Mapping, modeling, and visualization of the influences of

geomorphic processes on the alpine treeline ecotone,

Glacier National Park, MT, USA

Stephen J. Walsha,*, David R. Butlerb, George P. Malansonc, Kelley A. Crews-Meyerd,Joseph P. Messinae, Ningchuan Xiaoc

aDepartment of Geography, University of North Carolina, Chapel Hill, NC 27599-3220, USAbDepartment of Geography, Southwest Texas State University, San Marcos, TX 78666-4616, USA

cDepartment of Geography, University of Iowa, Iowa City, IA 52242, USAdDepartment of Geography, University of Texas, Austin, TX 78712-1098, USA

eDepartment of Geography, Michigan State University, East Lansing, MI 48824-1115, USA

Received 16 April 2001; received in revised form 25 January 2002; accepted 25 February 2002

Abstract

Spatially explicit digital technologies are integrated within a geographic information science (GISc) context to map, model,

and visualize selected direct and indirect geomorphic processes that influence the spatial organization of the alpine treeline

ecotone (ATE) in Glacier National Park (GNP), MT. GISc is used to examine alpine treeline and its biotic and abiotic controls

through the application of multi-resolution remote sensing systems, geospatial information and product derivatives, and

simulations of treeline spatial organization. Three geomorphic features are examined: relict solifluction terraces, evidence of

nonlinearity in the development of a catena, and the locations of isolated boulders. The significance of these features is in

constraining subsequent geomorphic and biogeographic processes, thus leading to disequilibrium. Exploration of these features

though GISc indicates that visualizations for characterizing the relations of geomorphic patterns and processes within a three-

dimensional context show promise for improved alpine slope models in the future by defining landscape attributes within a

spatially and temporally explicit context.

D 2002 Elsevier Science B.V. All rights reserved.

Keywords: Alpine treeline ecotone; Glacier National Park; Models; Slope; Solifluction; Spatial representations; Visualization

1. Introduction

The purpose of this paper is to examine the effects

of geomorphic processes and patterns that influence

the composition and spatial structure of the alpine

treeline ecotone (ATE) in Glacier National Park

(GNP), MT (Fig. 1). We do so using spatially explicit

digital technologies framed within a geographic infor-

mation science (GISc) context. We emphasize remote

sensing, geographic information systems, and scien-

tific visualizations to assess the effects of geomorphic

processes and patterns at alpine treeline. The sensi-

0169-555X/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved.

doi:10.1016/S0169-555X(02)00350-1

* Corresponding author. Tel.: +1-919-962-3867; fax: +1-919-

962-1537.

E-mail address: swalsh@email.unc.edu (S.J. Walsh).

www.elsevier.com/locate/geomorph

Geomorphology 53 (2003) 129–145

tivity of tundra to invasion is significant because

considerable areas of tundra exist just above the

treeline ecotone, and the sensitivity of the ecotone

and its use as an indicator of climatic change have

been debated (e.g., Rochefort et al., 1994; Kupfer and

Cairns, 1996). What the debates overlook, however, is

that a highly nonlinear response may occur at the ATE

because the ecotone is likely a balance of opposing

positive feedbacks (Malanson, 1997; Bekker et al.,

2001). Such positive feedback switches are likely to

have produced a system that can have a critical point

and be subject to small or large periods of change with

incremental climatic change. We believe, moreover,

that in addition to climatic factors, geomorphic pro-

cesses and site conditions are significant influences on

the location of the ecotone. Our past work has

illustrated how the elevation and position of the

ATE in GNP are constrained in many locations by

lithologic and structural controls and by geomorphic

processes such as snow avalanches and debris flows.

The locations of these processes are influenced by

lithology and structure (Butler and Walsh, 1990, 1994;

Walsh et al., 1990a,b; Walsh and Butler, 1997), topo-

graphic site conditions and associated soil and mois-

ture conditions (Brown, 1994a,b; Malanson and

Butler, 1994; Walsh et al., 1994b), and patterns of

snow distribution (Allen and Walsh, 1993, 1996;

Walsh et al., 1994a).

The general goal of this research is to emphasize

the use of GISc techniques to map, model, and

visualize critical geomorphic elements hypothesized

to be direct or indirect influences on the ATE and to

include these geomorphic elements in simulation

models. Remote sensing systems from satellites and

aircraft are described relative to their data acquisition,

processing, and integration subsystems. A multi-res-

olution approach is used to characterize the ATE and

to represent selected disturbance factors and landscape

changes observed and/or simulated over space and

time.

Fig. 1. Study area location: GNP, Montana.

S.J. Walsh et al. / Geomorphology 53 (2003) 129–145130

All spatially explicit data used in our treeline

research were organized within a multimedia-inte-

grated GIS so that multiple views of the landscape

could be accommodated by linking data spatially,

temporally, and thematically. In addition dynamic,

visualizations were developed to support the mapping

and modeling elements through animations. These

animations involved model outputs, spatial displays

of co-variable and multiple-variable combinations,

satellite image change detections, multi-resolution

image views, and characterizations of compositional

and spatial patterns of the ATE associated with

lithologic, topographic, and structural controls. Each

of the GISc methods and techniques examined was

selected on the basis of (i) their potential wide

applicability (i.e., geographically and thematically)

within geomorphology; (ii) their availability through

current technologies including hardware and software

systems that are commercially supported; (iii) their

representation of proven methods and technologies

used in allied sciences, particularly landscape ecology

and physical geography; and (iv) their considerable

utility in our previous, current, and planned geomor-

phic and vegetation studies in Glacier National Park.

Therefore, each GISc method and technique offers the

potential to make a real scientific contribution to the

study of landscape form and function without impos-

ing unreasonable learning curves on rare or experi-

mental hardware/software systems.

Here, we are particularly interested in the following

questions:

(i) Does the presence of relict solifluction terraces

aid the upward migration of treeline?

(ii) Can nonlinearities in catenae affect the advance

of treeline?

(iii) What is the role of individual boulders in aiding

upward migration of treeline?

These three questions have broader significance in

geomorphology. Relict solifluction terraces, a wide-

spread feature of alpine geomorphology, can affect

slope processes for centuries if not millennia. As such,

they create conditions that are paraglacial, i.e., dis-

equilibrium (sensu Renwick, 1992) conditions caused

by a significant relaxation time in process–pattern

relations following glacial climates (Church and

Ryder, 1972). Differences in solifluction terraces, as

might be determined through the application of GISc

techniques, could be an important aspect in the

development of alpine slope models. For example,

the second question, on nonlinearities in catenae,

arises from the potential effect of solifluction terraces

on Burns and Tonkin’s (1982) Synthetic Alpine Slope

model. They emphasized that slow soil development

could lead to geomorphic thresholds. GISc techniques

can identify locations where such process thresholds

may be imminent. Finally, although individual bould-

ers are not a definitive part of a slope and soil model,

they can be generated by solifluction processes that

isolate them or lead to their movement. Individual

boulders may also be, however, a widespread result of

glaciation, i.e., erratics. In some places, glacial erratics

or other individual boulders may affect subsequent

geomorphic–soil–vegetation relations by providing a

source of otherwise unavailable nutrients through

weathering, by providing a site for aeolian deposition,

and by providing a sheltered site for plant growth. In

this paper, we report on the application of GISc

techniques to the identification of such geomorphic

features.

The basic intent of this paper is to (i) summarize

what we have already accomplished in the recent past

through the application of GISc to the study of the

ATE, with particular attention devoted to geomorphic

topics and concerns; (ii) report on current activities in

GISc directed at selected questions of geomorphol-

ogy; and (iii) chart our future directions in basic and

applied GISc research that involves new theoretical

insights into the form and pattern of the ATE,

hypothesized biotic and abiotic controls, and how

GISc might be used to map, model, and visualize

the ATE within a space–time context.

2. Study area: Glacier National Park and Lee

Ridge, MT, USA

In Glacier National Park (GNP) (Fig. 2), using

topographic maps, Becwar and Burke (1982) esti-

mated that 80% of the transition from forest to tundra

occurred over a 550-m vertical range; in contrast, in

Rocky Mountain National Park, CO, 80% of the

transition occurred within a much narrower vertical

range of only 200 m. The variability in ATE elevation

in GNP is due to a combination of variability in

S.J. Walsh et al. / Geomorphology 53 (2003) 129–145 131

macroclimate, microclimate, topography, snow and

debris avalanches, and competition with tundra (But-

ler et al., 1992; Walsh et al., 1992; Malanson and

Butler, 1994). This variability makes the park an

excellent place to study spatial pattern; it is probably

more representative of the range of conditions found

in the Rocky Mountains.

In addition to an array of sites and conditions

available within the park, one site (Lee Ridge; Fig.

3A,B) is well suited for intensive study and instru-

mentation. Lee Ridge is located in the extreme north-

eastern corner of GNP and is bounded by Lee Creek to

the east, Gable Mountain to the south, Belly River to

the west, and the park border and Chief Mountain

Highway to the north. Our study area ranges in

elevation along a portion of Lee Ridge from f1825

to 2150 m, is oriented north to south along an elevation

gradient extending from lowest to highest, and

extends f 4 km along the north–south axis.

The Lewis Overthrust Fault is the dominant struc-

tural feature in the area, responsible for the emplace-

ment of Precambrian Belt Series formations over

relatively incompetent Cretaceous shales and mud-

stones (Whipple, 1992). Lee Ridge itself is comprised

of soliflucted colluvium derived primarily from the

Altyn Formation that forms the cliffs of Gable Moun-

Fig. 2. A Landsat TM composite of GNP. The TM data are presented in an RGB color model using channels 6, 4, and 1, respectively. Lakes

are represented as dark-toned, lobate features; closed canopy forests have moderately dark-toned features extending from valley floors to the

alpine; and sparse-vegetated tundra and non-vegetated rock, snow, and ice surfaces are represented as light-tone features on the satellite

image.

S.J. Walsh et al. / Geomorphology 53 (2003) 129–145132

tain above the ridge. Relict solifluction terraces (Car-

rara, 1990) dominate the current landscape of Lee

Ridge. Typical tread–riser morphology is widespread.

Field measurements reveal an average width of 1.25

m/individual tread–riser pair. Tread surfaces are com-

pacted and slightly indurated and are supportive of

large diameter Xanthoria elegans lichens, all condi-

tions indicative of the relict nature of the solifluction

Fig. 3. (A) A Landsat TM image looking across the east-central portion of the GNP and towards Lee Ridge. Lee Ridge is annotated for location.

(B) Lee Ridge as viewed from the north and represented by a drape of a digital orthoquadrangle image and a 30� 30-m digital elevation model.

S.J. Walsh et al. / Geomorphology 53 (2003) 129–145 133

processes on the ridge. Localized patches of bioper-

turbation by ground squirrels and grizzly bears (sensu

Butler, 1995) disrupt the surface of some solifluction

terraces and expose fine-grained sediments to erosion

from runoff. These exposed patches of fine-grained

sediment may also, however, serve as potential seed-

beds for tree expansion on the ridge. Individual

boulders of Altyn limestone are distributed randomly

across and along the ridge, probably the result of

rockfall and subsequent rolling and tumbling from

Gable Mountain. These boulders are also typically

lichen-covered, indicative of stability.

The vegetation of Lee Ridge varies with elevation

with a closed-canopy forest of primarily lodgepole

pine (Pinus contorta) at lower elevations; grading to

an open canopy forest of lodgepole pine, followed by

krummholz patches and forested fingers of lodgepole

pine; subalpine fir (Abies lasiocarpa), Engelmann

spruce (Picea engelmannii), and, to a lesser extent,

five needle pines (Pinus albicaulis and Pinus flexilis).

Many of the trees in these forested fingers grow in

distinct flagged form, attesting to the severity of the

climate on this windy, exposed ridge. Interspersed

with the matted and flagged krummholz patches and

forested fingers is the alpine tundra colonizing the

relict solifluction terraces (Fig. 4).

On lower portions of the ridge, an area of former

tundra appears to have been invaded by trees at the

end of the Little Ice Age. Upper sections of the

ridge now have patterns similar to those of the lower

section, suggesting that an upward encroachment of

the forest into alpine tundra is slowly occurring.

Such dynamic movements of treeline typify the

ATE, both currently and in the past. In some places

along higher portions of the ridge, fossil stumps and

snags that predate the Little Ice Age have been dated

by 14C to fAD 1400. These dead snags represent

the termination of a period when large, erect trees

(as opposed to low flag forms or krummholz) were

invading the tundra. The onset of colder conditions

associated with the Little Ice Age probably brought

about the death of these trees.

3. Previous studies on the alpine treeline ecotone,

Glacier National Park

Our previous research in Glacier National Park

has focused on a variety of regional and local

geomorphic and topographic controls that directly

impact the location and elevation of treeline. Snow

avalanche paths directly impact the elevation of

Fig. 4. Field photo of solifluction steps with tundra on risers.

S.J. Walsh et al. / Geomorphology 53 (2003) 129–145134

treeline by depressing the upper limit of tree growth

well below the climatic treeline (Walsh et al.,

1994a,b). The location of those avalanche paths is,

in turn, largely controlled by the regional pattern of

structural lineaments and bedrock units associated

with the Lewis Overthrust Fault (Butler and Walsh,

1990; Walsh et al., 1990a).

Other mass movement processes, notably debris

flows (Butler and Walsh, 1994; Walsh and Butler,

1997) and rock avalanche deposits (Butler et al.,

1998), also depress treeline in individual drainage

basins and along the leading edge of the Lewis

Overthrust. Debris flow sites are concentrated

beneath couloirs and gullies which also coincide

with (and are largely controlled by) the aforemen-

tioned spatial pattern of lineaments. Proximity to

semi-permanent snow patches, slope concavity, and

leeward slopes was shown to be favorable for debris

flow development and resultant depression of tree-

line.

The temporal pattern of snowmelt (controlled by

local topography, slope aspect, solar radiation poten-

tial, and wind patterns) also influences the elevation

of treeline in GNP. Brown (1994a,b) and Walsh et

al. (1994b) described three basins within eastern

GNP where treeline occurred at higher or lower

elevations than was to be expected given the cli-

matic parameters of the area. Snow patch distribu-

tion and snowmelt patterns played a strong role in

determining which sites were too dry for adequate

tree growth or which melted out too late in the

growing season to sustain tree growth (Walsh et al.,

1994a).

4. Approach

The aim of this current ATE research was to

assess the sensitivity and geomorphic characteristics

of alpine tundra that make it more susceptible to

invasion by woody species. This basic aim will be

met by developing models of tree species establish-

ment and growth that reflect causal mechanisms.

The models are based on geomorphic and biogeo-

graphic field data that include solifluction tread–

riser morphometry, surface induration/penetrability,

mapping of individual boulders associated with iso-

lated tree seedling establishment, mapping of the

pattern of animal disturbances of the soil surface,

and mapping of vegetation from multispectral air-

craft imagery—with ground-control for verification.

The models will be developed and validated at

multiple spatial and temporal scales using a GISc

approach. GISc indicators of the conditions above

treeline will be used to parameterize models, and

GISc indicators of past and present treeline will be

used to validate models. The results will allow the

interpretation of past and ongoing changes at and

above treeline.

4.1. Multimedia GIS databases

Geospatial data and Geographic Information Sys-

tems (GIS) techniques were used to represent mor-

phometric characteristics of disturbance factors (e.g.,

debris flows and snow avalanche paths) and process

variables (e.g., solar radiation potential, soil moisture

potential, snow accumulation, and ablation patterns)

hypothesized to influence the form and structure of

the ATE. A multimedia integrative GIS was devel-

oped as the analytical ‘‘backbone’’ of the research. All

static and derived data layers, in situ and remote

information, and cartographic and modeled surfaces

were assembled in the GIS database to support the

research. Digital displays were developed to examine

the relationships between scale, pattern, and process

relationships of debris flows and snow avalanche

paths and terrain characteristics and the geomorphol-

ogy of the alpine and the subalpine environments. The

static and derived data layers generated through

remote sensing and GIS approaches populated the

database for retrieval and transformation through data

visualizations that included temporal and spatial ani-

mations, flyovers, image change detections, three-

dimensional representations, image rotations, thematic

drapes, movie loops, and spatial simulations to sup-

port the mapping and modeling activities. Dynami-

cally linked data views (e.g., video, image graphics,

text, and maps) are examined in user-selected combi-

nations on the basis of commonly linked attributes

(e.g., feature IDs) within the multimedia GIS environ-

ment. Such visualization methods are highly interac-

tive, allow the user to change the extent of views (i.e.,

spatial and temporal scales), facilitate nesting of data

and scales, and accommodate the visualization of

hierarchy.

S.J. Walsh et al. / Geomorphology 53 (2003) 129–145 135

4.2. GISc: remote sensing data collection

During the summer of 1999, ADAR high spatial

resolution digital imagery was acquired. The ADAR

5500 system is a second generation, charge-coupled

device, frame camera system operated by Positive

Systems, Whitefish, MT. The ADAR 5500 operates

in four channels in the visible and near-infrared wave-

lengths (460–550, 520–610, 610–700, and 780–920

nm, respectively). The across-track field of view is

39j, and the radiometric resolution is 8 bits. Full-

frame image capture formats are 1500� 1000 pixels

with the spatial resolution varying between 50 cm and

3.0 m/pixel ground-sample distance. The nominal

spatial resolution of the imagery for our study was

1�1 m. On-board Global Positioning System (GPS)

technology spatially related each acquired image

frame to ground coordinates. Post-flight processing

modules facilitated (i) vignette correction of images

for exposure variation due to the internal effects of

sensors and optics, (ii) channel-to-channel registration

for automatic co-registration of the four multispectral

images, and (iii) file format translation for conversion

of the digital images to common file formats required

by our image processing applications software—

ERDAS Imagine.

Approximately 20 flight strips and 520 frames

measuring 1500� 1000 pixels were acquired. Follow-

ing atmospheric, geometric, and radiometric correc-

tions, initial processing was required to create a photo-

mosaic by referencing the ADAR frames to the

orthophotoquadrangles using a ‘‘camera model’’

approach that relied upon aircraft specifications and

geodetic control points established in the field during

the period of image collection. In addition to collect-

ing GPS coordinates for obvious natural features in

the field that were judged stable for our purposes (e.g.,

rock outcrops and stream/trail intersections) and/or

quasi-stable features (e.g., snow patches and water

bodies), registration markers were constructed of

plastic panels of 3� 3 m and displayed on the ground

in a ‘‘cross-hair’’ style for representation on the

acquired high resolution ADAR imagery. With the

natural and artificial ground markers spatially refer-

enced through GPS technology and subsequently

differentially corrected for higher spatial accuracies,

the mosaic image was developed as a seamless image

of the study site.

4.3. Basic image processing

Processing of the ADAR 5500 digital aircraft

data was achieved according to the following gen-

eralized set of considerations: (i) preprocessing of

the data to remove geometric and radiometric dis-

tortions in the data to reduce terrain-induced illumi-

nation biases (Meyer et al., 1993), and to perform

geographic referencing of the data to UTM coordi-

nates; (ii) preparation of a feature set for classifica-

tion of the ADAR and Landsat Thematic Mapper

(TM) satellite data through statistical clustering and

categorization of spectral responses for land use–

land cover (LULC) mapping; (iii) calculation of the

Normalized Difference Vegetation Index (NDVI), a

measure of plant greenness through use of the

visible and near-infrared spectral channels that are

sensitive to plant pigmentation and chlorophyll con-

tent, respectively, of the ADAR 5500 and Landsat

TM; and (iv) special-purpose processing (e.g., digital

enhancements) of the ADAR data to (a) clarify the

position of landscape boulders that serve as anchors

against the wind where soil fines may accumulate

and produce a more favorable environment for tree

seedling establishment, (b) define solifluction pat-

terns (e.g., treads and risers), and (c) differentiate

between tree/no-tree surfaces for evaluation of the

simulation models of tree growth and establishment

at Lee Ridge.

Principal Components Analysis (PCA) is often

regarded as a data compression technique that

enhances image views by representing scene spectral

variance from multiple remote sensing channels into

derived and transformed images that capture de-

creasing amounts of that variance through the com-

ponents (Walsh et al., 1990b). Fig. 5 is a view of

Lee Ridge with the first principal component of the

multiple channels of ADAR representing the soli-

fluction steps and risers. The image characterizes the

curvilinear pattern of the solifluction lobes and the

terraces running through Lee Ridge. The vegetated

solifluction risers are represented as interleaved,

dark-toned bands running across Lee Ridge, while

the non-vegetated solifluction steps are represented

as light-tone alternating bands. Lee Ridge was also

represented through an RGB (Red: channel 4;

Green: channel 3; Blue: channel 2) image of the

ADAR data to characterize isolated boulders occur-

S.J. Walsh et al. / Geomorphology 53 (2003) 129–145136

ring within the tundra. The imagery highlighted the

boulders, ranging in size from approximately 0.5 to

3.0 m in diameter, because of the spectral contrast

apparent between dense and less dense tundra and

exposed rock surfaces. While boulder size and

vegetation greenness and density played prominently

in the mapping of the boulders, the orientation of the

image to off-nadir views also aided in feature

characterization.

4.4. Data fusion: linking ADAR and Landsat The-

matic Mapper data

Using data from multiple sensors enhance land-

scape views and analytical power by integrating data

sets with different biophysical sensitivities. In this

instance, the ADAR data provided greater spatial

resolution (1�1 m, and thus the ability to discern

smaller objects and patterns on the landscape), while

the Landsat TM data provided greater spectral reso-

lution (seven spectral channels extending from the

visible to the near-infrared, middle-infrared, and ther-

mal-infrared; and thus the ability to better discriminate

among land cover and geomorphic features). The

process of combining two disparate sources of

imagery is referred to as data fusion and addresses

both differing spatial resolutions as well as differing

spectral resolutions. Several methods of data fusion

exist, including Intensity–Hue–Saturation (IHS)

transformations, texturization, and one based upon

PCA.

IHS transformation is a useful and relatively simple

approach that involves altering the data model from

the traditional RGB model to the IHS model. The data

Fig. 5. ADAR-5500 Principal Components Analysis (PCA) image of solifluction steps and risers on Lee Ridge. The steps and risers are shown

as curvilinear features running east–west across the image. The 4th principal component is shown.

S.J. Walsh et al. / Geomorphology 53 (2003) 129–145 137

sets are first resampled to a common pixel dimension

or spatial resolution and then transformed to the IHS

model. This method is commonly used when integrat-

ing Landsat TM and SPOT Panchromatic data (SPOT

Panchromatic has a 10� 10-m spatial resolution but

only one spectral channel that represents the broad

visible portion of the spectrum). Texturization, in

contrast, maintains the different pixel dimensions

and instead uses the finer-grained data (here, ADAR)

to provide textural information for each Landsat TM

pixel (each Landsat TM pixel is 30� 30 m, and thus

would contain f 900 ADAR pixels at a resolution of

1�1 m). Statistics are derived from the finer-grained

data and imported into the areal template provided by

the coarser-grained data.

For vegetation cover assessment, the finer-scaled

data can also be used to generate a vegetation index

such as NDVI that is compiled into the Landsat TM

30� 30-m template and used as an additional layer of

textural information. However, given the lower spec-

tral resolution of the ADAR data, not all vegetation

indices can be compiled to be equivalent with TM-

based indices. Texture statistics could also be gener-

ated from ADAR-based NDVI values and used to

inform the Landsat TM data layers. Also, in addition

to spatially compiling the 1�1-m ADAR data into

the 30� 30-m resolution template of Landsat TM, a

moving window or kernel can also be used to generate

statistics and/or vegetation index values to translate

neighborhood textural information from the ADAR

imagery to the TM imagery.

The third type of approach involves the use of

PCA, a derived set of orthogonal axes developed from

the spectral input channels of the remote sensing

system and represented by single layers of informa-

tion. Many times, the PCA layers from alternate

systems (e.g., derived from ADAR) are substituted

for one of the visible channels of the Landsat TM

imagery, as visible channels are known to contain

redundant information for many land cover types and

often have moderate to severe atmospheric scattering.

The process may also be used in the reverse direction

whereby a PCA transform is used to create one

channel of TM data used in conjunction with other

ADAR channels, again likely replacing one of the

ADAR visible channels.

While other approaches exist, the general fusion

methodology described herein relies upon using

higher resolution information to inform the co-

arser-grained analyses. In a more formal sense,

multilevel statistical models can also be developed

to integrate fine- and coarse-grained information in

a multivariate model where spectral channels and

their transformations serve as the descriptor varia-

bles. Also, within a context of data visualization,

overlaying higher spatial resolution data onto

coarser resolution data is quite common for enhanc-

ing selected landscape strata where additional infor-

mation is needed, such as edges and ecotones. In a

multiphased approach, Landsat TM data might be

used to characterize a landscape signature of a

disturbance, where the higher spatial resolution

ADAR data are then used to assess the nature of

that disturbance.

Lastly, the customization of data acquired via an

aircraft platform (as opposed to a satellite platform)

is an important issue to consider. Since the ADAR

data were acquired from an aircraft, mission spec-

ifications were set to highlight specific landscape

views and mapping goals. Such customization gives

the analyst control over maximizing the potential

utility of the data. For instance, timing an over-

flight to make use of low sun-angle conditions or

altering landscape views through experimentation

with sensor-terrain azimuth and orientation could

be used to enhance features such as solifluction

terraces, lineaments, and contacts between forest

edges. Moreover, the gain settings of the sensors

can be altered to enhance various segments of the

landscape such as the higher reflecting tundra or the

lower reflecting coniferous forest. In other words,

the analyst can interact more with mission specifi-

cations on aircraft- and ground-based systems than

on satellite-based systems. Ground-based spectral

radiometers and plant canopy analyzers might also

be used within a multi-resolution concept to inform

the ADAR data (or Landsat TM data) and to help

set mission specifications for optimizing landscape

characterizations from remotely sensed systems by

predetermining optimum spectral regions for map-

ping.

4.5. Digital elevation models

Digital elevation data consist of an array of

regularly spaced elevations. Elevation is used for

S.J. Walsh et al. / Geomorphology 53 (2003) 129–145138

the determination of potential energy: calculation of

climatic variables such as pressure and temperature,

vegetation and soil trends, and material volumes

including cut and fill. From the discrete digital

elevation data, calculating derivatives is possible at

any location. The 30� 30-m digital elevation models

for Lee Ridge and the immediate vicinity were

processed to characterize topographic elevation and

the primary derivatives—slope angle and slope

aspect. In addition, we have used the DEMs to

generate higher-order variables including solar radi-

ation potential and soil moisture potential after

Brown (1992) and Allen (1995), landform index

after McNab (1993), and wind potential after Allen

(1995).

4.6. Simulation models

We are developing nested models of treeline

response. The core model is a mechanistic model

of tree establishment and growth based on modifi-

cations to, and integration between, two existing

models; a biogeochemical cycling model (ATE-

BGC; Cairns, 1997; Cairns and Malanson, 1997,

1998), and a forest gap model (FORSKA; Leemans,

1989) modified to represent the form of mat

krummholz growth as well as upright trees. The

models are being validated using ADAR data,

digital orthophotoquads, and ground samples for

test slopes. The core model is embedded in a

mesoscale landscape model (Walsh et al., 1990a,b;

Brown, 1994a,b) and also generates a fine-scale,

cellular automaton model to analyze emergent spa-

tial pattern. Here, we use an even simpler model

(Malanson et al., 2000) that is preliminary to the

cellular automaton.

This basic simulation model embodies the re-

source-averaging hypothesis: trees need to gather

more resources and can do so over a larger area than

do tundra plants. When resources become few and

patchy along a gradient, a limit for trees will be

reached. Our simulation allows us to model the

relation between the spatial pattern of the alpine

treeline ecotone and the abiotic pattern of the environ-

ment, including geomorphic conditions at the boun-

dary. The simulation also allows us to examine the

change in the abiotic resources on a slope that can be

caused by geomorphic features.

5. Results

We address three questions that are examined here:

(i) What geomorphic features can we incorporate in

our models using GISc?

(ii) What vegetation features can we detect with

GISc that lead us to new hypotheses about

geomorphic constraints?

(iii) Can scientific data visualization help?

5.1. Modeling geomorphic features

Two geomorphic features can be mapped into a

GIS based on image analysis. Solifluction lobes

appear as a regular pattern in the image derived from

the ADAR data using PCA (Fig. 5). Isolated boulders

(which may serve as sites offering adequate soil

moisture, weathering generated nutrients, and protec-

tion from wind) appear as randomly distributed fea-

tures on the RGB image derived from ADAR data.

Will these patterns of geomorphic features lead to

differences in a simulation model? As a preliminary

test, we examined the differences in our resource-

averaging model between gradients created with, and

without, a specific effect that might be created by

solifluction lobes. To verify our simulations and to

explore how new inputs might change the current

treeline at Lee Ridge from current conditions, a tree/

no-tree image was generated through an ISODATA,

unsupervised classification of the ADAR-5500 digital

data (Fig. 6). Our basic question on Lee Ridge was:

could the fingers of trees extending up into tundra be

a response to linear patterns in the abiotic environ-

ment, i.e., the solifluction lobes. To the kinds of

variance added to the environmental gradient of

abiotic-site quality by the model (e.g., random var-

iance at 30% of the mean or a fractal surface of

variance), we increased the abiotic site quality of

every other column by 10% (note that on Lee Ridge,

the solifluction lobes run nearly perpendicular to the

advancing forest edge). Fig. 7A,B reveals that the

addition of solifluction lobes does produce the kinds

of finger-like distribution of trees that are seen on Lee

Ridge and elsewhere. The problem remains that the

fingers are not exactly parallel to the lobes—they

cross them at an angle of 20j, and in other places the

angle may be greater.

S.J. Walsh et al. / Geomorphology 53 (2003) 129–145 139

We are working to incorporate the pattern of bould-

ers in the model. Preliminary fieldwork indicates that

fine aeolian sediments collect on the leeward sides of

boulders on Lee Ridge, where winds are predominantly

from the west. The aeolian deposits are substantially

enriched in silt and clay when compared to exposed

soils on the adjacent wind-swept solifluction treads.

5.2. New vegetation features

Previously unobserved, the remote sensing product

revealed a ‘‘green wave’’ of denser tundra that is in

advance of the forest edge and which extends out and

beyond the most advanced fingers and outliers of the

forest edge (Fig. 8). The wave is now obvious on

analog aerial photography as well, but it is still difficult

to discern in the field (Fig. 9). We hypothesize that this

denser tundra is indicative of conditions deeper below

the surface, where finer-grained soils will allow the

establishment of trees. The green wave represents a

relatively abrupt transition from clear solifluction ter-

races where the treads are unvegetated to a condition in

which the treads are vegetated and the subsequent soil

and slope processes will approach equilibrium with

current forces more quickly.

5.3. Visualization

Scientific data visualization has aided our under-

standing of fine-scale geomorphic patterns and their

relationship with, and possible influence on, the

alpine treeline ecotone on Lee Ridge. Visualization

of geomorphic features on processed imagery

draped on DEMs allows us to determine how to

include them in simulation models. Future simula-

tions will use the data underlying the draped

imagery (elevation and both solifluction and iso-

lated boulders) as a direct input to the surface of

the variance of the abiotic site quality. Future

fieldwork will be aimed at testing hypotheses about

the green wave. Once we know what causes this

feature, it can then be incorporated in simulations

either as a direct input or dynamically by simulating

the process behind it.

6. Discussion and interpretations

Where are the emergent opportunities and chal-

lenges of GISc in geomorphology? GISc promises to

make spatial patterns a more fundamental part of our

Fig. 6. ADAR-derived image of tree (black)/no-tree (white) categorization of Lee Ridge.

S.J. Walsh et al. / Geomorphology 53 (2003) 129–145140

Fig. 7. Simulation results showing the influence (fingers) of incorporating solifluction lobes into the simulation model using (A) fractal variance

[top-left: no solifluction, fractal variance (D = 2.3); bottom-left: no solifluction, fractal variance (D= 2.7); top-right: solifluction, fractal variance

(D = 2.3); bottom-right: solifluction, fractal variance (D = 2.7)] and (B) random variance [top-left: no solifluction, random variance = 40%;

bottom-left: no solifluction, random variance = 80%; top-right: solifluction, random variance = 40%; bottom-right: solifluction, random

variance = 80%].

S.J. Walsh et al. / Geomorphology 53 (2003) 129–145 141

analyses in the development, parameterization, and

validation of models. On a variety of fronts, spatially

explicit models have improved our understanding of

Earth surface processes; these models will improve

as they integrate GISc methods into their develop-

ment and validation. In particular, more flexible

approaches to scale and resolution in the model

development stages will open new avenues of

Fig. 9. Field photo of the ‘‘green wave’’ on Lee Ridge.

Fig. 8. The ‘‘green wave’’ (viewed from the NE) as indicated on the NDVI image from the ADAR-5500 data overlaid onto an orthophoto-

quadrangle.

S.J. Walsh et al. / Geomorphology 53 (2003) 129–145142

research. The use of GISc to quantify spatial pattern

will also aid in parameterization, but may prove

more useful in validation given that many models

are not meant to reproduce exact matches of land-

forms (or ecotones) but only similar kinds, i.e.,

patterns.

Additionally, visualization becomes an integral

part of the research process. Scientific visualizations

in support of geomorphic studies may take many

forms, depending upon project goals and the nature

of the variable or variables to be examined. Varia-

tions to be visualized may occur in space, time,

and/or attribute. Whether the variable is spatially

discrete (e.g., sediment traps set out to capture

aeolian deposits at fixed locations) or spatially

continuous (e.g., elevation values secured from a

digital elevation model for the study area), or

temporally discrete (e.g., lithology) or temporally

continuous (e.g., soil moisture potential; point data

might be interpolated to create a continuous cover-

age) impacts the kinds of visualizations to be

applied and the type and complexity of data trans-

formation used to rectify differing data structures

and formats comprising the variables. Variables

might also be static, base variables (e.g., surficial

geology), or dynamic process variables (e.g., solar

radiation potential). Derived measures computed

within a GIS (e.g., surface roughness or distance

to an upwind barrier) might be calculated and

spatially referenced at a sediment trap location to

expand the dimensions of its attributes. These

variable conditions—space, time, and/or attribute—

might be viewed as orthogonal axes on which a

host of visualization approaches might be arrayed to

provide data views used as either part of the final

analysis or in generating additional hypotheses

through the consolidated visualization of formerly

disparate and disjointed data.

As we have shown, visualization of various forms

of data representing the extant landscape at different

scales can lead to new hypotheses. Visualization of

model results in three dimensions as animations of

data representing landscapes will also lead to new

insights about what models tell us and what they

miss. The future holds promise for more use of

visualization to open avenues for the study of spatial

relations through fieldwork and simulation modeling.

Animations of image change detections, image fly-

overs, rotations, and animated versions of cellular

automata are likely to produce new insights in the

future.

These techniques should lead to further refinement

of the Synthetic Alpine Slope model (Burns and

Tonkin, 1982) in which constraints on the processes

at a given slope, determined by patterns at greater

temporal and spatial scales (Malanson and Butler,

2002), can be incorporated more explicitly. For exam-

ple, the multiscale modeling advocated by Brown et

al. (1994) for vegetation patterns at the treeline

ecotone needs the input of geomorphic constraints,

but this approach could be adapted to modeling the

geomorphic processes as well. The combination of

patterns derived from GISc with mechanistic process

models leads to understanding processes within the

context of constraints imposed by broader scale top-

ography, geology, and the climate.

Acknowledgements

The USGS Biological Resources Division sup-

ported this work; special thanks go to our USGS PI,

Dan Fagre. David R. Butler acknowledges additional

funding in support of fieldwork described in this paper

from a Faculty Research Enhancement Grant from

Southwest Texas State University. George P. Malanson

acknowledges additional funding in support of model-

ing described in this paper from NSF grant SBR-

9714347. This research is a contribution of the

Mountain GeoDynamics Research Group. Graphics

were generated by Sean McKnight, Landscape Char-

acterization and Spatial Analysis Lab, University of

North Carolina.

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