Glacial geomorphology and geographic information systemsjnapiera/...Li_2007.pdf · Geographic Information Systems (GIS), and associated theoretical and technical advances in data

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Earth-Science Reviews

Glacial geomorphology and geographic information systems

Jacob Napieralski a,⁎, Jon Harbor b, Yingkui Li c

a University of Michigan-Dearborn, 4901 Evergreen Road, Dearborn, MI 48128, United Statesb University of Colorado at Denver, Campus Box 144, Denver, CO 80217-3364, United Statesc University of Missouri-Columbia, 8 Stewart Hall, Columbia, MO 65211-6170, United States

Received 22 June 2006; accepted 20 June 2007Available online 31 July 2007

Abstract

Recent developments in the field of glacial geomorphology have dramatically increased the need to acquire, maintain,manipulate, and analyze large amounts of landform, landscape and sediment data. The use of Geographic Information Systems(GIS) has provided new platforms and tools for analysis and visualization of geomorphic data. Glacial geomorphologists have usedGIS to integrate multi-source data, manage multi-scale studies, identify previously unrecognized spatial and temporal relationshipsand patterns in geomorphic data, and to link landform data with numerical models as part of model calibration and verification.GIS-based analyses associated with numerical modeling are improving our understanding of glacial landscape evolution and areallowing new quantitative and systematic examinations of spatial and temporal patterns of glacial landforms and processes. Thishas allowed for the development of insights and concepts that would be unlikely to arise using traditional methods alone. Keyrecommendations for future research and applications in glacial geomorphology include enhanced GIS education anddissemination, the development of standards and conventions for glacial geomorphic data, community projects to collect datainto readily accessible databases, and enhanced use of linked GIS — model frameworks to address major issues in glacialgeomorphology.© 2007 Elsevier B.V. All rights reserved.

Keywords: Geographic Information Systems (GIS); glacial geomorphology; ice-sheet reconstructions; glacial landforms; database

1. Introduction

A principal goal of glacial geomorphology is todescribe and explain the impacts that glaciers and icesheets have on landform and landscape development.This is achieved by integrating studies of landformswith empirical and theoretical studies of the processesresponsible for their development (Harbor, 1993).Assessing and analyzing the spatial distribution andtemporal evolution of glacial landforms is an impor-

⁎ Corresponding author. Department of Natural Sciences, Universityof Michigan-Dearborn, Dearborn, MI 48128, United States.

E-mail address: [email protected] (J. Napieralski).

0012-8252/$ - see front matter © 2007 Elsevier B.V. All rights reserved.doi:10.1016/j.earscirev.2007.06.003

tant approach to better understanding landformgenesis, and for revealing patterns and relationshipsof glacial landforms at various scales. The advent ofGeographic Information Systems (GIS), and associatedtheoretical and technical advances in data handlingand analysis, provides an opportunity for moresophisticated and effective analysis and modelingthat in turn can have a significant impact on ourunderstanding of glacial landform and landscapeevolution (Clark, 1997). The goal of this paper is toprovide a review of key issues in the development andapplication of GIS in glacial geomorphology, and tohighlight emerging areas of research and applications(e.g. Fig. 1).

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http://dx.doi.org/10.1016/j.earscirev.2007.06.003

Fig. 1. Emerging areas in GIS and glacial geomorphology include ice-sheet models linked to GIS, with the user able to experience model resultsthrough immersive visualization (Napieralski, 2005). In this case output during different time slices from an ice-sheet model is evaluated against amajor moraine along the western margin of the Scandinavian Ice Sheet during the last major glaciation (top diagrams). Each time slice (1000 yr)is checked against the moraine using a statistical method developed in GIS (APCA; Napieralski et al., 2007) and a bar graph is used to indicate the timeslice in which good correspondence occurs. Other GIS techniques (AFDA; Li et al., 2007) compare ice-flow orientation against suites of glaciallineations (bottom right) to indicate the potential times in which simulated ice-flow orientation best corresponds with field evidence. The visualizationsillustrate various glaciological conditions, such as surface or subsurface temperature (bottom left) and ice-flow orientation (bottom right).

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Earth scientists sometimes regard GIS as merelymapping tools (Wright et al., 1997), assuming the mainpurpose is to simply generate attractive maps. The toolsassociated with most commonly-used GIS packages al-low for advanced data integration and sharing, numer-ical and cartographic modeling, spatial analysis, andadvanced analyses of remotely sensed data integratedwith other data sources. GIS have already had a sig-nificant impact on many areas of the Earth sciences,including hydrology (e.g. Djuokic and Maidment,2000), watershed and natural resource management(e.g. Bhaduri et al., 2000), and mountain geomorphol-ogy (Bishop and Shroder, 2004). However, the use ofGIS in glacial geomorphology has progressed relativelyslowly, perhaps because of the emphasis placed on fieldinvestigations in glacial geomorphology and the lack offocused GIS education and technical training within thediscipline. Thus, although some of the geospatial capa-bilities offered by GIS have been applied to advancingknowledge in glacial geomorphology, there are stillmany areas in which significant advances are possible.To take advantage of GIS it is important to understandhow concepts and common spatial tools can be inte-grated into glacial geomorphology research, including

areas such as data assimilation and management, spatialanalysis, and landform mapping.

Many issues that are traditionally important in glacialgeomorphology, such as scale, space–time representation,data representation and integration, spatial variation andprocess, and visualization, are also key components of thelarger field of Geographic Information Science (GISc),which forms the basis for GIS. GISc is concerned with thephilosophical and scientific foundation of spatial theory,and is a dynamic multidisciplinary field of study thatutilizes skills and concepts from a variety of disciplines,such as cartography, geodesy, artificial intelligence,photogrammetry, and computer science (Goodchild,1992). Advances in GISc, such as improved methodsfor data extraction and drainage delineation, may havepotential uses in glacial geomorphology (O'Sullivan andUnwin, 2003), including improvements in defining alpinevalley morphometry and glacial valley networks (e.g.Duncan et al., 1998). Similarly, techniques and theoriesdeveloped to solve problems in glacial geomorphologyhave the potential for applications to other fields and toadvance more general theories and methods in GISc.

Key GISc issues that will be discussed in this revieware data integration and sharing, scale and representation,

Fig. 2. A conceptual diagram of the differences between four ice-sheetreconstructions, focusing on particular time–space data domains andthe data types used in the inversion procedures. The primary datadomains: thick black line marks the deglacial landforms; boxes insideglacial margin mark ‘events’ reflected by till lineations pre-dating thefinal decay phase; triangles schematically illustrate radiocarbon dates(which always reflect ice-free conditions); light gray inside ice margin(in Dyke and Prest, 1987) represents a ‘stretching’ of the deglaciallandform record for inferences about older non-deglacial events.

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ontology, numerical modeling, and visualization/anima-tion capabilities. Sample applications will be discussed toillustrate ways in which GIS are being used in glacialgeomorphology, including paleo-climate reconstructionsusing valley glacier extent, correlations between glaciallandforms and sediments, verification of numerical ice-sheet models, and reconstructions of ice extent, flowdirection, and subglacial conditions of paleo-ice sheetsand glaciers. This review expands upon and updates aprevious review by Clark (1997), which focused on therole of Geographic Information Technologies (GIT) inreconstructing dynamics of paleo-ice sheets, and reflectsconsiderable improvements and broader use of GIS inglacial geomorphology in recent years.

2. Recent trends in glacial geomorphology

Glacial geomorphology has traditionally focused onrecognizing and describing glacial landforms and land-scapes, and on advancing our understanding of glacialerosional and depositional processes and their interac-tions with ice and climate dynamics (Harbor, 1993;Martini et al., 2001). For example, the spatial distribu-tion of glacial landforms such as drumlins (ice-flowdirection) and end moraines (ice-marginal position)have been used to infer paleo-ice-sheet behavior(Punkari, 1995a,b; Kleman and Borgström, 1996;Colgan and Principato, 1998; Stokes and Clark, 2003),and to reconstruct ice-sheet extent and chronology(Kleman et al., 1997; Clark et al., 2000; Boulton et al.,2001). Understanding the relationship between patternsof glacial landforms and ice dynamics potentially pro-vides valuable insight into paleo-ice sheets and paleo-climates (Clark, 1997; Ehlers and Gibbard, 2003).

Recently, a significant emphasis in glacial geomor-phology has been on the development of subglacial re-gime studies as critical components of understanding ice-sheet and glacier behavior (Kleman, 1994; Kleman andHättestrand, 1999; Cuffey et al., 2000). The increasingquality and quantity of data that can be used to infer pastspatial patterns of subglacial conditions have allowed ice-sheet reconstructions to evolve from relying primarily onend moraines to reconstruct stages of ice-sheet retreat(Boulton et al., 1985) to sophisticated integrations ofproglacial and subglacial landform and sediment datawith ice-marginal data to examine the dynamics of ice-sheet advance and retreat (Fig. 2; Kleman et al., 2004).The sheer volume of data, the range of data formats, andthe need for a wide range of analytical approaches, areimportant reasons to use GIS in when relying on field datato reconstruct ice-sheet and glacier extent (i.e. inversionmodeling; Kleman et al., 1999).

Similarly, the application of new techniques such ascosmogenic nuclide dating (e.g. Lal, 1991; Bierman,1994; Gosse and Phillips, 2001) in glacial geomorphol-ogy (Fabel and Harbor, 1999; Bierman et al., 1999) hasimproved spatial and temporal constraints for paleo-ice-sheet reconstruction (Brook et al., 1996; Fabel et al.,2002; Stroeven et al., 2002; Li et al., 2005; Harbor et al.,2006). Thus the spatial distribution of glacial landformscan now be evaluated as the outcome of a sequence oferosional or depositional events (combination of syn-chronous and asynchronous events) from multiplestages of glaciation.

Current research in glacial geomorphology empha-sizes process modeling, including linkages betweenlandscape development and ice-sheet dynamics and


chronology, as well as relating erosion processes (andpatterns) to smaller scale landforms and landscape ele-ments. Numerical models of glacial processes andlandform development continue to evolve, reflecting im-provements in computation technology, the ability tomodel in 3-dimensions (e.g. Hubbard et al., 1998), andrecent efforts to verify model output against field ob-servations (Boulton et al., 2001; Näslund et al., 2003;Tarasov and Peltier, 2004; Napieralski et al., 2006; Liet al., 2007). In the future, simulated patterns of land-form development will continue to require additionalcalibration or verification using field evidence, includ-ing data observed/measured by remote sensing, GlobalPositioning Systems (GPS) and dating techniques.

3. Spatial and temporal scale issues

3.1. Time–space representations andglacial geomorphology

Although space–time relationships are important inboth GISc (Raper and Livingstone, 1995; Peuquet, 2002)and glacial geomorphology, most GIS applications inglacial geomorphology still emphasize space-dominantapproaches because of the widespread availability ofundated spatial data. Traditional applications of GIS inglacial geomorphology focused primarily on the spatialdistribution of glacial data, with much less emphasis onthe time dimension. Naturally, it is relatively easier tomap landforms compared to dating landforms and theirstages of development. Space-dominant approacheshave the advantage of being relatively easy to imple-ment, and allow for the production of landformmaps thatfit well with traditional geologic and geomorphic map-ping programs that have existed in many countries sincethe early twentieth century (Klimaszewski, 1990; Clarket al., 2004; Gustavsson, 2006). Using GIS to producetraditional maps that are useful for applications in areassuch as engineering geology, resource extraction andhydrogeology brings new data management and mapoutput capabilities to traditionally-valued products and isa useful first step in demonstrating the value of GIT.However, space-dominant approaches have limitationsthat ultimately affect inferences made about sequencesand processes of landform development.

Relying strictly on space-dominant approaches todistinguish and describe the development and distribu-tion of glacial landforms neglects issues related totiming of erosion or deposition. For example, it cannotbe assumed that moraines which mark the maximumextent of an ice sheet were formed at the same time.Rather, it is well accepted that many ice sheets expe-rienced non-synchronous growth and decay along their

margins, and thus maximum extents occurred at dif-ferent times along the length of many ice-sheet margins.In contrast to the space-dominant approach, the devel-opment of landforms along a “timeline” are establishedthrough dating techniques (e.g. radiocarbon, cosmogen-ic nuclides, varves), with little consideration of spatialvariations or patterns. Although some issues lend them-selves to either time- (e.g. occurrence of ice coverage orice-free conditions at a particular site) or space-dominant (e.g. spatial relationship between landforms)approaches, in most cases it is important to integrate spa-tial and temporal patterns to provide new insight intoglacial geomorphic issues.

An integrated space–time approach to glacial land-form data provides a more comprehensive perspectiveon landscape evolution. To achieve this, the spatialdistribution of data is linked to time, expressed as eitherabsolute or relative ages for the formation of a glaciallandforms or deposits. Relative dating is used to “stack”or order the landform and sediment record in a sequenceof development, and is often possible in areas with mul-tiple glaciations and cross-cutting features (e.g. Klemanet al., 1997; Clark et al., 2000; Boulton et al., 2001).Where absolute dating is possible using evidence suchas radiocarbon dates, cosmogenic nuclide exposure orburial ages, tephras, or paleo-magnetic data, the space–time relationships of glacial sediments and landformscan be analyzed within an integrated framework (Bishopand Shroder, 2004).

3.2. Spatial and temporal scale dependency

In glacial geomorphology, as in many areas of theEarth sciences, both the features of interest and the pro-cesses that create them occur over a wide range of spa-tial and temporal scales. The scale at which anygeomorphic study is conducted influences the outcomeof the analysis (Walsh et al., 1998), and thus multi-scaleanalyses are often required to develop a comprehensiveunderstanding of glacial landforms and processes(Levin, 1992). However, glacial landforms can beorganized into a spatial hierarchical structure (Ahnert,1988; Diakau, 1989), ranging from glacial striations(widths measured in millimeters) to drumlins (widthsmeasured in meters) to imprints left by major ice streams(widths measured in kilometers). Techniques, such asfractal analysis, autocorrelation, and semivariance, haveproved useful in examining the influence of scale onanalyses of fluvial, coastal, and morphotectonic features(e.g. Chase, 1992; Lifton and Chase, 1992; Sung andChen, 2004), and could equally be used in glacialgeomorphology. Scale-dependency analyses have been


used more widely in investigations of the surfacecharacteristics of glaciers, using the fractal responsesof ice as seen in remote sensing data (Bishop et al.,1998). Ice sheets and glaciated landscapes occur anddevelop over varying time scales and it is clear thatmuch of our field evidence for active glacial processeshas been collected on short time scales (up to decades atbest) and in environmental conditions during the currentinterglacial that are far different from those involved inthe formation of major landforms during glacial epochs(Harbor, 1992).

GIS are capable of handling sophisticated analysesacross multiple temporal and spatial scales, although theselection of appropriate spatial and temporal scale reso-lutions for analyses has significant impacts on compu-

Fig. 3. DEMs were used to analyze drumlins in within the Palmyra Drumlin Fresolutions to determine the morphometric characteristics, including height, lein drumlin size and shape determined using various DEM resolutions. Overalelongation increased (Nalepa and Napieralski, 2007).

tational efficiency (too high a resolution can result inunacceptable compute times) and the scales of land-forms that can be examined (Abert, 1996; Walsh et al.,1998). In many studies the data that are available for ananalysis have been collected on a variety of spatial andtemporal scales (see Fig. 2), and thus it is necessary tomerge low-resolution and high-resolution data. Thisrequires that some data to be “smoothed” or “simplified”so that all the data can be treated together (for example,see Clark et al., 2004). Coarse digital elevation models(DEMs) may affect or limit the quality of analysis thatcan be conducted. For instance, drumlins can be de-lineated from DEMs, but the cell-size of the DEM willinfluence estimates of drumlin size (area and height) andcalculations of elongation and volume (Fig. 3). As an

ield in Central New York. Contours were generated on a range of DEMngth, width, elongation, and volume. Vertical profiles reflected changesl, as the DEM resolution increased, estimates of volume decreased and


example, DEMs of 80, 30, 10, 5, 3, and 1 m were used toextract drumlins (north of Palmyra, NY) from the NewYork drumlin field. In this simple case study, drumlinsize and shape were affected by the resolution, such thatthe features became more streamlined (elongation ratio:length/width) and volume generally decreased as reso-lution increased from 80 m to 1 m. When switchingfrom coarse resolutions (80–30 m) to a fine resolution(10–1 m), drumlin orientation was slightly affected, asthe spine of the drumlin rotated several degrees (Nalepaand Napieralski, 2007).

To further illustrate the importance of spatial reso-lution, the type of analysis used to extract the location ofthe equilibrium line altitudes of former glaciers is sen-sitive the resolution of the DEM and this, in turn, im-pacts the level of accuracy reconstructions of pastglacier extent (Duncan et al., 1998). Topographic dataresolution is of particular importance in glacial geomor-phology, and in many areas even the highest resolutionelevation models traditionally available may not be suf-ficient to allow for the identification of subtle landforms(Abert, 1996). However the increasing availability ofhigh-resolution data from comprehensive satellite andshuttle-based programs is solving the traditional prob-

Fig. 4. Using a paper map, it is often difficult to distinguish spatial or temporaaway into separate layers, which can then be arranged into various themes.

lem of gaps that were common in data for more remoteareas (Gao and Liu, 2001), including the recent launchesof “polar” satellites such as ICESat in 2003 (http://icesat.gsfc.nasa.gov/).

3.3. Spatial analysis and geostatistics

One of the most compelling reasons to use GIS inglacial geomorphology research is the power and ease ofuse of advanced geostatistical and geospatial analysistools that are integrated into most software packages(see Dunlop and Clark, 2006; Napieralski et al., 2007).A geospatial analysis can include objective studies ofspatial correlations between sediments, landforms, andtopography. In addition, descriptive statistics can beused to partition and transform data into new combina-tions or classifications (i.e. empirical GIS spatialmodeling; Bishop and Shroder, 2004). Landform datacan be queried and separated according to commonattributes and viewed as new datasets, generating ad-ditional perspectives of patterns and correlations thatmay have otherwise gone unrecognized (Fig. 4).Advanced geostatistical tools, such as kriging and auto-correlation, can be used for spatial predictions and for

l landform patterns. GIS permits landform assemblages to be “stripped”

http://icesat.gsfc.nasa.gov/

http://icesat.gsfc.nasa.gov/


investigating variance within or between glacial geo-morphic studies (e.g. interpolation, prediction, and cer-tainty of glacial drift characteristics). For example,interpolation algorithms and methods, such as kriging,splines, and bicubic functions, have been developed andtested (Evans, 1972; Zevenbergen and Thorne, 1987;Desmet, 1997; Wise, 1998; Hutchinson and Gallant,1999), so that software users can focus more on findingthe appropriate digital terrain representation than under-standing the underlying mathematics of the geostatis-tical tool (Raseman et al., 2004). Of particularimportance are geostatistical tools that enhance ourunderstanding of scale dependencies and self organiza-tion, such as variograms and semivariance analysis(Hallet, 1990; Allen et al., 2004).

Spatial analyses of digital elevation data have beenused to establish altitudinal zonation in geomorphicfeatures, allowing for the reconstruction of paleo-climate conditions (such as atmospheric circulation pat-terns determined from erosional and periglacial features,e.g. Humlum, 1997), and have been used to generatetopographic profiles used to identify and delineatelandforms (e.g. Dunlop and Clark, 2006). Most GISprovide tools for exploring and displaying elevationdata, such as hillshading, slope angle, aspect, fractal,and curvature and many also have tools that automat-ically delineate topographic features, such as peaks orridges and concave or convex hillside allowing rapidexploratory analyses of DEMs from glaciated terrain(Montgomery, 2004).

In sum, the exploration of patterns and relationshipsusing geospatial and geostatistical tools in GIS is dra-matically enhancing our understanding of glaciatedlandscapes of glacial geomorphic processes, andrelationships and feedbacks between process controlsover a range of spatial and temporal scales. However,because of equifinality issues, it is always important toevaluate conclusions drawn using this approach in termsof what is already well known about the behavior andprocesses of glaciers and ice sheets.

4. Data representation and GIS in glacialgeomorphology

4.1. Data representation choices

In any geomorphic mapping project the features thatare identified can be represented in databases in a widevariety of ways. For example, drumlins can be repre-sented by a single line along the long axis, and classifiedin terms of orientation, spacing, or density. Alternative-ly, the boundaries of each drumlin can be digitized to

form a polygon, which provides an opportunity to cal-culate the area and to examine spatial parameters such asarea and elongation of the drumlin. Because each re-presentation allows for different analyses it is critical tochoose a suitable data representation approach based onthe needs/goals of the analysis.

The representation of topography is often critical inglacial geomorphology and is typically addressed usinga combination of cartographic principles and GIT.Digital elevation data, including measures of uncertain-ty, are typically derived from existing contour maps,photogrammetric interpretation of stereographic aerialimages, laser scanning, terrestrial surveying, or aerialand space measurements (e.g. Boulton and Clark 1990;Aber et al., 1993; Stokes and Clark, 2001). Becausemany surface processes are constrained by landscapegeometry and relief (Raseman et al., 2004), theintegration of digital elevation data with glacial geo-morphic data has provided new insight into these rela-tionships (Clark, 1997). For example, an analysis ofelevation, topography, and cosmogenic nuclide apparentexposure age for glacial erosional landforms in the north-ern Swedish mountains revealed a pattern of landscapeprotection and modification resulting from repeatedglaciation by the Scandinavian Ice Sheet. This suggestscold-based (non-erosional) basal conditions dominatedat intermediate elevations, whereas warm-based (ero-sional) basal conditions dominated at both low eleva-tions and in high elevation valleys (Fabel et al., 2002).Digital elevation data thus provide the basis for linkingtopographic influences with geomorphic processes, andalso providing opportunities to view the landscape inthree dimensions with an overlay of satellite or aerialimagery, or glacial modeling results (e.g., Dunlop andClark, 2006; Napieralski et al., 2007).

4.2. Ontology and taxonomy

Ontology is a philosophical discipline that examinesthe use and implications of formal specifications indescribing different types of object domains (e.g., Smithand Mark, 2001; Mark et al., 2004). This is important inglacial geomorphology because of the need to defineand describe landforms using formal specifications forlandforms (landform taxonomies) as part of landformprocess and development research (Rhoads, 1999).While some geomorphic features are clearly definedand have boundaries that are pronounced and well-established, the boundaries of many glacial landformsare frequently vague or subjective (Mark and Smith,2004; Harbor et al., 2006). For example, attempts tocategorize drumlins according to height, length, size


ratio, orientation and other physical characteristicsrequire decisions to be made about the definition of adrumlin and the capability to separate the drumlin fromthe surrounding landscape. Does the drumlin “begin”where it protrudes from the landscape or surroundingsediment? Or, are there subsurface characteristics thatshould be considered? Consequently, different analysesof a drumlin field may produce different results based onthe lack of a widely-accepted nomenclature and set ofdefinitions. GIS cannot directly answer these questions,but their use forces a more objective approach toaddressing these issues; the user is obligated to addressthe issue of how landforms such as drumlins are defined,and once specifications and rules have been createdautomated feature extraction will impartially delineateall drumlins using these criteria. Thus, with theimproving quality and quantity of the glacial landformdatabases, opportunities arise to scrutinize previouslandform classifications (see Abert, 1996 for anexample) and rectify “theory-laden evidence” (Rhoadsand Thorn, 1993), or interpretations that have beenpromulgated through the decades but are no longerlegitimate (e.g. Dunlop and Clark, 2006). Updatinglandform definitions is particularly important for theverification of numerical ice-sheet models, as sets ofglacial lineations, frozen bed zones, moraines, and otherglacial landforms are critical “building blocks” used inice-sheet and glacier reconstructions.

The taxonomy of glacial landforms needs to be clearlydefined to avoid errors that may occur when digitized ordelineated landform data based on different criteria areused in geomorphic analyses (Clark, 1997; Colgan et al.,2003). Landform terms have different meanings invarious parts of the world, for example streamlined fea-tures such as drumlins (Smalley and Unwin, 1968;Boulton, 1987; Shaw and Sharpe, 1987) and ribbedmoraines (Lundqvist, 1969; Markgren and Lassila, 1980;Hättestrand, 1997; Dunlop and Clark, 2006) are definedusing different criteria in different parts of the world(Colgan et al., 2003), leading to potential confusion andalternate outcomes of large-scale or comparative analyses.Objectively defining glacial features according to geo-morphometric parameters has become a significant andproductive challenge for geomorphologists. For example,distinguishing between cirque classes forced geomor-phologists to scrutinize the factors that control the spatialoccurrence andmorphology of the features (Unwin, 1973;King, 1974; Evans and Cox, 1974; Trenhaile, 1976;Evans, 1977; Gordon, 1977; Embleton and Hamann,1988; Federici and Spagnolo, 2004). Parameters such ascirque volume, concavity, gradient, altitude, and orienta-tion were combined as part of identifying the controlling

factors in cirque development (Gordon, 1977), howevercomparisons of cirques between mountain ranges haveshown wide variations in the significance of differentpotential controlling variables, illustrating the difficulty ofdefining cirques based on a limited set of geomorphiccharacteristics that happen to be significant in one region(Embleton and Hamann, 1988). Embleton and Hamannsuggested that other variables, such as duration or timingof ice coverage and subglacial thermal regime, need to beconsidered along with morphological characteristics tobetter explain variability in cirque forms. Thus it isessential that a wide range of morphometric character-istics be explicitly described in geomorphic studies ofglacial landforms, and that these data be included in GISdatabases, so that subsequent research aimed at linkinglandforms with the mechanics of possible land-formingprocesses over a variety of spatial and temporal scales arenot limited by a reliance on a highly restricted range ofparameters.

4.3. Data integration and sharing

Databases used in studies of glacial geomorphologycan include data from hundreds of sources (e.g. Clarket al., 2004) and include results that range from 19thcentury glaciological studies to recent aerial and satelliteimages and surveying (Evans et al., 2005). In addition, avariety of data formats are likely to be included, such aspoints (e.g. radiocarbon dates), lines (e.g. ice-flowindicators, till fabric analysis) and polygons (e.g. frozenbed zones), all then assimilated into a common theme forstatistical analyses or visualizations (Napieralski, 2005;Dunlop and Clark, 2006). Finally, data used for ananalysis can be merged from many scientific disciplines,thus producing a more comprehensive, multidisciplinaryapproach to a problem. For example, combining soilproperties (Sharpe et al., 1999; Paulen and McClena-ghan, 2000; Fenton et al., 2003), botanical evidence (e.g.Barnett and Singhry, 2000), geophysical data (e.g.Fenton et al., 2003), groundwater aquifer characteristics(e.g. Sharpe et al., 2003), and lithology data with land-form data may reveal previously unrecognized rela-tionships and correlations or produce new insight intothe processes that produce specific landforms and land-scapes. Efforts to accomplish large-scale data integrationwith traditional cartographic methods were relativelyinefficient, as paper maps are generally rigid illustrationsof spatial data that do not provide the ability to easilyquery data according to specific attributes. GIS haveprovided a platform that makes data integration mucheasier to accomplish in ways that also allow the user toaccount for different levels of data accuracy and precision.


The ability to share data through advanced informa-tion technology continues to improve, and it is verylikely that Web-based Geographic Information Systems(WebGIS), which are becoming increasingly routine(e.g. Pandey et al., 2000; Clark et al., 2004), will becomea standard means of data communication. WebGISprovide the basis for allocating high-end GIS and map-ping services through the Internet, so that users canintegrate local data sources with internet data sourcesfor display, query, and analysis in an easy-to-use Webbrowser. For example, the International Work Group onGeospatial Analysis of Glaciated Environments(GAGE), a subgroup of INQUA that was interested ininvestigatingmodern and ancient glaciated terrains usingGIS, compiled a glacio-tectonic database as a dataset thatis accessible on the internet at http://www.emporia.edu/earthsci/gage/gage.htm, and specific data can be down-loaded for use in other studies. Many software packageshave been designed for WebGIS, such as ArcIMS, In-ternet Mapper, andMap Xtreme, and these will increasedata sharing as viewing and display capabilities improve.In addition, web-based approaches (e.g. GoogleEarth)provide opportunities for geomorphologists who lackhigh-end computing capabilities to view and displaydata. This reduces the tendency for advanced analyses tobe limited to those who have the funding to be able topurchase highly sophisticated computer clusters andsupercomputers, increasing the range of individuals andgroups who can participate in the science, and thus thediversity of approaches and insights that are generated.

4.4. Visualization and animation

GIS have the functionality to produce a wide varietyof maps and diagrams to display data and the results of

Fig. 5. Themanner in which spatial problems are approached has drastically chawhich spatial data are acquired, analyzed, and displayed. Geomorphologists frewere then verified in the field. Now, visualizations and maps produced by GIS

analyses. Traditionally, maps were labor-intensive, finalsteps in a research project, often created by a carto-grapher who was not involved in other steps of theresearch. The ease with which layouts and maps can beproduced in a GIS has allowed a wide range of alternatevisual displays of data to become an integral part of theanalysis. As alternate displays reveal interesting patternsand relationships, new analyses and displays can becompleted on-the-fly. This multifaceted, adaptable ap-proach continues until a satisfactory result is attained,and research questions can be pursued more effectivelyand new insights may arise, increasing the breadth ordepth of research (Fig. 5). Thus, output from a spatialanalysis may actually function as a precursor to gener-ating new questions and formulating new hypotheses.

Many GIS now include photorealistic display capa-bilities. High-resolution imagery can be draped overDEMs to display complex glacial landscapes that theuser can explore using virtual fly-through utilities. How-ever, these approaches have limitations, such as theinfluence that azimuth-biasing (i.e. an illuminationsource or direction that influences the visualization)can have on the identification and delineation of subtlelandforms (Smith et al., 2001; Smith and Clark, 2005).Recent work to evaluate alternate approaches forvisualizing DEMs for landform mapping suggests thatthere was no single method that provides complete, un-biased visualizations; rather, alternate approaches suchas relief-shading, combined viewing, surface deriva-tives, and spatial enhancements each have shortcomingsthat a user must take into consideration (Smith and Clark,2005), In addition to standard 2- and 3-D displays (e.g.,Sharpe et al., 1999; Atkinson et al., 2000; Fenton et al.,2003), it is becoming increasingly common to viewglacial geomorphic data in 4-D (Buckley et al., 2004).

nged within the previous two decades, essentially a result of themanner inquently used aerial photos prior to field work to focus field efforts, whichare now a part of the analytical process (O'Sullivan and Unwin, 2003).

http://www.emporia.edu/earthsci/gage/gage.htm

http://www.emporia.edu/earthsci/gage/gage.htm


For example, in Napieralski (2005; Fig. 1) ArcGIS wasused to integrate numerically derived ice-sheet data andfield evidence in a simulation of the Scandinavian ice-sheet and high-resolution visualizations were createdand displayed using additional software.

5. Recent advances in GIS and glacial geomorphology

5.1. Data integration and database management

One of the most critical basic uses of GIS in glacialgeomorphology has been management and integrationof geomorphic and glaciological data. Efforts to createand maintain landform databases have been conductedon global, continental, and regional or localized scales.The INQUA Commission on Glaciation Work Grouphas undertaken one of the most comprehensivemapping efforts of glacial extent around the world(Ehlers and Gibbard, 2003). They used a digital-basedtechnique that would allow both users and compilersthe flexibility to query, select and view data usingvarious projections and scales (Ehlers and Gibbard,2003). Universal compilation guidelines were provid-ed to facilitate data integration and management, andthe end results included data layers containing glaciallimits, end moraines, ice-dammed lakes, and glacier-induced drainage diversions for several Pleistoceneglaciations (Ehlers and Gibbard, 2004a,b,c). At theXVI INQUA Congress in Reno Nevada, maps derivedfrom GIS databases were presented for Central Europe(Ber and Aber, 2003; Ber et al., 2003) and the Balticregion (Zelčs, 2003).

The INQUA datasets provide global data, and wereintended to support large-scale studies (e.g. 1:1,000,000)such as examinations of the extent and chronology of thePleistocene glaciations, and regional assessments ofglacio-tectonic landforms. Other databases have beencreated to support small-scale, more localized studies,including specific databases for Britain (Clark et al.,2004), the northern Midwest region of the United States(Aber, 1999), the northern United States (Colgan et al.,2003), North America (Aber et al., 1993) and Europe(Aber and Bluemle, 1991; Croot and Michalak, 1993).For example, Colgan and Principato (1998) collectedunpublished and published data from topographic maps,aerial photographs, and previous work, and used this toreconstruct the Green Bay and LakeMichigan lobes of theLaurentide Ice Sheet. Additional databases have beencompiled to study Pre-Illinoian glacial geomorphologyand dynamics in the central United States (Aber, 1999), toreconstruct the Laurentide Ice Sheet based on landformrecords (Clark et al., 2000), and to reconstruct ice-surface

geometry from erosional and periglacial features in theAlps (Kelly et al., 2004).

There are also numerous national mapping organiza-tions as well as university, local and municipal agenciesthat contribute landform and sediment data for glacialgeomorphology studies. Recent satellite missions, suchas NASA's Shuttle Radar TopographyMission (SRTM),have provided topographic data with almost globalcoverage, although at a relatively coarse spatial reso-lution (90–30 m) and excluding high latitude regions.The British Geological Society developed digitizedglacial landform data for Britain (Clark et al., 2004),with the specific aim of providing glacial geomorphol-ogists with data coverage that would stimulate newinsights into the glacial history of the British Isles, aswell as reveal gaps in the geomorphic record that are ahigh priority for future research. Databases have alsobeen developed for local areas using intensive surfaceand subsurface mapping, including use of groundpenetrating radar (Klempe, 2004).

5.2. Spatial analysis and spatial statistics

Spatial analysis tools and spatial statistics are used toanalyze relationships in data and to examine thestatistical significance of patterns and associations. Forexample, spatial analysis tools have proved useful in theidentification of anomalies in glacial landform andsediment data, and to examine the distribution oferosional, depositional, and relict landforms in glaciatedlandscapes (e.g., Fabel et al., 2002).

Spatial statistical analyses provide additional capa-bilities, and in particular have been used to analyzedistributions of landforms and to relate these to control-ling processes such as ice-flow patterns and subglacialconditions in ways that would have been difficult usingtraditional cartographic methods. Recent examplesinclude work relating subglacial bedforms and sedimentcharacteristics (Colgan and Principato, 1998), theinfluence of meltwater on the development of subglaciallandforms (Fisher et al., 2005), and the use of bedformattributes to determine flow sets (Fig. 6; Clark, 1997;Stokes and Clark, 2003). Particular emphasis has beenplaced on examining bedform density, packing, and par-allel conformity of lineation sets (e.g. Clark and Wilson,1994; Clark and Meehan, 2001; Stokes and Clark, 2001,2003) to provide insight into subglacial regimes (Clarkand Wilson, 1994) and to delineate ice divides, inter-lobate areas and zones of streaming ice (Punkari, 1993,1995a,b; Stokes and Clark, 2001, 2003). For example,Punkari (1993, 1995a,b) used these techniques to con-clude that zones of ice streams were dominated by fan-

Fig. 6. A spatial distribution of glacial bedforms reveals a distinct difference that can then be used to discriminate between flow events: (A)hypothetical lineation pattern, (B) an interpretation that assumes all of the flow evidence is of the same age, (C) an alternative that takes account of thecross-cutting lineations, and (D) shows how the spatial pattern and morphometry can assist in discriminating flow events (from Clark, 1997).

Fig. 7. An illustration of the orientation and distribution of glaciallandforms on the southern side of the Tampere interlobate zone(Finland). The orientations of different ice-flow indicators (plottedalong x-axis) and their frequency (y-axis) was used to indicate thelocations of ice lobes, streams, and melting beds (Punkari, 1993).


shaped drumlin fields and intensive scouring, while in-terlobate areas were generally devoid of drumlins(Fig. 7). Glacial sediments and landforms (e.g. moraines)have also been combined to infer relationships betweenmoraine type and icemargin positions (Maclachlan et al.,2003) and to extract possible sequences of ice margin po-sitions during stages of retreat (Engel and Pair, 2001). Inaddition, the compilation of glacial features relative tobedrock geology and surface-sediment types has resultedin thematic maps that illustrate the relationships betweenglacial features, such as drumlins, and ice-marginalpositions in the Midwest United States (Colgan andPrincipato, 1998). This work showed no spatial corre-lation between drumlin occurrence and bedrock lithol-ogy, but revealed that drumlins were grouped intoindividual fields associated with specific ice-marginalpositions, and that eskers and drumlins were spatiallycorrelated with the occurrence of sandy till.

More detailed examinations of the characteristics ofspecific landforms, using morphometric analyses, haveprovided new insights into landform development.Dunlop and Clark (2006) conducted a morphometricanalysis of ribbed moraines in North America andNorthern Europe using remote sensing imagery andDEMs. This included an assessment of basic physicalcharacteristics (e.g. length, width, height, slope, sym-metry, “connectability”; Fig. 8) as well as correlationsbetween moraine characteristics, location, topographic

characteristics, and proximity to other glacial landforms.The results highlighted the complexity of these morainesystems, and showed how an objective analysis usingGIS tools can produce glaciological interpretations con-trary to long-held views, such as those regarding ribbedmoraine genesis (Dunlop and Clark, 2006) and drumlinformation (Boulton, 1987; Shaw and Sharpe, 1987).Any set of morphological observations must be con-strained by hypotheses of landform genesis, so that the

Fig. 8. (A) Generalized overview of the Lac Naococane ribbed moraineridge in Central Quèbec and estimated ice-flow direction. (B) The con-nectivity of moraines is determined by sliding moraines along the axis ofice-flowdirection (solid line) and rotating themoraines to achieve a betterfit (shaded moraines were rotated). (C) Summary of ribbed morainemorphology from Northern and Central Quèbec, Sweden, and Ireland.Shown are themeans, ranges and “typical dimensions” of ribbedmoraineparameters forms by various ice sheets (modified fromDunlop andClark,2006).


observations allow for rejection of one or more of thehypotheses (Martini et al., 2001; Dunlop and Clark,2006).

Similarly, GIS-based morphometric analyses havebeen used very productively to reveal trends in theshape, orientation, and altitude of cirques in the Alps(Federici and Spagnolo, 2004), to relate elongation, arealextent, spacing, and orientation of drumlins to subglacialconditions (Smalley and Unwin, 1968; Boulton, 1987;Shaw and Sharpe, 1987; Lanier and Norton, 2003;

Maclachlan and Eyles, 2005; Cook and Regis, 2005),and to analyze the morphologies of glaciated valleys(Duncan et al., 1998). In addition to expanding the typesof analyses that can be performed, GIS have dramaticallyreduced the time involved in analysis; Clark and Wilson(1994) analyzed 4800 drumlins, and estimated that withtraditional methods they would have spent more time onthe analysis and would have been forced to work with asample size of 50–1600 drumlins.

The orientation of glacial bedforms has been an areaof particular focus in recent studies because of the po-tential to use position and orientation of landforms toreconstruct ice-sheet behavior (e.g., see Fig. 7; Kleman,1992; Kleman and Borgström, 1994; Clark, 1999).Orientation/rose diagrams, common tools in directionalanalyses (e.g. for drumlins, eskers, and till fabric) havebeen used to suggest possible ice-flow directions andpatterns, illustrate the presence of a shifting center ofmass, and explain the erosion–transportation–deposi-tion process of ice sheets (Punkari, 1993; Clark et al.,2000; Näslund et al., 2003). Orientation/rose diagramshave also been used to distinguish primary and sec-ondary ice-flow directions, which are critical in regionswith complex ice-flow histories that have resulted inpalimpsest landscapes. GIS have also been used to cal-culate ice-flow direction based on till fabric analyses(Treague and Syverson, 2002; Li et al., 2007) and todetermine the source areas of erratics by combiningremote sensing based flow directions with geologic data(Knight, 1996; Clark, 1997; Clark et al., 2000).

Spatial analyses have also prompted the reassess-ment and revision of glacial landform classifications.For example, previously unidentified landscape fea-tures in central Illinois were recognized using high-resolution DEMs and identified as end moraines(Abert, 1996). The relatively low relief of these ridgeswas ignored during earlier classifications but the closealignment of the ridges to previously mapped endmoraines became apparent using gridding tools andcross-section profile analysis (Fig. 9). As a result, moremapped moraines are available for future reconstruc-tions of the southern portion of the Laurentide IceSheet. The accessibility of high-resolution DEMs hasalso created opportunities to conduct more rigorousmorphometric analyses of glacial landforms (e.g.ribbed moraines, drumlins).

5.3. Glacial model calibration and verification

GIS-based modeling of geomorphic processes is arelatively new approach (Goodchild et al., 1993; Bishopand Shroder, 2004) that has great potential (Raper and

Fig. 9. GIS can be used to examine landform classifications suggested by prior work. Topographic profiles were used to indicate ridges that may beinterpreted as moraines (modified from Abert, 1996).


Livingstone, 1995). Recent work has focused ondeveloping GIS-based approaches that allows for theverification of output from stand-alone ice-sheet modelsusing geomorphic data (Napieralski, 2005; Napieralskiet al., 2006, 2007; Li et al., 2007). In this work GIS datahas been integrated with a stand-alone ice-sheet modelto calibrate the model to maximize the agreement be-tween ice-sheet simulations and field evidence (e.g. ice-sheet extent, ice core record, and isostatic reboundrates). To achieve this required the development ofnovel ways to statistically assess output from processmodels against a heterogeneous collection of field datathat are spatially and temporally distributed (Napieralskiet al., 2006; Li et al., 2007).

Although ice-sheet modelers have long been inter-ested in the reliability of model outcome, until recentlythere has been little emphasis on developing objective

ways to calibrate and validate model output using fielddata. With greatly improved computing capabilitiesand GIS software, it is now possible to assimilate outputfrom numerical models with field observations. Ice-sheetmodels are used to examine ice-sheet evolution and sothere is much interest in linking landforms to glacierdynamics, such as ice-marginal landform assemblages(Sharp, 1988; Hart, 1995; Evans et al., 1999) andsubglacial landform assemblages (Kleman, 1992;Kle-man and Hättestrand, 1999). The enormous amounts offield data pose a computing problem that can be resolvedbut, more importantly, the task of reducing these data tocoherent sets, whether flow patterns from one glacialstage or a previous glacial stage (Kleman et al., 2004),can be accomplished by analyzing the physical char-acteristics of landforms (e.g. orientation, correlation withsediment data).


In Napieralski (2005) a numerical ice-sheet model(from Hubbard, 1999) was used to simulate the growthand decay of the Scandinavian ice sheet and the outputwas compared to selected end moraines and glaciallineations. Key climatic input variables were altered togenerate various ice-sheet configurations and topogra-phies. Two GIS techniques were designed to statisticallyanalyze the level of correspondence between modeloutput and sets of major moraines and streamlines glaciallandforms (i.e. glacial lineations). Automated Proximityand Conformity Analysis (APCA; Napieralski et al.,2006) and Automated Flow Direction Analysis (AFDA;Li et al., 2007) compare simulated ice-marginal extentagainst sets of moraines (Last Glacial Maximum andYounger Dryas) and simulated ice-flow direction againstsets of glacial lineations (i.e. flow sets), respectively.APCA uses a system of buffers and overlays, common

Fig. 10. Automated Proximity and Conformity Analysis (APCA) uses a sagreement between simulated and observed ice extent by calculating the arcumulative plot of offset between features (determined by the area under thdistance and angle between simulated and observed features. Three ice-sheetthe application of APCA. Clearly, simulation 3 appears to match the morainesvisual comparisons can be subjective and APCA is used to determine(e.g. simulation 3, moraine B) and angle (e.g. simulation 1, moraine B areNapieralski, 2007).

GIS spatial analysis tools, to quantify the level of cor-respondence between model output and field evidence(Fig. 10). AFDA calculates the disparity (i.e. residual)between predicted basal flow direction and observedlineations; this residual is then plotted against their cor-responding time slices to assess temporal patterns ofcorrespondence between model output and field evi-dence (Fig. 11). Time series of one or more numericalmodels were then ranked according to their level ofagreement with suites of field data, indicating periods ofpredicted marginal stability and fluctuating correspon-dence between ice-flow orientation data during ice-sheetgrowth, maxima, and decay, and to support rigoroussensitivity analyses of model input parameters (seeNapieralski et al., 2007).

More generally, an integratedGIS-based approach alsoallows measures or controls of glacial processes to be

ystem of GIS-based buffering and overlays to quantify the level ofea-percent between predicted ice extent and major moraines (1). Thee curve) is used to quantify the level of correspondence, based on theconfigurations are compared against the same two moraines to illustratebetter, and APCA confirms this (large area under the curve). However,which moraines are reasonably well-matched, including distanceparallel, as reflected in the steep slope of the curve) (modified from

Fig. 11. Steps of Automated Flow Direction Analysis (AFDA). (A) Field-based glacial lineations and model outputs used in the analysis. (B) Overlaymodel outputs and field evidence to produce series residual datasets (offset between simulated and observed) for different time slices. (C) Plot of meanresiduals against their corresponding time slices to identify temporal patterns of agreement between predicted orientations and field observations. (D)Frequency analysis (rose diagrams) of selected time slices (e.g. d and f) provides detailed information on the distribution of residuals across the area(from Li et al., 2007).


mergedwith other fields to reconstruct current or previousglacial environments, including previous glacier extentsand subglacial regimes. For example, combining DEMsand reconstructed ice-surface topographies of ice sheetssubstantiated the occurrence of glacio-hydraulic super-cooling and basal freeze-on processes (Ensminger et al.,2002). In this work GIS were critical in providing a morerealistic spatially-distributed reconstruction, compared tosimple flow line calculations which, in turn, allowed theresearchers to identify new matches/mismatches withfield data in two and three dimensions.

6. Recommendations for the future ofGIS and glacialgeomorphology

This review has focused on several key issues in ap-plications of GIS in glacial geomorphology. Theapplications highlighted in this review have highlightedthe importance of managing databases, data sharing,spatial analyses, and linking spatial and temporal fielddata with process models. Many of these utilities andapplications are critical to productive multidisciplinaryand international collaborations, producing new knowl-

edge and data that have allowed for reassessment ofestablished theories and assumptions. However, thefull benefits of GIS thinking and expertise have not yetbeen fully utilized in glacial geomorphology. The fol-lowing recommendations are intended to stimulate moreadvanced use and appreciation of GIS in glacialgeomorphology.

6.1. Education

Although GISc and GIT have continuously improved,current applications in glacial geomorphology relyheavily on the experiences and skills of the user. It willtake considerable time to develop this experience in theglacial geomorphology research community. Glacialgeomorphologists need to be able to make appropriateprocedural decisions, customize GIS to handle the datainput and analysis, and to generate and interpret results(e.g. Plummer and Phillips, 2003). Traditional training inglacial geomorphology has emphasized the developmentof content knowledge and field skills; as digital databecome more readily available, it is important thatstudents of glacial geomorphology learn GIS and GIT as


a routine part of their coursework. In addition, glacialgeomorphology teaching that now commonly involvesaerial photos or remote sensing should be enhanced toinclude mapping and spatial analyses as a way to providenew insights into glacial geomorphology and to exposestudents to GISc issues and skills.

6.2. Standards and benchmarks

The wide range of data formats and GIS software isevidence of a rapidly expanding technology and dis-cipline. Current efforts should focus on developingstandards and benchmarks to improve data sharing andstimulate more comparative analyses. The methodologyby which GIS are “customized” for glacial geomorphicstudies need to be emphasized and made explicit inconference presentations, publications (e.g. Evans et al.,2005; Dunlop and Clark, 2006), technical reports andonline documents. Enough detail should be provided toallow for published work to be reproduced, includingdata acquisition and description, software, steps in anal-ysis (e.g. tools or scripts), and the process of generatingoutput and visualizations. This allows others toscrutinize previous studies and improve techniques orapproaches. While the design of GIS is typically not thefocus of glacial geomorphology, it does warrant at-tention for several reasons. First, it provides a basis forwhich knowledge, approaches, and methodologies canbe developed for future research, as current methodol-ogies can be used as a foundation for designing newtechniques. Second, it provides a means by which pre-vious and current GIS approaches and results can bescrutinized. If data manipulation or methodology isflawed, the final results may likewise be erroneous.

Open source software, which is freely distributedalong with source code, alleviates some of the high costsassociated with site licenses for software programs, andallows for the development of specific tools that may notbe readily available in the commonly-used softwarepackages (e.g. 3D display). Increasing use of opensource software would help to stimulate internationalcollaboration and to broaden the range of scientists whocan afford to use GIS applications in geomorphology.

6.3. Data quality and error reporting

Sources of error that occur during data acquisitionand analysis should be routinely assessed and reported.In current studies errors and error propagation are gener-ally neglected, yet they may have significant impacts onoutcomes and conclusions. Methods to quantify errors,for example the root mean square (RMS) error, should

be used to control the quality of the analysis and under-stand the impacts of errors on the results and the errorpropagation (e.g. Clark, 1997; Colgan and Principato,1998; Smith and Clark, 2005). Therefore, it is recom-mended that glacial geomorphology studies using GISshould include information on data quality, error levels,and error propagation. For example, recent efforts tobuild a database of glacial features of the British IceSheet included source information for each digitizedfeature (Clark et al., 2004). Errors associated withDEMs have also been reported, as this may influence ascale dependent spatial analysis of landforms (Dunlopand Clark, 2006). This effort provides the possibility totrace the data to its original source and resolve con-tradicting versions of landform type or location thatoccurred during the digitizing process.

6.4. Research and applications

There are many directions to be recommended forfuture applications and research in glacial geomorphol-ogy, and a few examples are provided here to suggestpossible new research avenues, such as spatial analysis,modeling, data integration, and artificial intelligence.

6.4.1. Spatial analysisMost GIS allow for analyses of the spatial attributes of

geographic data, such as landforms or sediment deposits.An exploratory analysis of glacial landform data mayreveal trends or abnormalities in the data and uncoverspecial characteristics that are of interest to glacial geo-morphologists. Therefore, glacial data should be morefully explored before interpretation or presentation. Forexample, ArcGIS and IDRISI include tools for analyzingspatial autocorrelations, which can yield new perspec-tives on landform distributions; these new perspectivescan then be better related to geomorphic processes. Inaddition, kriging and other geostatistical tools can beused to interpolate subsurface distributions of glacialmaterials. Many geostatistical and spatial analysis toolsdeveloped for other disciplines (e.g.hydrology, soilscience, and ecology) have great potential for use inglacial geomorphology (e.g. Maidment and Djokic,2000; Maidment, 2002; Clarke et al., 2002).

Spatial analyses can provide new insight in patternsand distributions of landforms relative to each other and tocontrolling variables. Cirques, drumlins, and ribbed mo-raines vary in spacing, orientation and elevation, and howthese characteristics relate to variations to glaciologicalparameters of former glaciers and ice sheets can providenew insights into the development of landforms. Sim-ilarly, linking the pattern of frozen bed areas predicted by


ice-sheet models with mapped sets of glacial lineationscan provide a way to constrain simulated subglacialthermal conditions for alternate ice-sheet reconstructions.

6.4.2. ModelingGIS-based modeling is currently limited by software

capabilities, and so advanced applications take the ap-proach of linking data from GIS to stand-alone models,either as a calibration or verification step (see Goodchildet al., 1993 for examples of linking GIS and environ-mental models). In glacial geomorphology such work hasfocused on climate–glacier interaction (e.g. Kaser, 2001)and subglacial conditions (Harbor, 1992; Copland andSharp, 2001; Truffer et al., 2001), though much of thecontemporary interest in glacial geomorphology empha-sizes the linkage between landform morphometry/patterns and glaciological processes (and perhaps post-glacial processes of modification). Future work willcontinue to advance numerical models that can be eval-uated against field observations in more quantitativemanners using GIS (e.g. Napieralski, 2005). Larger-scaleEarth systemmodels, such as the Grid Enabled IntegratedEarth System Model (GENIE) (see Gulamali et al., 2003or http://www.genie.ac.uk), will advance the study ofpaleo-ice-sheet characteristics (ice-flow direction, isostat-ic depression and ice topography, subglacial thermal con-ditions) and provide geomorphologists with uniqueopportunities to link output from large-scale modelswith mapped flow sets of glacial lineations, the distri-bution of frozen beds, and evidence from isostaticrebound to better constrain the glaciological conditionsfor landscape evolution.

6.4.3. Remote sensing and in situ dataMany of the analyses presented in this review

made use of data on external morphometry or land formpatterns based solely on remote sensing data, without useof data acquired in the field. Additional insights can beprovided by linking remotely sensed data with informa-tion on the internal structure of landforms. For example,many drumlin and ribbed moraine fields exhibit formsthat are similar in orientation or morphometry, but whichhave wide variations in sedimentological characteristics(e.g.Meehan et al., 1997; Dunlop andClark, 2006).Wheninternal structure, morphometry, and larger-scale patternsare analyzed together it is likely that additional insightswill be gained in glaciological processes and landscapeevolution.

6.4.4. Artificial intelligenceArtificial intelligence (AI) offers significant potential

to assess impacts of particular forcing parameters (e.g.

climate, topography) on complex, previously glaciatedterrain that interacted with processes over large spatialand temporal scales (Moody and Katz, 2004). Applica-tions of AI in geomorphology and subsurface geologyhave included artificial neural networks (Rizzo andDougherty, 1994; Bishop et al., 1999; Campolo et al.,1999; Gautam et al., 2000), cellular automota (Chase,1992; Luo, 2001), fuzzy sets (Burrough, 1989; McBrat-ney and Odeh, 1997; Oberthur et al., 2000), and geneticalgorithms (Seibert, 2000; Seibert et al., 2000).However this approach has not yet been widelyexploited in glacial geomorphology. For example,cellular automata, which can be used to model processesin 3D, may provide a new tool for re-evaluating theprocesses responsible for the formation and modifica-tion of glacial landforms. Cellular automota models relyon prior knowledge or sets of rules about processes,such as those that exist at the ice-land interface. As aresult, landforms can be partitioned into new data thatillustrate spatial relationships that would otherwise bedifficult to recognize with visual or qualitative techni-ques. Fuzzy set theory, which focuses on vague orundefined boundaries between objects or the inclusionof objects within several classes (Moody and Katz,2004), can potentially distinguish and help classifylandforms that are somewhat ambiguous (e.g. bound-aries of a drumlin). Overall, AI provides glacialgeomorphologists with new opportunities to evaluateand develop theories of landscape evolution and con-sider both spatial and temporal dynamics.

7. Conclusions

Deciphering glacial landform genesis and paleo-glaciation, the core of glacial geomorphology, relieson describing and analyzing the morphological andspatial characteristics of glacial landforms and depos-its, and linking these to process domains, all of whichcan be advanced through the use of GIS and GIT. How-ever, to take advantage of this potential requires anunderstanding of issues such as scale, data representa-tion, ontology, time–space representation, and modelingand visualization limitations. There has been notewor-thy progress to date, as many of the past and currentapplications of GIS in glacial geomorphology haveyielded new information on the relationships andpatterns of landforms, as well as dispelling or confirm-ing assumptions or conjectures on landform morphom-etry or genesis. However, future successful applicationsin glacial geomorphology will hinge on our ability tolink scientific thinking and skills within both disci-plines. Glacial geomorphologists must identify and

http://www.genie.ac.uk


appreciate the potential and limitations of GIS andembrace the concepts and theories that are the under-pinnings of GISc. In this way, spatial information relatedto glacial landforms will be better understood, andvarious GIT will enhance geomorphic field-basedprojects and numerical modeling efforts. The fullpotential of applications is also limited by user ex-pertise, so it is critical to enhance GIS education withinthe discipline of glacial geomorphology. Once the fullpotential of GISc is recognized and appreciated, therewill not only be more contributions of new knowledgeto glacial geomorphology, but also the potential to con-tribute to the ongoing development of GISc.

Acknowledgements

The authors would like to thank three anonymousreviewers for their insightful comments and suggestionswhich significantly improved the flow and content ofthis paper. A portion of this paper was completed whileNapieralski was supported as a US Department ofEducation GAANN fellow at Purdue University andthrough the Rackham Fellowship at the University ofMichigan, and this material was based upon worksupported by the National Science Foundation underGrant No OPP-0138486 to Harbor. Li's work was alsosupported in part by the Research Council of theUniversity of Missouri (grant no. URC-07-042).

References

Aber, J.S., 1999. Pre-Illinoian glacial geomorphology and dynamics inthe central United States, west of the Mississippi. GeologicalSociety of America, Special Paper 337, 113–1339.

Aber, J.S. and Bluemle, J.P., 1991. Great Plains Geotectonics.Miscellaneous Map 31, North Dakota Geological Survey.

Aber, J.S., Spellman, E.E., Webster, M.P., 1993. Landsat remotesensing of glaciated terrain. In: Aber, J.S. (Ed.), Glaciotectonicsand Mapping Glacial Deposits, Proceedings of the INQUACommission on Formation and Properties of Glacial Deposits,pp. 215–225.

Abert, C., 1996. Modeling glaciated terrains. Proceedings of theSixteenth Annual ESRI User Conference, California, p. 61.

Ahnert, F., 1988. Modelling landform change. In: Anderson, M.G.(Ed.), Modelling Geomorphological Systems. John Wiley & Sons,pp. 375–400.

Allen, T.R., Walsh, S.J., Cairns, D.M., Messina, J.P., Butler, D.R.,Malanson, G.P., 2004. Geostatistics and spatial analysis: charac-terizing form and pattern at the alpine treelin. In: Bishop, M.P.,Shroder, J.F. (Eds.), Geographic Information Science andMountainGeomorphology. Praxis Publishing, Chichester, UK, pp. 189–214.

Atkinson,D.M.,Deadman, P.J., Traynor, S., 2000.Adigital terrain andGISmodel of an Arctic esker near Lac du Gras in the Northwest Territoriesof Canada. Abstr. Programs — Sixth Circumpolar Symposium onRemote Sensing of Polar Environments, Yellowknife, Canada.

Barnett, P.J., Singhry, V.H., 2000. Satellite-based engineering geologyterrain studies for Ontario's far-north. Summary of field work andother activities 2000. Ontario Ministry of North Development andMines, pp. 38-1–38-5.

Ber, A. and Aber, J.S., 2003. Glaciotectonic map of Central Europe inscale 1: 1 500 000. Abstract with Programs — XVI INQUACongress, p 39–1.

Ber, A., Zelčs, V., Jakobsen, P.R., Kalm, V., Matoshko, A.V., Karakanov,A., Zagorodnykh,V.A.,Bitinas,A., Stephan,H.J., 2003. The nationalglaciotectonic maps of Central Europe. Abstract with Programs —XVI INQUA Congress, Paper No. 39-2.

Bhaduri, B., Harbor, J., Engel, B., Grove, M., 2000. Assessingwatershed-scale, log-term hydrologic impacts of land use changeusing aGISNPSmodel. EnvironmentalManagement 26, 643–658.

Bierman, P.R., 1994. Using in situ produced cosmogenic isotopes toestimate rates of landscape evolution: a review from the geomorphicperspective. Journal of Geophysical Research 99, 13885–13896.

Bierman, P.R., Marsella, K.A., Patterson, C., Davis, P.T., Caffee, M.,1999. Mid-Pleistocene cosmogenic minimum-age limits for pre-Wisconsinan glacial surfaces in southwestern Minnesota andsouthern Baffin Island: a multiple nuclide approach. Geomorphol-ogy 27, 25–39.

Bishop, M., Shroder, J.F., 2004. Geographic Information Science andMountainGeomorphology. Praxis Publishing,Chichester,UK. 486pp.

Bishop, M.P., Shroder, J.F., Hickman, B.L., Copland, L., 1998. Scale-dependent analysis of satellite imagery for characterization ofglacier surfaces in the Karakoram Himalaya. Geomorphology 21,217–232.

Bishop,M.P., Shroder, J.F., Hickman, B.L., 1999.High resolution satelliteimagery and neural networks for information extraction in complexmountain environment. Geocarto International 14 (2), 17–26.

Boulton, G.S., 1987. A theory of drumlin formation by subglacialsediment deformation. In: Menzies, J., Rose, J. (Eds.), DrumlinSymposium. Balkema, Rotterdam, pp. 25–80.

Boulton, G.S., Clark, C.D., 1990. A highly mobile Laurentide ice sheetrevealed by satellite images of glacial lineations. Nature 346,813–817.

Boulton, G.S., Smith, G.D., Jones, A.S., Newsome, J., 1985. Glacialgeology and glaciology of the last mid-latitude ice-sheets. Journalof the Geological Society of London 142, 447–474.

Boulton, G.S., Dongelmans, P., Punkari, M., Broadgate, M., 2001.Paleoglaciology of an ice sheet through a glacial cycle: the Europeanice sheet through the Weichselian. Quaternary Science Reviews 20,591–625.

Brook, E.J., Nesje, A., Lehman, S.J., Raisbeck, G.M., Yiou, F., 1996.Cosmogenic nuclide exposure ages along a vertical transect inwestern Norway: implications for the height of the Fennoscandianice sheet. Geology 24, 207–210.

Buckley, A., Hurni, L., Kriz, K., Patterson, T., Olsenholler, J., 2004.Cartography and visualization in mountain geomorphology. In:Bishop,M.P., Shroder, J.F. (Eds.), Geographic Information ScienceandMountain Geomorphology. Praxis Publishing, Chichester, UK,pp. 253–285.

Burrough, P., 1989. Fuzzy mathematical methods for soil survey andland evaluation. Journal of Soil Science 40, 477–492.

Campolo,M.,Andreussi, P., Soldati, A., 1999. River flood forecastingwitha neural network model. Water Resources Research 35, 1191–1197.

Chase, C.G., 1992. Fluvial landsculpting and the fractal dimension oftopography. Geomorphology 5, 39–57.

Clark, C.D., 1997. Reconstructing the evolutionary dynamics offormer ice sheets using multi-temporal evidence, remote sensingand GIS. Quaternary Science Reviews 16, 1067–1092.


Clark, C.D., 1999. Glaciodynamic context of subglacial bedformgeneration and preservation. Annals of Glaciology 28, 23–32.

Clark, C.D., Meehan, R.T., 2001. Subglacial bedform geomorphol-ogy of the Irish Ice Sheet reveals major configuration changesduring growth and decay. Journal of Quaternary Science 16,483–496.

Clark, C.D.,Wilson, C., 1994. Spatial analysis of lineaments. Computersand Geosciences 20, 1237–1258.

Clark, C.D., Knight, J.K., Gray, J.T., 2000. Geomorphologic reconstruc-tions of the Labrador sector of the Laurentide Ice Sheet. QuaternaryScience Reviews 19, 1343–1366.

Clark, C.D., Evans, D.J.A., Khatwa, A., Bradwell, T., Jordan, C.,Marsh, S.H., Mitchell, W.A., Bateman, M.D., 2004. Map and GISdatabase of glacial landforms and features related to the last BritishIce Sheet. Boreas 33, 359–375.

Clarke, K.C., Parks, B.O., Crane, M.P., 2002. Geographic InformationSystems and Environmental Modeling. Prentice Hall, Inc., UpperSaddle River, NJ. 306 pp.

Colgan, P.M., Principato, S., 1998. Distribution of Glacial Landformsand Sediments in Wisconsin and the Upper Peninsula of Michigan,an Application of GIS to Glacial Geology. Geological Society ofAmerica Abstract with Programs, vol. 30, no. 2, p. 11.

Colgan, P.M., Mickelson, D.M., Cutler, P.M., 2003. Ice-marginalterrestrial landsystems: Southern Laurentide Ice Sheet. In: Evans,D.A., Rea, B.R. (Eds.), Glacial Landsystems, Edwin Arnold,London, pp. 111–142.

Cook, D.B., Regis, R.S., 2005. Morphological analysis of theMenominee drumlin field using GIS. Abstracts with Programs —Geological Society of America 37 (5), 15.

Copland, L., Sharp, M., 2001. Mapping thermal and hydrologicalconditions beneath a polythermal glacier with radioecho sounding.Journal of Glaciology 47, 232–242.

Croot, D.G., Michalak, W., 1993. The European Glaciotectonic DataBase Project. In: Aber, J.S. (Ed.), Glaciotectonics and MappingGlacial Deposits, Proceedings of the INQUA Commission onFormation and Properties of Glacial Deposits, pp. 251–258.

Cuffey, K.M., Conway, H., Gades, A.M., Hallet, B., Lorrain, R.,Severinghaus, J.P., Steig, E.J., Vaughn, B., White, J.W.C., 2000.Entrainment at cold glacier beds. Geology 28, 351–354.

Desmet, P., 1997. Effects of interpolation errors on the analysis ofDEMs. Earth Surface Processes and Landforms 22, 563–580.

Diakau, R., 1989. The application of a digital relief model to landformanalysis in geomorphology. In: Raper, J. (Ed.), Three DimensionalApplications in Geographic Information Systems. Taylor & Francis,pp. 51–77.

Djuokic, D., Maidment, D.R., 2000. Hydrologic and HydraulicModeling Support with Geographic Information Systems. ESRIPress, Redlands, California.

Duncan, C.C., Klein, A.J., Masek, J.G., Isacks, B.L., 1998. Comparisonof Late Pleistocene and modern glacier extents in Central Nepalbased on digital elevation data and satellite imagery. QuaternaryResearch 49, 241–245.

Dunlop, P., Clark, C.D., 2006. The morphological characteristics ofribbed moraines. Quaternary Science Reviews 25, 1668–1691.

Dyke, A.S., Prest, V.K., 1987. Late Wisconsian and Holocene historyof the Laurentide ice sheet. Géographie physique et Quaternaire41, 237–263.

Ehlers, J., Gibbard, P., 2003. Extent and chronology of glaciations.Quaternary Science Reviews 22, 1561–1568.

Ehlers, J., Gibbard, P.L. (Eds.), 2004a. Quaternary Glaciations— Extentand Chronology, Part I: Europe. Developments in QuaternaryScience, vol. 2a. Elsevier, Amsterdam. 448 pp.

Ehlers, J., Gibbard, P.L. (Eds.), 2004b. Quaternary Glaciations —Extent and Chronology, Part II: North America. Developments inQuaternary Science, vol. 2b. Elsevier, Amsterdam. 440 pp.

Ehlers, J., Gibbard, P.L. (Eds.), 2004c. Quaternary Glaciations— Extentand Chronology, Part III: South America, Asia, Africa, Australasia,Antarctica. Developments in Quaternary Science, vol. 2c. Elsevier,Amsterdam. 388 pp.

Embleton, C., Hamann, C., 1988. A comparison of cirque formsbetween the Austrian Alps and the Highlands of Britain. Zeitschriftfür Geomorphologie. Supplementband 70, 75–93.

Engel, J.M., Pair, D.L., 2001. Combining surficial mapping with GISto investigate the glacial history of the Skaneateles, New Yorkregion. Abstracts with Programs—Geological Society of America33 (1), 25.

Ensminger, S., Evenson, E., Lawson, D., Alley, R., Larson, G., 2002.Using GIS to test for the occurrence of glaciohydraulic supercoolingand basal freeze-on processes on the Laurentide ice sheet. Abstractswith Programs — Geological Society of America 34 (2), 43.

Evans, I.S., 1972. General geomorphometry, derivatives of altitude,and descriptive statistics. In: Chorley, R. (Ed.), Spatial Analysis inGeomorphology. Methuen, London, pp. 17–90.

Evans, I.S., 1977. Worldwide variations in the direction andconcentration of cirque and glacier aspects. Geografiska Annaler.Series A. Physical Geography 59, 151–175.

Evans, I.S., Cox, N.J., 1974. Geomorphometry and the operationaldefinition of ciques. Area 6 (2), 150–153.

Evans, D.J.A., Lemmen, D.S., Rea, B.R., 1999. Glacial land systemsof the Southwest Laurentide Ice Sheet: modern Icelandicanalogues. Journal of Quaternary Science 14, 673–691.

Evans, D.J.A., Clark, C.D., Mitchell, W.A., 2005. The last British IceSheet: a review of the evidence utilized in the compilation of theGlacial Map of Britain. Earth Science Reviews 70, 253–312.

Fabel, D., Harbor, J., 1999. Application of in situ producedcosmogenic radionuclides techniques in glaciology and glacialgeomorphology. Annals of Glaciology 28, 103–110.

Fabel, D., Stroeven, A.P., Harbor, J., Kleman, J., Elmore, D., Fink, D.,2002. Landscape preservation under Fennoscandian ice sheetsdetermined from in situ produced 10Be and 26Al. Earth andPlanetary Science Letters 201, 397–406.

Federici, P.R., Spagnolo, M., 2004. Morphometric analysis on the size,shape, and areal distribution of glacial cirques in the Maritime Alps(Western French–Italian Alps). Geografiska Annaler 86A (3),235–248.

Fenton, M.M., Andriashek, L.D., Pawlowicz, J.G., Paulen, R.C.,2003. Application of three dimensional mapping of drift inAlberta for resource development. Abstract with Programs —Joint Annual Meeting of the Canadian Quaternary Associationand the Canadian Geomorphology Research Group, Halifax,Nova Scotia.

Fisher, T.G., Jol, H.M., Boudreau, A.M., 2005. Saginaw Lobe tunnelchannels (Laurentide Ice Sheet) and their significance in south-centralMichigan, USA. Quaternary Science Reviews 24, 2375–2391.

Gao, J., Liu, Y., 2001. Application of remote sensing, GIS and GPSin glaciology: a review. Progress in Physical Geography 25,520–540.

Gautam, M., Watanabe, K., Saegusa, H., 2000. Runoff analysis inhumid forest catchment with an artifical neural network. Journal ofHydrology 235 (1–2), 117–136.

Goodchild, M.F., 1992. Geographical information science. Interna-tional Journal of Geographical Systems 6, 31–45.

Goodchild, M.F., Parks, B.O., Steyaert, L.T. (Eds.), 1993. Environ-mental Modeling with GIS. Oxford University Press, New York.


Gordon, J.E., 1977. Morphometry of cirques in the Kintail–Affric–Cannich. Geografiska Annaler. Series A. Physical Geography 59,177–194.

Gosse, J.C., Phillips, F.M., 2001. Terrestrial cosmogenic nuclides: theoryand applications. Quaternary Sciences Review 20, 1475–1560.

Gulamali,M.Y., Lenton, T.M.,Yool,A., Price,A.R.,Marsh,R.J., Edwards,N.R., Valdes, P.J., Wason, J.L., Cox, S.J., Krznaric, M., Newhouse, S.,Darlington, J., 2003. GENIE: Delivering e-Science to the environ-mental scientist. UK e-Science All Hands Meeting, pp. 145–152.

Gustavsson, M., 2006. Development of a detailed geomorphologicalmapping system and GIS geodatabase in Sweden. In: Digital Com-prehensive Summaries of Uppsala Dissertation from the Faculty ofScience and Technology, 236. 36 pp. Uppsala. ISBN 91-554-6705-9.

Hallet, B., 1990. Spatial self-organization in geomorphology: fromperiodic bedforms and patterned ground to scale-invariant topo-graphy. Earth Science Reviews 29, 57–75.

Harbor, J., 1992. Numerical modeling of the development of U-shapedvalleys by glacial erosion. Geological Society of America Bulletin104, 1364–1375.

Harbor, J., 1993. Glacial geomorphology: modeling processes andlandforms. Geomorphology 7, 129–140.

Harbor, J., Stroeven, A., Fabel, D., Clarhäll, A., Kleman, J., Li, Y.K.,Elmore, D., Fink, D., 2006. Cosmogenic nuclide evidence forminimal erosion across two subglacial sliding boundaries of thelate glacial Fennoscandian ice sheet. Geomorphology 75, 90–99.

Hart, J.K., 1995. Recent drumlins, flutes and lineations at Vestari-Hagafellsjökull, Iceland. Journal of Glaciology 41, 596–606.

Hättestrand, C., 1997. Ribbed moraines in Sweden — distribution patternandPalaeoglaciological implications. SedimentaryGeology111, 41–56.

Hubbard, A., 1999. High-resolution modeling of the advance of theYounger Dryas ice sheet and its climate in Scotland. QuaternaryResearch 52, 27–43.

Hubbard, A., Blatter, B., Nienow, P., Mair, D., Hubbard, B., 1998.Comparison of a three-dimensional model for glacier flow withfield data from Haut Glacier d'Arolla, Switzerland. Journal ofGlaciology 44, 368–378.

Humlum, O., 1997. Younger Dryas glaciation in Söderåsen, SouthSweden: an analysis of meterologic and topographic controls.Geografiska Annaler. Series A. Physical Geography 79, 1–15.

Hutchinson, M.F., Gallant, J.C., 1999. Representation of terrain. In:Longley, P.A., Goodchild, M.F., Maguire, D.J., Rhind, D.W.(Eds.), Geographical Information Systems. John Wiley & Sons,New York, pp. 105–124.

Kaser, G., 2001. Glacier–climate interaction at low latitudes. Journalof Glaciology 47, 195–204.

Kelly, M., Buoncristiani, J.-F., Schlüchter, C., 2004. A reconstructionof the last glacial maximum (LGM) ice-surface geometry in thewestern Swiss Alps and contiguous Alpine regions in Italy andFrance. Ecologae Geologicae Helvetiae 97, 57–75.

King, C.A.M., 1974. Morphometry in glacial geomorphology. In:Coates, D.R. (Ed.), glacial Geomorphology: Proceedings of the 5thAnnual Geomorphology Symposium Series, 26–28 September1974, Binghampton, New York, pp. 147–162.

Kleman, J., 1992. The palimpsest glacial landscape in northwesternSweden— LateWeichselian deglaciation forms and traces of olderwest-centred ice sheets. Geografiska Annaler. Series A. PhysicalGeography 74, 305–325.

Kleman, J., 1994. Preservation of landforms under ice sheets and icecaps. Geomorphology 9, 19–32.

Klempe, H., 2004. Identification of Quaternary subsurface glacialdeposits using 3D databases and GIS. Norwegian Journal ofGeography 58 (2), 90–95.

Kleman, J., Borgström, I., 1994. Glacial landforms indicative of apartly frozen bed. Journal of Glaciology 135, 255–264.

Kleman, J., Borgström, I., 1996. Reconstruction of paleo-ice sheets:the use of geomorphological data. Earth Surface Processes andLandforms 21, 893–909.

Kleman, J., Hättestrand, C., 1999. Frozen-bed Fennoscandian andLaurentide ice sheets during the Last Glacial Maximum. Nature402, 63–66.

Kleman, J., Hättestrand, C., Borgström, I., Stroeven, A., 1997.Fennoscandian paleoglaciology reconstructed using a glacialgeological inversion model. Journal of Glaciology 43, 283–299.

Kleman, J., Hättestrand, C., Clarhäll, A., 1999. Zooming in on frozen-bed patches: scale dependent controls on Fennoscandian ice sheetbasal thermal zonation. Annals of Glaciology 28, 189–194.

Kleman, J., Hättestrand, C., Stroeven, A., Jansson, K.N., De Angelis,H., Borgström, I., 2004. Reconstruction of paleo-ice sheets:inversion of their glacial geomorphological record. In: Knight, P.G.(Ed.), Glacier Science and Environmental Change. BlackwellPublishing Ltd.

Klimaszewski, M., 1990. Thirty years of geomorphological mapping.Geographia Polonica 58, 11–18.

Knight, J.K., 1996. A Geographical Information System-basedSynthesis of the Labrodor Sector of the Laurentide Ice Sheet.Unpublished Ph.D. thesis, University of Sheffield, UK, 206 pp.

Lal, D., 1991. Cosmic ray labeling of erosion surfaces: in situ nuclideproduction rates and erosion models. Earth and Planetary ScienceLetters 104, 424–439.

Lanier, A.J., Norton, K.P., 2003. GIS based analysis of the ChautauquaDrumlin Field, Northwest Pennsylvania and Western New York.Abstracts with Programs— Geological Society of America 35 (6),425.

Levin, S.A., 1992. The problem of pattern and scale in ecology. Ecology73, 1943–1967.

Li, Y.K., Harbor, J., Stroeven, A.P., Fabel, D., Kleman, J., Fink, D.,Caffee, M., Elmore, D., 2005. Ice sheet erosion patterns in valleysystems in northern Sweden investigated using cosmogenicnuclides. Earth Surface Processes and Landforms 30, 1039–1049.

Li, Y.K., Napieralski, J.A., Harbor, J., Hubbard, A., 2007. Identifyingpatterns of correspondence between modeled flow directions andfield evidence: an automated flow direction analysis. Computer &Geosciences 33, 141–150.

Lifton, N.A., Chase, C.G., 1992. Tectonic, climatic and lithologicinfluences on landscape fractal dimension and hypsometry: implica-tions for landscape evolution in the San Gabriel Mountains,California. Geomorphology 5, 77–114.

Lundqvist, J., 1969. Problems of the so-called Rogen moraine.Sveriges Geologiska Undersökning, Series C, p. 648.

Luo, W., 2001. LANDSAP: a coupled surface and subsurface cellularautomata model for landform simulation. Computers and Geos-ciences 27, 363–367.

Maclachlan, J.C., Eyles, C.H., 2005. Quantitative morphologicalanalysis of the Peterborough drumlin field using GIS. Abstractswith Programs — Geological Society of America 37, 7.

Maclachlan, J.C., Eyles, C.H., Terlaky, V., 2003. Understanding theorigin of glacial landscapes using GIS imaging of deposits formedalong the southern margin of the Laurentide ice sheet. Abstractswith Programs — Geological Society of America 35 (6), 298.

Maidment, D.R., 2002. ArcHydro: GIS for Water Resources. Environ-mental Systems Research Institute Press, Redlands, CA. 203 pp.

Maidment, D., Djokic, D., 2000. Hydrologic and Hydraulic ModelingSupport with Geographic Information Systems. EnvironmentalSystems Research Institute Press, Redlands, CA. 216 pp.


Mark, D.M., Smith, B., 2004. A science of topography: from qualitativeontology to digital representation. In: Bishop, M.P., Shroder, J.F.(Eds.), Geographic Information Science and Mountain Geomor-phology. Praxis Publishing, Chichester, UK, pp. 75–97.

Mark, D.M., Smith, B., Egenhofer, M., Hirtle, S., 2004. Ontologicalfoundations for geographic information science. In: McMaster, R.B.,Usery, L. (Eds.), Research Challenges in Geographic InformationScience. John Wiley & Sons, New York.

Markgren, M., Lassila, M., 1980. Problems of moraine morphology,Rogen moraine and Blattnick moraine. Boreas 9, 217–274.

Martini, I.P., Brookfield, M.E., Sadura, S., 2001. Principles of GlacialGeomorphology andGeology. Prentice-Hall, Inc.,New Jersey. 381 pp.

McBratney, A., Odeh, I., 1997. Application of fuzzy sets in soilscience: fuzzy logic, fuzzy measurements and fuzzy decision.Geoderma 77 (2–4), 85–113.

Meehan, R.T., Warren, W.P., Gallagher, J.D., 1997. The sedimentologyof a Late Pleistocene drumlin near Kingscourt, Ireland. Sedimen-tary Geology 111, 91–105.

Montgomery, D.R., 2004. GIS in tectonic geomorphology andlandscape evolution. In: Bishop, M.P., Shroder, J.F. (Eds.),Geographic Information Science and Mountain Geomorphology.Praxis Publishing, Chichester, UK, pp. 425–453.

Moody, A., Katz, D.B., 2004. Artificial intelligence in the study ofmountain landscapes. In: Bishop, M.P., Shroder, J.F. (Eds.),Geographic Information Science and Mountain Geomorphology.Praxis Publishing, Chichester, UK, pp. 219–247.

Nalepa, N., Napieralski, J., 2007. Using GIS to ascertain efficient spatialand vertical resolution for determining drumlin morphometry: a casestudy of the Palmyra Drumlin Field. Association of AmericanGeographers Annual Meeting, San Francisco, CA, April 17–22.

Napieralski, J.A., 2005. Integrating a Numerical Model of theScandinavian Ice Sheet with Field Data using GIS. UnpublishedPhD thesis, Purdue University, 1–133 pp.

Napieralski, J., 2007. GIS and field-based spatio-temporal analysis forevaluation of paleo-ice sheet simulations. The ProfessionalGeographer 59, 173–183.

Napieralski, J.A., Li, Y.K., Harbor, J., 2006. Comparing predicted andobserved spatial boundaries of geologic phenomena: AutomatedProximity and Conformity Analysis (APCA) applied to ice sheetreconstructions. Computers and Geosciences 32, 124–134.

Napieralski, J., Hubbard, A., Li, Y.K., Harbor, J., Stroeven, A.P.,Kleman, J., Alm, G., Jansson, K.N., 2007. Towards a GISassessment of numerical ice sheet model performance usinggeomorphological data. Journal of Glaciology 53, 71–83.

Näslund, J.O., Rodhe, L., Fastook, J.L., Holmlund, P., 2003. Newways of studying ice sheet flow directions and glacial erosion bycomputer modeling — examples from Fennoscandia. QuaternaryScience Reviews 22, 245–258.

Oberthur, T., Doberman, A., Aylward, M., 2000. Using auxiliaryinformation to adjust fuzzy membership functions for improvedmapping of soil qualities. International Journal of GeographicInformation Science 14, 431–454.

O'Sullivan, D., Unwin, D.J., 2003. Geographic Information Analysis.John Wiley & Sons, Inc., New Jersey. 436 pp.

Pandey, S., Harbor, J., Engel, B., 2000. Internet Based GeographicInformation Systems and Decision Support Tools. Urban andRegional Information Systems, Park Ridge, Illinois. 36 pp.

Paulen, R.C.,McClenaghan,M.B., 2000. Integration of surficialmapping,stratigraphy and GIS to facilitate drift prospecting in glaciated terrain.Abstr. Programs — Geo Canada 2000, Calgary, Alberta.

Peuquet, D.J., 2002. Representations of Space and Time. GuilfordPress, New York. 380 pp.

Plummer,M.A., Phillips, F.M., 2003. A 2-D numerical model of snow/iceenergy balance and ice flow for paleoclimatic interpretation of glacialgeomorphic features. Quaternary Science Reviews 22, 1389–1406.

Punkari, M., 1993. Modelling of the dynamics of the Scandinavian icesheet using remote sensing and GIS methods. In: Aber, J.S. (Ed.),Glaciotectonics and Mapping Glacial Deposits, Proceedings of theINQUA Commission on Formation and Properties of GlacialDeposits, pp. 232–250.

Punkari, M., 1995a. Function of ice streams in the Scandinavian icesheet: analyses of glacial geological data from southwesternFinland. Transactions of the Royal Society of Edinburgh. EarthSciences 85, 283–302.

Punkari, M., 1995b. Glacial flow systems in the zone of confluencebetween the Scandinavian and Novaya Zemlya ice sheets.Quaternary Science Reviews 14, 589–603.

Raper, J., Livingstone, D., 1995. Development of a geomorphologicalspatial model using object-oriented design. International Journal ofGeographical Information Systems 9, 359–383.

Raseman, S., Schimdt, J., Schrott, L., Dikau, R., 2004. Geomorpho-metry in mountain terrain. In: Bishop, M., Shroder, J. (Eds.),Geographic Information Science and Mountain Geomorphology.Praxis Publishing, Chichester, UK, pp. 101–145.

Rhoads, B.L., 1999. Beyond pragmatism: the value of philosophicaldiscourse for physical geography. Annals of the Association ofAmerican Geographers 89, 760–771.

Rhoads, B.L., Thorn, C.E., 1993. Geomorphology as science: the roleof theory. Geomorphology 6, 287–307.

Rizzo, D., Dougherty, D., 1994. Characterization of aquifer propertiesusing artificial neural networks— neural kriging. Water ResourcesResearch 30 (2), 483–497.

Seibert, J., 2000. Multi-criteria calibration of a conceptual runoffmodel using a genetic algorithm. Hydrology and Earth SystemsSciences 4, 215–224.

Seibert, J., Uhlenbrook, S., Leiundgut, C., Halldin, S., 2000. Multiscalecalibration and validation of a conceptual rainfall-runoff model.Physics and Chemistry of the Earth. Part B: Hydrology, Oceans andAtmosphere 25 (1), 59–64.

Sharp, M.J., 1988. Surging glaciers: geomorphic effects. Progress inPhysical Geography 12, 533–559.

Sharpe, D.R., Russell, H.A.J., Pullan, S.E., Pugin, A., 1999. Anapproach to 3-D environmental geology mapping: a case fromthe Oak Ridges Moraine, southern Ontario. Abstracts withPrograms — Geological Society of America 31 (7), 81.

Sharpe, D.R., Russell, H.A.J., Logan, C.E., 2003. The role of expertknowledge in the construction of a 3-D geological model of theOak Ridges Moraine Region, Canada. Abstracts with Programs—Geological Society of America 35 (6), 76.

Shaw, J., Sharpe, D.R., 1987. Drumlin formation by subglacialerosion. Canadian Journal of Earth Sciences 24, 2316–2322.

Smalley, I.J., Unwin, D.J., 1968. The formation and shape of drumlinsand their distribution and orientation in drumlin fields. Journal ofGlaciology 7, 377–390.

Smith, M.J., Clark, C.D., 2005. Methods for the visualisation of digitalelevation models for landform mapping. Earth Surface Processesand Landforms 30, 885–900.

Smith, B., Mark, D.M., 2001. Geographic categories: an ontologicalinvestigation. International Journal of Geographical InformationScience 15, 591–612.

Smith, M.J., Clark, C.D., Wise, S.M., 2001. Mapping glaciallineaments from satellite imagery: an assessment of the problemsand development of best procedure. SlovakGeologicalMagazine 7,263–274.


Stokes, C.R., Clark, C.D., 2001. Identifying paleo-ice stream tracksusing remote sensing. Slovak Geological Magazine 7, 247–262.

Stokes, C.R., Clark, C.D., 2003. The Dubawnt lake paleo-ice stream:evidence for dynamic ice sheet behaviour on the Canadian Shieldand insights regarding the controls on ice-stream location andvigour. Boreas 32, 263–279.

Stroeven, A.P., Fabel, D., Hättestrand, C., Harbor, J., 2002. A relictlandscape in the centre of Fennoscandian glaciations: cosmogenicradionuclide evidence of tors preserved through multiple glacialcycles. Geomorphology 44, 145–154.

Sung, Q.C., Chen, Y.C., 2004. Self-affinity dimensions of topographyand its implications in morphotectonics: an example from Taiwan.Geomorphology 62 (3–4), 181–198.

Tarasov, L., Peltier, W.R., 2004. A geophysically constrained largeensemble analysis of the deglacial history of the North Americanice-sheet complex. Quaternary Science Reviews 23, 359–388.

Treague, J.J., Syverson, K.M., 2002. GIS analysis of northernWisconsin glacial till units. Abstracts with Programs—GeologicalSociety of America 34 (6), 505.

Trenhaile, A.S., 1976. Cirque morphometry in the Canadian Cordillera.Annals of the Association of American Geographers 66, 451–462.

Truffer, M., Echelmeyer, K.A., Harrison, W.D., 2001. Implications of tilldeformation on glacier dynamics. Journal ofGlaciology 47, 123–134.

Unwin, D.J., 1973. The distribution and orientation of corries innorthern Snowdonia, Wales. Transactions of the Institute of BritishGeographers 58, 85–97.

Walsh, S.J., Butler, D.R., Malanson, G.P., 1998. An overview of scale,pattern, process relationships in geomorphology: a remote sensingand GIS perspective. Geomorphology 21, 183–205.

Wise, S.M., 1998. The effect of GIS interpolation errors on the use ofdigital elevation models in geomorphology. In: Lane, S., Richards,K., Chandler, J. (Eds.), Landform Monitoring. Modelling andAnalysis, vol. 99. John Wiley & Sons, Chichester, pp. 139–164.

Wright, D.J., Goodchild, M.F., Proctor, J.D., 1997. Demystifying thepersistent ambiguity of GIS as “tool” versus “science”. Annals ofthe Association of American Geographers 87, 346–362.

Zelčs, V., 2003. Glaciotectonic map of Latvia. Abstract with Programs—XVI INQUA Congress, Paper No. 39-4.

Zevenbergen, L.W., Thorne, C.R., 1987. Quantitative analysis of landsurface topography. Earth Surface Processes and Landforms 12,47–56.

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