A study on Tamilnadu coastal deformation processes using SAR ... · and the complex information processing capability of modern digital electronics to provide high resolution imagery

INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES

Volume 1, No 4, 2011

© Copyright 2010 All rights reserved Integrated Publishing services

Research article ISSN 0976 – 4380

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A study on Tamilnadu coastal deformation processes using SAR

Interferometric data German Amali Jecintha .T1, V.E. NethajiMariappan2

1- Scientist-C, Centre for Remote Sensing and Geoinformatics, Sathyabama University Rajiv Gandhi Road, Jeppiaar Nagar, Chennai

2- Scientist-D, Centre for Remote Sensing and Geoinformatics, Sathyabama University Rajiv Gandhi Road, Jeppiaar Nagar, Chennai

[email protected]

ABSTRACT

SAR systems take advantage of the long-range propagation characteristics of radar signal and the complex information processing capability of modern digital electronics to provide high resolution imagery. The paper focuses on the differential interferometric SAR (Synthetic Aperture Radar) technique for the monitoring of terrain surface deformations. A series of ERS (Earth Resources Satellite) 1&2 and ASAR (Advance Synthetic Aperture Radar) images acquired at different dates before and after tsunami were used in this study. Two SAR images are combined to produce a SAR Interferogram to reveal information about the third dimension (elevation) of the object and to measure small displacements of objects between the two image acquisitions. Interferometric processing, the SLC images were accurately co registered and the complex interferograms are formed by multiplying each complex pixel of the first image by the complex conjugate of the same pixel in the second image. Coherence images and interferograms were then derived from pairs of co- registered SLC images. The interferogram thus generated is a complex image itself. A careful observation of the images reveals that closer are the fringes, more are the topographical changes or height variations which represents the main observation for the estimation of the coastal deformations processes.

Key words: SAR, Tamil Nadu Coast, coastal deformation, DEM, DinSAR

1. Introduction

Microwave Remote Sensing has advantages over Optical and Thermal Remote Sensing techniques in view of its all weather, day/night and penetration capabilities. Active micro wave remote sensing has its own illumination, thereby making measurements of both intensity and phase of the returned signal easier. Typical RADAR (RAdio Detection and Ranging) measures the strength and round-trip time of the microwave signals that are emitted by a radar antenna and reflected off a distant surface or object. The radar antenna alternately transmits and receives pulses at particular microwave wavelengths.

Radar images are composed of many dots, or picture elements. Each pixel (picture element) in the radar image represents the radar backscatter for that area on the ground: darker areas in the image represent low backscatter, brighter areas represent high backscatter. Bright features mean that a large fraction of the radar energy was reflected





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back to the radar, while dark features imply that very little energy was reflected. Backscatter for a target area at a particular wavelength will vary for a variety of conditions: size of the scatterers in the target area, moisture content of the target area, polarization of the pulses, and observation angles.

The deformation of the earth's surface is one of the prominent phenomena associated with many geological hazards, such as Tsunami, landslides, land subsidence, volcano eruption and earthquakes. Earthquake near coastal areas or at sea may in turn cause tsunami (massive tidal waves). Such hazards are severe threats to human life and property.

To monitor deformation of earth surface, geotechnical instrumentation, GPS-based systems and many other geodetic techniques are currently available. However, most of them are point-based measurement techniques and are too costly if a very large area needs to be monitored. Recently, a promising alternative area-based technique -- InSAR -- has been explored by researchers, which makes it possible to measure dense points in a study area accurately, economically, conveniently and efficiently (Gabriel et.al., 1989; Massonnet et.al., 1993; Biegert et.al., 1997).

InSAR is the abbreviation of Interferometric Synthetic Aperture Radar (InSAR). It derives information by using the interferograms, which are formed by phase differences between two complex SAR images of the same area but obtained at slightly different positions with the same sensor or two similar sensors. InSAR can be used to detect the terrain variations (Zebaker and Goldstein, 1986) and deformations (Dixon, 1995). Under favourable conditions, accuracy at a sub-centimetre level can be reached for deformation measurement, which is good enough for many monitoring purposes. However, some problems associated with the key processing procedures must be tackled before it can become a practically viable measurement technique for deformation monitoring, especially when the gradient of change is significant.

1.1 Synthetic Aperture Radar (SAR) Interferometry –Principles and Concepts

In order to improve the resolution of radar images, synthetic aperture radar (SAR) was developed in 1960s. It is based on the principle of Doppler frequency shift caused by the relative movement between the antenna and the target (Fitch, 1988). Figure 1 shows the imaging geometry of synthetic aperture radar while it is being used to take side-looking image of the ground.

Assuming a real aperture imaging radar with aperture length D moves from 'a'' to 'b', then to 'c', the slant range from any point, e.g. target O, to the antenna varies from Ra to Rb, then to Rc. It is obvious that Ra > Rb, and Rb <Rc, which means that at first the antenna is flying nearer and nearer to the point object until the slant range becomes the shortest Rb, then it go farther away.





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Figure 1: Imaging geometry of SAR

The variation of slant range R will cause the frequency shift of the received echo backscattered from point target O, varying from increase to decrease. By precisely measuring the phase delay of the received echoes, tracing its frequency shift, and then synthesizing the corresponding echoes, the azimuth resolution can be sharpened, as the area of the intersection of the three footprints shown in Figure 1.

1.2 Principles of Interferometric Synthetic Aperture Radar (InSAR)

SAR images (amplitude image) have been widely used for reconnaissance and environmental monitoring in remote sensing. In such cases, the phase component recorded simultaneously by SAR has been overlooked, indeed for a long time. Graham (1974) first reported that a pair of SAR images of the same area taken at slightly different positions can be used to form an interferogram and the phase differences recorded in the interferogram can be used to derive topographic map of earth surface. Such a technology is called Interferometric SAR (InSAR), or SAR interferometry.

Terrain height extraction is one of the most important applications for SAR images. Optical and IR images contain only the intensity of the energy received to the sensor. But SAR images contain distance information in the form of phase. This distance is simply the number of wavelengths of the source radiation from the sensor to a given point on the ground. SAR sensors can record this information because; their radiation source is active and coherent. This distance phase information in a single SAR image is mixed with phase noise from the ground and other effects. For this reason, it is impossible to extract just the distance phase from the total phase in a single SAR image. However, if two SAR images are available that cover the same area from slightly different vantage points, the phase of one can be subtracted from the phase of the other to produce the distance difference of the two SAR images. If the distance between the two orbital locations of the radar, called the baseline, is small then the surface scattering effects will be the same for the two images and thus are cancelled in the formation of the interferogram. Therefore the interferogram exhibits information related to the difference in distances from the surface to the two radar locations.





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Figure 2: Geomentry of InSAR

A1, A2- Radar Antennas B - Baseline ά – Angle with respect to horizontal. - phase difference

The geometry of InSAR may be understood with the help of Figure 1. In this figure, A1 and A2 are the two radar antennas that simultaneously view the same surface and are separated by a baseline vector B with length B and angle á with respect to horizontal. A1 is located at height h above some datum. The distance between A1 and the point on the ground being imaged is the range, while is the distance between A2 and the same point. The aim is to determine the elevation h of each point in the image. The topography z(y) can be inferred from the phase measurement to a precision of several meters, assuming that the 2 ambiguity inherent in any phase measurement can be solved (Equation 1)

Z (y) = h - <cos ……………………………….. (1) Where < is the look angle of the radar.

A SAR interferogram, viewed as a fringe pattern, shows the relative difference between phases of two images. The phase difference depends on the geometry of the two antenna tracks and the image point and thus is proportional to the difference in path delays from two antennas and is given by,

……………………………...(2)

Where is the wavelength.

To determine h, the interferometric processing steps that are generally followed can now be enumerated as:

• Selection of suitable pair of SAR images





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• Geometric registration

• Interferogram generation

• Phase unwrapping

• Extraction of elevations from phases

Among the various techniques used to generate detailed digital topography, ranging from classical ground and aerial surveying to modern satellite methods, SAR interferometry (InSAR) has been the subject of increasing interest during the last decade. This interest is motivated by some advantages of the InSAR which are: high spatial resolution (up to 5 × 10 m); height precision, with residual error generally less than 10 m; automated processing; good results over remote or not easily accessible regions and low magnitude image of the study, cost per square kilometer. Differential InSAR technique (DInSAR) can be used to estimate and monitor the ground surface displacement. (Pizzi and Pugliese, 2004).

1.3 Differential Interfereometry

Differential SAR Interferometry(DInSAR) is an advanced interferometric technique that can usually be applied to map surface displacements as well as those associated with landslides or erosion processes. The interferometric phase is sensitive to both surface topography and coherent displacement in between the acquisitions of an image pair. The basic idea of differential interferometric processing is to separate the two effects, allowing, in particular, to retrieve a differential displacement map. This goal is achieved by subtracting the topography related phase.

2. Objective

• To study the geomorphological changes of Cuddalore to Nagapattinam district in Tamil Nadu coast

• To derive the coastal deformation changes in the study area.

2.1 Study area

The study area covers part of Tamil Nadu coast, starting from Cuddalore to Nagapattinum districts. The major geomorphic features of this coastal tract is comprised of upland plain, flood plain, deltaic plain , coastal plains, sub aerial delta, strand plain, estuarine , strandlines, raised beaches, sand dunes, mangroves swamp and tidal flats. The large part of the delta is occupied by their distributary flood basins comprising brown and reddish gray silty clay and fine sands. The coastline of Nagapattinam is straightened by south bound long shore currents from the Kollidam river mouth to point Calimere as represented in figure 4.





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Figure 3: Magnitude of the SAR image

Figure 4: Study Area - South Tamil Nadu Coast

Four single look complex images were used in this study. Pre sunami images were ERS 1/2 which has C- band, VV polarization. Its frequency is 5.3 Ghz and wavelength is 5.66 cm. Its repetivity is 35 days and look angle is 20◦ -26◦

The post Tsunami images (in Table.1) were ASAR images which has C-Band, VV polarization. These images have azimuth (along track) resolution from 4 to 5 m and range (across track) resolution from 9 to 18 m. It operates at a wavelength of 5.6 centimeters. Its repetivity is 35 days and incident angle is 23 ◦





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Table 1: Data Acquisition and Data Description

3. Methodology

3.1 Interferometric processing

Six major processing stages were necessary to compute a coherence map, interferogram and differential interferogram. Thus interferometry processing stages can be divided into (a) data preparation (b) Co registration of radar images (c) Coherence interferogram, (d) Filtering of phase image, (e) topographic correction and unwrapping, (f) Geocoding of images.

Figure 5: Flow direction map

Sensor ERS1 ERS 2 ASAR ASAR Data of acquisition 07/05/96 08/05/96 11/05/2005 27/04/2006 Pass Descending Descending Descending Descending Orbit 25157 5484 16706 21716 Upper left lat 79.32 79.33 79.50 79.50 Upper left long 11.95 11.97 11.80 11.80 Lower right lat 80.00 80.00 79.78 79.79 Lower right long 10.85 10.88 11.46 11.46 Width 4904 4903 5155 5155 Height 25725 25744 24298 24278

Pre-Tsunami

SAR data

Pre-Tsunami

SAR data Post Tsunami

SAR data

Phase

Image

Phase

Image

Interferogram I Interferogram II

Topographical

Elevation

Without motion Differential

Interferogram

Topographical

Elevation

With motion





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3.2 Image reading

For the analysis of interferometry processing steps, two radar images acquired over the study area ERS1 and ERS2 were chosen as the reference and match image respectively in figure 4 representing the magnitude of the image. The ERS 1/2 SLC SAR images were delivered on a CD-ROM. In order to perform interfereometry processing, parts of the images have been cropped and saved. The intensity images (E) of the selected part of the reference image was defined as:

E = │g(x,y)│2……………………………………………….(3)

Where │g(x,y)│ is the amplitude of a complex number.

3.3 Image Registration

Before interferogram computation, the co registration process of lining up two images (reference and match) covering the same area must be undertaken. Co registration can be defined as a geometric image transformation function and subsequent resampling of the match image in such a way that each ground point is located at the same position in both images (Franceschetti and Lanari, 1999).

The registration process for repeat pass interferometric systems is generally broken into two steps: pixel and sub-pixel registration. Pixel registration involves using the magnitude (visible) part of each image to remove the image mis-registration down to around a pixel. Referring that after pixel registration, the two images are registered to within one or two pixels of each other in both the range and azimuth directions. The process identifies the pixel offset that produces the highest match between the two images, and therefore the best interferogram.

Sub-pixel registration was achieved by starting at the pixel registration offset and searching over upsampled versions of the phase functions for the best possible interferogram. When this best interferogram was found, the sub-pixel offset has been identified. This sub-pixel register function provides the weights for the sync interpolator needed to register one image to the other during the formation of the interferogram. The sub pixel accuracy of the co registration process was necessary to obtain coherent interferometric products.

3.4 Correction of flight path

To correct the orbits of the satellites that obtained the Reference image and the Match image requires at least one Ground Control Point (GCP) for each image. The Single-look Complex (SLC) images used in interferometric processing have pixels that are not square, and the image itself may be mirrored due to the collection geometry. This can make the process of identifying GCPs in the SAR image difficult. The range (X, or sample) spacing was about five times that of the azimuth (Y, or line) spacing. Viewer functionality has made identification of a GCP easier.





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3.5 Interferogram generation

Interferogram is constructed by a point-wise multiplication corresponding pixels in the reference and complex conjucate of match image. The phase image of the complex interferogram represented the phase difference between two SAR data sets while the amplitude image contains the useful information on the SNR of the observed phase. The images were delivered from two repeat SAR acquisitions, usually the resulting phase image was a repeat pass interferogram. Vertical fringes corresponding to the uniform surface of the earth are clearly visible and in some places are disturbed by the topography or some decorrelations.

3.6 Coherence Map

In the process of coherence map computation it was assumed that the reference phase has been subtracted from the interferogram. The complex coherence between two images is estimated based on the assumption that the accuracy of the phase observation of a uniform region was stationary. Therefore, it allows the computation of the local complex correlation between any two co-registered complex images by complex correlation computation over a small moving window. The coherence image of pre and post Tsunami Data was presented in Figure 6.

Figure 6: Coherence map of the study area Coherence images were useful for assessing the accuracy of the interferometric phase, thus coherence map are also suitable tool for land classification and change detection in the biomass as shown by Engdahl and Hyyppa (2000). As radar imagery was highly sensitive to change in soil moisture content, coherence maps are particularly useful for soil classification, in terms of highlighting different moisture content over arid regions.





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3.7 Differential interferogram

The differential interferogram (DF) was computed by subtracting the topography related phase (φDEM) from the complex radar interferogram (I). This was performed at each pixel location (I,J) according to the following equation:

DFi,j = I i,j . exp (-i.φi,jdem) ……………………………………..…………..(4)

Figure 7: Differential Interferogram of the study area

3.8 Phase unwrapping and DEM generation

The phase variation between two points on the flattened interferogram provided a measurement of the actual altitude variation, after deleting any integer number of altitudes of ambiguity (equivalent to an integer number of 2π phase cycles). The process of adding the correct integer multiple of 2π to the interferometric fringes was called phase unwrapping has been construed during the analysis.

Interferometric phases are unwrapped, an elevation map in SAR coordinates was obtained and thereby Digital Elevation Model (DEM) was generated. The SAR elevation map was then referred to a conventional ellipsoid (e.g. WGS84) and re-sampled on a different grid (for example UTM). DEM of the study area as shaded relief map (figure 8) shows that there was not much elevation as the study area was near the seas shore. However, the lower to moderate slope was observed all along the area and a steep slope were seen on top left corner. A coherence change detection study was carried out along with DEM to identify the spots affected by erosion in the view of pre and post tsunami. The eroded areas were more predominant along the shores for a distance of 300m from the sea shore.





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Figure 8: Shaded of the study area

4. Conclusion

The potentiality of multitemporal ERS-1/2 and ASAR datasets were used for assessing the impact of pre and post Tsunami affected Nagapattinam and Cuddalore districts. Analysis like image registration, flight path correction, orbital orientation was carried out as per the standard procedures obtained from literatures as well as the reference manuals. Additionally pre and post data sets are subjected to the analysis in turn detailed coherence map such as intensity map generation, phase unwrapping, geocoded DEM Generation, differential interferogram were produced. InSAR DEM provides elevation information of coastal areas, further slope and its changes to reference image were derived more approximately.

5. References

1. Biegert, E.K., Berry, J.L., and Oakley, S.D., (1997), Oil field subsidence monitoring using spaceborne Interferometric SAR: A Belridge 4-D case history, Atlantis Scientific Inc, available at http://www.atlsci.com/library/ papers/Oil_field_Subsidence_Monitoring_using_Space

2. Carnec, C., and Delacourt, C., (1999), Three years of mining subsidence

Monitored by SAR Interferometry, near Gardanne, France, In:Second International workshop on ERS SAR Interferometry, Fringe 99, Advancing ERS SAR Interferometry from Applications Towards Operations, ESA Publications Division, Liege, Belgium,





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3. Dixon, T.H., (1994), SAR Interferometry and Surface Change Detection, Report of a Workshop, Boulder, Colorado, USA.

4. Engdahl, M., and Hyyppa, J., (2000), Temporal Averaging of Multi temporal ERS-1/2 Tandem InSAR Data, Proceedings of IGARSS’00, Hawaii, USA, pp. 2224-2226.

5. Erdas field guide- Erdas Software version- 9.3.

6. ESA, (2008), Envisat: caring for the earth. Retrieved from: http://envisat.esa.int

7. Fitch, J.P., (1988), Synthetic aperture radar. Springer Verlag. New York, USA.

8. Franceschetti, G., and Lanari, R., (1999), Synthetic Aperture Radar Processing, CRS Press, New York, USA, 307p.

9. Gabriel, A.K., Goldstein, R.M., and Zebaker, H.A., (1989), Mapping small elevation changes over large areas: differential radar interferometry. Journal of Geophysical Research, 94(B7): pp. 83-91.

10. Graham, L.C., (1974), Synthetic Interferometric radar for topographic mapping. Proceedings of IEEE, vol.62, pp. 763-768.

11. Massonnet, D., Rossi, M., Carmona, C., Adragna, F., Peltzer, G., Feigl K. and T. Rabaute, (1993), The displacement field of the Landers Earthquake mapped by radar interferometry. Nature, Vol. 364, pp. 138-142.

12. Patterson, J. K., Edward, Makoto Terazaki and Masashi Yamaguchi, (2006), The impact of tsunami in coastal areas: Coastal protection and disaster prevention Measures—Experiences from Japanese coasts, Coastal Marine Science, 30(2), pp 414-424.

13. Pizzi, A., and Pugliese, G., (2004), InSAR-DEM analyses integrated with geologic field methods for the study of long-term seismogenic fault behavior: Applications in the axial zone of the central Apennines (Italy), Journal of seismology, 8(3), pp. 313-329.

14. Zebker, H.A. and Goldstein, R.M., (1986), Topographic Mapping from Interferometric SAR Observations. Journal of Geophysical Research, 91(B5) pp. 4993-4999.

Documents

A study on Tamilnadu coastal deformation processes using SAR ... · and the complex information processing capability of modern digital electronics to provide high resolution imagery