fluvial corridor n

ac

LisRenServch In

Accepted 1 April 2014Available online xxxx

Keywords:Fluvial geomorphology

basin managers, development of automated geographic information system (GIS)

1. Introduction

Fluvial corridor and riverscape concepts were introduced in the thropogenic pressures or changes in ood events at 1- to 100-year

ore studied by geo-agers to characterizeealth, and to providen, its sensitivity to

Geomorphology xxx (2014) xxxxxx

GEOMOR-04751; No of Pages 9

Contents lists available at ScienceDirect

Geomorp

l se1977; Vannote et al., 1980), riverscapes are longitudinally organizedand can be characterized by a set of nested units emerging at differentspatial and temporal scales and corresponding to both well-identiedareal features (e.g. river styles, erosion features, active channel) andtheir boundaries, andmore synthetic features (e.g., elementary sampling

nels) (Kondolf et al., 2003).Thus riverscapes are becoming more and m

morphologists, freshwater ecologists and manbiophysical features in terms of quality and hunderstanding of the network organizatio1960s by Leopold and Marchand (1968) and have been widely usedsince the early 2000s (Fausch et al., 2002; Wiens, 2002; Eros et al.,2010; Bertrand et al., 2013). Carbonneau et al. (2012) dene theriverscape as an ecological representation of rivers. This uvial object isa combination of broad scale units with energy, matter and biota trans-fers. In reference to the early notion of a uvial continuum (Schumm,

scale (Swanson et al., 1982; Gregory et al., 1991). Riverscape units areusually viewed as nested within each other from the network to thesegment reach, from the meso to micro-habitats (Allen and Starr,1982; Frissell et al., 1986; ONeill et al., 1986). These units therefore in-clude valley segments, geomorphic reaches, in-channel and oodplainfeatures (e.g. bars, rifes, pools, vegetation and islands, oodplain chan-features, linear features such as centerline). Riscribed (i) longitudinally, considering hydrauecological patterns, (ii) transversally, to focus oactions between channels and oodplains o

Corresponding author. Tel.: +33 6 01 31 07 26.E-mail address: [email protected] (C. Roux).

http://dx.doi.org/10.1016/j.geomorph.2014.04.0180169-555X/ 2014 Elsevier B.V. All rights reserved.

Please cite this article as: Roux, C., et al., Flu(2014), http://dx.doi.org/10.1016/j.geomorpnetwork, (iii) vertically, focusing on exchanges between the surfacewa-ters and groundwaters and (iv) in time, to highlight inuences of an-RiverscapeChannel reachGIS toolboxAutomated multiscale procedureRiver discontinuumnetworks. Since the 1990s, GIS toolboxes and add-in programs have been used to characterize catchments. How-ever, there is currently no equivalent to a planimetric and longitudinal characterization of uvial corridor net-works at multiple scales. This paper describes FluvialCorridor, a new GIS toolbox. This package allows the user:(i) to extract a large set of riverscape features such as the main components of uvial corridors from DEM andvector layers (e.g. streamnetwork or valley bottom), and (ii) to aggregate spatial features into homogeneous seg-ments and metrics characterizing each of them. The methodological frameworks involved have been previouslydescribed by Alber and Pigay (2011), Leviandier et al. (2012) and Bertrand et al. (2013) and this contributionfocuses on the GIS tools allowing the user to automatically operate them. A case study on the Drme River(France) is provided to illustrate the potential of the package both for geomorphologic understanding and targetmanagement actions. FluvialCorridorhas beendeveloped for ArcGISwith the related native Python library namedArcPy and tested on ArcGIS 10.0 and 10.1. Obviously, each component of the package can be used separately;however, it also provides a complete workow for uvial corridor characterization, even as the toolbox is contin-ually under development and revision. Case study database, FluvialCorridor package and guidelines are availableonline at http://umrevs-isig.fr.

2014 Elsevier B.V. All rights reserved.Received 17 October 2013Received in revised form 26 March 2014

tools is essential today to characterize riverscapes and explore biogeomorphologic processes over large channel

Article history: Both for scientists and riverFluvialCorridor: A new ArcGIS toolbox priverscape exploration

Clment Roux a,, Adrien Alber b, Mlanie Bertrand a,c,a University of Lyon, CNRS-UMR 5600 Environnement - Ville - Socit, ENS de Lyon, 15 Parvisb Direction Rgionale de l'Environnement, de l'Amnagement et du Logement (Rgion Centre),c IRSTEA-rosion torrentielle, neige et avalanches (ETGR)-Centre de Grenoble, National Resear2 rue de la Papeterie-BP 76, 38402, Saint-Martin-d'Hres Cedex, France

a b s t r a c ta r t i c l e i n f o

j ourna l homepage: www.everscape units can be de-lic, geomorphologic andn biomorphological inter-r hillslopes through the

vialCorridor: A new ArcGIS th.2014.04.018kage for multiscale

e Vaudor a, Herv Pigay a

Descartes, BP 7000, 69342 Lyon Cedex 07, Franceice Eau et Biodiversit, 5 avenue Buffon, 45064 Orlans Cedex, Francestitute for Environmental and Agricultural Sciences and Technologies, Domaine universitaire,

hology

v ie r .com/ locate /geomorphhuman pressures and its ability to adjust (Fausch et al., 2002;Wiens, 2002; Thorp et al., 2006; Le Pichon et al., 2009; Carbonneauet al., 2012; Bertrand et al., 2013). With the increasing availabilityof network-scale data, such analyses are becoming more and morecommon and automatic procedures to extract information are thusneeded. By sharing these types of tools, our intention is to better en-able researchers and river managers to characterize networks,

oolbox package for multiscale riverscape exploration, Geomorphology

which is a major challenge for planning and targeting restorationactions.

Previous signicant technical tools for characterizing rivers anduvialfeatures have already been developed (Table 1). In the text below, thenumerical superscripts refer to the rows in Table 1. Firstly,ArcHydroTools1

is available primarily as a method for basin and hydrographic character-ization. It provides a large set of tools used for two key purposes: ensur-ing either raster, vector or attributes treatments (e.g. stream burning,network generation, attribute transfers or accumulation) and attributeassessments (e.g. unique ID assignment, ordination, arc-length or water-shed area measurements). Additional tools have also been created inorder to investigate relationships between physical, chemical and biolog-ical parameters at a basin scale. Thus, STARS2, SSN2 or FLoWS3 allows forexploring and predicting catchment-scale information through spatialautocorrelation. Other researchers have attempted to provide elementsfor a longitudinal characterization of streamnetworks. TauDEM4 provides

2. Required extensions and toolboxes

FluvialCorridor toolbox has been developed for ArcGIS 10.0 andArcGIS 10.1, runningwith the related ArcPy and Python 2.6 or 2.7 librar-ies. Some ArcGIS extensions such as SpatialAnalyst, are required forensuring an optimal use and allowing all functionalities. Moreover,FluvialCorridor requires additional components (e.g. NumPy and SciPylibraries, ArcHydroTools). Further details about compatibility of the tool-box and installation of additional components are given within the setof guidelines attached to the toolbox.

3. General framework

FluvialCorridor has been developed based on the general workowinitiated by Alber and Pigay (2011). They developed a methodologicalframework for delineating and characterizing uvial features based on

e stu

ospial Rurce

atiort Synalynive

ice

IGN

2 C. Roux et al. / Geomorphology xxx (2014) xxxxxxtools to extract stream networks from DEMs attributing each link with ahydrologic and morphologic metric like upstream contributing area,slope, or cumulative distance. Assessment of some planimetric metrics(e.g. steepness index and concavity) has also been implemented withinthe Stream Proler5ArcGIS toolset. Multiple other examples of free,open-source software which carry out the morphometric characteriza-tion of catchments exist, such as GRASS6 (Neteler and Mitasova, 2002),SAGA GIS7 (Bhner et al., 2006), Multi Watershed Delineation8 tool(Chinnayakanahalli et al., 2006) or Geospatial Modeling Environment9.

As new accurate remote sensing techniques and data have becomemore widely available (e.g. LiDAR, drone imagery), several tools havealso been developed in response. River Bathymetric Toolkit10 (RBT) isone example among others. Especially designed for reach-scale studies,it enables the user to access uvial units (e.g. centerlines, banks, bankfullwater channel) and hydromorphic metrics, mainly related to the rivercross-sections. High resolutionDEMs are thus essential to use RBT. Devel-oped by Thomas Dilts, Riparian Topography11 is another ArcGIS toolboxwhich is useful for riverscape characterization. Also based onhigh resolu-tion DEMs processing, Riparian Topography provides a basis for exploringand modeling plant species distribution along reaches by extractingheight above river and inundation area rasters (Dilts et al., 2010).

Despite, or rather because of the proliferation of toolboxes to charac-terize channel reaches with more specicity, there remains a need foran automated process that allows the user to characterize riverscapeunits along river corridor networks in multiscale and planimetricways. According to the recent geomatics and statistical improvements(Alber and Pigay, 2011; Leviandier et al., 2012; Bertrand et al., 2013),development of such an ArcGIS toolbox, which accelerates such infor-mation production, is now possible. This toolkit can be considered asthe basis of knowledge production at a large scale, which is a new scien-tic frontier. This article describes a new toolbox named FluvialCorridor,designed to extract and characterize uvial features at a range of scalesalong a stream network.

Table 1List of existing tools for characterizing rivers or uvial features and dataset used in the cas

Material's name Developer

ArcHydroTools ESRISTARSSSN

United State National Oceanic and AtmCommonwealth Scientic and Industr

FLoWS United State Space-Time Aquatic ResoTauDEM David Tarboton-Utah State UniversityStream Proler Noah Synder and Kelin Whipple-US NGRASS Geographic Resources Analysis SuppoSAGA GIS System for Automated Geoscientic AMulti Watershed Delineation Kiran Chinnayakanahalli-Utah State UGeospatial Modeling Environment Hawthorne L. Bayer-Spatial EcologyRiver Bathymetric Toolkit ESSA Technologies and US Forest ServRiparian Topography Thomas DiltBD CarthageBD Topo

French National Geographical InstitutePlease cite this article as: Roux, C., et al., FluvialCorridor: A new ArcGIS t(2014), http://dx.doi.org/10.1016/j.geomorph.2014.04.018raw data available at a regional (over 1000 km2) scale (e.g. vector hy-drographic network, DEM, archives aerial photos). The combination ofinformation extracted from different sources allows the user to extractgeomorphic characteristics of uvial features at different spatial levels.Fig. 1 illustrates an example of the general framework involved duringa complete use of the FluvialCorridor package.

As the methodological framework, the FluvialCorridor toolbox issubdivided in four main processes (Fig. 2). The rst step consists inextracting unitary geographical objects (UGOs) that delineate the fea-tures of interest (e.g. the active channel, the valley bottom, etc.). Then,the spatial disaggregation of unitary geographical objects results in disag-gregated geographical objects (DGOs).Thirdly, the phase of characteriza-tion is processed. A set of metrics is calculated at the DGO databasescale, according the focus of interest. Finally, the spatial aggregation oftheDGOs into aggregated geographical objects (AGOs) allows delineationof physically meaningful uvial features. Fig. 2B shows those units froma theoretical point a view and Table 2 details the different tools of thepackage.

3.1. Extracted uvial units and spatial disaggregation

As a rst step, the toolset named Spatial components enables theuser to extract UGOs such as the hydrographic network, the valley bot-tom or centerline of polygon features, from a DEM or vector layers.

Once linear and polygon features (UGOs) have been extracted, theycan be disaggregated into elementary segments (DGOs) of a given user-dened and constant length using the Disaggregation processestoolset. This spatial disaggregation aims to provide uvial units at ahigher resolution for the characterization process. The spatial resolution(i.e. the disaggregation step)must be set precisely to ensure that spatialtrends are correctly detected (i.e. by analogy with the Nyquist-Shannonsampling theorem in information theory, the disaggregation step mustbe ner than the scale of investigation (Nyquist, 1928)).

dy over the Drme River (France).

Web-link

www.esri.comheric Administration and Australianesearch Organization

http://www.fs.fed.us/rmrs

s Modeling and Analysis Program http://www.nrel.colostate.eduhttp://hydrology.usu.edu/taudem

nal Science Foundation and NASA http://www.geomorphtools.orgstem GRASS http://grass.osgeo.orgses SAGA GIS http://www.saga-gis.orgrsity http://hydrology.usu.edu/mwdtool

www.spatialecology.com/gmehttp://essa.com/tools/rbthttp://arcscripts.esri.comwww.ign.froolbox package for multiscale riverscape exploration, Geomorphology

3C. Roux et al. / Geomorphology xxx (2014) xxxxxxBoth for linear and polygon UGOs, the disaggregation processes arebased on the segmentation of a polyline feature. A python scriptnamed SLEM (for Split Lines Each Meter) has been developed to splitpolyline features with a user-dened and constant length. Each line isprocessed separately, but it must be correctly oriented to ensure a con-sistent ordering of resulting segments within the network. Rotation ofwrongly-oriented streams is carried out by a specic tool namedSequencing. Finally, disaggregation of linear UGOs is directly conduct-ed by the SLEM tool, whereas polygon UGOs disaggregation is based onthe segmentation of the centerline and a Thiessen polygonization. EachDGO is spatially referenced into the network by: (i) Order_ID andRank_UGO elds of theUGO they belong to, and (ii) Distance eld or-dering them between each conuence from upstream to downstream.The Order_IDeld can be viewed as a streamorder. Unlike the Strahleror Shreve orders, Order_ID is dened increasingly from downstreamto upstream.

The Polyline disaggregation tool ensures another disaggregationprocess applied to linear UGOs. It provides (i) inection points of theinput polyline and (ii) arcs between each of them. These DGO-scaleunits are not segmented in the conventional sense of spatial disaggrega-tion since the length of disaggregated segments is not a user-dened con-stant. In that case, a DGO is dened as the arc between two successiveinectionpoints and they are attributed as anyotherDGO(i.e. Order_ID,Rank_UGO and Distance elds populate the nal attribute table).

3.2. Metrics calculation and spatial aggregation

Though the nal goal is to re-aggregate the basin-scale characteris-tics, these disaggregated continuums must rst be characterized withone or a set of metrics. The FluvialCorridor toolbox allows the user to as-sess such metrics through the Metrics toolset. For example, each DGOcan be described in terms of sinuosity index, channel width or cumula-tive linear of contact into the uvial corridor (i.e. so called ecotones inecology, it refers to the transitional zone between two different bio-physical features (Ward et al., 1999) such as gravel bars, vegetation orwater surfaces).

Finally, the toolset named Statistics enables to statistically aggre-gate the attributed DGOs by using the Hubert algorithm (Hubert,2000; Kehagias et al., 2005). Leviandier et al. (2012) compared a set ofseven existing methods for delineating homogeneous segments basedon univariate series. They concluded that the Hubert test of homogene-ity and theHiddenMarkovModels are especially successful for identify-ing longitudinal discontinuities. We chose the rst one, which providesa segmentation once the difference of the mean between two consecu-tive segments is signicant. So theHubert test is used to investigate sub-patterns encapsulated in the uvial continuum, locate the longitudinaldiscontinuities and aggregate DGOs into AGOs (i.e. homogeneousreaches). The number of AGOs depends on a criterion and any changeof its valuemodies constraints for the AGOs delineation (i.e. increasing relaxes constraints and enables the test to identify more homoge-neous reaches). Leviandier et al. (2012) showed this parameter not asa common condence level but rather as a level of the risk governingthe segmentation size.

Over an entire disaggregated network and for a given metric, theHubert test tool processes each DGO of a UGO from upstream todownstream. Hence the best segmentation is found (i.e. the one induc-ing the most relevant difference between two consecutive segmentswith respect to the limiting criterion ), DGOs are attributed withRank_AGO and AGO_Val elds. The Rank_AGO eld contains aunique ID for each AGO and AGO_Val is the mean value of the givenmetric over an AGO (Fig. 2B).

4. Automatic production of a multiscale geomorphic database

Through an archetypal and theoretical workow (Fig. 1), this section

details the different tools of the FluvialCorridor package. It also focuses

Please cite this article as: Roux, C., et al., FluvialCorridor: A new ArcGIS t(2014), http://dx.doi.org/10.1016/j.geomorph.2014.04.018on connections between the geographical units created during a pro-cess. According to the objectives of the case studies and the availableinput data, such a framework can be either simplied or expandedand conducted from a local to a regional scale.

4.1. Required input data

The full use of the FluvialCorridor toolbox presented in Fig. 1 requiresthree raw data: a DEM and two vector layers, the hydrographic networkof the study area and its active channel. External data can be joined toenrich the process with new information. Here, aerial photographs areso used to illustrate potential gains provided by such an external data.

Today, DEMs, aerial or satellite photographs and hydrographic net-works are widely produced over the world and active channel layerscan be obtained frommanual digitalization or from radiometric analysisof aerial or satellite photographs (Marcus et al., 2003; Wiederkehr,2012; Bertrand et al., 2013).

4.2. Extraction of UGO-scale units

4.2.1. Stream network extraction (UGO1)The stream network is extracted from the input DEM. The frame-

work is based on the widely used process introduced by O'Callaghanand Mark (1984) and involves the assessment of the drainage accumu-lation raster from the original DEM. Over this accumulation raster, thestream network is vectorized into a polyline feature according a user-dened drainage area to uniformly initiate the nal stream network.Setting a consistent drainage area is a crucial step to extract a relevantnetwork. Associated scale and resolution effects are discussed inTarboton et al. (1991). Following Saunders (1999) and others, such asLamouroux et al. (2008), a streamburning step can be added to the pro-cess to improvenetworkdelineation. Ensured by theArcHydroTools ESRIpackage, this optional step enables a vector layer from a pre-existingnetwork to be embedded into the original DEM.

4.2.2. Valley bottom extraction (UGO2)Within the FluvialCorridor toolbox, the framework used to extract

the valley bottom is the one previously developed by Alber and Pigay(2011).The Valley bottom tool is thus based on the extraction of tworasters (i.e. a reference altimetric plan and a relative DEM) from theinput DEM and a stream network. Accordingly, this unit is dened asthe set of DEM cells with an elevation value between the stream eleva-tion (assumed to be the minimal elevation in a user-dened buffer sur-rounding the stream network) and an empirically-dened thresholdcorresponding to the submerged oodplain for a uniform waterowheight (Williams et al., 2000).

4.2.3. Centerline extraction (UGO4)The Centerline tool, which involves a framework previously devel-

oped by Alber and Pigay (2011), can be used to extract a centerline forany long polygon (i.e. with one larger dimension compared to theother), including valley bottoms, active channels, or any other homoge-neous and continuous unitary geographical object. The process is basedon a Thiessen polygonization of the segmented boundaries of the inputpolygon.

4.3. DGO-scale database from spatial disaggregation

The spatial disaggregation can be conducted on any UGO covering areach or a network. At this stage of the toolbox use, we illustrate themethod with six DGOs layers (Fig. 1).

DGO1 andDGO6 represent channel patterns passing through inectionpoints. They are assessed by the Polyline disaggregation tool. ThenDGO2, DGO3, DGO4 and DGO5 relate to the spatial disaggregation of linear

or polygon units they belong to (i.e. UGO1, UGO2, UGO3 andUGO4). Linear


C4 C. Roux et al. / Geomorphology xxx (2014) xxxxxxDigitized streamnetwork DEM

Theoreticalnetwork

Stream network(UGO1)

Rank / DistanceWatershed areaElevationSlopeFlowsStream powerDrainage density

DGO1Channel pattern DGO2

Rank / DistanceSinuosityHalh-lengthHalf-amplitudeArc length

DGO3

Valley bottom(UGO2)

Raw

dat

ae

data

basefeatures (e.g. UGO1 and UGO4) are rst sequenced and oriented with theSequencing tool to have consistent results.

The resolution of the DGO-scale database is a user-dened parame-ter so that the user may modify the disaggregation step to investigatedifferent biophysical patterns within riverscapes. According to the rawdata precision, ne and detailed disaggregation enables the user toidentify very local trends by increasing the accuracy of metric calcula-tion. Conversely, managers can attempt to catch regional behaviorsthanks to a coarser-scale disaggregation. Thus, for each UGO, disaggre-gation scalesmust be set smaller than the scale at which forms and pro-cesses of interest occurs but coarse enough to be appropriate to theprecision of the raw input data.

4.4. Metric characterization of the riverscape

Fig. 1 provides a general overview of these metrics, either directlyextracted with FluvialCorridor package or derived from others or fromexternal data or processes (e.g. connectivity DEM, image analysis).

Firstly, channel patterns (i.e. DGO1 andDGO6) can be viewed as refer-ence axis for linear features such asUGO1 andUGO4. The Morphometry

A

Rank / DistanceValley bottom widthConfinement index

AGO

-sca

lesc

alD

GO

UGO

ExtractionDisaggregationTransfer informationAggregationIntermediate data or processesExternal data or processesAvailable metricsNon exhaustive list of derived metrics

Fig. 1. Example of an AGO-scale database extraction through the FluvialCorridor package. This trequired. Aerial photographs are used to illustrate potential investigations provided by externThen, they are disaggregated into a DGO-scale database to rene the accuracy of the morphoscale database is created by merging DGO-scale units into homogeneous reaches thanks to the

Please cite this article as: Roux, C., et al., FluvialCorridor: A new ArcGIS t(2014), http://dx.doi.org/10.1016/j.geomorph.2014.04.018Vector layers Aerialphotographs

onnectivityDEM

Eroded areasdetection

Active channel(UGO3)tool enables the user to assess a set of morphometric attributes (e.g. sin-uosity, half-length, half-amplitude and arc length) against linear featuresthanks to their related channel patterns. Their accuracy directly dependson the quality of the input linear feature and they can be stored into thechannel pattern or into the linear feature. Sinuosity is thus calculated foreach arc of a UGO as the ratio of the UGO arc length over the channel pat-tern segment length. Processes used to assess half-length, half-amplitude and arc-length are those described in Alber and Pigay(2011).

Then, DGO2 has been chosen to store geomorphic and topologicmetrics. Indeed, from the raw DEM input, FluvialCorridor package pro-poses to assess the watershed area, the mean elevation and the slopefor each segment of the entire network, from upstream to downstream.Thewatershed area is simply extracted from the drainage accumulationraster of the DEM. The elevation is stored into three different elds:(i) upstreamelevation, (ii) downstream elevation, and (iii) mean eleva-tion over a DGO. The slope is calculated by dividing the difference be-tween the upstream and downstream elevation by the DGO length.

Lastly, width values for polygon units such as the valley bottom orthe active channel can be calculated with the Width tool of the

SpatialdiscontinuitiesGO

Centerline(UGO4)

Rank / DistanceEroded Areas

Rank / DistanceSinuosityHalh-lengthHalf-amplitudeArc length

DGO5 DGO6Channel pattern

Rank / DistanceActive channel widthContact lengthConfinement indexShiftingWidenningEroded volumes

DGO4

heoretical framework involves all the tools of the package so that three input raw data areal data. Four UGO-scale units are created: spatial components and a reference centerline.metric, geomorphic and topologic metrics stored in the related attribute tables. An AGO-Hubert test.


A5C. Roux et al. / Geomorphology xxx (2014) xxxxxxMetrics toolset. Valley bottom width is thus assessed into the DGO3using the framework developed by Alber and Pigay (2011) which re-quires creating points at a constant length on the UGO2 boundaries.The resulting width values are measured as the projection of thesepoints on the centerline (i.e. UGO4). The active channelwidth is assessedinto the DGO4. The process is based on a Thiessen polygonization of thecenterline (i.e. UGO4) and the active channel width is assessed as the in-tersection between Thiessen polygons boundaries and UGO3. Anothermetric, named Contact length, can be calculated with the DGO4 and

Fig. 2. Implementation of the spatial disaggregation and aggregation procedure developed byFluvialCorridor package, from the raw data to the AGO-scale database extraction. The second rothe process.

Table 2Summary of tools included in the FluvialCorridor package, sorted according to four tool(iii) characterization with a set of Metrics, and (iv) spatial aggregation with Statistics tools

Tools Description

Stream network extraction of the stream network from a DEMValley bottom extraction of the valley bottom with a DEM and a stCenterline extraction of the centerline of a polygonSequencing orientation and ordination of a linear networkSegmentation disaggregation of entities, with a user-given constanPolyline disaggregation extraction of the disaggregated pattern of a linear neContact length assessment of the cumulative contact length betweeElevation and Slope assessment of the elevation and the slope along a linMorphometry assessment of a set of morphometric units along a liDiscontinuities assessment of the ratio between two consecutive DGWatershed assessment of the drainage area along a networkWidth assessment of the width of polygonsHubert test identication of longitudinal discontinuities thanks t

metric value into each AGO

Please cite this article as: Roux, C., et al., FluvialCorridor: A new ArcGIS t(2014), http://dx.doi.org/10.1016/j.geomorph.2014.04.018corresponds to the cumulative linear boundary shared between two ad-jacent biophysical features (e.g. water/bar).

4.5. Extraction of homogeneous reaches and longitudinal discontinuities

Once the DGO-scale database is characterized with at least onemet-ric, spatial aggregation can be conducted with the Hubert test tool ofthe Statistics toolset. This statistical test is univariate so that onlyone numerical metric can be assessed each run. For an entire network,

B

Alber and Pigay (2011). The rst row describes the general framework involved in thew illustrates the different databases and the related geographical objects creating during

sets: (i) extraction of Spatial Components, (ii) spatial Disaggregation Processes,.

Spatial componentsream network

Disaggregation processest steptwork passing through inection pointsn a set of polygons Metricsear networknear networkOs for a given metric

o the statistical test of Hubert and re-calculate Statistics


6 C. Roux et al. / Geomorphology xxx (2014) xxxxxxeach different UGO is processed successively. The nal output result in-cludes twoeldsmore than the input DGOs layer: Rank_AGO, a uniqueID for each AGO identied and AGO_Val, the mean value of the givenmetric over an AGO. This attribute table format allows to keep theDGO-scale characterization into the AGO-scale database.

Longitudinal discontinuities along the network can also be extractedinto a multi-point feature thanks to the Discontinuities tool of theStatistics toolset. The AGO_Val ratio of two consecutive AGOs istransferred into a point. This point is precisely located at the break be-tween the twoAGOs. This functionality enables to represent discontinu-ities breaks as consistent geographical objects.

5. Application example on the Drme River (France)

The present section illustrates how the toolbox is used for character-izing geomorphic features.We apply it to theDrmeRiver (France) con-tinuum to identify distinct geomorphic reaches along a river course andto distinguish unconstrained and constrained reaches by anthropogenicinfrastructure (e.g. embankments) or geological connements.

5.1. Background

5.1.1. Study areaThe Drme River is a 106 km long tributary of the Rhne River

(Fig. 3A) and its catchment of 1660 km2 lies between 800 and 2000 mof elevation. Located in the Southern French Alps, it drains the Vercorsand Diois massifs, a steep and mountainous terrain of Mesozoic lime-stone and marl (Pigay et al., 2004).

This continuumhas been chosen for illustrating the present GIS pro-cedure because its geomorphic pattern is constrained longitudinally interms of anthropogenic and natural connement (Landon et al., 1998).We focused our example on two reaches (C1 and C2 on Fig. 3A). Therst one is situated within the upper part of the Drme River, fromDie to Vercheny. This 15.3 km reach is characterized by two steep gorgesseparated and surrounded bywide alluvial valleys. The second one is inthe downstream part of the river, between Crest and Livron-sur-Drme.Along this13.8 km reach, originally highly braided, the channel has beenlocally constrained by embankments.

5.1.2. Available dataWithin France, several databases are available and provide a large

set of geographical data. Thus, respectively from the BD Carthage12

and the BD Topo12, we extracted the stream network and the DEMof the Drme River catchment. The stream network is 593 km long, in-cluding the main tributaries of the Drme River. The DEM has a 50 mresolution.

Over the past two decades, numerous scientic studies and integrat-ed river plans have been conducted on the Drme River, resulting in abroad range of available data. Thus, the last input of our study case, avector layer of the active channel, comes from the Crateur de Drmeproject, started in 2008 and supported by the ZABR and GRAIE programsand the Rhne-Mediterranean basin authority. It has been extractedthanks to image analysis of infra-red orthophotos.

5.2. Methods

The general workow is presented in the Fig. 3B.First, we used the Spatial components toolset to create aUGO-scale

database over the study area (Fig. 3B, step ). A consistent stream net-work of 101 kmhas been extracted thanks to the Streamnetwork tool.The raw DEM has been burned with the agree stream of the BD Car-thage and aminimumdrainage area of 65 km2 has been set empirical-ly both to reduce the drainage density and also to extract the entireDrme River. We proceeded to a quickmanual cleaning of the resultingnetwork in order to only select the Drme River main stem. Then, we

ran the Valley bottom tool. After having tested several elevation

Please cite this article as: Roux, C., et al., FluvialCorridor: A new ArcGIS t(2014), http://dx.doi.org/10.1016/j.geomorph.2014.04.018thresholds, the valley bottom was extracted and cleaned by removingalluvial fans and lling residual holes. We visually validated this UGOwith an existing layer from the Crateurs de Drme project. Finally, weused the Centerline tool with a 5 m disaggregation step to create a89.6 km centerline related to the valley bottom.

To distinguish conned from unconned reaches within our tworeaches, we had to compute a connement index, dened here as theratio between the active channel width and the valley bottom widthand viewed as an indicator of the potential of the stream's lateral mobil-ity. This metric must be stored into one of the geomorphic componentswhich are representative of the uvial continuum: the stream network,the valley bottomor the centerline.We chose the valley bottombecauseit is a physically meaningful riverscape unit and because it allows a con-sistent visualization of results. Hence, we created a DGO-scale databasethanks to the Disaggregation processes toolset (Fig. 3B step ). First,we ran the Sequencing tool to ensure a good orientation of the valleybottom centerline. This sequenced centerline was used to disaggregatethe valley bottom polygon every 100 m (Segmentation tool).

At the same time, we executed the Width tool of the Metricstoolset (Fig. 3B step and ) to calculate needed metrics. Along thecenterline and each 20 m, a rst set of points with the valley bottomwidth information (WVB) and another one with the active channelwidth (WAC) were created. Those attributes were then transferred intothe DGO-scale database thanks to a spatial join (Fig. 3B step ). Onlyone DGO did not receive a valley bottom width and nine had no valueof active channel width. This is due to either a lack of informationfrom the active channel layer or inconsistent DGOs (see Section 6.1).Lastly, we calculated the connement index (CI).

Finally, we used the Statistics toolset, with an criterion of 0.05, toaggregate the attributed DGO-scale database in two sets of AGOs(Fig. 3B step ). The Hubert test tool was run two times: one overthe CI in the reach C1 and another one overWAC in the reach C2.

5.3. Results

This section focuses on results presented in Fig. 3C1 and C2. They arebased on the two AGO-scale databases which have been produced fromthe 897 DGOs of the Drme River. Among this DGO-scale database,WVBvaries between 62.7 m and 4373.6 m (WVB: 831.7 m) andWAC between0.5m and 372.6 m (WAC: 81.4 m). The connement index CI is between2.1 104 and 0.76 (CI: 0.15).

5.3.1. Upper part of the Drme River: reach C1We chose the reach C1 to illustrate how useful the FluvialCorridor

toolbox can be to distinguish geologically conned reaches from alluvialvalleys, within which the active channel can freely shift. FollowingWiederkehr (2012), a CI of 0.3 can be used to identify geological con-nements, i.e. above this threshold, we assumed that the valley has asignicant control on the channel shift. In that way, we used the AGO-scale database constructedwith the connement index. The reach C1 in-cludes 105 DGOs aggregated into 6 different aggregated reaches whichextend from 700m to 3.2 km. As shown in the Fig. 3C1, and according tothe 0.3 threshold of connement index, Reaches 2, 3 and 5 can be iden-tied as conned reaches (i.e. respectively CI= 0.56; 0.33; 0.38). Con-sidering the connement index, the 3 others AGOs appear to beunconned (CI(1) = 0.16; CI(4) = 0.16; CI(6) = 0.23). This is stronglyconrmed visually on the orthophotos and by the contour lines ofelevation.

5.3.2. Lower part of the Drme River: reach C2We chose the reach C2 to illustrate how the FluvialCorridor toolbox

can be used to distinguish braided reaches from embanked reaches.The reach C2 is overlaid by 124 DGOs which have been aggregatedinto four homogeneous reaches in terms of active channel width.Those aggregated reaches vary from 1.1 to 5.1 km and two pairs clearly

emerge according to WAC.: Reaches 1 and 3 [84.4 m; 91.7 m] and


DEMagreestream

activechannel

FID DistanceRank_DGO Width_VB Width_AC Conf_IndexOrder_ID Rank_UGO121314151617181920

111111111

111111111

121314151617181920

120013001400150016001700180019002000

123.32378110.491

95.71298125.86233173.72395141.80922112.21320111.07617143.0410

23.9845043.7559629.8508052.7157955.6588636.2053630.2010710.8759935.41414

0.1944840.3960140.3118780.4188370.3203870.255310.26914

0.0979150.247565

1

0 1 km5

2

3 4

0 10 km5

Le Rhne

La Drme

La Roanne

La G

ervan

e

Le Bs

Crest

Livron sur Drme

Vercheny

Die

Drme basinDrme rivermain tributariessecondary tributariesmain cities

DGOs

Metrics

UGOsstream networkvalley bottomcenterline

disaggregated valley bottom

valley bottom width

Spatial joinattributed DGOs

6

AGOs :

AGOs :

homogeneous valley segmentsbased on confinement index

homogeneous valley segments based on activechannel width

0 1 km5

AB

C2 C1

110m

199m228m

240m229m

157m

110m109m

110m116m

141m170m

185m

242m

271m

166m

149m135m

123m

108m

0 50 100 m

37m

53m48m

14m

55m43m 44m

43m40m 44m

44m 45m

47m47m

46m40m

21m

0 50 100 m

FluvialCorridor tools1 "Spatial components" :

Stream networkValley bottomCenterline

"Disaggregation processes" : SequencingSegmentation2

"Statistics" : Hubert test6

"Metrics" : Width3 4&

(100m)(20m)(20m)

active channel width

(=0.05)

(=0.05)

5

0 1 km0,5

400

500

600

700

500

400

600

400 500

600700

0.16

0.23

0.56

0.16

isolines 100misolines 25mAGOs boundaries

confined reaches

=

active channel widthvalley bottom widthconfinement index (CI)0.23

active channel (water / bar)

C1

0.33

0.38

1

2

34

5

6

0 2 km1

194.0

91.7

196.8

84.4

AGOs boundariesembanked reachesactive channel width (m)91.7

active channel(water / bar)

embankments C2

1

2

3

4

Fig. 3. (A) Location of the Drme River basin in France and the two reaches C1 and C2 within the main hydrographic network. (B) Main steps of the study case workow. (C1) and (C2)Results over the two reaches of the Drme River: homogeneous reaches in terms of connement index in C1 and in terms of active channel width in C2.

7C. Roux et al. / Geomorphology xxx (2014) xxxxxx

Please cite this article as: Roux, C., et al., FluvialCorridor: A new ArcGIS toolbox package for multiscale riverscape exploration, Geomorphology(2014), http://dx.doi.org/10.1016/j.geomorph.2014.04.018

8 C. Roux et al. / Geomorphology xxx (2014) xxxxxxReaches 2 and 4 [196.8m; 194.0m]. A statistical analysis shows that thetwo narrow reaches are also quite uniform in width. Actually, theinterquartile range (IQR) of WAC in Reaches 1 and 3 (i.e. respectively22.4 m and 11.5 m) is signicantly lower than in the two others AGOs(i.e. IQR(2)=79.2mand IQR(4)= 51.4m). Those results are visually val-idated, bothwith the active channel layer and the orthophotos. It clearlyappears that Reaches 2 and4 are braided reaches, with a highwidth var-iability and free to extend or laterally shift. Conversely, an embankmentlayer from the Crateurs de Drme project conrms that Reaches 1 and 4are connedbyhuman infrastructures (i.e. embankments) resulting in aquite uniform distribution ofWAC.

5.4. Conclusion

The present organization is quite confusing because there are twoconsecutive conclusion chapters (i.e. 6 and 7).I think the Chapter 6"Conclusion" is not correctly located. This part of the paper refers tothe specic conclusions of the case study and so should be placedwithinthe chapter 5, as a sub-chapter 5.4."?>Actually, all the workows de-scribed above can be applied in a few hours depending on the studyarea extent and the preliminary requirements, such as the selection ofinput data and the empirical settings of parameters (e.g. the elevationthreshold for the valley bottom, the spatial disaggregation step). Thecase study presented took about 20min of computation. Also, presentedresults could surely be improved by increasing the DGO-scale databaseresolution, adjusting the criterion of the Hubert test or, certainly byintroducing other parameters (e.g. transversal slope threshold (Gallantand Dowling, 2003)).

6. Discussion and conclusions

6.1. FluvialCorridor limitations

As it is still in development, the FluvialCorridor toolbox shows someshortcomings. Geomatic or geometric limits are referenced within theguidelines includedwith the package. One of them has already been in-troduced in Section 5.2 and deals with the limits of the spatial disaggre-gation processes, especially for polygon inputs. Currently and asimplemented in the package, with too highly curved centerlines,Thiessen polygons used for the disaggregation do not cross the entireinput polygon. Thus, resulting DGOs can lead to problems during thecharacterization of the DGO-scale database, as mentioned in themethods section of the case study above. Similar issues occur at conu-ences which are very specic areaswhere themetric assessment can beinconsistent (e.g. uvial widths, ecotones).

Moreover, methodological shortcomings about the multiscale po-tential of the package are also noticed. Problems can occur with sometools used at a regional scale. Input parameters do not handle the inher-ent size effect of uvial longitudinal patterns. For example, in the case ofthe Valley bottom tool, valley bottom extraction is donewithin a buff-er, created around the different branches of the hydrographic network.The buffer size is user-dened and constant over the entire study length.Problems can therefore occur for upland areas, where the valley bottom,which is generally narrower in headwater systems, can be searched forwithin surrounding valleys. In order to avoid such problems, the buffersize, and perhaps the elevation threshold used for the valley bottomdef-inition, should be set for each stream of the network according to itscatchment area. This issue can be applied to other processes such asthe spatial disaggregation or aggregation and must be xed in thenext version.

Nevertheless, and as mentioned previously, the FluvialCorridor tool-box is still under development and geomatic and geometric weaknessesremain infrequent. The package still provides robust tools and frame-works to enrich scientic studies or management plans and produces

consistent information from local to regional scales.

Please cite this article as: Roux, C., et al., FluvialCorridor: A new ArcGIS t(2014), http://dx.doi.org/10.1016/j.geomorph.2014.04.0186.2. Other potential or derived metrics

The richness of the metrics characterization directly depends on theset of available metrics. As shown in Fig. 1, the FluvialCorridor packageenables the user to calculate ten geomorphic or landscape metrics. Da-tabases can also be characterized either by a combination of severalmetrics or by importing data from external databases or processes.The rst point is illustrated in the case study by the use of the conne-ment index (Fig. 3C1) and in the theoretical workow (Fig. 1) wherewe assessed the drainage density in the DGO2 as the ratio betweenthe cumulative stream length and the watershed area. External data-bases (e.g. ESTIMKART for the French territory (Lamouroux et al.,2010), CORINE Land Cover for the European Union) can be joined to aDGO-scale database to add information such as ows or channeldepth values, increasing the number of available and potential derivedmetrics. External databases can also be created from external processesand then joined to the attribute table of a DGOs layer (e.g. cumulativeeroded area from image and DEM analysis, Fig. 1). Possibilities areman-ifold and eld data can also be included when available. By joining sev-eral databases, one may access and investigate a very large range ofmetrics.

6.3. Added value and further improvements

As mentioned in previous sections, the FluvialCorridor toolbox pro-vides a complete and robust workow and offers multiple approachesto characterizing uvial continuums and riverscapes.

Compared to the rst version, this toolbox provides many options toexplore the database. User-dened metrics allow a more tailored ap-proach to the exploration of ones dataset, depending on the data avail-able and the geographical context. For example, the Valley bottomtool allows the operator to choose his/her own threshold in terms of el-evation. The Streamnetwork tool allows the user to choose the drain-age area value (in km2) used to initiate the hydrographic network andin the Hubert test tool, value is also chosen by the operator. As alast example, the constant length used to the spatial disaggregation ofUGOs is a user-dened parameter.

Further improvements are projected. Major efforts are currentlyunder way to include imagery analysis tools. By integrating colored orinfra-red orthophotographs, the user will be able to access newriverscape units (e.g. water bodies or backwaters, vegetated islands orforest cover, bars, anthropogenic elements, etc.) according to a specicresearch focus. Some others utilities are investigated such as a clusteranalysis module for exploring features typology after the spatial aggre-gation. Moreover, only one algorithm of aggregation (i.e. Hubert test) iscurrently available. One of the future improvements will be to imple-ment new statistical algorithms such as the multivariate Hidden Mar-kov Models (Baum and Eagon, 1963; Mari et al., 2011). Finally, andfollowing the pioneer contributions of Tormos (2010), we plan to intro-duce new procedures to exploremultiscalemodels, enabling the user topredict local reach-scale metrics from upstream reach, network orcatchment characteristics.

6.4. A geomaticworkow for answering different questions at a network scale

This paper can be viewed as a part of a largerwork, initiated by Alberand Pigay (2011), with a goal of developing a procedure to investigateand characterize networks and riverscapes at a range of scales. The pre-sented FluvialCorridor toolbox package goes beyond the simple concep-tual and geomatic framework by providing an automated GIS tool. It cantherefore be used in other contexts to answer to other questions. Bellettiet al. (2012) and Notebaert and Piegay (2013) demonstrated that thismultiscale approach is powerful and operational both in research andmanagement contexts. The latter used the entire workow to showthat the scaling effect observed on the oodplain width of the Rhne

catchment is mainly controlled by lithological variations rather than


by downstream distance. Bertrand et al. (2013) used this GIS packageand framework to assess the risks of environmental changes (i.e. interms of habitat diversity and trout distribution) induced by sedimentreplenishment on the Drme River.Wiederkehr (2012) used the proce-dure to provide a typology of channel patterns (e.g. single-bed ormulti-ple channels, braided, sinuous or straight channels) and compare thislevel of organization with the in-channel features (e.g. the inter-pool

Gregory, S.V., Swanson, F.J., McKee, W.A., 1991. An ecosystem perspective of riparianzones. Bioscience 40, 540551.

Hubert, P., 2000. The segmentation procedure as a tool for discrete modeling of hydrome-teorological regimes. Stoch. Environ. Res. Risk Assess. 14, 297304.

Kehagias, A., Nidelkou, E., Petridis, V., 2005. A dynamic programming segmentation pro-cedure for hydrological and environmental time series. Stoch. Environ. Res. Risk As-sess. 20, 7794.

Kondolf, G.M., Montgomery, D.R., Pigay, H., Schmitt, L., 2003. Geomorphic classicationof rivers and streams. In: Kondolf, G.M., Pigay, H. (Eds.), Tools in Fluvial Geomor-phology. Wiley, Chichester, pp. 171204.

9C. Roux et al. / Geomorphology xxx (2014) xxxxxxrunning project is focused on sedimentation patterns in groyne elds.Geomatic procedures are used to provide a typology of sedimentationpattern according to the inter-groyne geometry and associated hydrau-lic conditions.

The FluvialCorridor package is therefore constantly being rened andonly V01 is presently available. Nevertheless, this rst version of theFluvialCorridor toolbox has already been shown to be powerful enoughto explore riverscape features at reach to network scales from existingvector and raster layers.

Acknowledgements

The authors wish to thank all the researchers who actively participat-ed to the development of this research project, and notably B. Belletti,L. Bourdin, S. Dufour, F. Libault, B. Notebaert, M. Rival, E. Wiederkehr,and the different institutionswho funded the projects: RMCWater Agen-cy, SedAlp project (Alpine Space European Program). The authors alsokindly thank John C. Stella and Robin Jenkinsonwho reviewed the paper.

NB: the GIS toolbox, its guidelines and the database of the case studyare available online at http://umrevs-isig.fr.

References

Alber, A., Pigay, H., 2011. Spatial disaggregation and aggregation procedures for charac-terizing uvial features at the network-scale: Application to the Rhne basin(France). Geomorphology 125, 343360.

Allen, T.F., Starr, T.B., 1982. Hierarchy: Perspective for ecological complexity. The Univer-sity Chicago Press, Chicago (310 pp.).

Baum, L.E., Eagon, J., 1963. An inequality with applications to statistical prediction forfunctions of markov processes and to a model for ecology. Bull. Am. Math. Soc. 73,360363.

Belletti, B., Dufour, S., Pigay, H., 2012. Regional variability of aquatic pattern in braidedreaches (example of the French Rhne basin). Hydrobiologia 712, 2541.

Bertrand, M., Pigay, H., Pont, D., Libault, F., Sauquet, E., 2013. Sensitivity analysis of en-vironmental changes associated with riverscape evolutions following sediment rein-troduction: Geomatic approach on the Drme river network, France. Int. J. River BasinManage. 11, 1932.

Bhner, J., McCloy, K.R., Strobl, J., 2006. SAGA - Analysis and Modelling Applications.Gttinger Geographische Abhandlungen, 115 (130 pp.).

Carbonneau, P.E., Fonstad, M.A., Marcus, W.A., Dugdale, S.J., 2012. Making riverscapes real.Geomorphology 137, 7486.

Chinnayakanahalli, K., Hill, R., Olson, J., Kroeber, C., Tarboton, D.G., Hawkins, C., 2006. TheMulti-watershed Delineation Tool: GIS software in support of regional watershedanalyses. Utah State University, Logan (44 pp.).

Dilts, T.E., Yang, J., Weisberg, P.J., 2010. Mapping riparian vegetation with LiDAR data:Predicting plant community distribution using height above river and ood height.ArcUser Magazine, pp. 1821 (Winter 2010 Issue).

Eros, T., Schmera, D., Schick, R.S., 2010. Network thinking in riverscape conservation Agraph-based approach. Biol. Conserv. 144, 184192.

Fausch, K.D., Torgersen, C.E., Baxter, C.V., Li, H.W., 2002. Landscapes to riverscapes: Bridg-ing the gap between research and conservation of stream shes. Bioscience 52,483498.

Frissell, C.A., Liss, W.J., Warren, C.E., Hurley, M.D., 1986. A hierarchical framework forstream habitat classication Viewing streams in a watershed context. Environ.Manag. 10, 199214.

Gallant, J.C., Dowling, T.I., 2003. A multi-resolution index of valley bottom atness formapping depositional areas. Water Resour. Res. 39, 13471360.Please cite this article as: Roux, C., et al., FluvialCorridor: A new ArcGIS t(2014), http://dx.doi.org/10.1016/j.geomorph.2014.04.018Lamouroux, N., Pella, H., Vanderbecq, A., Sauquet, E., Chandesris, A., Capra, H., Souchon, Y.,Pont, D., 2008. Rapport ESTIMKART: Impacts quantitatifs sur les peuplementsaquatiques de la restauration hydraulique des cours deau. Guide de lapplication.Cemagref et Agence de lEau Rhne, Mditerrane et Corse, Lyon (54 pp.).

Lamouroux, N., Pella, H., Vanderbecq, A., Sauquet, E., Lejot, J., 2010. Estimkart 2.0: Uneplate-forme de modles cohydrologiques pour contribuer la gestion des coursd'eau l'chelle des bassins franais. Version provisoire. Cemagref, Agence de l'EauRhne-Mditerrane-Corse, Onema, Lyon (45 pp.).

Landon, N., Pigay, H., Bravard, J.P., 1998. The Drme River incision (France): From assess-ment to management. Landsc. Urban Plan. 43, 119131.

Le Pichon, C., Gorges, G., Baudry, J., Goread, F., Boet, P., 2009. Spatial metrics and methodsfor riverscapes: Quantifying variability in riverine sh habitat patterns.Environmetrics 20, 512526.

Leopold, L.B., Marchand, M.O., 1968. On the quantitative inventory of the riverscape.Water Resour. Res. 4, 709717.

Leviandier, T., Alber, A., Le Ber, F., Pigay, H., 2012. Comparison of statistical algorithms fordetecting homogeneous river reaches along a longitudinal continuum. Geomorphol-ogy 138, 130144.

Marcus, W.A., Fonstad, M.A., Legleiter, C.J., 2003. Management applications of optical re-mote sensing in the active river channel. In: Kondolf, G.M., Pigay, H. (Eds.), Toolsin uvial geomorphology. Wiley, Chichester, pp. 1941.

Mari, J.F., Le Ber, F., Lazrak, E.G., Benot, M., Eng, C., Thibessard, A., Leblond, P., 2011. UsingMarkov models to mine temporal and spatial data. In: Funatsu, K., Hasegawa, K. (Eds.),New fundamental technologies in data mining. InTech, Rijeka, pp. 511584.

Neteler, M., Mitasova, H., 2002. Open source GIS: A GRASS GIS approach, 689. Kluwer Ac-ademic Publisher (464 pp.).

Notebaert, B., Piegay, H., 2013. Multi-scale factors controlling the pattern of oodplainwidth at a network scale: The case of the Rhne basin, France. Geomorphology200, 155171.

Nyquist, H., 1928. Certain topics in telegraph transmission theory. Trans. Am. Inst. Electr.Eng. 47, 617644.

O'Callaghan, J.F., Mark, D.M., 1984. The extraction of drainage networks from digital ele-vation data. Comput. Vision Graph. Image Process. 28, 323344.

O'Neill, R.V., Deangelis, D.L., Waide, J.B., Allen, G.E., 1986. A hierarchical concept of ecosys-tems. Princeton University Press, Princeton (262 pp.).

Pigay, H., Walling, D.E., Landon, N., He, Q., Libault, F., Petiot, R., 2004. Contemporarychanges in sediment yield in an alpine mountain basin due to afforestation (theupper Drme in France). Catena 55, 183212.

Saunders, W., 1999. Preparation of DEMs for use in environmental modeling analysis,1999. ESRI User Conference. ESRI Online, San Diego, CA.

Schumm, S.A., 1977. The uvial system. Wiley, New York (338 pp.).Swanson, F.J., Janda, R.J., Dunne, T., Swanston, D.N., 1982. Sediment budgets and routing in

forested drainage basins. USDA Forest Service, general technical report PNW-141,Portland, OR (165 pp.).

Tarboton, D.G., Bras, R.L., Rodriguez-Iturbe, I., 1991. On the extraction of channel networksfrom digital elevation data. Hydrol. Process. 5, 81100.

Thorp, J.H., Thoms, M.C., Delong, M.D., 2006. The riverscape ecosystem synthesis:Biocomplexity in river networks across space and time. River Res. Appl. 22, 123147.

Tormos, T., 2010. Analyse l'chelle rgionale de l'impact de l'occupation du sol dans lescorridors rivulaires sur l'tat cologique descours d'eau. Ph.D. Thesis Institut des Sci-ences et Industries du Vivant et de l'Environnement, Paris, France (507 pp.).

Vannote, R.L., Minshall, G.W., Cummins, K.W., Sedell, J.R., Cushing, C.E., 1980. The rivercontinuum concept. Can. J. Fish. Aquat. Sci. 37, 130137.

Ward, J.V., Tockner, K., Schiemer, F., 1999. Biodiversity of oodplain river ecosystems:Ecotones and connectivity. Regul. Rivers Res. Manag. 15, 125139.

Wiederkehr, E., 2012. Apport de la gomatique pour une caractrisation physique multi-chelle des rseaux hydrographiques. laboration d'indicateurs appliqus au bassindu Rhne. Ph.D. Thesis cole Normale Suprieure de Lyon, Lyon, France (287 pp.).

Wiens, J.A., 2002. Riverine landscapes: Taking landscape ecology into the water. Freshw.Biol. 47, 501515.

Williams, W.A., Jensen, M.E., Winne, J.C., Redmond, R.L., 2000. An automated techniquefor delineating and characterizing valley-bottom settings. Environ. Monit. Assess.64, 105114.spacing) showing the two geomorphic organizations are not linked. Aoolbox package for multiscale riverscape exploration, Geomorphology

FluvialCorridor: A new ArcGIS toolbox package for multiscale riverscape exploration1. Introduction2. Required extensions and toolboxes3. General framework3.1. Extracted fluvial units and spatial disaggregation3.2. Metrics calculation and spatial aggregation

4. Automatic production of a multiscale geomorphic database4.1. Required input data4.2. Extraction of UGO-scale units4.2.1. Stream network extraction (UGO1)4.2.2. Valley bottom extraction (UGO2)4.2.3. Centerline extraction (UGO4)

4.3. DGO-scale database from spatial disaggregation4.4. Metric characterization of the riverscape4.5. Extraction of homogeneous reaches and longitudinal discontinuities

5. Application example on the Drme River (France)5.1. Background5.1.1. Study area5.1.2. Available data

5.2. Methods5.3. Results5.3.1. Upper part of the Drme River: reach C15.3.2. Lower part of the Drme River: reach C2

5.4. Conclusion

6. Discussion and conclusions6.1. FluvialCorridor limitations6.2. Other potential or derived metrics6.3. Added value and further improvements6.4. A geomatic workflow for answering different questions at a network scale

AcknowledgementsReferences

fluvial corridor n

Documents