The importance of temporal scale when mapping landscape change in permafrost environments using Interferometric Synthetic Aperture Radar

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1 Permafrost, Phillips, Springman & Arenson (eds) 23 Swets & Zeitlinger, Lisse, ISBN The importance of temporal scale when mapping landscape change in permafrost environments using Interferometric Synthetic Aperture Radar D.B. Sjogren Earth Science Program, University of Calgary, Calgary, Alberta, Canada B.J. Moorman Earth Science Program, University of Calgary, Calgary, Alberta, Canada P.W. Vachon Canada Centre for Remote Sensing, Natural Resources Canada, Ottawa, Ontario, Canada ABSTRACT: The properties of satellite interferometric synthetic aperture radar (InSAR) data were assessed to determine whether it is suitable to detect small landscape changes in a permafrost environment. The advantage of InSAR is that it can generate measurements with large spatial coverage with relatively fine horizontal and vertical resolution. Amplitude, coherence, and interferometric phase images were generated from SAR data from the ERS- 1/2, RADARSAT-1, and JERS-1 satellites. It was found that the variations between repeat pass period and technical characteristics of the satellites resulted in each system producing unique data sets. Decorrelation between images generally resulted in coherence that was too low to map the absolute rate and direction of surface motion. However, the relative decrease in coherence is useful for establishing the relative rates of ground surface change and linking these to geomorphic and hydrologic processes. 1 INTRODUCTION Recent climatic warming has initiated the melting of ground ice throughout permafrost regions. This is resulting in increased terrain disturbance. A potential consequence of these alterations to the land surface is damage to ecosystems and infrastructure (e.g. roads, buildings and pipelines). Before significant surface disturbance is initiated, melting ground ice causes small changes in the morphology, elevation and hydrology of the ground surface. Unfortunately, conventional techniques cannot provide the three essential elements required to detect the initial stages of ground ice melt: extensive spatial coverage (tens of square kilometres), frequent measurements (monthly or more frequently), and fine-resolution change detection measurements (sub-metre). Many remote sensing techniques easily cover the first two elements and with the high resolution interferometric products that may be extracted from SAR (synthetic aperture radar) images there is a possibility that the third required element may be met as well. Both the timing and the degree of surface change are highly variable in permafrost environments. Processes that vary in magnitude, frequency, and spatial extent/ distribution such as wind mobilization of snow, glaciohydrologic events involving stored water and slope movements are all superimposed on permafrost degradation which is primarily governed by seasonal and diurnal melt cycles. The superimposition of these events obscures longer term landscape destabilization due to melting permafrost. In this paper we highlight the importance of matching the temporal scale of the physical process with the repeat-period of interferometric SAR (InSAR). Also, we stress the complexity of using remote sensing imagery for isolating particular changes. Specifically, we intend to provide a general framework within which to evaluate different sources of InSAR for detecting various types of landscape destabilization in permafrost terrain. 2 METHODS Cross-track airborne interferometric SAR is now commonly used to create DEMs of the land surface (Gray et al., 1995; Orwig et al., 1995), and this method is now commercially available (Moorman et al., 1998). Repeat-pass interferometry has the potential for mapping a greater array of variables including, temporal change, however, there are also greater complications associated with it. In certain situations, repeat-pass satellite interferometric SAR can also be used to generate DEMs of the Earth s surface and produce associated temporal derivatives. Some of the applications demonstrated to date include, measuring glacier flow rates (e.g. Vachon et al., 1996), detecting frost heave in the active layer (e.g. Wang and Li, 1999), crust bulging associated with volcanic activity (e.g. Massonet et al., 1995), and land subsidence (e.g. Strozzi et al., 21). One of the limiting factors in using satellite SAR data for interferometry is the change in the character of the 157

2 ground surface between satellite passes. Even small changes result in decorrelation of the images, limiting the potential for generating a DEM (e.g. Vachon et al., 1995; Kenyi et al., 1999). This is particularly true in a permafrost environment during summer melt. Decorrelation between SAR images is of interest in detecting landscape changes. Areas where there is a relatively large change in the character of the ground surface over time (e.g. were ground ice is melting) will be expressed in a coherence image as areas of localized decorrelation. However, due to the slow rate of surface subsidence associated with the initiation of ground ice melt, longer observation periods are required for a detectable amount of surface change to develop. Unfortunately, as the repeat period of the satellite increases, so does the potential for widespread scene decorrelation due to other processes (e.g. vegetation growth, snow accumulation/ablation, or changes in the soil moisture content). The coherence between SAR scenes (i.e. the ability to interferometrically correlate the two images) is dependent on a number of factors other than just ground surface change (e.g. Vachon et al., 1996). In generating interferometric images from satellite SAR data, both the orbital and sensor characteristics of the satellite play a major role in the suitability of the data for interferometric processing. Orbital considerations include the repeat period of the satellite and its across-track repeatability. Sensor considerations include spatial resolution, incidence angle, radar wavelength, and signalto-noise ratio. For example, the longer wavelength of the JERS-1 sensor is theoretically less sensitive to the small changes that would affect the ERS-1/2 and RADARSAT-1 sensors, thus decreasing resolution, but at the same time decreasing the level of background noise. The optimal system for locating melting ground ice would show a loss of coherence in the areas of melt, while coherence is retained throughout the rest of the scene. The different system configurations of the sensors considered (Table 1) enabled examination of the loss of coherence within a scene due to geomorphologic and hydrologic processes that occur at different rates. For this project, amplitude, coherence, and phase images were generated from ERS-1/2 (tandem mode), RADARSAT-1, and JERS-1 image pairs for roughly Table 1. InSAR image pair parameters. Repeat period Baseline Satellite Band (days) Dates (m) ERS-1/2 C 1 Dec. 4/ (tandem mode) RADARSAT-1 C 24 Dec. 4/ JERS-1 L 44 May 14/ 91 Aug the same area. Because we were concerned with identifying particular processes and not mapping per se, the images were left in slant-range. The characteristics of the three systems are compared in relation to observed scene changes and the rate of landscape activity. Because our goal was to investigate the linkage between the coherence images and terrain processes, we compared two unsupervised classification techniques. The first was a variant on a K-means clustering (isodata) and the second was a non-parametric approach known as Narendra-Goldberg clustering (Narendra and Goldberg, 1977) that was developed for Landsat MSS data. Due to the highly skewed nature of the data (Skewness of ERS-1/2 1.69, RADARSAT-1.56, JERS-1 1.6) and the assumptions about the probability density inherent in the isodata method we elected to use the non-parametric approach. The Narendra-Goldberg clustering partitions the histogram by employing a valley seeking algorithm (Koontz et al., 1976). This method identifies breaks in the histogram and, therefore, can produce groups that have nonnormal frequency distributions. For the analysis we applied a 5 point (digital number) running mean to eliminate high frequency noise in the histogram. Also, we eliminated any groups that had less than 1 pixels. This technique allowed an unbiased, simple classification of the coherence images based on natural breaks in the histogram. 3 STUDY AREA A study area on southern Bylot Island in the eastern Canadian Arctic was chosen for this project as it contains a variety of terrain types and a number of subsurface massive ice bodies that are currently melting at varying rates (Klassen, 1993). The area s cold and dry climate results in little vegetation growth or snow accumulation, and thus the potential for detecting icemelt related changes is enhanced. Within the study area, there are two main geologic regions, Precambrian mountains consisting of highly resistant metamorphic bedrock, and a Cretaceous/ Tertiary platform that is composed of easily erodable sedimentary bedrock. The mountains act as a source area for the glaciers, some of which flow out onto the platform. The sedimentary platform consists of gently rolling uplands and broad U-shaped valleys carved out during past glaciations. Generally, the moraines surrounding the retreating glaciers are ice cored. Ground ice bodies observed on the island range in size from 1 m 3 to over 2 m 3. As the mean annual air temperature in the region is approximately 9.5 C, meltout of the ice-cored moraines does not occur spontaneously following deglaciation as in more temperate environments. 158

In all three data sets the coherence is variable across the scene, and is well correlated with terrain type (Fig. 3).

3, there is a lower coherence for the areas covered by the (relatively rapidly moving) glaciers.

) The dark area indicated by the arrow in Fig. 3 is a retrogressive thaw flow.

3 In all three data sets the coherence is variable across the scene, and is well correlated with terrain type (Fig. 3). The relationship between coherence and terrain type can be explained by the rate of surface activity within each terrain unit (Table 2). As can be seen in Fig. 3, there is a lower coherence for the areas covered by the (relatively rapidly moving) glaciers. The small dark spot in front of the terminus of the glacier on the left is an area of open water/slush from a spring that had a subglacial source (arrow in Fig. 1.) The dark area indicated by the arrow in Fig. 3 is a retrogressive thaw flow. Ground ice melts very rapidly in the summer and the resultant saturated mud flows can continue to move well into the winter. However, the amount of surface change expected from ground ice melt before a retrogressive thaw flow is initiated would be much less. Thus, a 1 day repeat period is of minimal use in detecting pre-landslide surface changes. In the RADARSAT-1 coherence image shown in Fig. 4, there is a lower overall coherence relative to the ERS-1/2 image. This is due to the longer repeat period between the image pair. However, this image displays the same patterns as the ERS-1/2 image. The glacial valley shows the greatest coherence while the glaciers show the least. Consequently, local areas where coherence is lost can still be detected in the valley (e.g. the wet area in front of the glacier on the left). Note that ground ice is most frequently found in glacial valleys. The JERS-1 data had low coherence values with only the valley floors and some of the upland terrain showing appreciable coherence. Differentiating between local Some ground ice bodies were found to still exist in areas not glaciated for tens of thousands of years. The ultimate result of ground ice melt in this environment is the development of thermokarst lakes, many of which can be observed throughout the lowlands. 4 RESULTS AND DISCUSSION The character of the interferometric fringes generated from the three data sets described in Table 1 are displayed in Fig. 2 (location is indicated in Fig. 1). The ERS-1/2 image has a high degree of coherence in this region and the grey cycles depicting the phase fringes are apparent. Only in the high relief terrain at the base of the image is there a decrease in coherence. The loss of coherence within the JERS and RADARSAT-1 interferograms at this location makes delineation of the phase fringes more difficult. For this reason phase unwrapping and DEM generation was not possible. Figure 1. A SAR image of the study area on southern Bylot Island acquired with the C-band, single-pass InSAR sensor on the CCRS CV58 in March The arrow indicates the location of a spring emerging from near the terminus of the glacier on the left. The region shown in Fig. 2 is outlined. LD refers to the look direction. Figure 3. ERS-1/2 tandem mode coherence image. The gently sloping valley bottom and plateau areas have a higher coherence than the glaciers and steep valley slopes. The dark area just in front of the glacier on the left is open water produced from a subglacial spring. Figure 2. Phase images of a location in the southern portion of the study area. E ERS-1/2, J JERS, and R RADARSAT

accumulation/erosion Ice movement Change in moisture content or snow accumulation/erosion Lack of vegetation and moisture, stays windswept and dry Moderate Slow Very slow * E1, E2, E3, E4 refer to

4 Table 2. The loss of scene coherence over time of different terrain types. Rate of decorrelation Terrain type, classification* Cause Rapid Open water, steep slopes, E1, R1 Glaciers, E2, R1 Intermediate slopes, E2, R2 Valley bottoms, E4, R3 Movement of water or slush, snow accumulation/erosion Ice movement Change in moisture content or snow accumulation/erosion Lack of vegetation and moisture, stays windswept and dry Moderate Slow Very slow * E1, E2, E3, E4 refer to the classes created in the ERS-1/2 image, R1, R2, R3 refer to the classes created in the RADARSAT-1 image (Figs. 5, 6, 7). Increasing coherence E4 Frequency 2 E3 1 E2 E Digital Number (a) 2 25 Increasing coherence Frequency 2 R1 1 R2 R3 (b) Digital Number 2 25 Figure 4. RADARSAT-1 coherence image. The glaciers and steep slopes have lost all coherence in the 24 days between images, while the valley floor still shows some coherence. Figure 5. (a) Histogram of the ERS-1/2 coherence image showing class breaks. (b) Histogram of the RADARSAT-1 coherence image showing class breaks. areas having rapid surface change would be difficult with this data set. The JERS-1 image was not included due to its low overall coherence. The non-parametric classification used to objectively test the visual interpretation of the images provides support for the interpreted terrain units. Due to the variety of differences from one image to another, not the least being the repeat period, the coherence image histograms dramatically change in character (Fig. 5). While the ERS-1/2 histogram is bimodal and negatively skewed the RADARSAT-1 and JERS-1 (not shown) histograms are unimodal, positively skewed, and have higher standard deviations. The non-parametric classification automatically generated 4 classes in the ERS-1/2 coherence image (Fig. 6) and 3 classes in the RADARSAT-1 coherence image (Fig. 7). In the ERS-1/2 coherence image the least coherent class consisted of open water and steep slopes. The next class grouped glacier surfaces and intermediate slopes. The most coherent class consisted Figure 6. Classified ERS-1/2 coherence image. Smoothed with a 5 5 median filter. 16

5 due to the nature of the imagery, there is a noticeable degree of spatial heterogeneity within each unit. Also, the class separability decreases with increasing repeat period. These initial results demonstrate the potential of this technique for investigating a variety of geomorphologic and hydrologic phenomena. However, inclusion of ancillary data (e.g. elevation) is expected to greatly improve the capabilities of interferometric SAR for detecting surface changes such as ground ice melt. ACKNOWLEDGEMENTS Figure 7. Classified RADARSAT-1 coherence image. Smoothed with a 5 5 median filter. of valley bottoms. A spatially limited class identified significant breaks in slopes. In the RADARSAT-1 image, open water, steep slopes, and glacier surfaces were classed together, having the least coherence. The second class consisted of intermediate slopes. The most coherent class consisted of the valley bottoms. The RADARSAT-1 image displays greater spatial heterogeneity within each terrain unit. We thank A.L. Gray (CCRS), K. Mattar (formerly of Intermap, now with DREO), and D. Geudtner (DLR) for their contributions to the airborne and satellite InSAR processing at CCRS. We thank B. Armour (formerly of Atlantis Scientific) for helpful discussion. The RADARSAT-1 data were acquired through CSA ADRO project #5 and are copyright CSA. The JERS-1 data were acquired through the NASDA JERS-1 Research Invitation and are copyright NASDA. The ERS data are copyright ESA. C. Livingstone (CCRS) expedited the acquisition of the CV58 data. We also thank F. Michel, L. Moorman, D. Kliza, and M. Elver for assistance in the field. Field logistical support was supplied by the Polar Continental Shelf Project. Thanks also go to the Hamlet of Pond Inlet for supporting this project. 5 CONCLUSIONS This investigation into the suitability of repeat-pass satellite interferometric SAR for detecting ground ice melt reveals a number of considerations in application of this technique to geomorphological analysis specifically, 1 By comparing coherence images from three sensors, the rate of surface change in different terrain types was estimated, and its impact on interferometric analysis determined. 2 ERS tandem mode data has the greatest coherence due to its short repeat period, but in general this is too short a period for detectable change to occur. 3 RADARSAT-1 data was less coherent than the ERS tandem mode data, but the 24 day repeat pass period makes it more suitable for detecting melting ground ice before landsliding is initiated. 4 Even with the longer wavelength of the JERS data, there was minimal coherence between the images due to the repeat pass period of 44 days and the seasonal timing of the imagery. 5 By utilizing both of the ERS-1/2 and RADARSAT-1 images, it is possible to objectively partition the different terrain units within the study area. However, REFERENCES Granberg, H.B. & Vachon, P.W Delineation of discontinuous permafrost at Schefferville using RADARSAT in interferometric mode. Proceedings 7th International Conference on Permafrost, Yellowknife, Canada, Collection Nordicana, No. 57, Centre d études nordiques, Université Laval, Quebec: Gray, A.L., Mattar, K.E. & van. der Kooij, M.W.A Cross-track and long track airborne interferometric SAR at CCRS. In: Proceedings, 17th Canadian Symposium on Remote Sensing, Canadian Remote Sensing Society, Saskatoon, Canada 1: Kenyi, L.W., Raggam, H. & Sharov, A Interferometric image analysis in the high Arctic: specific features of general approach. Proceedings, IEEE International Geoscience and Remote Sensing Symposium, Hamburg, Germany: Klassen, R.A Quaternary geology and glacial history of Bylot Island, Northwest Territories, Geological Survey of Canada Memoir 429: 93. Koontz, W.L., Narendra, P.M. & Fukunaga, K A graph theoretical approach to nonparametric cluster analysis. IEEE Transactions: Computing 25: Lewkowicz, A.G. & Duguay, C.R Detection of permafrost features using SPOT panchromatic imagery, Fosheim Peninsula, Ellesmere Island, N.W.T. Canadian Journal of Remote Sensing 25:

6 Massonet, D., Briole, P. & Arnaud, A Deflation of the Mount Etna monitored by spaceborne radar interferometry. Nature 375: Moorman, L.A., Geile, W. & Mercer, B New Frontiers: Environmental applications of high accuracy DEMs. Proceedings, 27th International Symposium on Remote Sensing of Environment, Tromsø, Norway: Narendra, P.M. & Goldberg, M A non-parametric clustering scheme for Landsat. Pattern Recognition 9: Orwig, L.P., Aronoff, A.D., Ibsen, P.M., Maney, H.D., O Brien, J.D. & Holt Jr., H.D Wide-area terrain surveying with interferometric SAR. Remote Sensing of the Environment 53: Strozzi, T., Wegmuller, U., Tosi, L., Bitelli, G. & Spreckels, V. 21. Land subsidence monitoring with differential SAR Interferometry. Photogrammetric Engineering & Remote Sensing 67(11): Vachon, P.W., Geudtner, D., Gray, A.L. & Touzi, R ERS-1 synthetic aperture radar repeat-pass interferometry studies: implications for RADARSAT-1. Canadian Journal of Remote Sensing 21: Vachon, P.W., Geudtner, D., Mattar, K., Gray, A.L., Brugman, M. & Cumming, I.G Differential SAR interferometry measurements of Athabasca and Saskatchewan Glacier flow rate. Canadian Journal of Remote Sensing 22: Wang, Z. & Li, S Detection of winter frost heaving of the active layer of Arctic Permafrost using SAR Differential Interferograms. Proceedings, IEEE International Geoscience and Remote Sensing Symposium, Hamburg, Germany:

Measuring rock glacier surface deformation using SAR interferometry

Permafrost, Phillips, Springman & Arenson (eds) 2003 Swets & Zeitlinger, Lisse, ISBN 90 5809 582 7 Measuring rock glacier surface deformation using SAR interferometry L.W. Kenyi Institute for Digital Image