Fluvial Remote Sensing for Science and Management (Carbonneau/Fluvial Remote Sensing for Science and...

9 Geosalar: Innovative RemoteSensing Methods for SpatiallyContinuous Mapping of FluvialHabitat at Riverscape Scale

Normand Bergeron1 and Patrice E. Carbonneau2

1Institut National de Recherche Scientifique, Centre Eau Terre etEnvironnement, Quebec, Canada2Department of Geography, Durham University, Science site,Durham, UK

9.1 Introduction

In 2002, a multidisciplinary group of researchers (biol-ogists, geomorphologists, engineers) member of CIRSA(Centre Interuniversitaire de Recherche sur le SaumonAtlantique) initiated the Geosalar project. The aim of thisresearch initiative funded by the GEOIDE Network Cen-tres of Excellence Program (http://www.geoide.ulaval.ca/)was to develop and apply geomatics technology to theproblem of modelling Atlantic salmon (Salmo salar) pro-duction in relation to fluvial habitat characteristics.

In the spirit of the riverscape approach fostered byFausch et al. (2002), the project proposed to developthe tools needed to increase the scope (extent of studiedarea/resolution) and spatial continuity of fish/habitat rela-tionship investigations. In their landmark paper, Fauschet al. (2002) demonstrated that the research commu-nity had so far failed to provide river managers withinformation and tools at the scale needed to efficientlyconserve stream fish populations, and they suggestedthat this gap contributed to the constant decline ofmany fish populations. Indeed, while stream managers

Fluvial Remote Sensing for Science and Management, First Edition. Edited by Patrice E. Carbonneau and Herve Piegay.© 2012 John Wiley & Sons, Ltd. Published 2012 by John Wiley & Sons, Ltd.

most-often face issues caused by large-scale anthro-pogenic disturbances of the habitat, the scientific informa-tion on which they must base their management decisionsarises mainly from studies conducted over relatively short(50–500 m) and spatially discontinuous river segments(Fausch et al., 2002). Because of their small spatial extent,such studies cannot easily account for the importanteffects of habitat heterogeneity and spatial organisationof habitat patches on fish distribution and abundance. Inhis dynamic landscape model of stream fish populationecology and life history, Schlosser (1991) emphasised therole of habitat heterogeneity in providing the varioustypes of habitat required by fish at different life stages forspawning, feeding and finding refugia from harsh envi-ronmental conditions. Because such a variety of habitattype can only be found on stream segments that are rela-tively long, studies conducted on a small-scale invariablyfail to include in their analysis all habitat components thatfish need to access in order to complete their life history.

Fausch et al. (2002) also argued that studying severalshort sample sections distributed along a river only pro-vided a fragmented view of the riverscape even when based

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on a logical statistical design. They therefore stressed theimportance of describing fluvial habitats in a continuousmanner in order to detect unique habitats (e.g. local coolwater input) or disturbance events (e.g. barrier to move-ments) at specific locations that can affect the distributionand abundance of fish over the entire riverscape.

However, they acknowledged that implementing theirapproach was a challenge due to the lack of appropriatetechnology to work at the intermediate scale (103−105 m)encompassing all necessary habitats (Figure 9.1). On theone hand, traditional field-based methods offer goodground resolution of fluvial habitat variables at the micro-habitat scale but they are labour intensive and not wellsuited to the continuous characterisation of long riversegments. On the other hand, satellite-based imageryoffer a large-scale synoptic description of entire fluvialsystems but their ground resolution is currently not suf-ficient for fine-scale habitat modelling purposes. One ofthe main focuses of the Geosalar project was therefore tofill the gap between these approaches by developing a newset of remote sensing methods allowing the productionof high-resolution spatially continuous maps of fluvialhabitat variables over long river segments. Typically, fourvariables are used to describe the physical habitat ofriverine fishes: bed material grain size, water depth, flowvelocity and water temperature. The emphasis of theGeosalar research effort was put on the quantification ofbed material grain size and water depth. Post-Geosalardevelopments later addressed the quantification of flowvelocity and water temperature. This chapter presentsthe innovative remote sensing methods that were devel-oped during the Geosalar project for the quantificationof river habitat variables over long spatially continuousriver segments. The usefulness of these methods is thenexemplified by applying them to the case of the Sainte-Marguerite River (Quebec, Canada) for the analysis ofAtlantic salmon juvenile and adult habitat.

9.2 Study area and data collection

The Geosalar research was conducted on the Principalebranch of the Sainte-Marguerite River (SMR) (48 ◦27′ N,69 ◦95′ W), a gravel-cobble bed river draining a Cana-dian Shield catchment of approximately 1000 km2 in theSaguenay region. Bed material is composed of well mixedigneous and metamorphic rocks. The lithological com-position of this mixture is stable along the channel lengthand thus no spatially dependent clast colour variationscan be observed. Suspended sediment load along the

channel is not altered by tributaries and therefore the sus-pended sediment load can be assumed as constant for thewhole channel. The SMR supports Atlantic salmon andbrook trout (Salvelinus fontinalis) populations that havebeen the subject of numerous ecological studies by CIRSAresearchers and their students since the creation of theresearch group in 1995. Field work for the various com-ponents of the project was facilitated by CIRSA’s researchstation located downstream from the confluence betweenthe main stem and North-East (1100 km2) branches ofthe SMR.

In August 2002, during the period of summer lowflow, the XEOSTM imaging system developed by GenivarInc. was fitted to a helicopter and used to obtain planview digital high resolution optical images covering theentire 80 kilometres of the main channel of the Principalebranch of the SMR. A first survey conducted at a constantaltitude of 155 m above ground resulted in a datasetcomprising of c. 5550 standard colour images with aspatial resolution of 3 cm. A second survey conducted atan elevation of 450 m above ground generated anotherset of 1600 colour images with a spatial resolution of10 cm. Image format was 3008 pixels × 1960 pixels in thestandard visible bands of red, green and blue. Images werecollected at 60% overlap. An onboard GPS provided theposition of the centre point of each image. These surveysprovided one of the first large scale hyperspatial (definedin Chapter 8) image datasets reported in the river scienceliterature. The project also supported a number of fieldefforts which provided a range of data concerning theabundance and spatial distribution of Atlantic salmon atvarious life stages.

9.3 Grain size mapping

Bed material grain size is one of the most fundamentaldescriptors of salmonid habitat. Generally speaking, thefreshwater stage of the salmon’s life cycle requires coarsesubstrate (Armstrong et al., 2003). The presence of clay,silt and sand is well established as having a negativeimpact on the survival of eggs (Sear, 1993) and juveniles(a group name for alevins, fry and parr). Consequentlythe quality of the substrate is often considered as a pri-mary indicator of the health of a salmon river. However,assessing the substrate status for an entire river has alwaysbeen problematic. Certain methods, such as the RiverHabitat Survey (RHS) protocol developed in the UK(Raven et al., 1997), employ walking surveys and visualappraisals in order to get a semi-quantitative sampling

9 Innovative Remote Sensing Methods 195

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Figure 9.1 Diagram used by Fausch et al. (2002) to illustrate the lack of appropriate conceptual models and technology to work at theintermediate segment scale (103−105 m) where information is least available. Reproduced from Fausch et al. (2002), with permissionfrom the American Institute of Biological Sciences.


of substrate quality (and other parameters) for very longreaches of a channel. However, methods such as the RHShave some key limitations. First they require more orless continuous access to the river banks. Whilst thismight be possible in many European rivers which nowflow through agricultural or urban landscapes, many keysalmon rivers of the world, notably in Scandinavia andNorth America, flow through undeveloped forested areasand, as a result, complete access to the entire channelfrom the ground is often difficult. Another limitationwith ground based approaches is that they frequently usevisual appraisal methods in order to save time in the fieldand allow for greater distances and channel lengths tobe sampled. Whilst this strategy does recognise the needto sample rivers over increasing scales as advocated byFausch et al. (2002), the resulting data is hard to reconcilewith documented habitat preferences which are collectedby manual sampling of individual clasts. Furthermore,the type of qualitative data collected by visual appraisalmethods is also incompatible with physical models ofsediment transport which could allow the prediction andvalidation of habitats distribution at catchment scales.There is therefore a clear need for substrate quantifica-tion approaches which are capable of delivering accurateand quantitative measurements of substrate with little orno field effort over riverscape scales.

Such requirements can be fulfilled by remote sensingapproaches and this section examines the contributionof the Geosalar project and others to the quantitativeassessment of sediment size with a focus on habitat.

9.3.1 Superficial sand detection

The detrimental effects of fine sediment in fluvial gravelson salmonid habitat quality are well documented (e.g.Chapman et al., 1986). During the incubation phase, ithas been shown that the presence of fine sediment withinthe gravel matrix reduces the survival rate of the eggs(Wu, 2000; Soulsby et al., 2001). Furthermore, Cunjaket al. (1998) suggested that during the juvenile life stage,sand deposition on the surface of the bed could blockaccess to the large interstitial voidspaces used by juvenilesalmon as shelter during overwintering. Clearly, from apurely visual perspective, it is relatively easy to detectthe presence of sand on the surface of a gravel barand therefore it can be deduced that an image of sanddeposited on gravels will somehow have the informationwhich allows for the identification of the sand patch.A method could therefore potentially be devised wherebyterrestrial photographs are taken in the field during a

ground survey which could then be analysed in orderto estimate sand content in a quantitative manner. Theproblem that must be solved is the manner in which theimage information can be converted to a quantitativemeasurement of sand area.

In remote sensing and image analysis terms, this is asegmentation problem requiring the delineation of a fea-ture in an image based on pixel properties. Once a featureis segmented, its type can be attributed in the processcommonly called ‘classification’. For example, a classicclassification application is the simple identification ofland-use types (e.g. forest, urban and/or agricultural) inan air photo or a satellite image. Basic segmentation andclassification approaches rely on the brightness values inthe image and assume that an object or land-use of anygiven type will most likely be of a specific colour or greylevel. For example, trees are assumed to be green, wateris assumed to be dark and sediment is generally light greywith a slight reddish hue depending on exact composition.However, in the case of sand identification, a close exam-ination of ground imagery reveals that the solution maynot be straightforward. Figure 9.2a shows a small imagecovering 20 × 20 cm of a sand patch with a few protrud-ing gravels and cobbles. Visually, the identification of theclasts in the image is quite natural. One could thereforeexpect that the frequency distribution of pixels brightnessvalues should reveal a clear bimodal distribution withone mode for the sand and a second mode for the clasts.However, the interpretation of Figure 9.2b is much moreambiguous. While this histogram does have two poorlydistinguished modes, the modal tails show considerableoverlap. Figure 9.2c shows the result of the applicationof Otsu’s segmentation algorithm (Otsu, 1979) whichwas specifically designed for bimodal histograms. It canclearly be seen that while the clast is effectively delineated,many sand particles were also delineated. This is simplydue to the fact that colour, or in this case grey level, is notthe key distinctive parameter. Sand grains having a coloursimilar to that of the clast will be falsely identified as clasts.Given that lithology exerts a dominant control on clastcolour, the presence of sand grains with the same colouras the clasts in any given river is highly likely. Carbon-neau et al. (2005b) therefore hypothesised that anotherimage property could act as a better discriminator in theclassification process.

Close observation of Figure 9.2a clearly shows that sandis characterised by its mixed colour. Grains of sharplydiffering colour are in close spatial proximity. This leadsto the suggestion that the defining feature of a patchof sand is not its average colour but the variability of


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this colour within a local spatial neighbourhood. Whilea simple metric such as local variance could be used toquantify local colour variability, the concept of imagetexture is most often employed. Image texture is definedas: ‘an attribute representing the spatial arrangement ofthe grey levels of the pixels in a region’ (Haralick andShapiro, 1985). This texture property therefore allowsthe production of a new image where the brightness ofeach pixel is in fact proportional to the texture of agiven neighbourhood instead of being proportional tothe reflected radiation. The result of this process is shownin Figure 9.2d. Here it can be noticed that the perceivedbrightness difference between the sand patch and the clasthas been greatly amplified. As a result, the histogram inFigure 9.2e shows a much greater separation betweenthe two modes. Consequently, the resulting segmentationshown in Figure 9.2f is now much less speckled and isa more accurate reflection of reality. After testing on a

range of 20 images, Carbonneau et al. (2005b) found thatthe texture based classification predicted values of sandcoverage in terms of a surface % of the whole image,with an R2 of 93% versus only 56% for traditional greylevel brightness segmentation. Therefore, this approachcan be effectively used during walkover surveys. All that isneeded is an image with a known scale, and sand coveragecan be sampled. Given that a digital picture takes secondsto acquire, this approach is very cost effective in the field.Furthermore, the approach can be combined to dubbedphotosieving methods that use terrestrial images in orderto measure particle sizes (Ibbeken and Schleyer, 1986;Dugdale et al., 2010). However it should be noted thatthis method is best used at periods of very low flow whensignificant areas of river bed are dry and exposed. In thecase of submerged bed material, the image histogram iscompressed in a manner which is similar to the shadingeffect discussed in Chapter 8 (see Figure 9.12 of that


chapter). The compressed brightness levels will reduceimage texture thereby making a clear segmentation ofsand and gravels more prone to errors.

9.3.2 Airborne grain size measurements

While the methods mentioned above are an improvementover visual surveys, they retain one key limitation oftraditional field based sampling from the ground: they donot allow for a continuous coverage at riverscape scales.The obvious solution is therefore to move towards theuse of airborne imagery. As mentioned in Chapters 2and 8, high resolution, hyperspatial imagery of fluvialenvironments carries a wealth of details. Figure 9.3 showsa hyperspatial image (spatial resolution: 3 cm) where itis relatively easy to distinguish coarse versus fine gravels(or sand) and deep versus shallow water.

This image, and others like it, leads once again tohypothesise that the information content in the imageshould allow for a quantitative measurement of bed mate-rial grain size and water depth. However, the reader shouldbe reminded that even hyperspatial imagery cannot rivalground based imagery in terms of spatial resolution. Inthe case of Figure 9.2a, the ground resolution of the imagewas 0.3 mm. The 3 cm resolution of Figure 9.3 is thereforetwo orders of magnitude coarser by comparison. Thishas important implications for any grain size mappingprocess. In the case of Figure 9.2a, the size of each pixelis similar to the size of an individual grain of sand and

much smaller than the clasts. In Figure 9.3, the pixel sizeis well within the gravel range. Consequently, individualclasts cannot be delineated in the airborne hyperspatialimagery. Therefore, photosieving methods mentionedabove, which rely on such particle delineations, will onlyfunction in the cases of particles in the cobble to boulderrange (Dugdale et al., 2010). However, textural methodssuch as that used to identify sand patches do not rely onthe delineation of the individual objects that create thetexture pattern (sometimes called ‘texels’). A key ques-tion is therefore the extent to which the texture metricsused to identify sand are in fact capable of a continuousmeasurement of the spatial distribution of particle sizes.

9.3.2.1 Dry gravel bar grain size mapping

Although fish live underwater, the determination of bedmaterial size on exposed gravel bars is important sinceat higher flows, these zones become inundated and maymake-up an important proportion of the available habitat.The quantification of bed material size from hyperspatialimagery is thus first explored for that simplest case: thatof a dry exposed gravel bar with a range of materials fromsands to cobbles. Figure 9.3 clearly allows identifyingsandy patches in the centre of the mid-channel barand coarser material can also be seen on either side.Carbonneau et al. (2004) hypothesised that image texturecan be correlated to local grain size in the image. Inorder to test this hypothesis, hyperspatial imagery of the

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Figure 9.3 Example of one the hyperspatial images of the Sainte-Marguerite River (Quebec, Canada) obtained for the Geosalarproject. This 3 cm ground resolution image was taken at an altitude of 150 m above ground.


SMR was used in conjunction with field data in orderto perform a direct empirical verification. With a sim-ple field procedure, local samples of median grain sizewere then taken in the field. These samples were pre-cisely positioned with a DGPS system which allowed theirlocation in the airborne hyperspatial imagery to be iden-tified. After experimenting with several types of imagetexture metrics, Carbonneau et al. (2004) opted for thetwo dimensional image semivariance. This is a similarmeasure to the texture discussed above. A flat uniformimage with nearly identical brightness values will have alow semivariance as well as a low texture. Inversely, animage with a strong ‘salt and pepper’ aspect where pixelsof very different brightness are in close spatial proximitywill have a high texture.

Figure 9.4 shows the result of this hypothesis test.Figure 9.4a shows the calibration relationship whichregresses the local semivariance with the grain sizeobserved in the field.

Figure 9.4b shows the results of an independent vali-dation where field based observations are tested againstthe predictions of the calibration equation. These resultswere quite encouraging since they established that theinformation in the image had the potential to yieldquantitative measurements of particle size with minimalfieldwork. Whilst fieldwork is indeed required for thecalibration of the relationship, this calibrated predictionof grain size can be applied to an entire image dataset in

a fully automated manner thus allowing for the mediangrain size of every exposed surface to be measured.

9.3.2.2 Grain size mapping along the wetted area

In most areas of river science and in lotic ecology inparticular, grain size measurements limited to dry exposedareas are not sufficient and particle size informationfor the wetted area is needed. For example, even forimages taken at summer low flow (e.g. Figure 9.3), asignificant fraction of the riverbed is underwater. Thecrucial question then becomes the extent to which theinformation content in this wetted but visible bed issufficient for particle size measurements.

In Carbonneau et al. (2005a), it was demonstrated thatin clear shallow flow situations, enough information iscontained in the images to extract particle sizes. How-ever, it was also demonstrated that the presence of thewater degraded the precision and accuracy of the grainsize mapping results. Figure 9.5 shows the results of thecalibration process in submerged areas. The calibrationequation in Figure 9.5a shows a strong correlation. How-ever, the validation equation in Figure 9.5b, with a slopeof 1.23, shows that larger particle sizes are over-predicted.This loss of data quality is not entirely surprising sincethe water interface inevitably degrades the image qualityfor submerged areas. Furthermore, the extent of thisdegradation will obviously be a function of water clarityand the exact, quantitative, relationship between water

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Figure 9.4 a) Calibration curve between the local semivariance of pixel brightness and the corresponding field measure of bedmaterial size (D50) on a dry gravel bar. b) Validation curve showing the relationship between the observed and predicted grain sizevalues. The dashed line shows the expected 1:1 relationship. From Carbonneau, P.E., Bergeron N.E., Lane, S.N. (2004),Catchment-scale mapping of surface grain size in gravel bed rivers using airborne digital imagery, Water Resources Research, 40,W07202, DOI:10.1029/2003WR002759. Copyright 2004 American Geophysical Union. Reproduced by permission of AmericanGeophysical Union.


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Figure 9.5 a) Calibration curve between the local semivariance of pixel brightness and the corresponding field measure of bedmaterial size (D50) for the wetted are of the channel. b) Validation curve showing the relationship between the observed andpredicted grain size values. The dashed line shows the expected 1:1 relationship. From Carbonneau, P.E., N. Bergeron, and S.N. Lane(2005), Automated grain size measurements from airborne remote sensing for long profile measurements of fluvial grain sizes, WaterResources Research, 41, W11426, DOI:10.1029/2005WR003994. Copyright 2005 American Geophysical Union. Reproduced bypermission of American Geophysical Union.

clarity and the quality of image-based grain size mappingoutputs is not yet known. This therefore leads to a crucialpoint about the application of remote sensing to particlesize mapping: the measurement of particle sizes for largeareas at high spatial resolutions comes at the cost of a lossof accuracy and precision for each individual measure-ment. If a single particle size measurement derived fromthe methods of Carbonneau et al. (2004) or Carbonneauet al. (2005a) is compared to those derived from groundbased methods, it will inevitably be found that the qualityof individual measurements are inferior. It is thereforeimportant to appreciate the value of these airborne meth-ods in view of the large spatial extent and high resolutionof the coverage they provide.

9.3.3 Riverscape scale grain size profile and fishdistribution

Automated airborne grain size mapping methods basedon imagery can be extended to entire channels providedsuitable imagery is available. Using the image analysismethod described above on the Geosalar image data setof the Sainte-Marguerite River, Carbonneau et al. (2005a)proceeded to show an upstream profile of median grainsizes (Figure 9.6). This profile was constructed by takingthe median of all available grain size measurements withineach 20 m reach of the entire 80 km length of the river.The only breaks in this continuous profile are near km 18

and 80 and are associated with short periods of cameramalfunctions where no images were collected.

9.3.4 Limitations of airborne grainsize mapping

As mentioned previously, grain size mapping methodsbased on airborne imagery generate data whose quality issomewhat lesser than ground based methods. At the out-set, users interested in the application of this technologymust realise that these methods do not measure the sizeof each visible clast with millimetric accuracy. Instead,they measure the median diameter of a patch of gravel(usually 1 m2) with precisions in the area of ±10–30 mm.Another key limitation of such image based methods isthe requirement that the patch of clasts be visible in theimagery. This precludes any size measurements below theexposed surface of the gravel layer. Furthermore, in thecase of the wetted perimeter, the feasibility and resultingquality of grain size mapping process is heavily dependenton the clarity of the water. Currently, only Carbonneauet al. (2005a) and Carbonneau et al. (2012) have pub-lished remotely sensed grain size values in the wettedperimeter. Therefore, it is still difficult to empiricallydefine the required threshold of water clarity. However,as a rule of thumb, a simple visual appraisal is suggested:if the river bed cannot be visually seen from the banks orfrom a bridge due to high turbidity, then conventional


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Figure 9.6 Upstream profile of airborne grain size measurements (D50) versus upstream distance along the main stem of theSte-Marguerite River (Quebec, Canada). From Carbonneau, P.E., N. Bergeron, and S.N. Lane (2005), Automated grain sizemeasurements from airborne remote sensing for long profile measurements of fluvial grain sizes, Water Resources Research, 41,W11426, DOI:10.1029/2005WR003994. Copyright 2005 American Geophysical Union. Reproduced by permission of AmericanGeophysical Union.

colour photography will not do any better. In terms ofdata acquisition, this clearly indicates that images mustbe acquired during periods of low rainfall, turbidity andwater discharge.

Another limitation of the grain size mapping methodsdeveloped during the Geosalar project is the need forfield calibration data. While the required fieldwork isgenerally not onerous, this does increase the final costand limit the method to accessible rivers. However, afew authors have sought to lift the need for calibrationdata and there has been significant progress in this area.Dugdale et al. (2010) have developed a method wherebythe grain size mapping algorithm is calibrated by directon-screen measurements. This approach, dubbed ‘AerialPhotosieving’, therefore mimics traditional ground-basedphotosieving methods. The method relies on hyperspatialimagery. With this imagery, the user measures the b-axis(intermediate axis) of the coarse clasts which are visible inthe image. This measurement is done directly on-screenand requires no fieldwork provided that the scale of theimage (i.e. the pixel size) is accurately known. This useof on-screen data thus enables the grain size mappingprocess to be applied to areas which are inaccessible orto archival imagery. However, the method was foundto result in a slight systematic overestimation of sizesand relies on the presence of coarse clasts that can bedistinguished in the image (approximately larger than2–4 pixels). Furthermore, significant progress has beenmade by other authors working in coastal environments.Buscombe and Masselink (2009) and Buscombe et al.(2010) have developed a method based on Fourier analysiswhich can derive particle sizes without any calibration

from field or on-screen data. This method has beensuccessfully applied to coastal environments and showsmuch promise for river environments.

For most river sciences applications, it is suggestedthat the advantages offered by the airborne grain sizemapping methods far outweigh their limitations. Indeed,in the Sainte-Marguerite River study case, the methodallowed over 3 million bed material size measurementto be obtained over the entire 80 km-long studied riversection. Clearly, such a large volume of data points wouldbe impossible to collect with any other methods. Longawaited for by stream ecologists, such a level of habitatdescription can now start being incorporated into newstudy designs that will help improve the understandingand modelling of fish/habitat interactions.

9.3.5 Example of application of grain sizemaps and long profiles to salmonhabitat modelling

Figure 9.7 shows an example of the unique map productthat can be obtained by combining the automated air-borne methods of grain size measurements for both thewet and dry portions of the channel.

Despite some loss of accuracy and precision comparedto ground based methods, such an image provides a high-resolution, synoptic description of grain size over theentire image. Moreover, the process can be quickly andeasily reproduced for the hundreds of images necessaryto cover the entire riverscape.

As part of the Geosalar project, Hedger et al. (2006)showed how such grain size maps could be used toimprove the prediction of juvenile Atlantic salmon density


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on the Ste-Marguerite River. Using historical fry and parrdensity data obtained from 1997 to 2004 at 48 parcels(5 m × 20 m) distributed along the river, they derivedsubstrate preference models using substrate size (D50)measurements obtained, 1) directly inside the parcel attime of density estimation using the traditional Wolmancount method and 2) inside the larger grain size mapof the image including the fishing parcel obtained usingthe automated airborne grain size mapping methods.They showed that, although the shape of the relationshipsbetween juvenile salmon density and D50 were similarfor the two models, the relationship was stronger usingmean image D50, suggesting that the habitat surroundingthe location of the fishing parcel had a direct effect onfish density. Clearly, this example shows that one benefitof automated methods of grain size measurements is toallow multi-scale analysis of fish habitat relationships, apossibility that would be labour intensive using traditionalground based methods.

The grain size profile information obtained from theautomated grain sizing methods allowed the identificationof distinct sequences of downstream grain size finingalong the Sainte-Marguerite River. Indeed, it was observedthat, rather than exhibiting a single longitudinal decreaseof grain size from headwater to mouth, the river could besegmented into a number of discrete sedimentary links,each characterised by a node of coarse sediment supplyfollowed by a gradual downstream fining of substrate.The sedimentary link concept was originally developedfor high mountain river environments where the supplyof coarse sediment is mainly related to tributary inputs,valley-side landslides and tributary fan contacts (Riceand Church, 1998). However, using the Geosalar grain

size data set, Davey and Lapointe (2007) adapted andextended the original concept to account for sedimentarylinks of lower mountain landscapes of North EasternCanada where coarse sediment inputs are often relatedto supply zones (rather than point sources or nodes)originating in bedrock canyon reaches or valley bottomdeposits of glacial drift.

Because the downstream changes in substrate and asso-ciated slope along sedimentary links are accompanied bychanges in channel morphology and hydraulics, theycreate a longitudinal sequence of aquatic habitat typesmoving from steep, fast flowing and turbulent boulderbed channels at the head of links to meandering, slow-flowing, low-gradient sand channels at the downstreamend. Rice et al. (2001) were the first to demonstratethe usefulness of sedimentary links for ecological mod-elling by applying the concept to explain the longitudinalstructuration of benthic macroinvertebrate communi-ties. Davey and Lapointe (2007) then showed how suchinformation on the large-scale variations of substratesize could help understand the spatial organisation ofAtlantic salmon spawning habitat. Studying the locationof Atlantic salmon (Salmo salar) spawning sites alongeight sedimentary links of the Sainte-Marguerite River,they found that the centroıd of the spawning sites on eachlink tended to occur towards the middle to downstreamend of the cobble-gravel fining sequence (Figure 9.8b).Within these zones, D50 was always in the suitable rangeof 40–60 mm. Higher upstream, bed material size was toocoarse to allow female fish to dig their redds. Below, theabsence of spawning activity was probably related to poorembryo survival associated with the high percentage ofsand in riffle substrates.

Similar progress in the understanding of the large-scale pattern of juvenile salmon spatial distribution wasobtained by analysing the spatial correspondence betweensemi-continuous measures of parr density and grainsize variations of the sedimentary links of the Sainte-Marguerite River (Figure 9.8a). Clearly, peaks in parrdensity correspond to the heads of sedimentary linkswhere boulder-rich reaches offer both good summerfeeding habitats and abundant bed interstices providingwinter shelters to juveniles.

These two examples demonstrate that the grain sizeinformation derived from high resolution images of riverscan help quickly identify ‘hot spots’ in the production ofsalmonid within the riverscape, even for rivers whereonly few or sparse fish or habitat information is currentlyavailable.


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Figure 9.8 Illustration of the spatial correspondence between the sedimentary link structure of the Sainte-Marguerite River anda) the distribution of Atlantic salmon parr densities (Data extracted from Bouchard and Boisclair (2008)) b) the centroıd ofspawning sites (blue stars), on each sedimentary link. Adapted from Davey and Lapointe, 2007.

9.4 Bathymetry mapping

Juvenile salmon also express a strong preference for rela-tively shallow flows and therefore water depth is anotherimportant habitat descriptor (Armstrong et al., 2003).However, in this case, there is a wealth of literature on thesubject and much detailed literature (see Chapter 3 and(Legleiter et al., 2009; Legleiter et al., 2011). One classicapproach is to establish an empirical calibration betweengeolocated depth measurements and image brightnessvalues according to the method proposed by Lyzenga(1978). In this part of the Geosalar project, a Real-TimeKinematic (RTK) GPS was used to efficiently collectover 1000 geolocated depth measurements. However,this empirical approach was initially developed for largerwater bodies sampled with coarse resolution imagery. As aresult, its application to hyperspatial image data sets posedcertain specific problems which were addressed during the

project. The key issue was found to relate to the numberof images in hyperspatial data sets and to the stability ofthe lighting conditions. In the case of the hyperspatialimage dataset for the Ste-Marguerite river, the acqui-sition of 5550 images over 80 kilometres meant thatlighting conditions slightly changed over the duration ofthe flight (see the discussion on radiometric normalisationin Chapter 8). This led to a challenging condition where itwas impossible to calibrate the depth mapping process forall 5550 images. For example, Figure 9.9 shows calibra-tion and validation relationships of depth versus imagebrightness in the case of raw imagery where radiometricnormalisation is a significant issue. These relationships,from Carbonneau et al. (2006), were created from groundpoints which span three separate images. An effort wasmade to select an area which was thought to be repre-sentative of the river. This area contained a mid-channelbar with a typical fining structure going from cobblesto sparse sandy patches. The bathymetry of the area was


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epth

[m]

y = 109exp(−0.59x)

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(a)

(b)

Figure 9.9 a) Calibration relationship of field-measured depth values versus image brightness in the red band with regressionequation allowing for the prediction of depth from image brightness values. Three parallel bands can be seen which correspond tothree slightly different levels of base illuminations in the imagery b) Validation relationship testing the predictions of the calibrationequation versus additional, independent, field data. From Carbonneau, P. E., Lane, S. N., and Bergeron, N. 2006. Feature based imageprocessing methods applied to bathymetric measurements from airborne remote sensing in fluvial environments. Earth SurfaceProcesses and Landforms, 31(11), 1413–1423. Reproduced from Carbonneau et al. (2006), with permission from Wiley-Blackwell.


gradually varied and no significant over-arching vege-tation was present. Given the lack of uniformity in theillumination conditions, this has led to three differentcalibration and validation relationships which are clearlyvisible in the figure. However, Carbonneau et al. (2006)noted that these three relationships were parallel to eachother suggesting that the optical attenuation of the light asit passes through the water column occurred at the samerate in all three images. Therefore, what was required wassimply a re-adjustment of the base illumination in eachimage. The complete solution involved an adaptive pro-cedure whereby portions of dry exposed sediments wereused to establish a wet/dry interface (Carbonneau et al.,2006). Immediately adjacent to the dry portion of thisinterface, the water depth could be assumed to be zero.This feature-based property allowed for an adaptive recal-ibration of the image brightness which minimised driftin the depth measurements artificially caused by changesin the illumination of the imagery. This recalibration wasapplicable to the whole image dataset.

Figure 9.10 shows the result of this correction proce-dure when applied to the data in Figure 9.9. The threeparallel relationships have now collapsed into a singlerelationship which makes the process of depth mappingreliable. Figure 9.11 shows a typical result of the finaliseddepth mapping process when applied to an image. It canbe seen that depth was set to zero at the interface of thegravel bar and then increases smoothly up to a seeminglyconstant, saturated, value of 1.5 m. This saturation valueis another key limiting factor in such approaches. Unsur-prisingly, the success and feasibility of depth mappingfrom standard colour imagery depends on the visibility ofthe river bed in the imagery which is dependent on waterclarity. It is therefore not uncommon to observe a satu-ration depth below which the riverbed is not visible anddepth mapping cannot operate. In the case of Figure 9.10,this depth was found to be roughly 1.5 m. Given thatwater clarity conditions can be highly variable, there isno fixed value for this saturation depth. Even for a givenriver, changes in the suspended sediment load will leadto large changes of water clarity. Therefore, the empiricaldepth mapping described in Carbonneau et al. (2006) isonly valid for depth data collected on the day of imageacquisition.

The problem of modelling river bathymetry from imagedata remains a complex one. Legleiter and Roberts (2009)have used computer simulations in order to thoroughlyexamine the limitations and weaknesses of this approach.They conclude that one key parameter which couldimprove image-based bathymetry mapping is improvedradiometric resolution (see Chapter 1 for a definition).

Unfortunately this seems to go against the current trendsin the instrumentation used for Fluvial Remote Sens-ing (FRS). Rather than using bespoke imaging equip-ment, FRS is increasingly using standard photographyequipment. The work of Legleiter and Roberts (2009)is a clear indicator that the FRS community may soonhave to consider a move towards more advanced sen-sors more suited to the specific requirements of riverineenvironments.

9.5 Further developments in the wakeof the Geosalar project

Because of the momentum it generated, the Geosalarproject continued to stimulate research and developmentafter its completion in 2008. Issues related to the man-agement and visualisation of the extraordinary volume ofdata generated by the new remote sensing methods weremore specifically addressed. Work leading to the devel-opment of in-house airborne image acquisition systemswas also conducted.

9.5.1 Integrating fluvial remote sensing methods

The Geosalar project delivered one of the largest hyper-spatial image databases currently available. Additionally,it prompted the development of some important FRSmethods which were needed to analyse the images. Bothinside and outside of the Geosalar project, the significantrecent progress in fluvial remote sensing methods (seeMarcus and Fonstad, 2010) has generally focused on sin-gle papers presenting single methods. There is currently asevere paucity of papers which analyse the riverine envi-ronment with a range of integrated hyperspatial remotesensing approaches. However, the Geosalar project pro-vided a needs-driven impetus to the development of anintegrated interface which could allow users to manip-ulate and, crucially, analyse the large volume of data inthe image database. In 2003–2004, a prototype FluvialGeographic Information System (FGIS) was developedby Geosalar researchers. The goal of this system was pre-cisely the integration of the depth and grain size mappingmethods along with automated channel width measure-ment done directly from the images. This early prototypesuccessfully produced the data seen in Figure 9.7. How-ever, one of the key limitations of the FGIS prototypewas georeferencing of the image data. Georeferencingcan be defined as the process whereby an image raster ismapped to real world coordinates. This process is crucialin order to preserve the spatial relationships (e.g. the


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(a)

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Figure 9.10 a) Final calibration curve between red band brightness and water depth after application of the correction procedure.The three distinct bands seen in Figure 9.9 have merged into 1 after the correction procedure, b) Final validation relationship testingthe predictions of the calibration equation versus additional, independent, field data. From Carbonneau, P. E., Lane, S. N., andBergeron, N. 2006. Feature based image processing methods applied to bathymetric measurements from airborne remote sensing influvial environments. Earth Surface Processes and Landforms, 31(11), 1413–1423. Reproduced from Carbonneau et al. (2006), withpermission from Wiley-Blackwell.


1.0

> 1.5

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Figure 9.11 Example of a bathymetric map obtained from the analysis of an hyperspatial image using the red band brightness versuswater depth relationship of Figure 9.10a.

downstream distance) between objects and features indifferent images. In the case of the Geosalar hyperspatialimage database, the coarser imagery with a spatial resolu-tion of 10 cm was manually georeferenced over the courseof the (entire) summer following image acquisition. Thisprocess rested on ground control points collected dur-ing an intensive field campaign. Unfortunately, this fieldeffort only produced sufficient field data in order to geo-reference the coarser imagery (two ground control pointsper image). It was found that manual georeferencingof the c. 5500 3 cm images was too labour intensive tobe feasible. As a result, the early FGIS functioned with-out explicit georeferencing information. In the FGIS, theinter-image spatial relationships were estimated basedon the known overlap between successive images andthe known dimensions of each pixel. In essence, theFGIS measured distances downstream by counting pixelsand accounting for image overlaps whilst this processwas functional, it likely induced some error because itapproximated that, in the short 90 m span of a singleimage, the channel was straight.

Recently, theFGISwas takenbeyondtheprototypestagefor applications in commercial settings. A new system wasdeveloped and dubbed simply the ‘Fluvial InformationSystem’ (FIS). The FIS maintained the initial FGIS objec-tive of integrating a range of remote sensing methods.However, the FIS benefited from recent developments inthefieldofgeoreferencing. Asdiscussed inChapter8of thisvolume,Carbonneauet al. (2010)developedanautomatedgeoreferencing approach which was much better suitedto large hyperspatial image databases. Furthermore, the

FIS implemented a River Coordinate System as describedby Legleiter and Kyriakidis (2006) in order to produce aunique, locally orthogonal, coordinate system which givesthe position [s,n] of any point in the river as a combinationof distance downstream [s] and distance across stream[n]. With this coordinate system in place, the FIS thenintegrates a range of remote sensing methods which arewell established in the literature (e.g. Carbonneau et al.,2004; Fonstad and Marcus, 2005; Carbonneau et al., 2006;Legleiter and Kyriakidis, 2006). Carbonneau et al. (2012)successfully applied the FIS in order to demonstrate thatFRS technology is now at the point where it can contributeto the broad range of river sciences. These authors showedthat hyperspatial image data can be successfully acquired,managed, processed and analysed in order to producemeaningful habitat parameters at sub-metric resolutionsfor an entire river in the Scottish highlands. In addition tothe data presented in Carbonneau et al. (2012), the FIS isalso capable of producing innovative habitat visualisationsas discussed below.

9.5.2 Habitat data visualisation

The availability of continuous maps for two key habitatparameters (water depth and bed material grain size) openup important new avenues in terms of habitat mapping.In line with the recommendations of Fausch et al. (2002),it is now possible to examine the spatial distribution offish in a spatially explicit description of fluvial habitatat the riverscape scale with metric resolution. However,


the representation and usage of this data poses onceagain new challenges. The depth map and grain size mapoutputs of the Geosalar project represent over 3 millionmeasurements spread over an 80 km channel with anaverage width of 22 m. Visual representations of such adataset are problematic. Since the width of the channel isless than 1% of its length, any scaled map representationswill lose all the detail in the representation. For thisreason, Carbonneau et al. (2012) use a new synoptic,abstract, representation of river channels where the entirechannel is represented as a rectangular field where widthsare presented in a normalised manner. The main goal ofthis visualisation strategy is to achieve a synoptic view ofa given parameter which preserves both downstream andcross-stream variability. The cross section data are firstreprojected to a constant width of 100 pixels. Experiencehas shown that for small rivers, 100 pixels preserves lateralfeatures (albeit with a loss of scale due to the reprojection).Second, successive cross-sections are concatenated (i.e.stacked) in the horizontal direction. This transforms theriver into a rectangular array where each vertical columnrepresents one cross section and downstream distanceis given horizontally. This abstract river visualisationallows for the display and viewing of the entire river ina single compact figure. In essence, this new synopticview straightens the channel and widens the width toallow lateral patterns to become visible. While scaling andshape are lost due to the strong cross-stream exaggerationand the elimination of curvature, patterns of variabilityin both the cross stream and downstream direction cannow be observed. Figure 9.12 gives an example. On theleft panel, an 11 km stretch of the SMR is represented asa rectangle where the horizontal scale gives the distanceupstream in kms and the vertical scale gives the channelwidths as a normalised percentage. The colour scheme inFigure 9.12 is designed to give ‘at a glance’ information

on habitat suitability. Here the depth and grain size datawere combined to produce a dichromatic range of coloursranging from yellow to blue. Simply put, yellow areas inFigure 9.9 represent good juvenile salmon habitat whilstdark, white and/or blue areas are unsuitable.

9.5.3 Development of in-house airborneimaging capabilities

While the airborne optical images of the Geosalar projectwere contracted to a private firm, the need to bettercontrol both the cost and parameters of the imagesstimulated researchers to develop their own airborneimaging capabilities.

At INRS, Normand Bergeron’s laboratory developeda helicopter-based imaging system capable of acquiringhigh resolution images over hundreds of kilometres ofriver (Figure 9.13). The system integrates a FLIR SC660thermal imaging camera (0.3 megapixels) and CanonEOS 550D digital SLR (18.7 megapixels) with a precisehardware triggering system in order to acquire thermaland optical imagery at 20 cm and 3 cm resolution respec-tively (from 300 m altitude). The two cameras are fixedto a pan-tilt unit allowing the operator to ‘frame’ theimage from inside the helicopter, thereby aiding imageacquisition quality. The system is mounted within a heli-pod, so that it can be used with any suitably licensed andequipped helicopter operator. The helicopter’s GPS posi-tion and attitude is logged using specialised Matlab codeand stored alongside the images on a laptop computer.This acquisition system allows imagery to be obtainedrapidly (i.e. 40–50 river kilometres per hour), and ata fraction of the cost normally associated with similarremote sensing work.

This platform was recently successfully used toobtain a continuous coverage of more than 500 km of

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Figure 9.12 Dichromatic plot of D50 and depth. This plot represents an 11 km stretch of the St-Marguerite. Here river width isnormalised from 0 to 100% and successive cross sections are stacked horizontally in order to achieve an abstract, synopticrepresentation of this entire reach which allows us to conserve lateral variability as well as downstream variability. The colour key onthe right gives the combined values for depth and grain size at any given point.


(a) (b) (c)

Figure 9.13 Photographs of helicopter-based image acquisition system. a) Helipod attached to helicopter. b) Interior of the helipodcontaining the imaging system. c) FLIR SC660 (thermal) and Canon EOS 550D mounted on pan-tilt.

(a) (b)

Figure 9.14 a) Optical (3 cm spatial resolution) and b). Thermal infrared (TIR) (20 cm spatial resolution) imagery of the samesection of the Milnikek river in the Gaspesie region (Quebec, Canada). On the TIR image, the darker blue section on the rightcorresponds to a cold thermal anomaly created by the input of a small groundwater fed tributary.

the Matapedia River (Quebec, Canada) and of all itstributaries accessible to Atlantic salmon (Figure 9.14).This work demonstrates that producing spatiallycontinuous maps of fluvial habitat over entire riverscapesis now at hand.

9.6 Flow velocity: mapping ormodelling?

While flow velocity constitutes one of the most importantfluvial habitat variables, its measurement at riverscapescale probably represents the major remaining challengein terms of habitat mapping. Although large-scale par-ticle image velocimetry (LSPIV) can be used to obtaindetailed surface flow velocity fields from shore-basedvideo imagery of short river reaches (see Chapter 16),its application to longer river segment appears imprac-tical. Fujita and Hino (2003) demonstrated that LSPIV

measurement of velocity fields from helicopter-basedimagery is possible by stabilising the images from themoving helicopter using known coordinates of fixedpoints on the image. Although the possibility of repeatingthis procedure at several contiguous locations along theriver seems feasible, no one has yet attempted to do so.Another potential avenue which shows promise is along-track interferometric SAR from high resolution satelliteplatforms. Romeiser et al. (2007) have demonstrated thatTerraSAR-X data can be used to calculate surface veloci-ties for large to medium rivers. The TerraSAR-X satelliteis a synthetic aperture radar sensor/platform. Romeiseret al. (2007) use a Doppler effect in order to deduce sur-face flow velocity. Since TerraSAR-X data has a spatialresolution of 3 m, this method cannot be applied to riversless than a few tens of meters wide.

An alternative approach which was made possible byGeosalar innovations is the direct estimation of flowvelocities based on known discharge and image-based


depth and grain size measurements. In traditional flowmodelling methods, water depth and bed roughness areiteratively solved based on flow and continuity (i.e. con-servation of mass) equations. However many researchershave considered the problem of the hydraulic roughnessexerted by the bed and its relationship to flow depth andbed particle size. For example, Richards (1982) present aclassic form (equation 9.1) of the ‘law of the wall’ wherethe logarithmic velocity profile is integrated over the flowdepth in order to give an equation where dimensionlessvelocity (mean column velocity V/shear velocity u∗) iscalculated as a function of water depth, H, and D65 (the65th percentile of the local grain size distribution). Suchequations have long been difficult to apply in flow mod-elling because the detailed maps of depth and grain sizewere simply not available.

V

u∗= 5.75 log

(H

D65

)+ 6 (9.1)

With the grain size and depth data discussed earlier in thischapter, and with a simple continuity assumption thatin a given river cross-section, the discharge will partitionitself laterally in proportion to the depth and rough-ness, it is possible to make estimates of local velocities.Figure 9.15 shows an example of such velocity estimatesfor a 1 km stretch of the St-Marguerite River using therectified projection where the river is represented as arectangle. Figure 9.15 provides reasonable estimates ofvelocity which quite clearly captures the fast flowing areasassociated with flow constriction and the slow flowingareas associated with an increase in cross-sectional area.Clearly such a simplified estimation of velocity will not beas precise or accurate as a fully calibrated and validatedcomputer fluid dynamics model. However, this lowerprecision is offset by the potential to function over largeareas and even entire rivers. Furthermore, the type of data

presented in Figure 9.15 is well suited to juvenile salmonidhabitat models since they can detect areas of slow flowwhich are well known to be preferred by juvenile salmon(Armstrong et al., 2003).

The use of precision as the standard quality met-ric in this case is inappropriate. An alternate validationapproach can be found in geostatistical methods. In suchmethods, the quality of a dataset is estimated not by thelikely error of a single point but by the overall properties ofthe dataset. For example, Carbonneau et al. (2003) usedthe scaling properties of photogrammetrically deriveddigital elevation models (DEMs) as part of the qual-ity assessment procedure. The scaling properties of theDEM were compared to the established scaling prop-erties of natural surfaces in order to demonstrate thatthe DEMs were not dominated by error (which also hasclear scaling properties). A similar approach is applicableto the simplified velocity estimation method presentedabove. Lamouroux et al. (1995) present an alternativetype of velocity prediction. These authors show that withthe readily available reach scale parameters of discharge,mean roughness, mean depth and mean width, the shapeof the velocity distribution can be accurately predicted.Rather than predicting single point, localised, velocities,Lamouroux et al. (1995) show that we can predict theprobability distribution of velocities.

The approach of Lamouroux et al. (1995) was there-fore used to validate the data presented in Figure 9.15.The required discharge data was obtained. Mean depth,mean roughness and mean width were derived fromthe image data and from the image processing methodsdescribed above. When applied to the model of Lam-ouroux et al. (1995), this resulted in a prediction envelopeshown in Figure 9.16. This figure shows the prediction ofLamouroux et al. (1995), in green, for the reach inFigure 9.15 overlain on the actual calculated velocity

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Figure 9.15 Flow velocity (V) map estimated from bed material grain size, discharge and water depth for a 1000 m reach of theSainte-Marguerite River (Quebec, Canada).


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Figure 9.16 Velocity validation. This figure shows, in blue,the distribution of the Normalised velocity (V/u∗)(see Equation 9.1) for the data presented in Figure 9.15. Ingreen, we show the velocity distributions as predicted by themethods of Lamouroux et al. (1995). Both distributions have aconsistent shape.

distributions in Figure 9.15. Overall, the distributions aresimilar which indicates that the direct velocity estimationapproach described here could potentially provide a valu-able contribution to catchment scale velocity estimations.

9.7 Future work: Integrating fishexploitation of the riverscape

Now that new remote sensing methods are available todescribe the riverscape at metric resolution, we suggestthat the necessary next step is to provide biological infor-mation at similar scope on how fish exploit the mosaicof habitats comprising the riverscape. While catch perunit effort fish sampling techniques such as electrofish-ing or snorkelling can reveal population-level patternsof fish/habitat relationships, they cannot help understandthe processes causing the fish movements that are creatingthese patterns.

We therefore suggest that more progress will be madeby aiming to document how individual fish move acrossthe riverscape throughout their life time in order to accessthe various habitats they need at different life stagesto complete their life-cycle. The well-known distinctionin fish spatial behaviour between ‘stayers’ and ‘movers’exemplifies that given a set of habitat conditions, differentfish will exploit the habitat differently (Grant and Noakes,1987). However, it appears that the more individual fish

are tracked in natural environments, the more researchersare discovering new spatial behaviour corresponding todifferent tactics of habitat exploitation by fish.

For example, Bujold (2010) recently used stationarypassive integrated transponder (PIT) systems to providecontinuous remote monitoring of the longitudinal move-ment of PIT-tagged juvenile salmonids along a 2.5 km-long section of a small tributary of the Sainte-MargueriteRiver (Quebec, Canada). This approach allowed the iden-tification of a new type of spatial behaviour characterisingfish that were called ‘commuters’ because of their ten-dency to enter the tributary at sunrise, travel upstreamoften as far as 2.5 km, and return to the Sainte-MargueriteRiver before sunset. This peculiar spatial behaviourapplied to 16% of the PIT-tagged individuals, a grouptoo large to be ignored when investigating fish/habitatrelationships.

Similarly interesting results were obtained by Roy, M.(unpublished data, Department of geography, Universityof Montreal.) in a study of juvenile Atlantic salmonindividual mobility using a large array of passiveintegrated transponder antennas buried in the bed ofa natural stream. This system, described in details inJohnston et al. (2009), allowed the continuous remotemonitoring of PIT-tagged fish locations at high spatialand temporal frequency, from which four types of dailybehaviour were identified: stationary, sedentary (lowmobility), floater (frequent movements in restrictedhome range) and explorer (movements across the reach).The surprising result is that most individual fish werefound to exhibit all types of daily behaviour during thestudy period, thus challenging the traditional descriptionof a population composed of fractions of sedentary andmobile individuals.

The studies described above are examples amongstothers suggesting that how fish exploit the habitat mosaicof the riverscape is probably far from being a resolvedissue. More research is thus required in order to assessthe geographic path of fish moving from one habitat tothe other throughout their lifetime. Such informationwill allow predictions to be made of how fish are affectedby anthropogenic disturbances causing a modification ofthe available habitat or a reduction of the connectivitybetween the different habitats comprising the riverscape.

9.8 Conclusion

Although the riverscape approach proposed by Fauschet al. (2002) was immediately perceived by stream


ecologists as the necessary next step in the analysisof fish/habitat relationships, its application remaineddifficult due to the lack of appropriate technology tocharacterise fluvial habitats at the appropriate scale andresolution. One of the main contributions of the Geosalarproject was to participate in the development of newremote sensing methods that are now making the conceptof riverscape ‘real’ (Carbonneau et al., 2012). Imageanalysis methods are indeed now available to producespatially continuous high-resolution maps of importantfluvial habitat variables, such as bed material size andwater depth. These methods are currently applied onhyperspatial images but the constant improvement ofthe resolution of satellite sensors suggest that they maysoon be applied to satellite imagery. Now that significantimprovements were made to the quantification of fluvialenvironments over long river segments, it appears thatthe new necessary next step is to provide equally detailedbiological data describing the exploitation by fish of themosaic of habitats comprised within the riverscape.

Acknowledgements

The authors wish to thank the researchers who par-ticipated in all components of the Geosalar project.A special thank to Julian Dodson, PI of the project.The project was funded by the GEOIDE Network Cen-tres of Excellence Program (http://www.geoide.ulaval.ca/)with contributions from our partners : Hydro-Quebec,Genivar, Ministere des Resources Naturelles et de la Faunedu Quebec, Aquasalmo inc., ZEC Sainte-Marguerite, Fon-dation pour le Saumon du Grand Gaspe. Special thanksalso to Keith Thompson and Nicholas Chrisman whowere, in turn, directors of GEOIDE. The comments andsuggestions of Steve Rice and Herve Piegay on an earlierversion of this chapter are gratefully acknowledged.

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