Day 3 Question 4: Remote Sensing of the Cryosphere

Question 4:

Prepare an essay to discuss the generic procedures for a cryospheric remote sensing project (such as mapping the extent and depth of snow cover, land ice, or sea ice). What are the major pitfalls at each phase, how to deal with them from both theoretical and practical perspectives? Whenever possible, you are encouraged to use your own examples to make the case.

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Introduction

Climate fluctuates on all time and spatial scales, and these fluctuations are reflected in the continually varying distribution of ice on Earth (Paterson, 1994). Careful observation and analysis of changes in the extent and volume of ice may contribute to the reconstruction of past climates and aid in the understanding of current and future climate changes. Changes in the mass balance and morphologies of glaciers and ice sheets provide insight into changing climatic conditions on various spatial scales and temporal scales. Accordingly, changes in the Earth’s cryosphere, or frozen portions, and their links to climate, need to be studied on these various scales.

Glacial ice presently covers about 3 % of the Earth’s surface and contains nearly 75 % of its fresh water (Martinson et al., 1998). The mass balance of these ice bodies has a direct influence on sea level. Glaciers are important on regional scales as reservoirs of water for hydroelectricity and irrigation, and can effect humans through sudden events like ice dam collapses, or through glacier advances which can threaten human structures. On a larger scale, glaciers, and especially ice sheets, can modulate sea level and effect climate through the albedo feedback and by effecting the thermohaline circulation, and, in the case of the ice sheets, by posing as a barrier to atmospheric circulation.

The Greenland ice sheet stretches from approximately 60 N to 80 N, covers an area of approximately 1.7×106 km2, has an estimated volume of 2.6×106 km3, and contains roughly 8% of the volume of ice on Earth (Thomas, 1993). The Greenland ice sheet plays a crucial role in the global climate system on scales larger than the ice sheet itself. This large ice sheet directly influences global climate by forming an orographic barrier to atmospheric circulation in the Northern Hemisphere and through the albedo feedback (Barry and Kiladis, 1982). In turn, the mass balance of the ice sheet is influenced by the local climatic environment, particularly the exchanges of mass and energy between the surface of the ice sheet and the atmosphere.

Greenland's contribution to sea level is ultimately controlled by its mass balance. If the ice sheet were to melt completely, it would cause sea levels to rise by 7.4 m (IPCC, 2001). Although there is no evidence that this will happen in the future, recent modeling results suggest that the Greenland ice sheet may have contributed much more to sea level rise during the warm Eemian than the great ice sheets of Antarctica (Cuffey and Marshall, 2000). Repeat remote altimetry of the ice sheet reveal that at present, the ice sheet is thinning rapidly along its margins and is contributing an estimated 0.13 mm y-1 to global sea level rise (Krabill et al., 2000).

With an estimated total volume of 180,000 km3, glaciers and small ice caps, outside Antarctica and Greenland, comprise less than one percent of the ice volume on Earth, yet they play a significant role in the global hydrologic cycle, particularly the transfer of freshwater to the oceans (Williams and Hall, 1993). These smaller glaciers are potentially the most sensitive to climate fluctuations and may contribute significantly to the rise of sea level in the next century (Oerlemans and Fortuin, 1992). This results from their potential to rapidly gain or lose mass, and therefore many of the smaller glaciers are expected to disappear completely by 2100 (IPCC, 2001; Thompson, 2000; Thompson et al., 2000). Careful and continuous monitoring of these ice fields may help reveal small climatic changes that may otherwise go undetected.

Remote sensing of the cryosphere

Satellite imagery is an excellent tool for monitoring isolated ice fields as it allows more extensive and frequent observations. Thus, the response of ice fields to smaller scale climatic perturbations may be inferred. These data have the potential to provide regional and synoptic scale observations (Gurney et al., 1993) that are otherwise unavailable and offer a less expensive alternative to continuous field monitoring. Satellite data are well dated and may eventually provide a decadal- to century-scale record of glacial extent and environmental change. Furthermore, satellite imagery allows for a uniform digital inventory of land ice (Williams and Ferrigno, 1997). Nevertheless, satellite sensors are still limited in their measurement resolution and some variables cannot be observed from these platforms. Therefore, ground-based observations and field research will continue to play an important role in science in the coming years. Rather than eliminating field research, satellite observations offer new ways to address questions and a wider range of questions that may be addressed (Gurney et al., 1993) and may aid in more efficient and productive field campaigns.

Ice-extent changes

The areal extent of an ice cap may be determined through land-cover classification of a satellite image (Albert, 2002). Classification schemes rely on the spectral reflectance signatures of ground features in both short and long wavelengths. Different materials exhibit different spectral signatures which are utilized in grouping features into land cover types. In theory, the unique spectral signatures of snow and ice make them readily distinguishable from other materials in most conditions. In practice, however, the identification of snow and ice is not always straightforward. The albedo of a snow or ice surface changes dramatically with age and dustiness. This factor is especially acute near the margins, where exposed ice becomes dusty and may develop a surficial cover that resembles the surrounding glacial moraines. Additionally, snow and ice are not Lambertian; instead, their reflectance is highly dependent on the slope and aspect of the surface, the zenith angle and azimuth of the sun, and the zenith angle and azimuth of the satellite sensor. Since the geometry of the sun and satellite are effectively fixed throughout the recording of the image, additional variation in the reflectance of ice measured by the satellite may be caused by the slope and aspect of the surface. Generally, steep ice walls and crevasses in the image are heavily shaded and have very low reflectances. Shaded ice has a depressed spectral curve that approaches the spectral curve of water. Another factor that confounds the identification of ice is that edge pixels are likely to be combinations of ice and the surrounding surface material, resulting in a mixed signal. While fuzzy classification schemes can usually help determine the proportion of class cover in edge pixels, the environmental factors generally pose larger errors than mixture problems, and fuzzy classifiers tend to offer little or no advantage over hard classifiers.

Glacier extent mapping from satellite data has become more common and is the focus of the Global Land-Ice Measurement from Space (GLIMS) project. Bayr et al. (1994) used a a ratio image of Landsat TM band 4 to band 5 to delineate glacier area, while Rott (1994) used a band 3 to band 5 ratio image. Paul (2000) found that the band 4 to band 5 ratio technique yielded the best results for glacier mapping, especially in regions with low insolation. Unsupervised classification techniques, specifically the isodata algorithm, were used to map the Southern Patagonia Icefield by Aniya et al. (1996). Supervised techniques, in combination with elevation data was used to map glaciers by Gratton et al. (1990) and by Sidjak and Wheate (1999). Fuzzy classification schemes have also been used in glacier mapping by Serandrei-Barbero et al. (1999). Landsat images have been used to map alpine glaciers with the primary problem being the distinction between moraine-covered ice and ground surface. Albert (2002) analyzed a wide set of classification algorithms for delineation of snow and ice features, with particular consideration for the GLIMS project.

Delineation of ice extent of a single glacier for multiple dates allows for studies of ice-extent changes. However, it is possible for a glacier to advance while it is thinning and losing mass (Meier, 1984). Thus, observations of areal extent may not relate directly to changes in mass balance, and observations of the volumetric change of an ice cap may be a better indicator of mass balance changes (Popovin, 1996). This may limit the use of remote observations to monitor changes in the mass balance of ice fields and determinations of glacier sensitivities to climate change.

Thickness change and motion

Various methods have been developed to generate elevation information over ice fields from satellite sensors. These include synthetic aperture radar interferometry, shape-from-shading algorithms, and radar and laser altimetry. These and similar methods may be employed to reproduce the surface topography of the ice cap at a given time. Mass balance changes may be inferred directly from the change in volume between two observations (Krabill et al., 1999).

Synthetic aperture radar interferometry (InSAR) permits measurement of the relative position of points on the Earth’s surface as well as their displacement over time (Bürgmann et al., 2000; Meade and Sandwell, 1996), and has recently been applied to glaciological studies (e.g. Joughin et al., 2003). Topographic mapping of features may be obtained with sub-meter scale accuracy from radar images taken from slightly different perspectives. Any movement or disturbance of the surface between successive images may be mapped with sub-centimeter accuracy (Bürgmann et al., 2000). Each time the satellite passes over a ground feature it emits a radar pulse and measures the travel time required for that pulse to reach the surface and be reflected back. In this way, the distance between the surface feature and the satellite are measured precisely. On the next pass the distance to the same feature will change slightly due to the new viewing angle and any displacement that has occurred. When the effects of perspective are removed, the phase difference between the two images results in an interferogram consisting of a concentric pattern of interference fringes each representing a displacement of 28 mm (Kerr, 1996).

InSAR provides a systematic way to precisely map the grounding line of tidewater glaciers (Rignot et al., 1997). These methods have been employed to determine ablation rates on individual glaciers in the Arctic and Antarctic (e.g. Reeh et al., 2002). Excess water vapor in the tropics, however, hinders the utility of InSAR for monitoring glaciers there. Atmospheric water vapor may delay radar echos, and this delay may vary between successive images introducing large errors (Meade and Sandwell, 1996). For this reason, InSAR applications have been most suitable in arid or polar regions. Repeat measurements at multiple wavelengths may allow the humidity phase shift to be isolated, although this idea has not yet been tested. If removal of this signal is demonstrated, InSAR may prove to be an ideal tool for monitoring tropical ice caps as it is also capable of peering through thick cloud cover (Clery, 1997).

Similar to InSAR, radar altimetry may determine ice elevations to better than 10 cm. Yet the coarse spatial resolution and retracing problems limit the use of such systems in alpine areas. For these areas, laser altimetry may be better suited (Dozier et al., 1987). Laser altimetry utilizes short pulses of infrared radiation to measure ice sheet and land surface topography, and visible-green light to measure clouds and aerosols. The distance from the feature being measured to the satellite is inferred from the time taken for the laser pulses to travel to these targets and return (Isbell and Jones, 1998). Airborne laser altimetry was used to survey the entire Greenland ice sheet in 1993 and 1994, obtaining elevations to better than 10 cm accuracy (Krabill et al., 1999). Twenty-two flight lines were re-surveyed in 1998 and 1999 revealing widespread thinning of the ice sheet at elevations below 2000 m (Dahl-Jensen, 2000; Krabill et al., 2000). Utilizing existing satellite imagery, shape-from-shading algorithms attempt to recreate topography from variations in shading of a uniform surface caused by changes in relief. However, these studies are limited to broad, homogeneous, light-colored features such as the polar ice sheets and sandy deserts.

Melt detection

Another interesting application of remote sensing of the cryosphere involves the strong microwave signal differences between dry snow facies and wet snow facies. As soon as liquid water is present in snow, the microwave signal changes dramatically. Exploiting these differences, algorithms have been developed (Abdalati and Steffen, 1995) and recently utilized to derive a melt history of the Greenland ice sheet. This history reveals that the ice sheet has recently been exhibiting much greater areas of melt, and areas that were once labeled as dry snow, where the snow never melts, are being renamed.

Methods and potential pitfalls in remote sensing of the cryosphere

Aside from the many problems already mentioned, studies of ice extent change on alpine glaciers have several problems, which although they are not unique to these types of studies, they may be exacerbated. Here I will use the case of ice-extent change on the tropical Quelccaya ice cap as an example of the typical problems encountered.

The first step in any remote sensing study is data acquisition. In order to clearly delineate ice extent, the imagery should be free of any cloud cover and free of non-perennial snow cover. Clouds, while not impossible to delineate from ice, often obscure the edges of glaciers, precluding the proper mapping of the glacier extent. Non-perennial snow cover may also extend far beyond the margins of the ice cap, and is indistinguishable from perennial snow. Furthermore, illumination and view angles in all images should be similar, so obtaining imagery from the same time of year is ideal. With these constraints, it is often difficult to find imagery, especially from satellites with the high spatial resolution required for studies in alpine regions.

Once appropriate data are collected, the next step in a change detection study is the geometric correction and registration of the imagery. Proper geometric correction is essential if absolute areas are to be reported. Accurate geometric correction in montane areas requires the use of a high-resolution digital elevation model (DEM) to de-warp the image. Although DEMs of proper resolution for alpine research exist, they are seldom available to the public. If the satellite sensor is in a low enough orbit and the mountains are high enough, some degree of pixel distortion from elevation may also be present in the image.

For the image registration, very few ground control points are available in the mountains that are easily distinguishable on both maps and satellite imagery. For example, in the regions surrounding the Quelccaya ice cap, no roads or road intersections exist. The most easily distinguishable features in the imagery, both for the original registration, and eventually for the co-registration of the two images, are stream confluences. The problem with using streams (as opposed to roads) is that streams fluctuate and meander over time and with changes in the local hydrography. Once images are coregistered, there are still many issues with delineating ice extent, especially in areas of debris-covered moraines, shadows, steep slopes, and crevasses.

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