Geomorphology 137 (2012) 74–86

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Geomorphology

j ourna l homepage: www.e lsev ie r.com/ locate /geomorph

Making riverscapes real

Patrice Carbonneau a, Mark A. Fonstad b,⁎, W. Andrew Marcus c, Stephen J. Dugdale d

a Department of Geography, University of Durham, Durham, United Kingdomb Department of Geography, Texas State University, San Marcos, TX, 78666, USAc Department of Geography, University of Oregon, Eugene, OR, 97403-1251, USAd Institute National de la Recherche Scientifique, Centre Eau Terre Environment, Quebéc, Quebéc, Canada

⁎ Corresponding author.E-mail address: mfonstad@txstate.edu (M.A. Fonstad

0169-555X/$ – see front matter © 2011 Elsevier B.V. Adoi:10.1016/j.geomorph.2010.09.030

a b s t r a c t

a r t i c l e i n f o

Article history:Received 19 March 2010Received in revised form 14 June 2010Accepted 8 September 2010Available online 28 March 2011

Keywords:Remote sensingRiversLandscape ecologyRiverine habitatGeomorphology

The structure and function of rivers have long been characterized either by: (1) qualitative models such as theRiver Continuum Concept or Serial Discontinuity Concept which paint broad descriptive portraits of how riverhabitats and communities vary, or (2) quantitative models, such as downstream hydraulic geometry, whichrely on a limited number of measurements spreadwidely throughout a river basin. In contrast, authors such asFausch et al. (2002) and Wiens (2002) proposed applying existing quantitative, spatially comprehensiveecology and landscape ecology methods to rivers. This new framework for river sciences which preservesvariability and spatial relationships is called a riverine landscape or a ‘riverscape’. Application of thisriverscape concept requires information on the spatial distribution of organism-scale habitats throughoutentire river systems.This article examines the ways in which recent technical and methodological developments can allow us toquantitatively implement and realize the riverscape concept. Using 3-cm true color aerial photos and 5-mresolution elevation data from the River Tromie, Scotland, we apply the newly developed Fluvial InformationSystemwhich integrates a suite of cutting edge, high resolution, remote sensingmethods in a spatially explicitframework. This new integrated approach allows for the extraction of primary fluvial variables such as width,depth, particle size, and elevation. From these first-order variables, we derive second-order geomorphic andhydraulic variables including velocity, stream power, Froude number and shear stress. Channel slope can beapproximated from available topographic data. Based on these first and second-order variables, we produceriverscape metrics that begin to explore how geomorphic structures may influence river habitats, includingconnectivity, patchiness of habitat, and habitat distributions. The results show a complex interplay ofgeomorphic variable and habitat patchiness that is not predicted by existing fluvial theory. Riverscapes, thus,challenge the existing understanding of how rivers structure themselves and will force development of newparadigms.

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© 2011 Elsevier B.V. All rights reserved.

1. Introduction: views of the river

“…we perceive a need to conceptualize rivers not as samplingpoints, lines, or gradients, but as spatially continuous longitudinaland lateral mosaics. As such, heterogeneity in the river landscape,or riverscape, becomes the focus of study” Fausch et al. (2002)(p. 3)

In this seminalwork, Fauschet al. (2002) argue that the temporal andspatial scales of the processes that drive stream ecology often remainunsampled in our basic methodologies. Indeed, for over a century, rivermorphology and habitat has either been sampled with highly localized,high resolution, point sampling methods (e.g. Heggenes, 1996; Buffin-Bélanger et al., 2006) or broadly spaced surveys yielding average trends

in river response at watershed scales (e.g. Leopold and Maddock, 1953;Allan and Johnson, 1997; Rice and Church, 1998). One consequence ofthis approach is that the broadly spaced averagingapproacheshavebeenthemajor sourceof input into theory. This has led to thewidely-acceptedview that the flow of water, other materials, and energy produces anenvironment that changes relatively smoothly and predictably throughspace (Vannote et al., 1980; Leopold, 1994).

The continuous view of river systems and its characteristic gradual,averaged, variation of key parameters like grain size, depth and widthhas been strongly challenged, however, by a number of authors whoargue that discontinuities and variations are key to characterizingriver systems (Ward and Stanford, 1983a,b; Fausch et al., 2002; Wardet al., 2002a,b; Wiens, 2002; Thorp et al., 2006, 2008, 2010). This bodyof literature establishes an important conceptual framework of riversas holistic systems where process scales range from small microhab-itats to entire watersheds. The need to test these concepts withquantitative data, thus, making these conceptual, highly-abstractedriverscapes ‘real’, has created the impetus to change our approach to

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sampling the physical and biological properties in river systems.Indeed, over the past decade, a growing number of researchers havedelivered high-resolution, large-scale observations of the sizes of rivergrains, depths, widths, vegetation composition and land cover clas-sification (e.g. Carbonneau et al., 2004; Verdú et al., 2005; Booth et al.,2007; Lejot et al., 2007; Feurer et al., 2008; Pavelsky and Smith, 2008;Hauet et al., 2009; Vericat et al., 2009). These variables can now bemeasured with the objective of continuously sampling small-scaleriver and/or physical habitat features such as depth and grain sizeover entire watersheds (see review by Marcus and Fonstad, 2008).

These data are now beginning to provide the evidence to support aprofound change in our continuous viewof river systems. For example,fluvial geomorphologists are beginning to use various digital terrainmodeling technologies to see a tremendous topographic diversitywithin the paradigm of downstream hydraulic geometry (Smith andPavelsky, 2008; Fonstad and Marcus, 2010). Similarly, stream (lotic)ecologists have been strongly influenced by the view of gradualdownstream changes in energy and nutrients, as exemplified by theseminal River ContinuumConcept (Vannote et al., 1980). Lotic ecology,however, now recognizes many of the same non-smooth structuresthat are seen, predicted andmodeled in landscape ecology theory; thatis, a ‘riverscape’ built mostly on patches, functional units, connectivity,and a relationship between geodiversity and biodiversity (Poff andWard, 1990; Thorp et al., 2006).

The goal of this paper is to demonstrate that recently developedmethods in fluvial remote sensing enable quantitative documentationand analysis of the ‘riverscapes’ that have been conceptually advocatedby a growing number of authors (e.g. Fausch et al., 2002;Wiens, 2002;Thorp et al., 2006). We show that we can now measure variabilityacrossmultiple scales ranging frommetric to kilometric and, thus, takethe riverscape concept from the realm of theory and into the realm ofpractice and reality. This riverscape approach allows us to testhypotheses and models belonging to the ‘smoothly-varying’ and the‘complex patchwork’ schools of thought. Furthermore, we show thatthese new approaches in remote sensing can be used to extract riverinformation of similar type and quality to those measured in the fieldand with similar levels of logistical complexity to traditional methods.Crucially, these new methods allow us to combine large-scale studyareas with high (metric) resolution data. For example, Carbonneauet al. (2005) present a grain size mapping data with metric resolutionextending the full 80 km length of the St-Marguerite river in Quebec,Canada. We, therefore, aim to show that the data generated with thisnew technology challenges classical models of how river environ-ments are structured.We argue that the acceptance andwider usage ofthese new approaches and the resulting new views of the river willmake a hugely important contribution to river sciences.

2. Riverscapes: a framework for understanding rivers

2.1. The riverscape concept

The concept of the “riverscape” dates to at least the 1960s, whenLeopold and Marchand (1968) used the term to describe the large-scale physical, biological, and aesthetic nature of rivers. The mostcommon use of the term today, originating in the early 2000s (Fauschet al., 2002; Poole, 2002; Ward et al., 2002b; Wiens, 2002) representsan ecological perspective that portrays rivers as a combination ofbroad scale trends in energy, matter, and habitat structure as well aslocal discontinuous zones and patches. The large and local extentvariations affect the movement and dynamics of biological popula-tions (Ward, 1998).

Wiens (2002) stated that theories of landscape ecology theory canaid in understanding rivers. Foundational principles that can transferto rivers include (1) patches differ in quality, (2) patch boundariesaffect flows, (3) patch context matters, (4) connectivity is critical,(5) organisms are important, and (6) scale is important. Rivers may

differ from typical terrestrial landscapes because of the very strongdirectional gradients associated with the flow of water, the resultinglongitudinal water-borne connectivity of habitat patches, and theunidirectional watershed network structure. Rivers do possess, how-ever, structured variations along the cross-stream and downstreamdirections and, in common with terrestrial landscapes, riverine eco-systems display structures which reflect the spatial organization ofhabitat patches.

In the riverscape context, a “patch” is an area of similar physicaland biological attributes from the standpoint of an individual or-ganism (Pringle et al., 1988). The scale of the organism and itsmobility thus determine the scale of what is considered a patch. Thehabitat patches of crayfish are considerably smaller than that of theSteelhead Trout. These variations in patch scale are one reason whymulti-scalar mapping systems are needed. Furthermore, the physicaland biological processes that form patches are varied. They can resultfrom geomorphology and/or hydraulics (such as in pool-riffle se-quences or sediment links, Richards et al., 2002), be partly the result ofautotrophic activity (such as in algal mats, e.g. Power and Stewart,1987), be from heterotrophic activities (such as in the geomorphiceffects of beavers and fish, e.g. Johnston and Naiman, 1990), resultfrom deliberate or inadvertent human modifications causing habitatfragmentation (as with dams, e.g. Lake, 2000; Bowen et al., 2003), andof course, the many combinations thereof. The resultant patchiness ofthe riverscape partitions resources into specific areas that affectpopulation dynamics (Pringle et al., 1988, Townsend, 1989).

The spatial relationship between patches is characterized by theconcept of habitat connectivity (see Ward, 1998), a fundamentalconsideration in (terrestrial) landscape ecology. Similarly, the con-nectivity of patches within riverscapes should be an important factorin the isolation or integration of ecological populations. In loticsystems, the downstream advection of water dominates the connec-tivity between patches in rivers (Fausch et al., 2002). Ways exist,however, in which upstream–downstream connectivity can be ham-pered or severed. Discrete hydraulic barriers such as waterfalls andhydraulic jumps can hamper the upstream–downstream movementof organisms. In the cross-stream direction, as between channels andbackwater areas, connectivity may be time-dependent and intermit-tent such as during flood-pulses. Therefore, in a manner similar toterrestrial systems, connectivity and patch dynamics in lotic systemswill be variable across space and time. Thorp et al. (2006) argue thatadapting and applying crucial terrestrial spatial ecology theories, suchas the Hierarchical Patch Dynamics model of Wu and Loucks (1995),to streams will lead to a major change in understanding of riversystems. The quantitative application of this and other models to riversystems, however, has proven difficult because of a paucity of suitabledata. Riverscapes are in desperate need of accurate mapping.

2.2. Mapping riverscapes

A notable expansion in the number of techniques for mapping andsampling rivers has occurred since the mid-1990s. Classic approaches,such as pebble counts, visual summaries of reaches, and surveys ofchannel cross-section remain valuable, but these standard techniquesare now often accompanied by segment-scale indices based on quali-tative assessment of air photos and GIS analysis (Fitzpatrick, 2001).These classic approaches, still dominant in many agencies and indus-tries, are being augmented by several new in-situ techniques. Newtechniques ofmeasuring river processes at specific locations include theincreasing use of acoustic Doppler current profiling (ADCP), and thewidespread usage of sonar-based bathymetry techniques, especially formonitoring large rivers (Parsons et al., 2005). Furthermore, terrestriallaser scanners now allow for ultra-high resolution measurements oflocal topography (Hodge et al., 2009a,b). In addition to these in-situtechniques, new techniques are also allowing measuring of mobileobjects and organisms. These ‘Lagrangian frameof reference’ (Doyle and

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Ensign, 2009) techniques include passive integrated transponder (PIT)tagging (e.g. Johnston et al., 2009), RFID— radio frequency identification(e.g. Bubb et al., 2002), and ‘GRiFTers’ formeasuring spatial distributionsof water velocity (Stockdale et al., 2008).

One of the more profound changes in riverscape science has beenthe rapid development of quantitative measurement of river environ-ments using a huge range of remote sensing technologies and plat-forms. Many of these technologies capture the topography of riverlandscapes. At the very largest extents, active spaceborne altimetryand passive optical remote sensing are being used to monitor floodsand flood waves at global scales (Alsdorf and Lettenmaier, 2003). Theproposed Surface Water Ocean Topography (SWOT) satellite wouldfurther increase the spatial resolution of active spaceborne interfer-ometric radar (IFSAR) approaches, allowing water surface elevations,slopes, and potentially discharges to be mapped worldwide (Alsdorfet al., 2003).

At finer spatial resolutions, light detection and ranging (LiDAR)technologies have markedly improved remote sensing of rivertopography. Airborne LiDAR is now a primary method for measuringriver topography over large distances at resolutions of a meter orbetter (Casas et al., 2006; Jones et al., 2007; Hilldale and Raff, 2008;Notebaert et al., 2009). Terrestrial-based LiDAR can be used over shortdistances (reach-scales) to measure high-resolution topography andestimation of particle sizes from mm-scale topographic maps (Hodgeet al., 2009a,b). A few groups have experimented with bathymetricLiDAR; a technology that uses active green wavelength LiDAR tech-nology to detect the water surface and the channel-bottom (McKeanet al., 2008, 2009a,b).

Optical imagery can also be used to map river bathymetry (Lyonand Hutchinson, 1995;Winterbottom and Gilvear, 1997; Gilvear et al.,2007), even in the absence of ground-based depth measurements(Fonstad and Marcus, 2005; Walther et al., 2011). In addition to thesenew approaches, significant advances in photogrammetric methodsnow enable very high resolution mapping of river bathymetry (Laneet al., 2010).

Awide number of techniques have also been developed formappingriver features other than topography. Thermal imagery detects areaswhere groundwater andsurface-water injection, canopies, shadowsandother factors alter the thermal regime and create a patchy habitatmosaic (Handcock et al., 2006). Passive optical remote sensing has longbeen used in river studies to detect planform changes (e.g. Gilvear et al.,1995; Winterbottom and Gilvear, 1997; Leys and Werritty, 1999) andqualitatively locate certain river surface features, such as broad habitatareas (Ward et al., 2002a). In thepast decade, however, an explosion hasoccurred in optical technologies for measuring the riverscape (Marcusand Fonstad, 2008). Ultra-high-resolution optical imagery now allowsthe extraction of particle sizes for the dry, exposed sediment of an entirechannel (Carbonneau et al., 2004; Verdú et al., 2005). Furthermore,Carbonneau et al. (2005) found that shallow submerged particle sizescould be mapped in clear water, albeit with slightly more error. Byextending the number of optical bands to multispectral and hyperspec-tral levels, optical imagery has given researchers the means to mapbiotypes (Marcus, 2002), fluvial wood (Marcus et al., 2003), and betterdiscriminate water depths and bottom types (Legleiter et al., 2004). Itmay also be possible to extend these new techniques to existing imagesto analyze historical changes in rivers, and new technologies shouldallow evermore riverscape variables to be extracted over long distancesin an increasingly automated fashion.

2.3. Contributions to river theory

Already, the application of these new technologies is shedding newlight on theories of the continuous versus discontinuous structure ofrivers. The widely-spaced measurements of rivers over the past half-centuryhavehighlighted the viewof rivers asbeingdominatedby large-scale, continuous changes as captured in the theories of downstream

hydraulic geometry and the River Continuum Concept. As higher-resolution data became available, rather than continuity at finer scales,researchers encountered discontinuities, however, as captured insedimentary links (Rice et al., 2001) or in even more pronounced andcomplex patterns that overwhelm gradually-varying downstreamtrends in rivers (Carbonneau et al., 2005; Fonstad and Marcus, 2010).Inmany respects, these discontinuities are data in search of a paradigm.The increasing volume of high resolution river data is leading us toquestion the origins of river geodiversity. Can we generalize theseorigins beyond case studies of individual rivers and, thus, establish anew paradigm for riverscape complexity? Being able to generalize inthis way might profoundly affect theory and practice in river science.

The above examples do not even address biocomplexity in rivers.While technological progress has allowed for a step-change in theability in measuring the abiotic components of riverscapes, the samecannot be said for stream biota, particularly animals. At present, wecannot map individual organisms at high-resolution and at watershed-wide scales. Yet such data or models are essential to exploring the linksbetween biological diversity and geodiversity. For example, habitatconnectivity cannot be seen as a purely physical parameter. The abilityof an organism tomove between resource patcheswill be defined by itsbasic mobility and can be further complicated by behavioral in-teractions (Tischendorf and Fahrig, 2000). Therefore, a physicalmapping of suitable habitat patches for any given organism can onlyyield partial information; biotic and behavioral knowledge is alsoessential. Progress is, therefore, needed in terms of quantifying physicalhabitats and populations in a spatially explicit riverscape framework(e.g. Le Pichon et al., 2006, 2009). One avenue for such progress is infusing detailed riverscape maps with agent-based models of organisms(Railsback and Harvey, 2002).

3. Realistic, feasible mapping of riverscapes

Our goal in “making riverscapes real” is to quantitatively implementa growing body of emerging concepts and argue for a holistic approachto river science which utilizes high resolution data throughout awatershed. To obtain results from this riverscape approach, a moveaway is needed from qualitative arm-waving and theory and towardsrigorous hypothesis testing on the basis of quantitative data. Currentlythis is difficult to do because of the extreme theory-ladenness of ob-servations, where the scale and extent of observations are tailored to fitpreconceived notions about where phenomenon will be observed.Whereas all observation is somewhat theory-laden (Brown, 1996;Rhoads and Thorn, 1996), extreme theory-ladenness leads to a situationwhere observation can lend support to a theory even if that theory isfalse because of sampling bias in favor of the theory. Several studies inthe philosophy of science and in geomorphology have warned thathaving observations too tightly bound to theory makes it more difficultfor the scientific community to falsify incorrect theory (Brown, 1996;Rhoads and Thorn, 1996; Goodwin, 1999). In river science, this con-straint was imposed historically by the difficulty (indeed, the impos-sibility) of sampling at high resolutions over basin extents. We are nowat a stage where methods and technology, however, are capable ofdelivering riverscape data with reasonable resources and logisticalrequirements. In this section, we demonstrate that riverscape data andanalysis are within the grasp of the wider river sciences community interms of data collection, processing, and analysis.

As an example, we focus on the River Tromie in Scotland. The Tromieis a small, shallow, clear water, gravel bed stream flowing in theSpeyside area of the ScottishHighlands (Fig. 1). In the fall of 2008, APEMLtd. (an environmental consultancy firm in the UK with the requiredinfrastructure for very high resolution airborne remote sensing)conducted an aerial survey of this 16 km river using a 16 megapixelcolor digital camera to acquire 193 3 cm resolution images eachcovering an area of approximately 100 m by 150 m. Each image centerhad an associated GPS point and compass heading. The airborne survey

Fig. 1. The River Tromie study area in Scotland, UK. The River Tromie is a small, shallow, gravel-bed stream flowing north through Glen Tromie in the Speyside area of the ScottishHighlands. It flows roughly 16 km from its source at Loch an t-Seilich to its confluence with the River Spey near the town of Kingussie. The left panel shows the location of GlenTromie within Britain. The right panel shows the course of the river centerline with 4 high resolution image samples. Each image has dimensions of 150 m×100 m.

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was accompanied by a ground campaign to collect calibration data. Bedmaterial was sampled with a photosieving approach according to themethods of Carbonneau et al. (2004, 2005). Differential GPSwas used toestablish the topography of the bed which was combined with mea-surements of the water surface to produce geolocated depth measure-ments according to the methods of Carbonneau et al. (2006). A DEMwith a resolution of 5 m was purchased to provide baseline elevationsand slope for the river corridor. Finally, discharge datawere obtained forthe beginning and end of the channel. In terms of costs, Dugdale et al.(2010) cite a cost of £1500 (≈2500 $US) for the acquisition of 3 cmcolor imagery over a 10 km reach. These amounts are roughly doubledwhen taking into account the processing time, software and computingfacilities needed to georeference the images, but such costs are wellwithin the budgets of many management agencies or research grants,even for rivers in excess of 100 km. The field data to accompany theairborne surveys generally represents a fewdays ofwork at or very nearthe time of flight and is, therefore, no more onerous than conventionalriver fieldwork (e.g. Carbonneau et al., 2004). We, therefore, argue thatfor very high resolution color imagery, riverscapes can be adequatelysampled within the means and constraints of standard modern rivermanagement and science.

The organization and pre-processing of remote sensing data hasbeen an obstacle to implementing new methods that capture rivermetrics over large scales. Recently, two of the authors (Dugdale andCarbonneau) in close partnership with APEM Ltd., have developedsoftware specifically for the task of managing and analyzing river-scape image datasets. This new software environment, the ‘FluvialInformation System’ (FIS), is a river-analog to GIS and operates withina MATLAB environment. The FIS integrates established fluvial remotesensing methods developed and published over the past seven yearsto manage and analyze large high resolution remotely sensed riverdata sets and extract meaningful geomorphological data from rawdata. It achieves a high level of automation and efficiency bymanagingremotely sensed data as individual, sequential, rasters.

Specifically, the FIS processing chain includes: automated imagegeoreferencing following Carbonneau et al. (2009), image classificationwhich allows for water width and depth measurements from imageryand ground data following Carbonneau et al. (2006) or Fonstad andMarcus (2005), and measurements of particle sizes following themethods of Carbonneau et al. (2004, 2005) or Dugdale et al. (2010).Most importantly, all the derived data (i.e. width, depth and grain size)are calculated from the georeferenced imagery and, therefore, retain full

geolocation. As an additional, step, the FIS produces a ‘river coordinatesystem’ (RCS). Based on Smith and McLean (1984) and Legleiter andKyriakidis (2006), this coordinate systemtakes the orthogonal grid usedin the image georeferencing (usually a Transverse Mercator projection)and transforms it into a curvilinear frame of reference fitted to the river.This new coordinate system has a downstream dimension which fol-lows the river centerline at each point, a cross-stream dimensionwhichis locally orthogonal to the centerline, and, therefore, treats the river as afully 2D ribbon flowing in the landscape in a curvilinear course. The RCSallows for spatial inquiries in a frame of reference (e.g. distancedownstream) that is directly meaningful to ecohydraulic analyses. Inthis work, the FIS is used to produce primary data from remotely sensedimages. Our aim, however, is not to imply that the FIS is a uniquesolution. On the contrary, it is hoped that other river scientists willfollow the example of the FIS and proceed to integrate a range ofappropriate analysis methods within a spatial framework specific torivers. At the time of writing, reports of such integrated tools andapproaches are exceedingly rare. Thorp et al. (2010), however, describea GIS based approach to river characterization which is not whollydissimilar to the FIS.

The River Tromie imagery was one of the key datasets used todevelop and test the FIS. The resultant data set allowedwetted widthsto be extracted at 1 m downstream intervals, grain sizes for each 1 m2,and depths at the time of image acquisition at a resolution of 1 m2. Asis discussed further in this paper, accuracy assessment for such high-resolution, large extent, data is problematic. Field data shows, how-ever, that the data for grain sizes is within previously reported valueswith an estimated precision of 15–30 mm for the grain sizes and 10–15 cm for depth.

After raw data processing, the FIS allows for the automatedextraction of the “first-order” variables, i.e., those based directly onthe imagery. Fig. 2 shows downstream plots of river width (m), cross-section average depth (m), D50 (mm), cross-sectional area (m2), andelevation (m). In these stacked graphs, the left is the downstream endand the right is the upstream end of the river. Rarely, a location willappear to have a negative or zero depth, usually because whitewaterhas returned a much brighter-than-normal brightness value.

In terms of data storage, the 16 km River Tromie complete datasetsummarized in Fig. 2 is approximately 4 gigabytes. In terms ofcomputation and labor times, the processing of the georeferencing,river coordinate system, grain size and depth maps can be done in aseries of overnight jobs. The bulk of labor time, therefore, is spent on

Fig. 2. Downstream plots of width, depth, D50 grain sizes, cross-sectional area, and elevation. The resolution of these data is one meter.

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correcting errors in the image classifications to identify the vegeta-tion, the water and the dry exposed gravel. As a result, the limitingfactor for the scale of usage of the FIS is not computation power, butthe labor time needed to correct the image classifications. Theproduction of the data in Fig. 2 required approximately 1 person/weekfor a 16 km river. Therefore, we can state that a 50 km, single threadchannel will produce approximately 12 gigabytes of data and requireapproximately three person/weeks of labor, assuming the users arealready familiar with the FIS system. A 100 km, single thread channel,will produce 25 gigabytes of data and require about six person/weeksof work. These amounts of data storage and labor are quite reasonableand single thread channel lengths up to 250 to 300 km are well withinthe means of academia and the private sector.

In total, the mapped area of the River Tromie is approximately170,000 m2. Within this area, we have near-continuous metricresolution data for width, depth and grain size. Including the imageacquisition, roughly three weeks were required to collect and pre-process this data. Using conventional methods, acquiring such adataset is simply not possible. We, therefore, argue that fluvial remotesensing methods have now reached a stage where remotely senseddata allow fundamental science questions to be examined within ariverscape framework.

4. Riverscape geomorphology

The previous section detailed the transformation of image datainto first-order hydraulic variables at very high resolutions along theentire length of the River Tromie. As we have already mentioned, themeasurement of the first-order variables can, with the knowledge of

discharge, allow estimation of other measures, the “second-order”variables, from the following basic hydraulic equations:

V = QA= ð1Þ

S =ΔzΔn

ð2Þ

F = V ffiffiffiffiffigD

p.ð3Þ

τ = ρgRS ð4Þ

Ω =ρgSQw

: ð5Þ

In Eq. (1), V is the cross-section average velocity (m/s), Q isdischarge (m3/s, external data), A is the cross-section area. In Eq. (2), Sis the slope, z (m) is the local elevation and n (m) is the downstreamdistance coordinate. In Eq. (3), F is the Froude number and g is theacceleration of gravity (m/s2). In Eq. (4), τ is shear stress, ρ is thedensity of water, R is the hydraulic depth (m) approximated here asthe cross-section depth. In Eq. (5),Ω is the stream power per unit area(W/m2) andw is thewettedwidth per unit length. These variables aredisplayed in the stacked graphs in Fig. 3. The compression of the entireriver length into the figure space may sometimes make it look as ifmore than one data point exists per cross-section, but this is an artifactof the compressed figure display; each section has only one value pervariable. Aswith thefirst-order variables, these second-order variablesshow very little continuous (or gradual) downstream trends. Instead,

Fig. 3. Downstream plots of derived measures: slope, velocity, Froude number, shear stress, and stream power per unit area.

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segment-to-segment variation dominates the mean of each variableand the amount of scatter about that mean. For example, unit streampower is both smaller on average, and its variance is smaller in thecentral segments of the River Tromie than at the upstream anddownstream ends. By themselves, these data are extremely useful intesting scientific theories of river dynamics, and, provided repeatimage surveys can be funded, they could allow for a vast improvementin the ability to monitor river channel change. For example, theeffectiveness of the downstream hydraulic geometry theory of riverforms could be directly tested by using these data to test the statisticalfit of the power laws, and whether such laws are significant predictorsat different locations and scales (Fonstad and Marcus, 2010). Fur-thermore, high resolution abiotic data of this type can be used, alongwith existing knowledge of preferred types of physical habitats for agiven organism, to define and examine the spatial distribution ofpredicted patches of habitat. Here, we proceed with a preliminaryexamination of habitat patches for juvenile Atlantic Salmon.

In Scotland, and within the wider area of the North Atlantic,Atlantic Salmon (Salmo salar) is without a doubt the most studied fishspecies. The total biomass production of this anadromous fish iscritically dependant on the juvenile stages of its lifecycle when it mustsurvive in riverine habitats. During these early life stages, AtlanticSalmon are observed to have consistent physical habitat preferences(Armstrong et al., 2003). Very broadly speaking, a higher probabilityexists of finding juvenile salmon near coarse particles, shallow waterand low flow velocity. As the first order variables of particle size andwater depth were extracted from the imagery, we can make a pre-liminary habitat analysis based on two of the three habitat preferencevariables using queries in the FIS. In accordance with the literaturecited in Armstrong et al. (2003), we define ‘suitable habitat’ for juve-

nile salmon as having depths ranging from 10 cm to 60 cm and grainsizes ranging from 25 mm to 250 mm.

We then use the FIS to query the entire longitudinal and lateralextent of the river at 1 m resolution to create a binary two dimen-sional map of physical habitat where a value of 1 indicates suitablehabitat and a value of 0 indicates un-suitable habitat. The resultingdataset has over 170,000 points which are distributed along the 2Dcurved path of the river with an average width of 20 m. Consequently,the representation of such a high resolution, large extent, 2D datasetin a concise figure format is challenging. Here, we use a slightlyabstract representation of the river wherewidths are normalized from0 to 100%. The entire river then becomes a rectangular raster wherethe vertical dimension is a normalized cross-stream distance and thehorizontal dimension is distance downstream. In this new format, thewidth scale of the river needs to be exaggerated by a factor of severalthousands relative to length in order to be visible. This simplifiedformat (Fig. 4) makes a complete view of the river much easier todisplay and communicate.

The binary classification of Fig. 4 shows that preferred habitat forjuvenile salmon in the River Tromie is arranged into a patch-likedistribution, This figure, produced from quantitative data, challenges,however, our preconceptions of what the actual shape of a ‘habitatpatch’ might look like. Whereas previous research has examined thesize, composition and connectivity of lotic habitat patches (e.g. Pringleet al., 1988; Rieman and McIntyre, 1995; Lake, 2000; Le Pichon et al.,2009; Winemiller et al., 2010), literature describing their morphologyremains largely conceptual. Very little exists to constrain the actualgeometry of these hypothetical patches. Are they roughly elliptic? Dothey extend across the channel, thus creating discontinuities whichare oriented preferentially downstream? What relationship, if any,

Fig. 4. Graph of habitat distribution for juvenile Salmon, showingwhich streamareasmeet the requirements of depth (0.1 mbdepthb0.6 m) and particle size (25 mmbD50b250 mm)for juvenile Atlantic Salmon habitat. White is habitable, black is not habitable. Lateral variability is fully preserved in the analysis but the river is reprojected to a rectangular display toallow for the entire channel to be displayed.

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can we hypothesize between the broad hydraulic and hydrologicdescriptors of a channel-hillslope system and the geometry of its lotichabitat patches?

These questions have been difficult to address in the past but, as weargue here, modern remote sensing approaches can begin to provideanswers. In the case of Fig. 4, the majority of patches can be seen toextend to a full channel width despite that we have preserved all thelateral variability in the analysis. This suggests that for a small shallowstream, or at least for the River Tromie, habitat patchiness and dis-continuity have a preferential downstream orientation. The majority ofunsuitable habitat also seems to extend across the channel albeit withsome exceptions. Visual assessment would, therefore, suggest that fishmoving in the downstream directionwould need to cross small areas ofunsuitable habitat to access additional habitat. Given the implicitconnection provided by flowing water, this might not be a significantimpediment, especially for adult fish. In the case of juvenile fish, how-ever, very short displacement distances have been reported by Crisp(2000) and Johnston et al. (2009). The work of Johnston et al. (2009) isinformative. Using advanced fish tagging and tracking methods, theseauthors have found that in autumnand earlywinter conditions, juvenilesalmonmove in a range of only a few tens of meters. This observation isconsistent with previous observations that juvenile salmon minimizemovements to preserve their energy reserve and minimize the risk ofpredation (Cunjak et al., 1998; Armstrong et al., 2003). Given the lowdisplacement capacity of juvenile salmon, it becomes important toaccurately characterize the spatial distribution of available habitat toquantify the distances a juvenile fish may have to range. The methodspresentedhere, therefore, offer thepotential todo this over entire rivers.

This can be accomplished quantitatively by further analyzing thedata from Fig. 4. For each location in Fig. 4, we can use a GIS query tomeasure the distance to the nearest suitable habitat pixel. Given thatthe cross-stream distance is normalized to 100%, cross-stream dis-tances are not metric. The resulting figure (Fig. 5), shows anotherrepresentation of the river with normalized widths and with thedistances between nearest suitable habitats as grayscale values; verybright values mean that a salmon would have to swim a long distance

Fig. 5. Graph of distance to the nearest hab

to get to a preferred habitat pixel, whereas a dark value means thatsuch a preferred area is close by. For the River Tromie, this distancemap (which is inversely related to connectivity) shows a strikingpattern: the river appears to be separated into discrete sections bytight zones extending across the channel with maximum expanses ofunsuitable habitat slightly shorter than 50 m. Additionally, severalsmall patches exist where unsuitable habitat areas have a length scaleof about 5–20 m. In terms of the potential swimming performances ofjuvenile salmon, these distances are small. Crisp (2000) reports typicalmovement distances of 100 m and Béall et al. (1994) report dispersaldistances as high as 2 km. Ultimately, these data, therefore, paints thepicture of a well connected river system where juvenile salmon couldpotentially access vast areas of habitat. Crucially, this observation issupported by quantitative evidence which encompasses microhabitatpatches at watershed extents. This analysis, however, is only based ongrain size and depth data. Information on flow velocity at matchingresolution and scales might drastically alter theses results. As such,these results should be considered as a demonstration of potentialrather than a detailed ecological analysis.

Figs. 2 to 5 represent the data in a fairly conventional manner thatmakes it is difficult to undertake a scale dependent analysis becausesome scales cannot be visualized at the resolution of the figure. Thetraditional representations do not explicitly plot features as a functionof scale and, thus, make it near-impossible to test hypotheses aboutscale dependent features, such as how habitat patches structure inriverscapes might vary as a function of spatial (and temporal?) scales.Can riverscape patterns be measured quantitatively at more than onescale simultaneously?

Several new approaches offer a potential solution. One possibilityis wavelet analysis, which measures the variance explained by cyclesof various lengths at different positions down the length of a dataset(e.g. Lane, 2007; McKean et al., 2008). One difficulty with waveletanalysis is that even normal Fourier analysis is seldom used in fluvialgeomorphology, and interpreting wavelet analyses of river data isfraught with difficulty. An alternative is the “hyperscale graph” con-cept described in Fonstad and Marcus (2010). Two variables from the

itable area, based on analysis of Fig. 4.

Fig. 6. A) Hyperscale graph of the correlation between river width and distancedownstream for the River Tromie. The correlations are from window lengths of 2 mat the bottom of the graph to 16,000 m at the top of the graph. Pearson correlationvalues are ramped from −1 in blue to +1 in red. Insets on the left give a close-upview of the small scale (0–150 m) features. B) Hyperscale graph of the correlationbetween river depth and distance downstream for the River Tromie. Resolution andextent are as in (a).

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same river are compared with one another using some type ofcomparison function, such as regression or correlation. The compar-ison of the entire river length is a comparison at the largest scalepossible. If the comparison is repeated, but restricted to a smallerpiece of the river at different locations, this represents a different scaleof measurement. In the hyperscale framework, these scale restrictionsare accomplished by windowing the river dataset with windows ofdecreasing size. The results are plotted as values of the comparison atparticular locations down the river (the X-axis), and for a particularwindow size, equivalent to a scale, (the Y-axis). Ultimately, hyper-scale graphs appear as triangles. The summit point of the triangle isthe single result we obtain whenwe compare the entire data series fortwo variables. The second topmost line in the hyperscale graph iscomposed of three points. The first point, on the left, is the result forthe comparison off all the data bar the last two points. The second(middle) point is the result for the comparison for all the data bar thefirst and last points. Finally, the third point is the result for thecomparison off all the data bar the first three points. As we movevertically downwards in the graph, towards smaller scales, smallersubsets of points (i.e. smaller scales) are tested. As we slide the testregion downstream in the data, we are in effect testing different sub-regions of the stream. Finally, the bottom line of the graph gives theresults for the comparison of all point pairs along the data, i.e. first andsecond, second and third, third and fourth, etc.

Here, we present an addition to the original concept proposed byFonstad and Marcus (2010). Given that each point of a hyperscalerepresents the result of some statistical operation such as a regressionor a correlation, we can perform a statistical test (ANOVA) to ensurethat the resulting point is statistically significant. This becomesespecially important at small scales where the subsample size reachessingle digits. For this work, we have chosen a stringent 99.5% confi-dence level. Points that fail the significance test are made completelytransparent and, thus, become invisible. All the remaining visiblepoints in our hyperscale graphs are statistically significant. Further-more, we use a presentation format for hyperscale graphs whichincludes five inset strips showing amagnified view for the small scalesranging from 0 to 150 m. These small scales are of geomorphic im-portance because we expect to see the signature of the riffle-poolsequence at these scales.

For the River Tromie, we used this hyperscale graph approach tolook at simple cross-correlations between four pairs of river variables(Figs. 6 and 7). For example, we can ask the simple question: Do riverwidth and depth display strong correlations with distance down-stream? The hyperscale graph in Fig. 6(a) shows the correlationvalues (r) for width versus distance downstream for the entire river,from a scale of 2 m (at the bottom of the graph) up to the scale of16,000 m (the entire river, at the top of the graph). Positive correla-tions are represented by red hues, with stronger reds representingstronger correlations. Negative correlations are represented by bluehues, with stronger blues representing stronger correlations. As statedpreviously, all visible correlations in Figs. 6 and 7 are statisticallysignificant at the 99.5% level.

In Fig. 6(a), relatively few points have failed the significance test.These failed points can be seen as white gaps between the colors. Aswould be expected, at larger scales where sample sizes are in excess of1000, even low correlations are statistically significant. In the insetsshowing the smaller scales, however, statistically significant correla-tions and anti-correlations occur even when sample sizes fall below 50data point pairs (i.e. 50 m since we are working with 1 m resolutiondata). This is strongevidence that the data in Fig. 6(a) are not dominatedby random noise processes. Across most scales, the general trend isthat width and distance are positively correlated in the downstreamhalf of the river (the right of the graph), and negatively correlated inthe upstream half (the left of the graph). Of importance is the onlysignificant tributary input is at approximately 3.8 km downstream.Examination of Fig. 2, however shows an obvious decrease in width

startingapproximately at 8–10 km. Thepattern of large scale correlationbetween width and depth, therefore, indicates that, in the downstreamhalf of the river,width decreases downstreamcontrary to the predictionof downstreamhydraulic geometry (DHG). This graph suggests that thiswidening feature is dominant at large scales up to 12 km. Consideringthis scale and the lack of a tributary, a few likely explanations for thislarge feature would involve local controls overriding the downstreampatterns predicted by DHG (see Wohl, 2004). For example, a change inbedmaterialmay occur, perhaps as a result of the river cutting through aQuaternary deposit or variations in the underlying geology, althoughthis is not supported by D50 data in Fig. 2. Alternatively, aclose examination of the imagery suggests that ephemeral tributaries,activated only during very high rainfall events,may have left a signaturein the data.

Fig. 7. A) Hyperscale graph of the correlation between river width and depth for theRiver Tromie. B) Hyperscale graph of the correlation between river width and grain size(D50) for the River Tromie. Resolution and extent are as in Fig. 6.

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Strong features also appear at medium scales in the two to six kmmagnitudes. In these cases, the tributary input carries a clear signature.In Fig. 6(a), a streak of positive, moderate (0.2–0.5), correlationfeatures influence scales up to nearly 7 kmwhich originates before thetributary input point at 2 km. Further downstream at the 4 km point,another strong positive feature appears. Both of these features havea paired negative correlation which occurs downstream, therefore,indicating an increase ofwidth downstream followed by a contraction.At small scales, we see alternating positive and negative correlations.Typically, each km of river has 5 or 6 correlation or anticorrelationstreaks which imply a physical spacing of 180 m–200 m. Given anaverage river width of 20 m, this is slightly in excess but very close tothe classic rule of thumb of 5–7 river widths for the spacing of the rifflepool sequence. The possible constraint on bed widening mentionedearlier could also explain the slightly higher spacing observed here.

Fig. 6(b) shows a hyperscale graph for the variables of depth anddistance. Again, few points in the graph have failed the significancetest. At very large scales upwards of 8 km, the depth responds in a

similar manner to the width. In the upper half, depth is positivelycorrelated with width. If we combine the observation with the nega-tive correlation of width seen in Fig. 6(a) for the upper half, the river isdeepening and narrowing. Conversely, in the lower half Fig. 6(b)shows negative depth correlations and positive width to distance,respectively. This indicates a channel that is widening and shallowing.At medium scales, in particular at scales of 4 km, we can see a suc-cession of deepening and shallowing reaches. The width in Fig. 6(a)has an approximately inverse response. A similar medium scalefeature also originates at roughly 12 km downstream. This is wellaway from tributaries and seems to have a paired streak of positiveand negative correlations extending to scales of three to five kms. Thisimplies a gradual increase followed by a decrease in depth; an ob-servation that does not sit well with current theories. The increase indepth downstream is contrary to the prediction of DHG and thegradually varied increase and decrease do not conform to thesedimentary links concept proposed by Rice and Church (1998),which predicts abrupt change at tributary input points, as opposed toour observed gradual change, change at tributary input points.Intriguingly, if we examine Fig. 6, we can see two similar but fainterpaired correlation–anticorrelation streaks at 12 km downstream. Thepaired streaks are in opposite order, i.e. the positive correlation isfurther upstream. This suggests a constriction which is altering thewidth depth ratio at a pinch point. Inspection of the imagery suggestsa localized bedrock outcrop.

Fig. 7 shows a second pair of hyperscale graphs for the RiverTromie and shows the correlation of width to depth (Fig. 7(a)) andwidth to grain size (Fig. 7(b)). Once again we see that few points havefailed the significance tests. At larger scales, these graphs show muchweaker correlations than Fig. 6(a). In Fig. 6(a), the distance upstreamvariable is monotonically increasing, whereas in Fig. 7(a) and (b) bothvalues vary at multiple scales and, therefore, correlations are smaller.Similarities among the figures, however, do exist. Once again, thedominant feature seems to initiate near the 8–10 km mark. Fig. 7(a)indicates that the channel is widening and deepening at this locationand Fig. 7(b) shows that it is also coarsening. In addition, two otherpoints exist with stronger correlations: one near the bedrock outcropand one near the tributary. This last point is of particular interestbecause the width and depth are anticorrelated (Fig. 7(a)). Verifica-tion with Figs. 2 and 6(a) shows that the width is reducing while thechannel is deepening. This suggests incision. The corresponding areain Fig. 7(b) shows positive correlation between width and D50 and,therefore, fining.

One crucial point is that hyperscale observations are not always inagreement with established theories. Indeed, Figs. 6 and 7 show a mixof responses. Some of these are in good agreement with establishedtheories, such as the riffle-pool sequence and the sedimentary linkconcept (Rice and Church, 1998). Much of the response in thesefigures does not correspond to any known theory or paradigm. Is thisa reflection of complex local morphologies and/or soil/bedrock pro-perties? Do we need to re-examine some of the fundamental theoriesin river sciences?

Here, we argue that high resolution data combined with multi-scalar analyses, such as presented in Figs. 6 and 7, are a promisingavenue of research. Hyperscale analyses are in their infancy. We haveshown here, however, that they have the capacity for a synopticvisualization across the full range of scales and space sampled by theremote sensing approach. Furthermore, our initial use of a simplecorrelation function makes few assumptions about the mathematicalrelationships that link fluvial variables. This allows us to re-examinetrends in river response without preconceived theoretical ideas. Thehyperscale approach concept, however, can easily be adapted to testmore precise relationships if more precise hypotheses needs to betested. A prime example of this is given in Fonstad and Marcus (2010)where hyperscale graphs are used to test DHG. One key priority for thenear future is to apply hyperscale analysis to study and compare

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multiple rivers as a function of watershed characteristics and also toassess channel change across time.

5. Discussion

High-resolution, hyperscale measurements of riverscapes raiseprofound questions about the nature of geomorphological investiga-tions. The results in the previous sections have shown that data forentire riverscape can be collected, processed, and analyzed in a shortamount of time, at low cost, and with a resolution several orders ofmagnitude beyond what is often required in fluvial studies. To ourknowledge, the data presented in this paper is the first instance ofmetric resolution width, depth and grain size data being published forthe large majority of a river. The results have showed an enormousamount of patchiness and variability in the river environment at sub-reach scales. They have also shown how continuous geomorphic andhydrologic data can be transformedwith relative ease into habitat datathat could be useful in management decisions. Future studies will beable to use these data to map or measure additional variables such aslocalized depth averaged flow velocity, fluvial wood, aquatic biotypes,connections between channels, floodplains and hyporheic zones, andprovide data for biogeochemical budgets. These advances alonewouldbe worthy of continued improvement and widespread usage, but anumber of substantive issues exist that raise the importance of criti-cally examining this approach to the measurement of rivers. From ascientific standpoint, the production of hyperscale data allows differ-ent theories to be tested at the scales for which they were originallydeveloped (Fonstad and Marcus, 2010), as well as for other scales.From a practical standpoint, the same metrics can be extracted fromhyperscale data as from conventional methods.

A range of problems and issues still need to be addressed, however,by the river remote sensing research community. The FIS and relatedapproaches are still in their infancy and we see three related areaswhich will require further research. First, the current river coordinatesystem, implemented in the FIS, cannot support braiding channels.Second, the RCS cannot support channel networks. While the FIScould manage each tributary or each channel braid as a separate river,we could not interrogate complex channel networks as a system, a keylimitation requiring further development. Third, systems like the FISideally will be integrated into standard GIS to document and analyzeinteractions between the river and its basin.

Another key problem facing the wider community is the signi-ficant paucity of biological data. In the river ecology part of riversciences, almost no data exist at the resolution and extent that we cannow collect for geomorphology and hydrology. This is particularly thecase for mobile organisms. One approach to solving this data questionmay be to encourage more use of rapid survey techniques (e.g.Bateman et al., 2005). These techniques deemphasize the levels ofmeasurement precision usually required by biologists (particularlyexperimental biologists), and instead emphasize broad coverage inmany river habitats. Tagging and tracking technology providesanother potential solution, as with PIT tags and RFIDs. Followingtagged organisms, such as individual fish, however, still requires alarge amount of personnel time. Ideas are currently developing to useUAV technology to track such tagged organisms without the need forhigh-intensity human interaction.

Nearly all of our understanding about riverscape dynamics has beenfrom the Eulerian (place-based) frame of reference; an increasing needexists for data to be collected from the Lagrangian (individual-based)frame of reference as well (Doyle and Ensign, 2009). This frame ofreference could be useful in areas such as mobile organisms, fluvialwood, and the mobility of individual sediment particles. Remotelysensed data also provide a Eulerian framework (such as water depth)withinwhichLagrangian studies (suchas taggedfish) canbe interpreted.

Another problem with the type of riverscape remote sensing dataadvocated here is the crucial issue of quality assessment and trust.

Traditionalmeasurements of the riverscape aremade at specific pointsand/or along well-defined lines, and acceptability is gauged partly bythe precision of the individual measurements. Fluvial scientists usethese precisionmeasures to establish a level of trust for each dataset. Adifficulty with hyperscale, remotely-sensed data is that its very naturedefies the use of point precision for establishing trust. For example,here we present a dataset with 170,000 measurements of depth andgrain size along with approximately 16,000 measurements of width.The total size of the dataset, therefore, exceeds 350,000 points. If wedecide to collect validation data equivalent to 1% of the data, wewouldneed 3500 validation points consisting of field based grain size, depthand width. This is more data than presented in most conventionalstudies in the literature. Moreover, to insure that the validation statis-tics are applicable to the entire dataset, this validationdatawouldneedto cover the full extent of the river. This would limit the applicability ofthese techniques to accessible (which usually implies roaded) rivers.We are not advocating ignoring error simply because the process ofdata collection is inconvenient, but we are arguing that innovation isrequired in the areas of accuracy assessment for this type of remotesensing data.

The methods we are presenting here are now well documented inthe literature and baseline information on performance is known. Forexample, Dugdale et al. (2010) present a detailed discussion of pub-lished errormargins formethods ofmapping grain sizes. Furthermore,alternative approaches to quality control are present in the literature.Lamouroux et al. (1995) found that the statistical distribution ofvelocities in a channel reach followed predictable statistical models.Suchmodels could be used to validate remotely sensedmeasurementsof water velocity once they reach a higher density than the basic cross-sectional averages presented here. Carbonneau et al. (2003) used 2Dsemivariance analysis in order to compare the statistical properties of aDEM to the expected properties of natural surfaces and to look for thepresence of random noise in the DEM generation process. Similarly,the use of a significance test in our hyperscale graphs ensures that ourresults are not dominated by noise processes. While accuracy (i.e.systematic bias) issuesmay still be present in the absolutemagnitudesof our data, the trends we are observing in Figs. 6 and 7 are not madeinsignificant by poor data precision.

Approaches that rely on existing published knowledge and geo-statistical methods to modernize procedures for error assessment are,therefore, urgently in need of development (Marcus et al., 2003). Suchapproaches could include forward modeling (Legleiter and Roberts,2009), computer simulations (Hodge, 2010), and sensitivity analysis(Fonstad and Marcus, 2005). This small body of literature detailingpossible avenues of research for a change in our error assessmentculture must be expanded. Otherwise, the potential of fluvial remotesensing approaches to understand and monitor rivers could besignificantly damaged. For example, we have several decades of aerialphotographs of rivers, some of which are wholly applicable todetermining water depth and reconstructing floodplain topography.We will never be able to point validate the level of precision of theseproducts, at least over large areas. Does that mean we should not usethese data?

In addition to the question of trust, more systematic thinking isneeded about temporal resolutions and repeated surveys. Temporalvariability is a key element in the response of rivers. To our knowledge,hyperscale surveys of the quality shown above, however, have not yetbeen repeated at multiple epochs for any river, although approacheshave been developed for visualizing hyperscale data at one location(Strandhagen et al., 2006). Therefore, hyperscale data have not yetbeen applied to channel change detection and monitoring. Given thatthe cost associated with high resolution remote sensing data is nowdropping, it is possible for amultitemporal dataset to be acquired. Suchdatasets would greatly increase our ability tomonitor and study riversat the scales that are actually of importance in geomorphology, loticecology and freshwater conservation (Fausch et al., 2002).

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Finally, currently a paucity of discussion by the geomorphic andbroader river research community is related to data ethics (Marcusand Fonstad, 2008). The type of hyperscale data in this paper has thepower to resolve vehicles and houses. In the very near future it couldquite conceivably resolve individuals, and already can resolve indi-vidual habitat niches for specific species. Privacy issues clearly arisingfrom such data and, for urbanized rivers, this could eventually becomean impediment to data acquisition. Furthermore, the type of basinwide, high-resolution details on river environments could prove con-tentious in areas where the interpretation of conservation laws isdebated. Once we are able to map where all of the fish are in a river, isthis a map that should be readily given to everyone? For example,freshwater pearl mussel (Margaritifera margaritifera) populations arean endangered across Europe and yet they are still the target ofpoachers seeking pearls (Geist, 2010). For this reason, their location isoften kept secret. Therefore, public distribution of detailed habitatmaps for this organism could have devastating consequences onfragile pearl mussel populations.

6. Conclusions

The multiscale structure of river environments raises the distinctpossibility that how we see and measure a river determines whattheories we believe are applicable to those environments (Fonstadand Marcus, 2010). In the past, our measurements of rivers have beenmostly determined by a code; that is, we make measurements in asomewhat standardized way so that these measurements could becompared clearly with those collected by other researchers. In tryingto be clear, however, this standardization has had adverse effects. Oneeffect has been the somewhat uncritical acceptance of a particularview of the river, which we term the ‘smooth’ view. In this view,changes in river geomorphology, ecology, hydrology, and hydraulicsare ‘smoothly-varying’ through space; the spatial autocorrelation ofriver forms and processes is very strong, particularly in thedownstream direction. Such a ‘smooth’ river environment could,thus, be measured by discrete observations separated by significantdistances without significant loss in information. Moreover, certaintypes of river measurements (such as cross-sections) can preservequantities useful in understanding (such as discharge continuity).These abstractions having been made, it becomes logistically easier tofocus on the relative precision of making these individual measure-ments as a means of comparing the quality of observation rather thanconstantly testing the applicability of the ‘smooth’ abstraction againstthe actual spatial structure of rivers.

While the gradually-varying view of the river still has someweightin river sciences it appears more andmore to be a theoretical ideal. Anincreasing number of authors are finding exceptions with a greaterand greater divergence from this established model. Instead of agradually-varying set of hydraulic environments, we find turbulentstructures at many scales. Instead of a smoothly-varying change ingrain sizes in the downstream direction, we see sediment links,pulses, and legacies of past events. Instead of a gradual continuum ofsubaqueous and riparian organisms, we see patches, interfaces,mosaics, and regions. Smoothly-varying average river trends exist,but they are often overwhelmed by heterogeneity. In any particularsituation, it is not immediately evident if this heterogeneity is pri-marily imposed from outside, generated internally, and/or is a recordof past history. What is evident, however, is that by skipping over thisriver complexity, we have imposed a way of seeing the river that maybe fine for some applications in some rivers (for example, large riverfloodplain modeling), but at the same time destroys our ability tointegrate other parts of our river knowledge (for example, therelationship between hydraulics and small, mobile organisms).

What is thus required is a way or ways of conceptualizing the riverthat will not automatically destroy different kinds of information atdifferent measurement scales. Such observations having been made,

we also need ways of manipulating the observations to be able toanalyze interrelationships between parts at particular scales relevantto the processes at work. In essence, what we need is a way to movefrom seeing a ‘smoothly-varying’ river with a fixed measurementmethod to approaches that allow us to see a spatially complex riverwith fluid measurements. This article has dealt with one suchapproach, based on recent advances in remote sensing and imageprocessing. Advances in sonar, terrestrial LiDAR, acoustic Dopplervelocimetry, and tagging and remote monitoring of individualorganism and objects provide other avenues for characterizing rivers.

The geospatial mapping and analysis of riverscapes is in themidst ofa revolution. Advances are progressing so fast that debates will inevi-tably occurwithin the river science community about the relativemeritsof precision, accuracy, cost, and logistics. Also, the training of riverscientists becomes entangled in these debates; how much generaliza-tion and specialization is necessary to grow employable, thoughtfulriver researchers? It is important that river scientists contribute stronglyto these debates, lest they be overshadowed by those pushing particulartechnology or methodological views. Moreover, an urgent need existsfor testable theories in the areas of river discontinuity, multiscalarstructures, and spatially distributed interconnections between geomor-phology and hydraulics, ecology, and humans.

Acknowledgments

The authors would like to thank APEM Ltd. for crucial support anddedication in the development of FIS and for their permission to use theRiver Tromie dataset. Thanks also go toAllan James,Michael Bishop, andSteve Walsh for inviting this contribution and for organizing the 41stBinghamton Geomorphology Symposium. A Durham University Inter-national Fellowship for Marcus and additional Durham Universitysupport for a remote sensing workshop in May, 2008 supported theauthors convening in Durham, England, where the article wasconceived. The manuscript benefited in its early stages from conversa-tions with Stuart Lane. We thank JosephWheaton and Carl Legleiter forextremely constructive and useful reviews.

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