Linking geomorphic Systems theory and remote A conceptual

Linking geomorphic Systems theory and remote sensing Nora Jennifer Schneevoigt, Lothar Schrott 181

Linking geomorphic Systems theory and remote sensingA conceptual approach to Alpine landform detection (Reintal, BavarianAlps, Germany)

Nora Jennifer Schneevoigt, Zürich, Lothar Schrott,Salzburg

1 Introduction

Alpine regions are moving into the focus of scientificattention: chapter 13 of Agenda 21 is dedicated tomountains, 2002 was declared «International Year ofthe Mountain» and German Geographers' Day 2003

was entitled «Alpine World - Mountain World: Islands,Bridges, Borders». The sustainable preservation ofalpine regions represents a global issue, because atenth of the world's population lives in mountains,while a multiple indirectly depend on mountain re¬

sources. Geomorphologie activity in alpine regions is

significantly greater than in its foreland. Therefore,mountain environments display quick changefulnessin time and space (Caine 1974) and react very sensi¬

tively to global change (Kääb 2002).

As higher regions often cannot be observed fromthe ground, remote sensing (RS) applications lendthemselves to the investigation of the alpine sedi¬

ment cascade. While geomorphologic research oftenuses geographical information Systems (GIS) cou¬pled with RS data, only few genuine RS techniquesare employed. Difficulties in the aecurate designationof landforms add to the general intricaey of handlingalpine data in RS. Yet objeet-oriented Classificationconstitutes a new and promising approach to com¬bine the advantages of GIS and RS. This study aimsat illustrating the role of objeet-oriented RS as a toolin high mountain geomorphology within the contextof Systems approaches to alpine sediment fluxes anddeposits.

2 Allocation of landforms in alpine regions

In order to deteel landforms by RS, a compilation oftheir distinctive features is required. The better theknowledge of the target classes (Tab. 1), the greaterthe chances of differentiating between them in imagedata (Schneevoigt et al. 2006). Varying geomorphicprocess activities result in a patchwork make-upof landforms. Hence, activity Status and processesinvolved are vital for their detection: texture andspectral characteristics of individual landforms can be

very heterogeneous.

2.1 Target landformsA landform is defined by its particular shape. How¬ever, strict delimitations of landforms rarely exist inlandscape, as many forms show no clear boundaries(Fig. 1 and 2). Moreover, landforms often form part ofother landforms - scale and given interest determinewhere a speetator would set a division. Dependingon geographie Situation, age, maturity and marked-ness of a landform. its geomorphometry also variesenormously (Rasemann et al. 2004). Besides, detec¬tion of landforms in RS data can be hindered by thefact that many landforms originate from interactingprocesses and display partially interfingered, complexassemblages (Fig. 2). Equifinality blurs underlyingprocesses as well: different processes can render thesame landform shapes, which however should beardifferent names according to the building process. Forinstance, a strict Separation of avalanche from debrisflow deposits is not always possible in situ (Fig. 1B) norhence in a satellite scene.

This fuzziness of high-mountain terrain features (Kääb2002) urges consideration of context for sound RSclassifications: landforms result from spatially distrib¬uted and interlinked geomorphologic processes whichare consecutively modelling landscape by Alling and

emplying different types of Stores. Monocausal, linearprocess-form relations cannot be established becauseof interactions which vary spatially and temporally.Further investigation is still needed to fully understandlandform development in high mountains (Becht et al.2005; Jordan & Slaymaker 1991; Schrott et al. 2002).RS applications benefit from increased knowledge ontarget classes (Tab. 1), as more possibilities of featureanalysis arise in Classification hierarchies.

Deciphering sediment flow and storage is Iricky sincea focus on Single events or forms ignores greater con¬text (Fig. 2). whereas a large scale point of view cannotfully grasp complexity (Fig. 1B). A systemic approxi-mation unites these antipodes: on the one hand, itfacililates the delimitation of Single components byallowing the zooming in onto systemic details. On theother, its holistic approach places all components intoSubsystems and these into greater superordinate Sys¬

tems, thus restoring the entirety of landscape. HenceSystems theory. paralleled by objeet-oriented RS, helpsin overcoming the scale problematics in space. As thetemporal aspect of RS is limited, the following sectionfocuses on the micro time scale of the present.

182 Geographica Helvetica Jg. 61 2006/Heft 3

crest regions rockwalls valley bottom

cirques &hanging Valleys

avalanche &debris flow tracks

avalanche &debris flow tracks

avalanche &debris flow deposits

less inclinedbare rock

Vegetation coveredslopes

talus sheets& cones

rockfall partly/fully overgrown

free faces free faces alluvial fans rockfall deposits

snow & ice loose Sediments floodplains moraine deposits

Tab. 1: Target groups of Alpine landform ClassificationZielklassen der alpinen Georeliefformen-KlassifikationClasses cibles pour la Classification des formes du relief alpin

2.2 Systemic approaches to alpine sediment lluxesand deposits

With the onset of process-orientated geomorphology.Strahler (1952) adapts the concept of open Systems togeomorphology: in- and Output of energy and mattercharacterise these flow Systems striving for a steadyState, i.e. an equilibrium of transfers in the System as

a whole. Hack (1960) refers to dynamic relationshipsbetween process components: as soon as a Systemcontains negative feedback loops, it shows a tendencytowards establishing a dynamic equilibrium, as, for this

reason, a capability of compensation is given. Land¬forms thus develop to a State of maturity that is in a

continual State of destruction and reconstruction.

Chorley (1962) and Chorley & Kennedy (1971) fur¬

ther promote general Systems theory, focussing on innercomplexity: they perceive environment as a hierarchyof organised Subsystems and mountainous regions as

dynamic cascading Systems. Although not all processeswork in downslope direction, «sediment cascade» gen¬erally designates alpine material transport. It is definedas a structure in which the Output of one Subsystemforms the input of another (Fig. 3). Any movement is

determined by regulators at the interfaces between

processes and forms (Fig. 3), i.e. by the disposition,presence or absence of geomorphological variables(e.g. storage potential, slope). Internal thresholds of theindividual variables interfere with external thresholdsof other regulators in the same or surrounding Subsys¬

tems, allowing for reaction intervals and different reac¬tion patterns even in adjacent forms. This explains the

complexity and nonlinearity of alpine Systems, wherehigh rates of energy transfer result in rapid sedimentturnover (Caine 1974; Chorley & Kennedy 1971).

Despite its benefits, the Systems approach initially didnot gain general acceptance due to limited implemen¬

tation. With more powerful Computer Systems in the1990s, paradigms in geomorphology changed: with theincreasing improvement of memory space, the Systemsapproach regained vitality. Recent research on sedi¬

ment transport Systems increasingly simulates sedi¬

ment cascades in (peri-)glacial environments (Bal-lantyne 2002; Becht et al. 2005: Otto & Dikau 2004;Rothenbühler 2006; Zemp et al. 2005).

2.3 The Alpine sediment cascade of the ReintalThe project Sediment Cascades in Alpine Geosystems(Sedimentkaskaden in alpinen Geosystemen - SEDAG)modeis qualitative and quantitative sediment turnover(Becht et al. 2005; Schmidt & Morche 2006; Schrottet al. 2002: Unbenannt 2002), including the identifica-tion of different storage types (Tab. 1) and their spatialpatterns. RS hence represents a helpful tool, especiallyconsidering the higher, inaccessible parts of the valley(Fig. 1B; Schneevoigt et al. 2006). The conceptionalAlpine sediment cascade (Fig. 3) by Schrott et al.

(2002) is based on Systems theory, synthesising Chor¬ley & Kennedy's hierarchical cascading Systems con¬

cept (1971) and Caine's alpine sediment transfer model(1974). Il shows the spatial distribution of storage typesin the Reintal (Fig. 1) in three Subsystems cascadingdownslope via processes. Today, 79% of the sedimentStores representing geomorphic process units in theReintal valley bottom are inactive, overgrown (Fig. 1B,

2) and decoupled from the cascade.

As hardly any sediment leaves the subcatchment, theReintal equals a nearly closed sediment System (Unbe¬nannt 2002). An imbalance in favour of input preventsthe onset of a steady state or dynamic equilibrium. Itleads to quickly growing sediment Stores, exemplified in

high Sedimentation rates of 18 to 27 mm a' (Schrott et

al. 2002 and this issue). These Stores form buffers whichcan trap material for centuries and millennia (Jordan &


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Fig. 1:A) Geographical location ofthe Reintal (Bavarian Alps, Germany). B) ASTER scene (29.5.2001) in RGB3-1-2 draped over digital elevation model (DEM), view from north-north-west.A) Geographische Lage des Reimals (Bayerische Alpen, Deutschland). B) ASTER-Szene (29.5.2001) in RGB 3-1-2 über ein digitales Höhenmodell (DHM) gelegt, Sicht aus Nord-Nord-West.A) Situation geographique du Reintal (Alpes bavaroises, Allemagne). B) Scene ASTER (29.5.2001) en RGB 3-1-2elrapee sur un modele numerique de terrain (MNT), vue du nord-nord-ouest.Source: DEM generated by SEDAG partners, data: Bavarian Geodetic Survey; ASTER image provided by theASTER science team

Slaymaker 1991), introducing major temporal variationsinto the System. The heterogeneity of alpine regions andrecent climate change add to the difficulty of designinguniversally applicable schemes of sediment fluxes. Thusoverall, quantitative modeis of sediment cascades fullydescribing landscape development are still lacking.

3 Optica) RS in alpine geomorphology

For a better understanding of the complex materialflow Systems engendering landscape variability andnatural hazards (Kääb et al. 2005), RS constitutesan adequate tool. It permits global, regulär coverage

of remote areas at a wide ränge of scales. However,extreme altitudinal differences within small horizon¬tal intervals in alpine terrain may result in offsets ofseveral pixels or hundreds of meters if scanned at dis-advantageous angles. Illumination and shading varyenormously because of relief influences (Fig. 1B). Yethigh mountain geomorphologists increasingly recog¬nise the potential of RS (Bishop & Shroder 2004).Thelong-distance perspective facilitates paltern detectionand monitoring of otherwise inaccessible landscapesections: remotely sensed imagery and digital elevationmodeis (DEM) complement one another for geomor¬phological analyses based on applications from the RSCommunity (Giles & Franklin 1998; Kääb 2002).

184 Geographica Helvetica Jg. 61 2006/Heft3

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Fig. 2: Alpine landform assemblage around the Hintere Gumpe floodplain (Reintal. Bavarian Alps). Photo over-laid with labeis of landforms. view facing south-east.Vergesellschaftung von alpinen Georeliefformen um die Schwemmebene der Hinteren Gumpe (Reintal, Bayeri¬sche Alpen). Foto mit Bezeichnungen der Georeliefformen, Blick nach Süd-Ost.Assemblage de formes du relief alpin autour de la plaine d'inondation appellee Hinlere Gumpe (Reinted, Alpesbavaroises). Photo avec denominalions eles formes du relief, vue en direction du sud-est.Photo: L.Schrott2001

3.1 GIS versus RSWhereas GIS approaches to the analysation of RSdata are manifold, studies employing RS methoclologyoccur less frequently in high mountain geomorphol¬ogy. GIS-based spatial analysis in the form of Statisticalarithmetics, neighbourhood relationships and Clustersallows for a differentiation of patterns in landscape.While descriptivecalculations per cell (e.g. slope. aspect.curvature) have been applied for two decades now.(semi-) empirical landscape modelling has only been

operationaliscd in GIS environments of late (Bishop&Shroder 2004). Several studies confirm the potential ofoptical RS imagery and GIS for the assessment of geo¬morphic forms and processes (Etzelmüller el al. 2001:McDermid & Franklin 1994; Walsh et al. 1998).

Snow and ice monitoring represents the foremost

research topic when it comes to genuine RS. as fieldmeasurements quickly reach their limits here. Formore than two decades now. the füll ränge of possi¬bilities concerning optical satellite data on snow appli¬cations has been exploited (Bishop & Shroder 2004:Kääb et al. 2005; Paul 2000: Paul et al. 2002. 2004:Schaper 2000). For example, the Global Land IceMeasurements from Space (GLIMS) programme cen¬

tres on the Advanced Spaceborne Thermal Emissionand Reflection Radiometer (ASTER) sensor. scenesof which also form the basis of this study (Fig. 1). Forclimate moniloring, an inventory of global land-iceextension is thus being developed by a worldwide con¬sortium combining RS and GIS (Kääb 2002).

Recent developments lead to a coalescence of GISand RS: current objeet-oriented Software unites RS


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Fig. 3: Conceptual Alpine sediment cascade of the Reintal (Bavarian Alps, Germany)Konzeptionelle alpine Seclimentkaskade des Reintals (Bayerische Alpen, Deutschland)Modele concepluel de la cascade sedimeniaire alpine du Reinial (Alpes bavaroises, Allemagne)Source: Schrott et al. (2002)

applications with GIS statistics into one desktop envi¬ronment. While pixel-oriented RS analyses the spec-tral characteristics of each pixel individually, objeet-oriented RS also considers the neighbourhood of a

pixel. It assumes that adjacent pixels showing certainsimilarities belong to one group, so that the entirescene is divided into image Segments (Fig. 4) beforeclassifying it in a second, separate step (Fig. 5). Thismethod produces more homogeneous results throughspectral generalisation, thus smoothing out irregulärpixel-dominated patterns and creating more realisticforms (Baatz & Schäpe 2000; Koch et al. 2003).

3.2 Segmenting images into objeetsThe idea of image segmentation arose in the 1970s,but like Systems approach only became operationaltwenty years later with high-capacity Computers. Seg¬

mentation techniques are used to deal with intensifiedin-class variability brought forth by increasing spectraland spatial image resolution (Schiewe & Tufte 2002).The spectral properties of image objeets are addressedby mean values, Standard deviations and ratios of the

incorporated pixels. Image objeets also comprise geo-melric features, as image objeets vary in shape andextent (Fig. 4). Neighbourhood relationships, sub- and

superordinations, morphometric and class-related fea¬

tures can be analysed as well (Baatz & Schäpe 2000;Benz et al. 2004; Blaschke & Hay 2001). As objeet-based class descriptions hence reach far beyond spec¬

tral information, they are well suited for three-dimen-sional alpine applications, where spectral input alonedoes not suffice (Giles& Franklin 1998; Schneevoigtet al. 2006).

The objeet-oriented Software eCognition segments animage in a knowledge free way via region-growing, anautomatized heuristic optimisation method assessingthe potential increase of spectral heterogeneity in a

weighed merge of two pixels or segments considered.Next to this colour criterion based on spectral infor¬mation alone, shape parameters can be used to cor-rect highly textured data which otherwise would pro¬duce frayed and distorted segments. This constitutesan advantage especially in high mountain data. Yetit must be applied carefully. as it implies an arbitrarydivergence from the given spectral information basedon pure arithmetics (Baatz & Schäpe 2000).

From the colour and shape input, the region-growingalgorithm produces image objeets with a minimizedaverage heterogeneity in any desired resolution orscale. However. vital information for all classes oftencannot be displayed in one Single resolulion, while seg¬mentation must be applied to an entire image with onescale parameter. Multiresolution segmentation allowsa simultaneous depiction of several image levels seg-mented on different scales (Baatz & Schäpe 2000;Benz et al. 2004). For example. a small scale parameter


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Objektorientierte Klassifikation. Oben: Datengrundlage der objektorientierten Analyse: ASTER-Satellitenszene(15 m Auflösung), DUM (5 in Auflösung) und fünf DHM-Ableitungen (5 in Auflösung). Unten links: Segmentie¬

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best conveys the heterogeneous Reintal ground sur¬

face, whereas the delineation of a mask encompassingthree altitudinal Subsystems (Fig. 3, 5) implies a veryhigh scale parameter. The final Classification should beexecuted on an intermediate level, not too detailed butshowing also relatively small landforms.

This meets the demand for hierarchical and scale

dependent RS classifications. Dikau (1994) states thatthe hierarchical Organisation of topography has notyet been taken appropriately into account in studieson mountain geomorphology. Multiscalar approachescan correct this defect (Fig. 4).

scape units as on the valley bottom of the Reintal(Fig. 2). Thus, RS constitutes a valuable tool in theelaboration of the Alpine sediment cascade, par¬ticularly on account of its high detection capacityin otherwise inaccessible crest regions and rock-walls. Objeet-oriented Classification rules should betransferable to other regions, as they depend less onreflection values, atmospheric conditions and arbi-trarily selected training areas than pixel-based ones

(Blaschke et al. 2002). Application of the approachto different study areas will show whether or not itprepares the ground for a semi-automatic landformClassification scheme.

3.3 Hierarchical landform Classification at four levelsin the Reintal

An ASTER satellite scene, a DEM (5 m resolution,generated in the Arclnfo spline algorithm Topogridfrom data by the Bavarian Geodetic Survey) and itsderivatives were assessed at four levels ranging fromvery small spectral units to the altitudinal strata mask

(Fig. 4). The finest level, LI, was segmented based onspectral information only, while the coarsest layer, L4,

represents a strata mask made in a separate objeet-oriented project. All segmented levels were thenclassified individually (Schneevoigt et al. 2006) witha knowledge base containing features immanent ina class itself and inherited ones passed on by parentclasses in the Classification hierarehy. Thus, working onmultiscalar levels focussing on differently sized land¬

form features mimics the Systems approach, allowingfor detailed and general views at once (Fig. 4).

The LI Classification renders ground land cover, levelL3 eastern and western walls of cirques and hangingValleys, all of which leads to a sound L2 landform Clas¬

sification shown in Figure 5. The good fit of the resultsto ground truth is demonstrated in Schneevoigt et al.

(2006). Detection limitations are reached with land¬

forms such as moraine and rockfall deposits (whichhave been overprinted by more recent processes forcenturies or millennia) and some complex alluvialfans. The majority of classes such as cirques, rock-walls, floodplains, fine and coarse Sediments were wellassessed, and some could even be differentiated morethan previously expected, e.g. the Vegetation coveredslopes and talus cover, leading to twenty final thematiclandform classes (Fig. 5).

4 Conclusions

RS applications with geometrically medium-resolved,multispectral image data allow the Classification ofAlpine landforms or geomorphic process units to a

great extent, even when dealing with such small land-

AcknowledgementsWe thank the Remote Sensing Laboratories, Univer¬sity of Zürich (T Kellenberger. K. Itten), the Centerfor Remote Sensing of Land Surfaces (S. Schiefer,M. Braun) and the Remote Sensing Research Group(H.-P.Thamm, G Menz), both University of Bonn, fortheir Cooperation. SEDAG has been funded by theGerman Research Foundation since 2000 (Sehr 648/1-3). N.J. Schneevoigt was supported by the GermanNational Scholarship Foundation, SEDAG and theInternational Fellowship Programme of the GottliebDaimler and Karl Benz Foundation.

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Summary: Linking geomorphic Systems theory andremote sensing. A conceptual approach to Alpinelandform detection (Reintal, Bavarian Alps,Germany)Although the global importance of high mountains is

increasingly being recognised, their geomorphic proc¬ess System has not been completely understood as yet.While Systems theory and geographical informationSystems (GIS) approaches have been long-serving inalpine geomorphology, the implementation of remotesensing (RS) tools is still rare. However, objeet-ori¬ented image analysis lends itself to alpine applications,as it unites the benefits of RS and GIS. The Systemsapproach and the objeet-oriented Classification of anASTER satellite scene with digital elevation informa¬tion are parallelized in the Reintal (Bavarian Alps). Ina hierarchical, multiscale data segmentation and Clas¬

sification, alpine landforms can be detected with highaccuracy. Hence, RS techniques represent a valuabletool for high mountain geomorphology.

Zusammenfassung: Parallelen zwischen geomorpho¬logischer Systemtheorie und Fernerkundung. Einkonzeptioneller Ansatz zur Erkennung alpinerGeoreliefformen (Reintal, Bayerische Alpen,Deutschland)Obwohl die globale Bedeutung von Hochgebirgenzunehmend erkannt wird, ist ihr geomorphologischesProzesssystem noch nicht ganz entschlüsselt. Währenddie Hochgebirgsgeomorphologie Systemtheorien undGeographische Informationssysteme (GIS) seit langemverwendet, nutzt sie Fernerkundungswerkzeuge kaum.Dabei bietet sich objektorientierte Bildinterpretationfür alpine Anwendungen an, da sie Vorteile von Fer¬

nerkundung und GIS vereint. Der systemtheoretischeAnsatz und die objektorientierte Klassifikation einerASTER-Satellitenszene mit digitalem Geländemodellwerden am Beispiel des Reintals (Bayerische Alpen)parallelisiert. Durch hierarchische, multiskalige Seg¬

mentierung und Klassifikation können Reliefformenmit hoher Genauigkeit detektiert werden. Somit stel¬

len Fernerkundungstechniken ein wertvolles Instru¬ment für die Hochgebirgsgeomorphologie dar.

Resume: Des liens entre la theorie systemiquegeomorphologique et la teledetection. Une approcheconceptuelle appliquee ä la detection des formes durelief alpin (Reintal, Alpes bavaroises, Allemagne)Bien que l'importance globale des hautes montagnessoit de plus en plus reconnue, la systematique des

processus geomorphologiques n'a pas encore ete suf¬

fisamment etudiee. Alors que les approches se basant

sur la theorie systemique et les Systemes d'informa¬tion Geographique (SIG) ont ete employees depuislongtemps en geomorphologie alpine, l'usage d'outilsde teledetection est encore peu frequent. Cependant.l'analyse d'images orientee-objet se prete aux appli¬cations alpines, puisqu'elle reunit les avantages de lateledetection et des SIG. Dans l'exemple du Reintal(Alpes bavaroises), l'approche systemique est utili-see parallelement ä une Classification orientee-objetissue d'une vue satellitaire ASTER et de l'informationnumerique de terrain. Une segmentation et une Clas¬

sification hierarchique multiscalaire permettent alorsde classifier les formes du relief avec un haut degred'exactitude. Les techniques de teledetection repre¬sentent donc un outil valable pour la recherche geo¬

morphologique alpine.

Dipl.-Geogr. Nora Jennifer Schneevoigt, RemoteSensing Laboratories, Department of Geography, Uni¬

versity of Zürich, Winterthurerstrasse 190, CH-8057Zürich, Switzerland.e-mail: [email protected]. Dr. Lothar Schrott, Department of Geographyand Geology. University of Salzburg, Hellbrunner-strasse 34, A-5020 Salzburg, Austria.e-mail: [email protected]

Manuskripteingang/received/meinuscrit entre le

30.1.2006Annahme zum Druck/accepted for publication/aeeeptepour Timpression: 26.9.2006

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Linking geomorphic Systems theory and remote A conceptual