Presented at SatNav 2003 The 6th International Symposium on Satellite Navigation Technology Including Mobile Positioning & Location Serivces Melbourne, Australia 22–25 July 2003
Quantitative Subsidence Monitoring: The Integrated InSAR, GPS and GIS Approach Linlin Ge, Eric Cheng, Xiaojing Li, and Chris Rizos School of Surveying & Spatial Information Systems The University of New South Wales Sydney NSW 2052, AUSTRALIA Phone: +61-2-9385 4177 Fax: +61-2-9313 7493 Email:
[email protected]
ABSTRACT Errors in radar satellite orbit determination are common problems in radar interferometry (InSAR). For example, when trying to locate a radar test site with known geographic coordinates using the geocoding information in SAR image (the latitude and longitude of the four image corners), the location may be well away from the true position. Another example is when there is indeed a significant signal in the differential InSAR result, there is uncertainty as to whether it is due to ground deformation or atmospheric heterogeneity. As a consequence, InSAR is considered mostly as a qualitative tool for subsidence monitoring. Even after these are all corrected, it is necessary to export the InSAR results to a GIS so that they can be overlaid as layers over orthophotos and plans, in order to interpret the results. To aid InSAR applications it is therefore necessary to use both GPS and GIS. Results are presented with an application to monitoring subsidence due to underground mining in Appin, southwest of Sydney, Australia. KEYWORDS: Interferometric Synthetic Aperture Radar, Geographic Information Systems, Global Positioning System, radar applications.
1. INTRODUCTION The fact that no satellite has been launched for the express purpose of radar interferometry has meant that inaccurate orbit determination for satellites such as JERS-1, ERS-1 and ERS-2, has had to be tolerated in applications to derive topography and topographic change information from radar images (Graham, 1974). For example, the authors had to locate a radar test site near the Appin township, southwest of
Sydney (Figure 1), using both the known GPS coordinates of the site and the geocoding information in the SLC (single look complex) data (i.e. the latitude and longitude of the four image corners). There are three collieries at the test site, namely, Appin, Westcliff and Tower.
Figure 1. Location of Test site.
Because the geodetic coordinates of the test site and the SAR images are independently derived, in a process external to the SAR imaging itself, this method is referred to as external location. Results of the external location on both C- (ERS-2) and L- (JERS-1) band SAR images are shown in Figure 2.
Figure 2. Results of the external location on both C- (ERS-2) and L- (JERS-1) band SAR images.
On the other hand, the test site was also located based on certain ground features (e.g. rivers, railways, and small towns) in the SAR image and on the map for the same region. Since the relative locations of the features within the same radar image are used, this method is termed
internal location. The location of the Tower Colliery using this method is illustrated in Figure 3.
Figure 3 Results of the internal location on both C- and L-band (images copyright of ESA and NASDA).
Comparing Figures 2 and 3, it can be seen that the location determined by the external method is well away from the true position indicated by the internal method based on features in the images. Also when one checks the coordinates of the same feature in the InSAR results and on the map, there is significant discrepancy. This is because the SLC data is produced in the data transcription using the satellite orbit information without any input from ground control points. When there is indeed a significant signature in the differential InSAR (DInSAR) result, one sometimes is not sure whether it is due to ground deformation or an artefact of atmospheric heterogeneity. Figure 4 shows a significant disturbance detected in the differential InSAR result, which was later shown to be due to a cold front, after comparison with the observation of a weather radar system (Hanssen et al, 1999). Because of these problems, InSAR is often regarded as a qualitative tool for the purpose of subsidence monitoring.
Figure 4. The effect of a cold front on: (A) the DInSAR result; and (B) as measured by a weather radar system.
In addition there is the problem of swapping between the geocoded master image and the interferogram (or height/height change image), or reading the latitude and longitude of a pixel, in order to link the InSAR result to the real world. Hence, it is necessary to export the InSAR results to a GIS so that they can be overlaid over GIS data such as orthophotos and mine plans (in the case of mining subsidence), in order to interpret the results. The authors describe in this paper how to use both GPS and GIS to assist radar interferometry, so that it can find application as a quantitative tool. 2. USING GPS TO GEOREFERENCE INSAR RESULTS Corner reflectors (Figure 5) or permanent scatterers (natural reflectors such as the twin towers in Figure 6) are surveyed using GPS receivers so that they are well-defined not only in the image domain with precise image coordinates (row/column), because they are very bright in the radar image; but also in the object domain with precise geographic coordinates (latitude/longitude/height). These image and geographic coordinates are used to establish a more accurate and realistic georeference for the geocoded InSAR products (i.e. the DEM and the deformation image).
(A)
(B)
Figure 5. Corner reflector (A) imaged by ERS-2 radar (18 July 2002) (B).
(A)
(B)
Figure 6. Natural reflector as a GPS ground control point, the twin tower on: (A) aerial photo and (B) geocoded master image.
Assume the image coordinates of a reflector are (Irow, Icol), and its geographic coordinates are (Glat, Glon). Then the transformation between the two coordinates in the same projection, referenced to the same datum, can be expressed as [Glat Glon] = [Irow Icol 1]
a a b b c c 1
2
1
2
1
2
(1)
or L=BX
(2)
Where for multiple reflectors, L can be extended to a matrix of GPS coordinates, B a matrix of image coordinates, and X remains a 2x3 matrix of transformation coefficients. Therefore, at least 3 (corner or natural) reflectors have to be identified in the radar image in order to solve for the six transformation coefficients. As a matter of fact, 6 corner reflectors as shown in Figure 5 were deployed in our test site.
When there are more reflectors available, least square estimation can be introduced into the data analysis. Assuming the vector of observation errors is ∆ , then the observation equation is L=BX+ ∆
(3)
Therefore, the least squares estimation (Giordano and Hsu, 1985) of the transformation coefficients is ^
X = (BT P B)-1 BT P L
(4)
where P = D −∆1 , D ∆ is the variance matrix of ∆ . The next thing to do using GPS is to check and correct the InSAR product. With the horizontal coordinates (latitude and longitude) corrected in the previous step, focus is then on the third component, i.e. the height in the DEM. In addition to the reflectors, GPS-surveyed geodetic marks are also used in this step. Both reflectors and geodetic marks are located or identified in the DEM using their horizontal coordinates. Their heights are then read from the DEM and compared with GPS heights. The DEM is then shifted or rotated to minimise the difference between the two heights. Figure 7 illustrates an InSAR-DEM derived from ERS tandem data after applying such corrections (master image: 22434 (acquired by ERS-1 on 29 October 1995); slave image: 2761 (acquired by ERS-2 on 30 October 1995)).
Figure 7. An InSAR-derived DEM after applying GPS corrections.
The approach to deal with the third component in differential InSAR (DInSAR) result (height change) is very different. As shown in Figure 4 the tropospheric delay heterogeneity could lead to a misleading DInSAR result. Hence, GPS data are used to estimate the differential tropospheric delay among the GPS stations in the same radar image. These differential delays are then interpolated to generate an image that can be used to correct the atmospheric disturbance in the DInSAR result on a pixel-by-pixel basis (Ge, 2000).
3. GIS-ASSISTED INTERPRETATION OF INSAR RESULTS After the GPS corrections have been applied, the InSAR/DInSAR products can be exported in GIS format for interpretation. It is also useful to import some GIS data (e.g. orthophoto) in the InSAR format, for example, the MFF (multiple file format) format for Atlantis EarthView InSAR software, so that the GIS data can be used to assist GPS corrections. Figure 8 shows the display in ArcMap (a core application of the ArcGIS Desktop) of a differential InSAR result over a period of 132 days from 9 November 1993 to 21 March 1994. Both the master and slave images were acquired by the L-band JERS-1 satellite. The InSARDEM derived from ERS tandem data as shown in Figure 7 was used to remove the topographic fringes in the interferogram. The interferogram was then phase-unwrapped and converted to a height change image. This image was enhanced and imported to a GIS as a layer. The geocoded master image was also imported to the GIS in order to double check the georeferencing. In addition to these two layers, the mine plan and aerial photo of the area have also been imported to the GIS.
-
(a) OVERALL RESULT
(b) ZOOM-IN APPIN COLLIERY
(C) ZOOM-IN WESTCLIFF COLLIERY Figure 8. Differential InSAR result over the period of 931109 - 940321 (JERS-1, 132 days).
From Figure 8 it can be seen that four regions have experienced significant subsidence, two in the Appin Colliery (Figure 8b), one in the Westcliff Colliery (Figure 8c), and one in the Tower Colliery (where the mine plan is not available). It seems the extent and magnitude of subsidence in Westcliff is much bigger that those in Appin, probably due to different
geological conditions. By checking the subsidence against the mining progress information given in Tables 1 and 2 (for example, in Figure 8b the Appin longwall LW25 start date is 27 January 1994 and the slave imaging date is 21 March 1994), it can be concluded that the delay between mining and subsidence appears to be less than two months, if any. Longwall LW24 LW25 LW26 LW27 LW28a LW28b
Start (YYYYMMDD) 19930428 19940127 19941208 19951020 19960801 19970211
Finish (YYYYMMDD) 19931210 19941027 19950805 19960624 19961231 1990701
Table 1 Mining Activities at Appin Colliery.
Longwall LW18 LW19 LW20 LW21 LW22
Start (YYYYMMDD) 19920310 19930117 19930831 19941128 19960510
Finish (YYYYMMDD) 19921210 19930805 19941023 19960331 19970613
Table 2 Mining Activities at Westcliff Colliery.
Figure 9 shows the display in ArcMap of a differential InSAR result over a period of 44 days from 21 April 1995 to 4 June 1995. Both the master and slave images were acquired by the JERS-1 satellite. Again the InSAR-DEM derived from ERS tandem data was used to remove the topographic information. Two regions have experienced significant subsidence, one in the Appin Colliery (Figure 9b), and one in the Westcliff Colliery (Figure 9c). Again it seems the extent and magnitude of subsidence in Westcliff is much larger that those in Appin. By checking the subsidence against mining progress in Tables 1 and 2 (for example, in Figure 9b the master and slave imaging dates 21 April 1995 and 4 June 1995 fall between the Appin longwall LW26 start and finish dates 8 December 1994 and 5 August 1995 respectively), it can be concluded that the DInSAR result has picked up the correct longwall responsible for the subsidence.
(a) OVERALL RESULT
(b) ZOOM-IN APPIN COLLIERY
(C) ZOOM-IN WESTCLIFF COLLIERY Figure 9. Differential InSAR result over the period of 950421 - 950604 (JERS-1, 44 days).
Comparing the results between Figures 8 (height change in mm) and 9 (height change in cm), the extent of subsidence is greater for the longer time span (132 days). The magnitude, however, is almost the same, especially in Westcliff, which suggests that the subsidence process ended after a period of about 44 days directly above the longwall. If the mining methods used at Appin and Westcliff were the same, the DInSAR results also imply that Westcliff is more susceptible to subsidence. From the induced subsidence, it can be inferred that in the Appin Colliery, mining is progressing from top to bottom one longwall after another (Figure 8b vs Figure 9b), while mining in each longwall starts at the top-right (northeast) and finishes at the bottom-left (southwest) (Figure 8b), even if information about mining activities given in Tables 1 and 2 were not available. In Westcliff Colliery mining is progressing from bottom to top, one longwall after the other (Figure 8c vs Figure 9c), however the mining direction within each longwall cannot be inferred. With the DInSAR results already in the GIS format, many more map-quality products can be generated. For example, the subsidence can be displayed in 3D; profiles across the subsidence area can be produced; and so on. Figure 10 shows subsidence profiles derived from the 931109 - 940321 JERS-1 DInSAR result for Westcliff. Hence, with the help of GPS and GIS, the InSAR result can not only be used to pin-point the longwall responsible for the subsidence, but also compared quantitatively with ground geodetic survey (such as line levelling) along the profiles.
Figure 10. Subsidence profiles derived from the 931109 - 940321 JERS-1 DInSAR result for Westcliff.
Such precisely georeferenced and well-archived DInSAR results could have a big impact on the mining industry. The history of mining disasters includes cases of subsidence and collapse into workings, and also a number of inrushes into underground mines, where deficiencies in surveying, or in maintenance and interpretation of plans, were prime factors. Moreover, in the established coal mine fields of eastern Australia it is becoming increasingly difficult to select underground minesites which avoid major engineering structures, both on the surface and underground (highways, bridges, buildings, abandoned underground workings, and so on). The Tower Colliery, also covered in the radar image, is a representative example, where the surface topography overlying the mine consists of several steep-sided river gorges. The surface is traversed by a freeway which crosses one of the gorges. Consequently, a major surface subsidence monitoring program has been in place for several years, including intensive conventional, GPS and EDM surveying, plus real-time monitoring of critical components of the bridge structure. However, current subsidence monitoring techniques are relatively time-consuming and costly. Hence, the monitoring is usually constrained to very localised areas, and it is very difficult to monitor any regional deformation induced by underground mining. In addition, even in the localised area, the monitoring points are not usually close enough to assist in understanding the mechanisms involved in ground subsidence. The integrated InSAR-GPS-GIS technique described here is both accurate and gives a fine spatial characterisation of the ground deformation, so that it can be used to complement current techniques to measure, on a costeffective basis, the ground subsidence due to underground mining.
4. CONCLUDING REMARKS An application of the integrated InSAR-GPS-GIS technique to monitoring subsidence due to underground mining in Appin, southwest of Sydney, has been demonstrated. The integrated technique can be used as a viable operational and quantitative tool for ground subsidence monitoring to complement current geodetic techniques.
ACKNOWLEDGMENTS: The authors wish to thank Mr Yufei Wang of UNSW for assistance with
GIS, Mr Andrew Nesbitt of BHP for providing GIS data, A/Prof Makoto Omura of Kochi Women's University, Japan, for providing L-band data, and ACRES (the Australian Centre for Remote Sensing) for providing SAR images and satellite programming.
REFERENCES Ge L (2000) Development and Testing of Augmentations of Continuously-Operating GPS Networks to Improve their Spatial and Temporal Resolution, PhD Thesis, School of Surveying and Spatial Information Systems, The University of New South Wales, Sydney NSW 2052, AUSTRALIA, UNISURV S-63, xvi+230pp. Giordano A, Hsu F (1985) Least Square Estimation with Applications to Digital Signal Processing. New York, Wiley, 412pp. Graham LC (1974) Synthetic interferometer radar for topographic mapping. Proc. IEEE, 62, 763-768. Hanssen RF, Weckwerth TM, Zebker HA, Klees R (1999) High-resolution water vapor mapping from interferometric radar measurements, Science, 283, 1295-1297.
