in decorrelated areas. Using Permanent scatterers technique deformation can be detected in areas of high temporal decorrelation. The core idea is to use long time series of SAR data and phase coherent radar targets or permanent scatterers (PS) [1]. Purpose of this project is to study the possibilities to use INSAR techniques in Finland. This project started in early So far the analysis of the coherent targets has been done and the results are presented in this paper. In the future comprehensive analysis of the phase values of stable targets will be carried out. 2 PROCESSING A total of 24 ERS SAR acquisitions were coregistered on the sampling grid of the common master image. Image acquired on 24 May 1999 was selected as the master image in order to get low normal baseline dispersion (± 900 m). Software used for calculation of coherence images and interferograms was Atlantis EVInSAR. Coherence images were calculated using 5 x 5(25) window. The topographic phase for differential interferograms was retrieved from the reference DEM (source National Land Survey, NLS) using Delft precise orbits. Fig. 2. Multitemporal SAR image of the study area: mean coherence (blue), mean amplitude (green), standard deviation of the amplitude (red). Multitemporal SAR image (Fig. 2) was formed using the coherence and amplitude images. Amplitude values were calibrated [6] (ratio of the mean amplitude of all pixels for each acquisition and mean amplitude of all acquisitions) and degraded to square images to make them compatible with coherence images. The mean amplitude, coherence and

4 standard deviation of amplitude were calculated. Amplitude dispersion index D A was calculated from the amplitude images. D A σ A = m A Where σ A is the standard deviation of amplitude and m A is the mean amplitude for the pixel [1]. To test traditional 2-pass DINSAR differential interferogram with small normal baseline and two-year temporal baseline was processed (Fig. 3). Atmospheric effects were further studied by forming 4 differential interferograms from 4 Tandem pairs and the NLS DEM (Fig. 4). Fig. 3. Differential interferogram from the city center of Turku and the corresponding coherence image (B = 8 m). Analysis on the amount of permanent scatterer candidates or coherent targets was carried out. Several test areas for each land-use class were digitized from the multitemporal SAR image. Land-use classes studied were forest, field, archipelago, settlement (built-up) and city center (urban). Classes could be distinguished visually from the multitemporal SAR image. Phase stability can be estimated using amplitude stability [1]. Permanent scatterers or coherent targets were extracted using different threshold values for amplitude stability (amplitude dispersion index) and coherence images. Test areas were overlaid on these images and the amount of pixel values above threshold value in each test area was calculated. Results for the amount of permanent scatterer candidates are presented in Table 2. Table 2. The amount of stable targets in different land-use classes using amplitude stability and coherence as criteria (#/km 2 ). Ampl. stab. Forest Agric. City Built-up Archip. Cohere nce Forest Agric. City Built-up Archip. < > < > < > >

5 Fig. 4. Differential interferograms from the four Tandem pairs show atmospheric effects and ice motion (B = 140m), (B = 14 m), (B = 29 m) and (B = 82 m). 3 RESULTS AND DISCUSSION Because of long winter season and vegetation, the stability and coherence values were lower than in more southern areas [1]. The minimum value for amplitude stability was The maximum coherence was 0.89 and the mean coherence was The amount of permanent scatterers estimated using amplitude stability was highest in cities and settlements. But it very interesting to our future studies that also forested areas had stable targets. Reason for this could be that forests on the coast are rocky. In archipelago the digitized area includes small islands and lots of water, but still there are some possible permanent scatterers on the rocky islets. The final amount of permanent scatterers will be higher than the amount of candidates [1]. The final amount cannot be recovered before atmospheric and other error sources are modeled and removed using the PS-candidates and we can examine the stability of phase values. Nonetheless, the amount of 3-4 PS/km 2 is enough for removing the atmospheric effect [2]. Therefore, it should be possible to carry out the Permanent scatterers analysis (at least in the city center of Turku). The amount of permanent scatterers in different land-use classes was different when coherence was used as criterion. Particularly, there were more coherent targets in the fields than in the forests. Most of the coherent targets located in cities and settlements as could be expected. Reason for small amount of coherent targets in fields and in forests was that several adjacent pixels affect the coherence value of the pixel. If a permanent scatterer is surrounded by noncoherent pixels, it can t be identified.

6 The long-term phase values of permanent scatterers candidates in Turku city center were examined. The phase values appear to be very noisy. In addition, the differential interferograms from the Tandem pairs showed strong atmospheric effects. These effects have to be removed before deformation is measured. Clear displacement signal is not visible in the 2-pass differential interferograms. Permanent scatterers analysis is needed to avoid the effects of temporal decorrelation and to examine the possible deformation. 4 CONCLUSIONS This study has so far proved that there is fairly good possibility to carry out permanent scatterers analysis in the heavily forested areas in Finland. Results of the analysis of the amount of coherent targets showed that in the urban areas the number of PS is higher than required 3-4 PS/km 2. The determination of land uplift anomalies still seem plausible, since forested areas and archipelago also have reasonably high amount of PS. In the near future the analysis of the phase values of the stable targets will be carried out. Also more data form the area will be ordered. The urban subsidence results are verified with field measurements. A new precise leveling, to be completed in 2004, will be used as a reference data for the land uplift study. 5 ACKNOWLEDGMENTS ERS SAR data set was provided by ESA for the CAT-1 project: Land subsidence and land uplift determination due to the post glacial rebound in the boreal forest zone using repeat pass satellite SAR interferometry and Permanent scatterers technique. Weather data set was provided by the Finnish Meteorological Institute. 6 REFERENCES 1. Ferretti A., Prati C., Rocca F., Permanent Scatterers in SAR Interferometry, IEEE Trans. on Geosc. and Rem. Sens., Vol. 39, No. 1, Colesanti et al., Monitoring landslides and tectonic motions with the Permanent Scatterers Technique, Engineering Geology, Vol. 68/1-2, Lambeck, K., Smither C., Ekman, M. Tests of glacial rebound models for Fennoscandinavia based on instrumented sea- and lake-level records, Geophys. J. Int., 135, , Turun Sanomat Turun keskusta vajoaa yhä sentin vuodessa. 5. Finnish Meteorological Institute. 6. Lyons, S. and Dandwell D., Fault Creep Along the Southern San Andreas from InSAR, Permanent Scatterers, and Stacking, J. Geophys. Res., 108(B1), 2047, 2003.