ERS-SAR Interferometric Products for the Urban Application Domain

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1 ERS-SAR Interferometric Products for the Urban Application Domain E. Stabel 1, P. Fischer 2 Abstract The potential of spaceborne earth observation (EO) to meet some of the requirements for urban monitoring is high but not fully recognized. Especially the potential of radar interferometry for the urban application domain is not yet evaluated. The main objective of the project described in this paper was to analyze the benefits and the limitations of ERS-InSAR data analysis for urban monitoring. The evaluation focuses on three main applications: monitoring small surface movement (e.g. subsidence, dunes, crustal movements), change detection analysis (urban sprawl monitoring) and the generation of Digital Elevation Models (DEMs). Using the technology of repeat-pass radar interferometry, there are three different products to process for the urban application domain: Coherence, DEM and differential InSAR. The method of InSAR processing was tested at two validation sites, Cairo and Taipei, which are different in size and urban structure and which are also located in different climatic and vegetation zones. The results of the interferometric analyses have finally to be integrated into urban information systems for further value adding processing or thematic analyses. In future, the recently available ready-to-use interferometric products (e.g. Spot Image Coherence products) will reduce the complex processing effort required to use radar interferometry data. There will be a load-and-go solution to simply use interferometric products in urban monitoring. Keywords: spaceborne earth observation, SAR interferometry, coherence analysis, digital elevation model (DEM), differential radar interferometry, urbanism, megacities, data integration, value added products 1 2 Saarland University - Dept. of Physical Geography - PB D Saarbruecken. e.stabel@mx.uni-saarland.de University of Applied Sciences Trier - Geomatics Division P.O. Box D Birkenfeld. fischer-stabel@umwelt-campus.de - 1 -

2 1 MONITORING THE EFFECTS OF URBANISM The unplanned Megacities represent the extreme manifestation of the recent worldwide trend to uncontrolled growth of urbanized areas. Especially, these Megacities affect and are affected by natural cycles. They import water, energy and materials which are transformed into services. The procedure is triggering local, regional and global problems. When these cities are expanding into the surrounding agricultural areas, pastures, forests and other land use areas the impact on the environment is significant. There is not only direct impact by the growth of the cities on the environment, but also growing infrastructure, sewage, air pollution and solid waste problems. That means that monitoring the existence, distribution, changing patterns and growth of settlements in each order of magnitude plays a very important role in conservation of natural resources and planning economic growth in a prudent manner. Megacities and other urban areas are also endangered by various sorts of natural hazards. One hazardous phenomenon, relatively unknown by the EO community, is the mid-term ground displacement in some Megacities. The combination of the widespread pumping of groundwater especially from shallow depths (problematic aquifers and weak geology) with the increased building density results in land subsidence of some cm per year. Traditional ground based methods of gathering information and mapping urban areas are no longer sufficient to serve the requirements of local and regional governments or companies. Therefore, the implementation of urban information systems (UIS) is an option for many urban agglomerations to integrate EO data and GIS methods for better management of the targeted areas. Unfortunately the spaceborne EO activities within this application domain are mainly focused on missions carrying optical sensors or are waiting for data from the now operational very high resolution missions (e.g. IKONOS). The potential of space EO for the urban application domain is therefore not fully realised. Also the synthetic aperture radar (SAR) instrument has been recently proven to give useful information for urban land use mapping (XIA & HENDERSON 1997). On the other hand, the potential of the radar interferometric products for the urban application domain is not yet evaluated. To close this gap, we have examined the applications for which radar interferometric spaceborne products can play an important role. We also examined the limitations of this new technology. It is not our intention to replace existing valid systems but to underline the additional potential that interferometric products have for integrated urban monitoring. Integrated urban monitoring is only possible with the availability of urban information systems integrating the different sources of information including the one coming from spaceborne Earth Observation (see fig.1)

3 Fig.1: Data integration in a generic megacity information system (ESA, 2000) 2 AIMS AND OBJECTIVES The potential value of the ERS-SAR instrument must be viewed based on the two major benefits of the Synthetic Aperture Radar (SAR) instrument: the Image Acquisition possibility by night and under cloud coverage (<==> optical sensors) the availability of 2-D and 3-D information by receiving and analysing Amplitude and Phase Information. The phase information of the radar signal is able to deliver important additional data from the earth s surface that cannot be detected by optical instruments or even backscatter analysis. The main objectives of the project was therefore find out the benefits but also the limitations of ERS-InSAR data analysis for the urbanism application domain. The evaluation focuses on three main application-themes InSAR technology has a high potential for: Change detection analysis (urban sprawl monitoring): Settlements can either be detected by optical sensors looking for geometric housing patterns or street nets, or by SAR systems classifying textures. A different approach to explore settlements is followed in this study by the use of the coherence information. Detection of small surface movement: The detection of small surface movements is one of the most challenging applications of the SAR instrument. It has a special - 3 -

4 relevance for detecting hazardous phenomena like subsidence, dunes, crustal motion, etc. The detection is based on differential InSAR processing. Generation of Digital Elevation Models (DEMs): DEM s are important base information for civil engineering activities, environmental planning (e.g. fresh air streams) or in the field of cell planning for mobile telecom. For differential processing there is a need of DEM s that remove the terrain hight influence. The generation of DEM s from space products is of high importance because of the very limited availability of products for most of the regions on earth. 3 RADAR INTERFEROMETRY In this study, we used the repeat-pass radar interferometry (InSAR). Repeat-pass radar interferometry is a technique to extract information about the Earth s surface using the phase difference between the signals arriving at the antenna during repeated observations of the same platform (PRATTI et al 1994, SOLAAS et al 1996). The distance information from antenna to Earth is encoded in the phase. The satellite orbits have a small degree of drift such that they are not returning to the exact location on subsequent orbit repeats. These repeats are generally parallel and separated by a distance, called baseline, on the order of a few hundred meters. This baseline between passes provides the different viewing angles necessary for interferometry to work. Repeat-pass interferometric SAR uses two antenna positions to acquire two SAR images. The phase difference is directly related to the difference in path lengths between the point on the Earth surface and the two positions of the antenna. The phase difference in the SAR images acquired in two passes at corresponding locations, allow a measurement of the incident angle of the incoming radiation. The combination of distance with incident angle and with the location of the SAR platform gives a three-dimensional localization of points on the Earth s surface. In repeat-pass SAR interferometry, the interferograms are formed from repeated observations of the same platform. With respect to the Earth, the ERS-1 and ERS-2 satellites go through 35- days cycles of orbits which means that the satellite returns to the same position every 35 days. After this period of time, one or more orbit repeat cycles, the same area may be imaged again to acquire additional SAR images. The basic requirements to apply repeat-pass SAR interferometry can be described as (SOLAAS et al 1996): Terrain of sufficient and stable backscatter, without or only with very slow changes, in the order of C-band wavelength between 2 passes Similar atmospheric conditions during the acquisitions Stable viewing geometry Preservation of inherent phase information within the SAR processor The whole chain of InSAR processing includes the selection of appropriate input data (SLC/I products fitting the baseline and weather conditions requirements), the image co-registration and the generation of the interferogram. Coherence generation, altitude of ambiguity, phase - 4 -

5 unwrapping, DEM construction, computing of map projection are other processing steps to name but a few. Details related to the technique of InSAR processing can be found in GENS & VAN GENDEREN (1996). There are three different interferometric products relevant for the urban application domain: Coherence Maps DEM s Differential Radar Interferometry Products. 3.1 Coherence for change detection analysis While processing the interferogram a coherence image is produced. The coherence measures the correlation between master and slave image, which means the changes between two image acquisitions. High coherence means no or small changes whereas no coherence indicates a high degree of change. A certain grade of coherence can be correlated to a special landcover type or gives information about weather changes between the two acquisitions of the SAR images. The coherence image is important for many applications such as geological, agricultural, ice dynamics and soil moisture monitoring. For city monitoring the coherence tool can provide information useful in detecting the expansion activities. The coherence in settled, mainly built-up areas is higher than in areas covered with vegetation. A coherence change in a certain area from low to high value could give an indication of a replacement of vegetation by buildings. 3.2 DEM Generation The two required SAR images, each containing brightness and phase information, allow the production of one interferometric dataset from which height and other information is extracted. The generation of a DEM includes the processing of the coherence and interferogram image. The SAR interferogram contains fringes (set of coloured bands) with one complete fringe representing a full wavelength shift caused by two different viewpoints. This means that one fringe re-presents a difference in path lengths caused by difference in terrain altitude. The value of one wavelength shift is dependent on the perpendicular baseline: data sets with 100 m baseline are producing an interferogram fringe containing 81 m height difference; a fringe made by a baseline of 500 m contains 16 m of difference in terrain altitude. The formula to get an approximated value is: 8100 / BASELINE. A higher baseline will produce a more precise topographic information but will also decrease coherence. For DEM only baselines between 100 and 600 m are considered. 3.3 Differential InSAR Processing This processing type is used for the surface deformation detection. It needs data sets acquired with very close viewing points around m. Very small baselines allow to neglect the topographic influence on the results. The interferograms are producing fringes with one complete fringe representing a shift of half a wavelength of the backscattered microwave, while the radar wave covers the round-trip distance forth and back (MASSONET 1997). For the SAR instrument on ERS-1 and ERS-2 (C-band) a fringe marks a change of 3 cm of - 5 -

6 ground motion in the direction towards the satellite. For higher baselines the motion effects can be extracted if an input DEM is available that can be used to model and remove the topographic phase influences. 3.4 Input Data Selection The main purpose of this step is to choose applicable couples of SAR images. The two SAR acquisitions should be repeated as closely as possible spatially, temporally and physically. It is only possible to combine two SAR images from the same sensor or from two very similar sensors (ERS-1 and ERS-2). The couples of SAR images from the interesting time period are chosen using existing catalogues and software (e.g. ESA Multimission Catalogue Interactive EOLI). Next step is to search for applicable perpendicular baseline values in the baseline listings available at European Space Agency. Typical values for maximum usable baseline separation are less than 600 m for DEM generation, between m for the coherence application and m for differential interferometry. The data used for repeat-pass interferometry are required as SLC/I (Single Look Complex) products. The time separation should be one day for generating the DEM and high coherence images. For the differential processing the time difference is dependent on the velocity of the surface changes, but is around some months. In cities a time separation up to some years is possible because there are in general only a few changes. The weather conditions must be dry and without changes between the two acquisitions. If no weather data is available from local weather stations information can be found in information systems like the ISIS system for Europe. 3.5 Software Tools The interferometric processing have been done with the Earth View InSAR software version (Atlantis 1997b). For the conventional post-processing, the multitemporal colour composites and other data combinations Atlantis Earth View version (Atlantis 1997a) and ERDAS imagine 8.3 have been used. 4 TEST CASES: CAIRO AND TAIPEI With it's 13 million inhabitants, Cairo as the largest and most dynamic African town is representative of the sustainable development problems related to Megacities. Egyptians regard the uncontrolled urbanization of their arable land as an ecological disaster. New towns or suburbs have been built up in the desert to protect the Nile valley, where the majority of the Egyptian farmland is located. However, no regular and systematic observation of urbanization had been undertaken until now. Several interferometric products were tested in the study area of the city and the surroundings of the greater Cairo area. The city of Cairo is characterized by a very dense settlement structure without any vegetation and an uncontrolled growth and building activity. It is built on a slopeless, flat area. The mountains which start at the east border of Cairo are very dry, without vegetation and with a small slope. There is a very strong borderline between the watered agricultural Nile valley area and the mountainous desert area which contains rocky and sandy desert. The city of Cairo suffers under man-induced as well as under natural - 6 -

7 problems. These are: The uncontrolled growth of the city in the surroundings in the last decades. There is a huge loss of agricultural land due to the building activities. The influence of building activities on mesoclimate because of disturbing fresh air streams. The moving dunes near the city presenting a rising danger for built-up areas. The city subsidence possibly appearing in regions with high exploitation of groundwater in combination with a weak geology. On the other hand Taipei (3 million inhabitants) is one of the booming towns in SE-Asia where an immense economical, political and ecological power is combined with an enormous town development and with migration rates that reach and exceeds those of known Megacities of the third world. As a representative of this booming towns, Taipei is concerned with all the typical problems resulting from the urbanization process. In addition, because of the weak geological formation, the Taipei Basin has in some areas an annual ground displacement rate of up to 10 cm since the mid seventies. Risks caused by the ground displacement are mainly the destruction of infrastructure networks (especially gas, water supply, telecommunications infrastructure), road and railroad networks and an increasing instability of buildings. 5 RESULTS OF THE InSAR PROCESSING The results of the interferometric research are directly depending from the quality and availability of the data as well as from external variables. The main influencing factors are: Landcover: A higher density of vegetation produces a lower coherence. Slope: A flat area and a lower slope of the surface are leading to a bad matching of the two acquisition images Perpendicular baseline: The optimal baseline is dependent on slope and height difference of the area as well as on the product which should be processed. Temporal separation: In general a smaller time difference between the acquisition of master and slave image produce a high coherence. This is applied for vegetation covered areas. For urban areas it was shown by FRUNEAU 1998 that the coherence is remaining high on cities for long period, because there is no big seasonal changing. Weather conditions: Images need to be acquired under dry weather conditions to get a high coherence and to exclude inhomogenities due to different soil moisture or due to atmospheric artifacts. 5.1 Coherence Maps The stable situation in build-up areas should lead to a high coherence which could be seen already in several investigations (e.g. FRUNEAU 1998). In our research the first coherence result was unexpected bad above all the coherence in the city. The baseline of 415 m of the low coherence image of Cairo (September-1995-tandem) gave the idea to test the influence of the baseline to the coherence. In addition the variation of - 7 -

8 coherence value due to time separation was also studied. It was noted that a decreasing baseline of the tandem data presented a higher coherence for the Nile valley and the mountainous area. A separation between the built-up area and the agriculture land was not possible with this baseline. The investigation of time separation and baseline due to the coherence presented interesting results: The decreasing of the baseline together with a bigger time difference produced very good coherence of the mountains, very low coherence of the vegetated Nile valley but a relatively high coherence of the city. The difference between the agricultural used Nile valley and the settled area of Cairo became more significant. The best result was found with a baseline of 86 m and a time difference of 7 months. There was no conclusive explanation for the low coherence in the area of Cairo. Whereas the coherence image of Taipei (see fig.2) shows an excellent separation between build-up areas with high coherence and forest, ocean and river with very low coherence. Fig.2: Coherence map of the Taipei area. The white pixels are representing a high coherence (build-up areas)

9 5.2 DEM s and Differential Processing As mentioned before, the Digital Elevation Model is an important tool to visualize the surface structure of landscapes but the DEM is also required to remove the topographic effect during the differential processing. Due to the lack of external DEMs for Cairo the attempt was to use the DEM generated from the ERS tandem data from September The quality of the tandem derived DEM was unexpected bad due to very low coherence in the Nile valley. The second tandem data set from January 1996 produced a slightly better DEM, but again the vegetation covered Nile valley had a very low coherence. Even the interferogram (fig.3) shows the difficult situation in the valley. Fig.3: Interferogram of the Nile valley (greater Cairo area). Fringes are representing height information An attempt to improve the DEM quality in the low coherence areas was made with the Iterative Disk Masking phase unwrapping algorithm (Earth View Atlantis 1997b). Nevertheless, the vegetated areas were put to 0 m altitude value (threshold due to low coherence). To avoid the disturbance of the vegetation another subarea was selected containing more mountainous area and less parts of the Nile valley. But the result was still a DEM with a lot of height misclassifications

10 A better DEM was derived from another data set with a two month time separation and a baseline of 63 m ( ). The mountainous area appears well designed whereas the Nile valley information was poor due to the low coherence. Very strong errors appeared in the City of Cairo because the phase unwrapping is working well only in small islands with acceptable coherence. Also the altitude of ambiguity of 160 m with a total surface height difference of 200 m does not produce good DEM data in this area. The processing of differential interferometry was not possible due to the low quality DEM (low coherence in the Nile area). It was not possible to generate a simulated SAR image from the DEM from which the tiepointing and coregistration should be done. This means that the removal of height influences cannot be generated. To enable the differential processing an external DEM is needed (actually not available) or an improvement of the internal DEM derived from ERS has to be done. Further investigations related to the differential InSAR processing in urban areas are under way (Taipei, Bangkok, Hanoi). 5.3 Other Products Beneath the coherence maps and DEM s, we also derived traditional products from the data available. Averaged intensity images derived from superimposed images (see fig.4) for mapping purposes and RGB colour multitemporal intensity composites for further change detection were processed. Fig.4: Greater Cairo area: average intensity image (ERS-SAR)

11 6 DATA INTERGATION ISSUES Radar interferometry offers some high sophisticated analysing potential not only in the urban application domain. Being a relatively new technology, the analysing process is actually accompained by problems which makes this technology difficult to use in an operational environment. Some additional research has to be done to be able to understand fully all the interfering artefacts, before the benefits and limitations of SAR interferometric processing can be realized. This is reality especially in micro and meso scaled applications. In addition, the artefacts occurring in the different climate and vegetation zones have to be evaluated in detail. For instance the tropical regions, where a lot of the future monstercities are concentrated, will have different patterns than the Mid-European towns in the meso-climate zone or the urban areas in desert regions. In our opinion, the big obstacle using this technology in urban management is the accurate feature extraction from the raw data itself and not the integration or fusion of the value added information with other geo-spatial data. With the advent of radar based value added products (e.g. Multilayer Thematic Products MTP, Spot Image Coherence Product) some of the problems the end user has within the radar-based feature extraction chain will be solved. In addition, the MTP availability in ready-to-use and ready-to-integrate formats (e.g. Dimap/XML) the new high level products will offer a successful approach to solve some of the transdisciplinary problems we have in the urban application domain by multi source data fusion. 7 REFERENCES Atlantis (1997a): Earth View version User s guide. Atlantis Scientific Inc., Ontario. Atlantis (1997b): Earth View InSAR version User s guide. Atlantis Scientific Inc., pp.246, Ontario. Bally, P.; Angleraud, Ch. & Somer, Y. (1999): The Spot Image Coherence Product and Dimap: A new format for a new product.- In: Proceedings of the CEOS SAR Workshop, ESA-CNES, Toulouse, October 1999, European Space Agency, Paris. Bamler, R. & Schaettler, B. (1993): SAR Data Acquisition and Image Formation. in: SAR Geocoding: Data and Systems, Karlsruhe. Dong, Y. & Forster, B. & Ticehurst, C. (1997): Radar backscatter analysis for urban environments. in: Int. J. Remote Sensing, Vol. 18, No. 6, pp European Space Agency / ESRIN (2000): Demonstration of potential value of ESA Earth Observation Data and Products. End-to-end demonstrator: MEGA CITIES.- Fruneau, B. & Rudant, J. & Classeau, N. & Obert, D. & Raymond, D. (1998): Small displacements detected by SAR interferometry on an urban site Influence of tropospheric inhomogenieties. Paris. Gens, R. & Van Genderen, J.L. (1996): SAR Interferometry - Issues, Techniques, Applications.- International Journal of Remote Sensing, 17, Haynes, M. & Capes, R. & Lawrence, G. & Smith, A. & Schilson, D. & Nichols, G. (1997): Major urban subsidence mapped by differential SAR interferometry. - Fringe 96, ESA Workshop on Applications of ERS SAR Interferometry, Zurich. Hellwich, O & Streck, C. (1996): Linear structures in SAR Coherence Data. in: IGARSS 96, Remote Sensing for a Sustainable Future, Vol.1, pp Pratti, C. & Rocca, F. & Monti Guarnieri, A. & Pasquali, P. (1994): Report on ERS-1 SAR interferometric techniques and applications. ESA rapport 10179/93/YT/I/SC, p. 122, Frascati, unpublished

12 Rocca, F. & Prati, C. & Monti Guarnieri, A. (1996): Possibilities and limits of SAR interferometry. AGARD SPP Symposium on Remote Sensing: A Valuable Source of Information, CP-582, Toulouse. Solaas, G. & Gatelli, F. & Campbell, G. (1996): Initial testing of ERS tandem data quality for InSAR applications ESA RS/ED 96.D002/1.0, Frascati. Stow, R. (1996): Application of SAR Interferometry to the Imaging and Measurement of Neotectonic Movement applied to Mining and other Subsidence / Downwarp Modelling. Fringe 96, ESA Workshop on Applications of ERS SAR Interferometry, Zurich. Ticehurst, C. & Forster, B. & Dong, Y. (1996): Using backscatter from radar images for classifying and determining the bulk density of the urban environment. Int. Archives of Photogrammetry and Remote Sensing, Vol. 31, P. B7, Vienna. Villasenor J. & Zebker H. (1992): Temporal decorralation in repeat-pass radar interferometry Proceedings of IGARSS 1992, pp Wegmueller, u. & Werner C. & Nuesch, D. & Borgeaud, M. (1995): Land-Surface analysis using ERS-1 SAR Interferometry ESA bull. No. 81, pp Xia, Zong-Guo & Henderson F.M. (1997): Understanding the relationships between radar response patterns and the bio- and Geophysical parameters of urban areas.- IEEE Transaction on Geoscience and Remote Sensing. Vol. 35, No. 1, January Vitae E. Stabel received a Diploma in Geography from the Saarland University. After completing research at the environmental research centre she joined the earth observation department of the European Space Agency as trainee. Currently she is a full time PhD student. Her research addresses river dynamics and river morphology using different remote sensing technologies and ground survey information. Peter Fischer finished in 1991 his studies of Geography, Computer sciences and Hydrology. He received his Ph.D. in 1995 for his thesis related to the realization of a GIS-based environmental information system. Between 1992 and 1996 he was responsible for different GIS projects in the framework of the German Environmental Specimen Banking Program. Within this period he was also involved in the design and implementation of environmental management systems at different organizations. In 1997 he joined the European Space Agency as systems engineer doing research in geo-spatial data integration and the definition of new high level EO-products for end users. Since 2000 he holds a professorship for Geomatics technology at the University of Applied Sciences Trier