Optical remote mapping of rivers at sub-meter resolutions and watershed extents · 2009-09-24 · Optical remote mapping of rivers at sub-meter resolutions and watershed extents W

Optical remote river mapping at sub-meter resolutions and watershed extents 1

Copyright © 2007 John Wiley & Sons, Ltd. Earth Surf. Process. Landforms (2007)DOI: 10.1002/esp

Earth Surface Processes and LandformsEarth Surf. Process. Landforms (2007)Published online in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/esp.1637

Received 9 September 2007;Revised 23 October 2007;Accepted 26 October 2007

Introduction

Ever-accelerating advances in technology and methods are altering river science. Among these methods, opticalremote sensing of rivers has experienced some of the most dramatic advances over the past decade. Case studies haveestablished the feasibility of measuring in-stream parameters ranging from wood cover to stream depth to substratesize. To date, however, these studies have largely been proof-of-concept studies at local or reach scales.

This article asserts that it is time to move beyond locally based efforts and create initiatives that support opticallybased, spatially continuous, sub-meter resolution river mapping at watershed extents. To support this thesis, we firstmake the case that continuous, sub-meter resolution remote mapping of rivers at watershed extents is a desirable andnecessary goal to advance river science. We then cite recent studies showing that remote mapping of many riverparameters is now possible, develop a map of stream depths to demonstrate feasibility and utility at basin extents andoutline significant obstacles and conceptual issues that remain. Finally, we contend that addressing these issues andopportunities requires broader-scale collaborative research and funding initiatives.

Review

Optical remote mapping of rivers at sub-meterresolutions and watershed extentsW. Andrew Marcus1* and Mark A. Fonstad2

1 Department of Geography, University of Oregon, Eugene, OR, USA2 Department of Geography, Texas State University, San Marcos, TX, USA

AbstractAt watershed extents, our understanding of river form, process and function is largely basedon locally intensive mapping of river reaches, or on spatially extensive but low density datascattered throughout a watershed (e.g. cross sections). The net effect has been to charac-terize streams as discontinuous systems. Recent advances in optical remote sensing of riversindicate that it should now be possible to generate accurate and continuous maps ofin-stream habitats, depths, algae, wood, stream power and other features at sub-meterresolutions across entire watersheds so long as the water is clear and the aerial view isunobstructed. Such maps would transform river science and management by providingimproved data, better models and explanation, and enhanced applications.

Obstacles to achieving this vision include variations in optics associated with shadows,water clarity, variable substrates and target–sun angle geometry. Logistical obstacles areprimarily due to the reliance of existing ground validation procedures on time-of-flight fieldmeasurements, which are impossible to accomplish at watershed extents, particularly inlarge and difficult to access river basins. Philosophical issues must also be addressed thatrelate to the expectations around accuracy assessment, the need for and utility of physicallybased models to evaluate remote sensing results and the ethics of revealing informationabout river resources at fine spatial resolutions. Despite these obstacles and issues, catch-ment extent remote river mapping is now feasible, as is demonstrated by a proof-of-conceptexample for the Nueces River, Texas, and examples of how different image types (radar,lidar, thermal) could be merged with optical imagery. The greatest obstacle to developmentand implementation of more remote sensing, catchment scale ‘river observatories’ is theabsence of broadly based funding initiatives to support collaborative research by multipleinvestigators in different river settings. Copyright © 2007 John Wiley & Sons, Ltd.

Keywords: remote sensing; rivers; fluvial; geomorphology; hydrology

*Correspondence to: W. AndrewMarcus, Department ofGeography, University of Oregon,Eugene, OR 97403-1251, USA.E-mail: [email protected]

2 W. Andrew Marcus and Mark A. Fonstad


To make our case, we focus on illustrative case examples. The article emphasizes digital analysis of optical imagery(as contrasted to qualitative air photo interpretation) and the mapping of in-stream or active channel features ratherthan floodplain environments, where classic vegetation monitoring techniques play a major role. In discussing thesetopics, our goal is to prompt a broader conversation within the research and management community regarding whetheroptical remote sensing of rivers at watershed extents deserves greater emphasis in research agendas and funding. Thepaper therefore highlights and evaluates recent advances and capabilities specific to optical remote sensing of rivers.For a longer-term perspective and more comprehensive review of remote sensing history, techniques, sensors andapplications in geomorphology, we refer readers to the work of Gilvear and Bryant (2003) and Mertes (2002).

Rationale for Optical Mapping of River Featuresat Sub-Meter Resolutions and Watershed Extents

The case for optical imageryOptical imagery records reflected sunlight in the visible and shortwave infrared spectra. Because it records reflectedsunlight, optical imagery is distinct from thermal imagery, which records long wave emissivity, and from lidar andradar, which send out a signal and record the timing and intensity of its return (Campbell, 2007).

Relative to other sensor types, the major advantage of optical imagery for remote sensing of rivers is that it directlyrecords sub-aqueous information, a phenomenon familiar to anyone who has seen the bottom of a stream with thenaked eye. In contrast, radar and lidar are often scattered and reflected at the water surface or absorbed in the watercolumn, while thermal imagery records near-surface emissivity. The ability of sunlight to penetrate water means thatoptical imagery has the potential to capture surface features (e.g. riffles), water column characteristics (e.g. depth orturbidity) and stream bottom parameters (e.g. algae and substrate) (Figure 1), so long as the water is relatively clearand the line of view is not obstructed by trees or other features.

Optical imagery is also widely available through government mapping agencies and commercial vendors that collectand archive airborne and satellite optical imagery. The avoidance of special missions to collect imagery can reduce costs,although purchasing existing high resolution images can be expensive in some nations, especially for large portions ofa river. Optical cameras are sufficiently simple, however, that an increasing number of researchers and managementagencies are collecting their own imagery using platforms ranging from balloons to drones to private aircraft.

Figure 1. Photos of (a) surface turbulence, bars and in-stream habitats, (b) water column characteristics such as depth and clarityand (c) stream bottom parameters such as algae and substrate. In clear-water streams, optical imagery has the advantage of seeingthrough the water column to the stream bottom. This figure is available in colour online at www.interscience.wiley.com/journal/espl



Figure 2. A map of water depths based on historic color imagery (digital orthophotoquads), discharge data from a local gage andthe HAB model (Fonstad and Marcus, 2005). The ability to generate maps such as this enables change-over-time analysis even whentime-of-flight field measurements were not collected. This image shows the lower end of Soda Butte Creek, Yellowstone NationalPark, on 22 August 2001.

The availability of historic optical imagery also enables change-over-time analysis that can extend back to the earlydays of aerial photography. Geomorphologists have long used air photos to document changes in channel parameterssuch as width and sinuosity. In addition, recent advances enable recovery of water depths if stereo photos are availableor if synchronous historic color images and discharge are available (Figure 2).

Finally, people are used to interpreting raw output (i.e. photos) from optical sensors. Optical imagery thus isparticularly well suited for non-experts, public meetings and rapid response situations, where time for extensive imageprocessing or explanation is limited.

The strong case for using optical imagery does not imply a weak case for thermal or active sensing of rivers. Inrecent years and at watershed extents the use of non-optical sensors has outpaced the use of optical imagery, parti-cularly in the context of lidar mapping of valley bottoms (Jones et al., 2007), radar measurement of cross-sectionaldischarge (Spicer et al., 1997; Costa et al., 2000) and thermal mapping of streams (Torgersen et al., 1999, 2001).However, these sensors provide different information than optical imagery, and should therefore be viewed as com-plements rather than replacements for optical imagery.

The need for continuous, watershed-extent, sub-meter-resolution mappingMost research on fluvial geomorphology, river hydrology, and aquatic ecology has derived from (1) locally intensivedata collected in a small number of river reaches or (2) spatially extensive but low density data scattered throughout awatershed, as when we use widely dispersed cross sections to infer hydraulic geometry. To be sure, these localmeasurement techniques have enabled major advances in river science since the 1950s. Moreover, continuous,watershed-wide mapping of sinuosity, width and bar area has been possible with air photos. In general, however,existing techniques do not provide meter-resolution, watershed-extent maps of many parameters such as waterdepth, sediment size, wood distribution and stream power that are of critical importance to scientists investigatingstream behavior and to biota that experience the river at the sub-meter scales. The net effect of using locally intensivemeasurements, or low density spatially extensive measurements, has been to characterize streams as discontinuoussystems. Yet the reality is that rivers vary continuously along their lengths at scales ranging from centimeters (aswith turbulence) to meters (e.g. depth, bed sediment size) to kilometers (e.g. wood loading) to tens and hundredsof kilometers (e.g. discharge).

Recent research indicates that optical imagery can provide fine resolution, watershed coverage of in-stream features,and thus portray rivers as continuous systems. Optical imagery at this resolution can be obtained from both satellites



Figure 3. (a) True color IKONOS satellite imagery at 4 m resolution for the confluence of Soda Butte Creek and the Lamar River,Yellowstone National Park, USA, (b) a linear enhancement of the IKONOS image that indicates how in-stream variations can bedetected even from satellites and (c) an example of stitched together, airborne imagery at a 1 m resolution for the Brazos River,Texas, USA. Optical satellites can now provide fine spatial resolution, while aircraft imagery can cover wide spatial extents. Thisfigure is available in colour online at www.interscience.wiley.com/journal/espl

and aircraft (Figure 3). These advances in remote sensing of rivers have the potential to transform river science byproviding the following.

• Improved data, with continuous characterization of multi-scalar variations of many stream parameters such aswater depth, sediment size, wood and stream power. In fact, we will be able to measure or estimate parameters ata resolution and extent never before monitored (similar optimism was shared by Lane and Chandler, 2003). Muchas with the invention of the microscope or telescope, we may expect this to lead to the following.

• New observations of system components and behavior. In turn, this should promote the following.

• New understandings, better models, and new theories as spatially continuous data facilitate a deeper understandingof stream form, process and function across multiple spatial scales, and

• enhanced applications and social utility, as watershed-wide, detailed maps and models provide better guidance onriver monitoring and management.

Spectral Mapping of In-Stream Features

ApplicationsPrior to the late 1990s, the overwhelming majority of image-based river maps were based on visual interpretation orphotogrammetric analysis of air photos. A few researchers explored multispectral mapping of river features usingsatellite-based imagery, but large pixel sizes limited most analyses to large river settings and features that coveredlarge expanses. Multispectral mapping of smaller streams and in-stream features such as wood and riffles onlyemerged in the mid-1990s as airborne multispectral sensors provided pixel resolutions substantially smaller than thestream width. Since that time, the increasing accessibility of fine resolution optical imagery from aircraft, satellites anddrones has promoted a corresponding increase in investigations of potential applications of optical imagery in streams.

The following sections briefly summarize potential river mapping applications, early studies, and recent achieve-ments. Rather than being comprehensive, the examples provide an overview of the range of potential mappingapplications of high spatial resolution, multispectral imagery. Specific analytical approaches and common problemsare summarized in later sections of the article.

Turbidity and suspended sediment. Some of the earliest remote sensing of rivers focused on turbidity and sus-pended sediment estimates in large river settings (Aranuvachapun and Walling, 1988; Li, 1993; Mertes et al., 1993),river bay mouths (Klemas et al., 1973), estuaries (Reddy, 1993) and reservoirs (Harrington et al., 1992; Nellis et al.,



1998). Turbidity was an early focus of investigation because it is readily observed at the resolution of large satellitepixels, is relatively constant at reach scales and can be easily measured in the field in large river systems.

Organic litter, dissolved organic carbon and chlorophyll can cause variations in turbidity (Woodruff et al., 1999), souse of turbidity to estimate suspended sediment requires numerous adjustments or assumptions. Remote measurementof turbidity can also be complicated by variations in depth (Lafon et al., 2002) and sun angle (Morel and Bélanger,2006). Despite these obstacles, recent research indicates that satellite imagery can be a useful tool for monitoringlarge-extent variations in both turbidity and suspended sediments in larger rivers and river estuaries (Chen et al.,2007). To date however, with the exception of the work of Wass et al. (1997), little research has examined high spatialresolution mapping of turbidity in smaller inland streams.

Depth. The large majority of research on optical mapping of water depths focuses on near-shore, marine applica-tions (briefly reviewed by Fonstad and Marcus, 2005). Lyon et al. (1992) initiated much of the contemporary work onmapping river bathymetry by combining ground-based measurements and an optical model to classify depths into fiveintervals in the relatively large St. Marys River, Michigan. Gilvear et al. (1995) and Winterbottom and Gilvear (1997)started the present wave of high resolution river depth mapping by showing that spatially continuous, accurate depthmaps could be generated in small streams using multiple regressions of spectral response versus measured depths.Brasington et al. (2003) have shown that this depth-mapping approach can be used to calculate sediment transportrates via the morphological method.

Subsequent work identified complications in depth estimates associated with in-stream habitats and channel mor-phology (Marcus et al., 2003; Legleiter and Roberts, 2005) as well as substrate and in-stream vegetation (Legleiteret al., 2004; Gilvear et al., 2007; Lejot et al., 2007). Many of these variations can be adjusted for, however, bysegregating the stream by substrate type (see, e.g., Marcus et al., 2003) or by using band ratios (Legleiter et al., 2004;Legleiter and Roberts, 2005). Recent innovations include techniques that enable depth mapping without ground-baseddata at the time of flight (Fonstad and Marcus, 2005) (Figure 2) and simple approaches for correcting between-imageillumination differences that can confuse depth estimates (Carbonneau et al., 2006). Remote depth mapping is nowsufficiently advanced that Gilvear et al. (2007, p. 2241) noted that ‘airborne remote sensing provides the way forwardfor synoptic mapping of small streams (<20 m)’. The Compagnie Nationale du Rhône is already using optical remoteimagery to map bathymetry of large, clear-water side channels (Lejot et al., 2007).

Substrate size. Work on mapping sediment size in rivers is relatively recent, in part because current analyticaltechniques require special data collection flights to obtain centimeter-resolution imagery for delineating sizedifferences within gravels. Carbonneau and others have taken the lead in demonstrating that variations in textureprovide a robust approach for estimating variations in D50 along a river (Carbonneau et al., 2004, 2005a; Figure 4(a)).Accurate assessment of sand versus larger clast sizes can also be achieved using ground-based imagery at millimeterresolutions (Carbonneau et al., 2005b). Estimates of D50 derived from airborne imagery can be an effective tool fordetermining juvenile Atlantic salmon habitat use (Hedger et al., 2006).

The textural techniques, however, are not applicable to mapping fine grained sediment size distributions. Instead,Rainey et al. (2003) used subpixel linear mixing models to extract fine sediment sizes in estuarine sediments. Themixing approach is based on the optical theory that small spaces between fine grains act as blackbody cavities, therebyfilling in spectral absorption curves to a degree proportional to the grain size.

In-stream habitats. Hardy et al. (1994) achieved ‘close’ correspondence between ground surveys and maps ofpools, eddies and runs based on remote multispectral videography, although they did not quantitatively define themeaning of ‘close’. Subsequent efforts to remotely map microhabitats encountered various problems. Wright et al.(2000) showed that ground survey errors as small as 50 cm could lead to poor coregistration of ground maps and highresolution imagery, creating poor supervised classifications of in-stream habitats. Spatial and spectral resolution arealso critical, with pixel sizes substantially smaller than the size of the habitat units and multiple spectral bandsnecessary to achieve high accuracies (Legleiter et al., 2002; Marcus, 2002).

Marcus et al. (2003) used 1 m resolution, hyperspectral imagery to overcome the limitations of spectral resolutionand pixel size, and mapped directly to printouts of the imagery to insure close coregistration of imagery and field data.Using these techniques they achieved overall producer’s accuracies ranging from 68% in a third order stream to 86%in a fifth order stream (Figure 4(b)), with the size of the pixel relative to the in-stream habitat unit being the biggestlimitation on accuracy. Goovaerts (2002) improved these accuracies to 94% in the fifth order stream using a co-krigingalgorithm.

Submerged vegetation and algae. In contrast to the relatively large body of literature on mapping algae andsubmerged aquatic vegetation (SAV) in marine environments (reviewed by Vahtmäe et al., 2006), little work has beendone on remote mapping of SAV or algae in rivers. The potential for successful mapping, however, seems high.Submerged vegetation and algae are clearly visible to the naked eye (Figure 1(b), (c)) and have distinct spectralsignatures that allow them to be mapped within active channels (Figure 5(a)). Preliminary studies indicate that maps



Figure 4. Examples of (a) D50 sediment size map developed from 3 cm spatial resolution, true color imagery for the SainteMarguerite River, Quebec, Canada (Carbonneau et al., 2004, Figure 3), and (b) Bisson habitat classes mapped for the Lamar River,Yellowstone National Park, USA, following methods detailed by Marcus et al. (2003).

based on hyperspectral imagery are effective at detecting both submerged aquatic vegetation (Williams et al., 2003)and algae (Marcus et al., 2002).

Wood. The potential to map wood with fine resolution, multispectral imagery is particularly high, because wood hasa clear spectral signature relative to other active channel features, especially in the shortwave infrared. As with thefirst attempts to map microhabitats, however, early attempts to map woody debris encountered problems associatedwith inaccurate coregistration of field maps and imagery at sub-meter registrations (Marcus et al., 2002). Subsequentwork that coupled multivariate techniques to isolate the wood signal with spectral thresholding achieved classificationaccuracies of 83% using hyperspectral imagery (Marcus et al., 2003; Figure 5(c)) and 89% using digital air photos(Smikrud and Prakash, 2006). Marcus et al. (2003) note that thresholding techniques can be used with multispectral orhyperspectral imagery to detect wood that makes up only a portion of a pixel.

Other applications. The list of optical river mapping applications can be extended if one includes work based oncoarse resolution satellite imagery, measurement of parameters outside the low flow channel or topics receivingrelatively little research. Notable applications that fit these more general criteria include forecasting and detection ofice breakup on large rivers (Gatto, 1990; Pavelsky et al., 2004; Morse and Hicks, 2005), flood detection and monitor-ing (Barton and Bathols, 1989; Kishi et al., 2001; Brakenridge et al., 2005; Ip et al., 2006), detection of levee slides(Hossain et al., 2006), estimating erosion and deposition (Brasington et al., 2003; Lane et al., 2003), mapping generalhabitat types such as standing water and forest (Novo et al., 1997), measuring suspended chlorophyll (Karaska, 2004)and mapping derived variables such as stream power (Jordan and Fonstad, 2005).

This list of potential applications is expanding rapidly, especially if one includes fusion of optical data with lidar.Research is now under way to extract other hydraulic variables such as water surface slopes, velocity, Froude numbersand discharge using combinations of lidar and multispectral imagery. If successful, these approaches will enablemapping of many hydrologic parameters at meter resolution. Even without these additional applications, however, a



large number of important fluvial parameters can now be detected and mapped with optical remote sensing at accura-cies that are useful to river scientists and managers.

Remote mapping methodsThe number of remote sensing techniques applied to rivers is large and growing steadily. Initial studies either usedground-based data to drive supervised classifications (e.g. Wright et al., 2000; Marcus, 2002) or to derive correlationsbetween the ground measurement and spectral reflectance (e.g. Winterbottom and Gilvear, 1997). More recently,investigators have developed ways to bypass ground-based measurements (Fonstad and Marcus, 2005) or extend themto larger areas (Carbonneau et al., 2004), a critical step in enabling the use of these techniques at watershed extents.Overall, there have been four general approaches to characterizing rivers remotely: photogrammetry, empirical spec-tral classification and regression, physically based spectral approaches and spatial/textural approaches. Many tech-niques are hybrids between these categories.

Figure 5. Matched filter maps of algae (a) and wood (c) and the corresponding true color images created from 1 m resolution,PROBE-1 hyperspectral imagery in Cache Creek ((a), (b)) and the Lamar River ((c), (d)), Yellowstone National Park, USA. Images(c) and (d) modified according to Marcus et al. (2003, Figure 5). This figure is available in colour online at www.interscience.wiley.com/journal/espl

www.interscience.wiley.com/journal/espl



Photogrammetry. Photogrammetry, the science of making precision measurements from photographs, supplied themethods for the first analyses of rivers over wide extents. Analysts can couple a single, panchromatic aerial imagewith information on plane height, image size, camera dimensions and geometry relative to the ground to preciselymeasure lengths and widths of river landforms. If stereo imagery is available, analysts can measure the verticaldimension of the river landscape using either mechanical equipment (hard copy photogrammetry) or, more recently,digital computers, scanners and photogrammetric software (soft copy photogrammetry). While photogrammetricapproaches have been applied to above-water projects for some time, refraction-correction algorithms have allowedphotogrammetry to be applied to below-water areas as well (Butler et al., 2002; Westaway et al., 2000, 2001, 2003).Although the finding of tie points in stereo imagery is a laborious and tedious process, stereo photogrammetry can beapplied retroactively to archived imagery, which is an advantage over many other techniques. Current photogrammetricresearch has focused on extending classical photogrammetric concepts to oblique photography situations (Chandleret al., 2002; Ashmore and Sauks, 2006), such as bankside, non-metric cameras used for measuring river channel change.

Statistical analysis of spectral data. Some of the earliest applications of multispectral remote sensing to rivers usedsupervised classification or regression techniques (Campbell, 2007). These approaches are taught in introductoryremote sensing classes and remain a commonly used technique for remote sensing of rivers. Supervised classificationis used where the researcher is interested in discrete types of river environments such as pools and riffles or wood andnon-wood (Figure 5(c)). The Bisson et al. (1982) habitat classification, for example, describes low-flow salmonidhabitats in terms of discrete aquatic units that have different spectral signatures (e.g. whitewater in riffles or opticallydark water in pools) and are amenable to supervised classification (Figure 4(b)). Alternatively, ratio-level data such aswater depth or particle size are amenable to regression techniques. Both the classification and regression approachesrely on ground-based field data to calibrate and validate the relations.

Supervised classification techniques have been used to map in-stream habitats and forms (Legleiter et al., 2002; Marcus,2002; Whited et al., 2002; Marcus et al., 2003; Gilvear et al., 2004). Wright et al. (2000) extended this approach tofuzzy classifications of in-stream units, while Legleiter and Goodchild (2005) investigated fuzzy classifications coupledwith unsupervised classification. Techniques used to extract ratio-level data rather than nominal categories include thespectral unmixing of fine particle sizes by Rainey et al. (2003) and the spectral angle mapper approach to habitatclassification of Leckie et al. (2005). Regression-based approaches are used widely in the mapping of water depth(Winterbottom and Gilvear, 1997; Whited et al., 2002; Marcus et al., 2003; Lorang et al., 2005; Lejot et al., 2007).

Physically based models. Lyzenga (1978, 1981) laid the groundwork for characterizing water bodies without groundcalibration data through use of optical physics. To date, the physics-based approaches have yielded the best results inlake, ocean and coastal areas that (relative to rivers) are optically deep, geometrically simple, have homogenoussubstrates, and exhibit low surface turbulence. Much of this theory is appropriate for rivers (see, e.g., Hedley andMumby, 2003), but the requirements for fine resolution, multi- or hyperspectral data on ambient light fields make itdifficult to tailor these bottom-up approaches to complex river settings. Rather than taking a pure physics-basedapproach, early researchers therefore combined physically based and ground-based calibration/regression approaches(e.g. Lyon et al., 1992; Lyon and Hutchinson, 1995).

More recently, Legleiter et al. (2004) further developed the theoretical basis for physically based optical approaches inrivers, as well as suggesting some approximations to simplify the use of such models in complex riverine settings. Inparticular, they demonstrated that a log-transform of the green-over-red band ratio correlates linearly with water depthacross a wide range of substrate types and bottom albedos. Legleiter and Roberts (2005) refined some of this theory toextract water depth from a wide range of bottom environments. Fonstad and Marcus (2005) combined a simplified versionof the Beer–Lambert law of light absorption with the Manning equation and conservation of discharge to extract waterdepths in clear-water rivers without ground-based depth measurements. Most recently, Gilvear et al. (2007) documentedthe optical physics required to accurately constrain both depth and substrate characteristics of in-stream habitats.

Spatial/textural approaches. The limited spectral information and radiometric resolution in many aerial images hasprompted development of spatial techniques to map river features. The local spatial structure of a river can beobtained by moving a small window over an image to extract digital values beneath the window and derive theautocorrelation or semivariance structure. Once calibrated against field measurements, the spatial structure can be usedto estimate quantities such as particle size (Carbonneau et al., 2003, 2004, 2005a, 2006; Verdu, Batalla, Martinez-Casasnovas, 2005; Hedger et al., 2006) or in-stream habitats (Goovaerts, 2002; Maruca and Jacquez, 2002). Couplingof spatial approaches with object-detection algorithms can generate accurate measurements of the size and sorting ofindividual particles imaged at very close range (Butler et al., 2001, 2002; Graham et al., 2005). Textural measures shouldalso be useful in characterizing the surface turbulence of rivers, although this application remains, as yet, unexplored.

To date, all the spatial approaches require spatial resolutions that are smaller than the features of interest. In the caseof sediment size in particular, this may limit the application of spatial techniques to imagery acquired throughapplication-specific, low elevation, high resolution flight missions.



Obstacles and Issues

The general approaches and applications discussed above have been successful in characterizing river parameters atpoint to reach scales in a number of environments (Table I). Significant obstacles and conceptual issues need to beaddressed, however, before widespread application of these techniques is feasible. Understanding these issues iscritical to developing future lines of research, to extending the applications to watershed scales and to makinginformed decisions regarding appropriate use of remote sensed river measurements.

The obstacles to optical remote sensing of rivers are varied in their nature (Aspinall et al., 2002; Priestnall andAplin, 2006). We focus here on obstacles specific to rivers, which include optical conditions, special logisticalconcerns and philosophical issues related to mapping with high resolution remote sensing imagery. We do not addressissues common to a wide range of image mapping and surveying applications outside river environments such asplatform and sensor constraints, weather (e.g. cloud cover, haze) and survey equipment and personnel training.Throughout the presentation we highlight problems where additional research could make a significant and immediatecontribution to remote sensing of rivers.

The optical environmentThe optical environment of rivers is complex and variable through both space and time. Reflectance from water isalmost always significantly lower than from other landscape features, so the range of digital radiance values availablefor analysis is small compared with most terrestrial features. It is these optical considerations that place the mostsevere constraints on where and when optical remote sensing of rivers is feasible.

The most obvious optical constraint is that the stream must be visible from above. Optical remote sensing ofstreams cannot be done where trees, bridges, woods or other obstacles overhang the stream (Figure 6(a)). Theno-obstruction criterion is most constraining in headwater streams in forested environments, or along stream banks inlarger, forested streams.

Figure 6. Examples of variations in the optical environment that can confuse remote sensing of rivers: (a) obstruction by treesand shadows, (b) turbidity, which prevents viewing of the full water column and stream bottom, (c) variations in substrate thatcreate different reflectances for similar depths and (d) sun glint (specular reflectance), which prevents viewing of the water column.This figure is available in colour online at www.interscience.wiley.com/journal/espl




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The need for clear-water conditions can also be a significant constraint (Figure 6(b)), except when the goal is remotemeasurement of suspended sediment, turbidity or surface features (e.g. wood, emergent bar sediment size, surfaceturbulence). Techniques for remote mapping of features such as submerged aquatic vegetation, depth or substrate relyon light penetrating to the stream bottom, being reflected and returning to the sensor. Streams that fail to meet thiscriterion are not ‘clear-water streams.’ There is currently no firm geographical understanding as to which streams areclear water or not, how this varies with stream order or geographic region and how it varies temporally. Likewise,there is not yet an automated, image-based technique for determining whether light has penetrated to the streambottom or not, which would enable remote determination of whether streams were clear water.

Even in clear-water settings, the natural spatial and temporal variability of streams can generate different reflectionsfor similar features. Identical depths, for example, may have different reflectances due to surface turbulence, variationsin substrates (Figure 6(c)), changes in turbidity or shadow (Figure 6(a)). Shadows pose a particular problem. Mostresearch to date has manually masked out the shadowed portions of streams. Yet shadows create sharp edges, whichshould be detectable with appropriate algorithms. Moreover, shadows rarely obscure all of the outgoing radiation fromthe water, so it may be possible to recalibrate shadowed pixels to extract meaningful in-stream information.

The flip side of this problem is that different features can exhibit similar reflectances. Wind blowing waves alongthe water surface can have a spectral signature almost identical to a riffle, while turbidity or shadow can make waterappear deeper that it is relative to nearby sites with clear water or direct sunlight.

Compounding these problems, water is particularly sensitive to the sun–target–sensor geometry; most river pixelslook very different when observed at different times of day or from different angles. Images taken at a low angle mayrecord reflected images from near-channel objects such as trees or sky signal from clouds, although this effect is morerare in vertical aerial photos taken from a fair distance above the water. The most extreme version of this effect isglare from specular reflectance (Figure 6(d)), which can wash out all river information. Most of these problems can beeliminated or reduced with careful flight planning relative to sun angle, although the consistently low sun angels inpolar environments may pose a particular challenge in this regard.

Bidirectional reflectance and variations in lighting across individual images pose a problem when mosaickingdozens or hundreds of aerial images into long mosaics. Standardizing the image contrast currently requires skilled,manual adjustments by remote sensing specialists. Carbonneau et al. (2006) developed techniques to normalizelighting conditions in one attempt to deal with this type of issue. Some automated compilation procedures exist inmost software packages, but their performance has not been tested in river settings. Spaceborne imagery may over-come compilation concerns by providing large extent images of rivers, but at the cost of spatial, radiometric andspectral resolution, issues that are particularly significant in the low light environment of rivers (Legleiter et al., 2002;Priestnall and Aplin, 2006).

LogisticsRivers pose a number of logistical obstacles related to image acquisition, field calibration and validation, and imageprocessing. Acquisition of fine spatial resolution, optical imagery poses problems for a variety of reasons. Aircraftoperators use straight flight lines that rarely follow the curvature of rivers, especially when close-to-the-ground, highspatial resolution imagery is required. River flights thus typically generate multiple flight lines that must be stitchedtogether, or resort to higher elevation airborne or satellite imagery at the cost of lower resolution.

More difficult to accommodate is river investigators’ need to have imagery when the water is clear, clouds areabsent (to increase illumination of the relatively dark water) and field teams are available (if time-of-flight ground dataare required). These timing constraints are particularly problematic when working with providers who are trying to fita river flight into a calendar filled with other missions in different locations. Given the difficulties in relying on outsidevendors, it is not surprising that increasing numbers of river researchers are acquiring their own aircraft, platforms andsensors to record river information (e.g. Lejot et al., 2007) or are developing techniques to use existing airborne orsatellite imagery (e.g. Lane, 2000; Fonstad and Marcus, 2005).

Associated with the issue of flight timing is the timing of field data. Timing of field data is not an issue for somefeatures such as wood and substrate, assuming no major flows mobilize the feature between the time of image fielddata acquisition. Timing is an issue, however, for the many techniques that require calibration and validation data tomap features that vary with discharge and time, such as depth, in-stream habitats and algae. These projects requirefield teams to collect data at or near the time of flight, which is impossible when field teams have to cover multiplerivers or an entire watershed, or when one is using historic imagery. Solving this issue is one of the major obstaclesto remote mapping of rivers at watershed scales. One hopeful line of inquiry is the development of models that donot require field calibration (see, e.g., Fonstad and Marcus, 2005). For hydraulic variables, another alternative may bethe coupling of lidar and field based bathymetry with 2D models to generate simulated calibration and validation



hydraulic data for the time of flight. In this manner, topographic field data that are not synchronous with the dayof flight might be used to provide hydraulic ground truth data throughout a watershed. Sensitivity analysis appliedto river imaging algorithms might provide an alternative for estimating confidence intervals associated with historicimagery.

Once imagery and field data are acquired, investigators and river mangers are constrained by the lack of lack oftrained personnel and absence of automated algorithms for river data preparation and analysis. Because the analysesrequire a relatively high level of expertise, and because there is not a history in hydrology and geomorphology oftraining people in these techniques, it can be difficult to find staff or students who can implement even the existingmethods, much less develop solutions to problems. Availability of automated (i.e. menu driven or publicly available)software and/or data sets and tutorials for training could help to make remote sensing of rivers accessible to a broaderaudience.

Finally, high spatial resolution remote sensing of rivers at watershed extents can be expensive compared withconventional techniques, especially if new imagery has to be flown. Parties interested in using the new techniquesneed to weight the expense of new imagery and materials for analysis against the information cost of using traditionalmethods that do not provide continuous coverage. The price of digital imagery has come down dramatically over thepast decade as more image sources become available, making the use of remote imagery ever more appealing from abenefit–cost perspective.

What is a ‘valid’ measurement?Spatial resolution, spatial extent and accuracy versus precision. Because nearly all observational fluvial geomor-phology is based on traditional point-based field methods, the accuracy and precision of a new technique such assynoptic measurements from remote sensing are often suspect when viewed by the wider research community. Part ofthe suspicion is rooted in the belief that high precision is one of the most important goals in field measurement. Whencompared with most point-based in situ equipment, remote sensing techniques are not as precise. Why use them whencharacterizing modern streams? The typical answer by researchers developing remote sensing for river applications isthat the synoptic information generated by remote sensing gives a high level of accuracy when compared with thespatially poorer in situ measurements. However, there is very little theory about what levels of accuracy and precisionare required to answer specific research questions, and this lack of application theory is an obstacle to wide use ofremote sensing techniques in fluvial geomorphology.

By way of example, Table I lists some recent accuracies reported for remote-sensing-based river mapping. Formany classically trained field geomorphologists, agreement between ground and remote-sensing-based measurementson the order of 80–90% may seem unacceptable. Yet the accuracy metrics do not tell the whole story. In part, this isbecause accuracy metrics are subjective in certain cases. For example, the producer’s accuracy (Congalton and Green,1999) reported by Marcus et al. (2003) for mapping of wood is based on a spectral thresholding approach, where thethreshold is chosen by the user (Figure 5(c)). Changing the threshold could have increased the accuracy to 100%, butat the cost of classifying many non-wood features as wood (i.e., decreasing the user’s accuracy).

Even the relatively objective R2 accuracy approach of comparing ground-based with remote sensing measurementscan be misleading. Fonstad and Marcus (2005), for example, noted that almost all errors in their remote-sensing-baseddepth estimates fell within a ±15 cm range. Such a range might be fine for many applications and settings, particularlywhen one considers that the data provide spatially comprehensive measurements for every pixel in the river. More-over, the field-based measures used for validation were at the resolution of a stadia rod, while the remote measurementswere for a ~1 m pixel. In the gravel and cobble bed streams examined by Fonstad and Marcus, a 1 m pixel wouldoften encompass ±10 cm variations depending on the location and size of clasts.

A number of researchers have therefore argued that remote sensing maps are more accurate at documenting localvariability and overall trends than is suggested by R2 or ‘percent correctly classified’ values. Winterbottom and Gilvear(1997), Marcus et al. (2003) and Fonstad and Marcus (2005) all demonstrated that cross sections and bathymetricmaps derived from remote sensing provide highly realistic portrayals of overall stream bathymetry (Figure 7).Likewise, Legleiter et al. (2002) and Marcus (2002) argued that pixel-scale variability of in-stream units (Figure 4(b))is missed by field teams, who tend to lump features together into large, homogenous units. As a result, much of the‘misclassification’ error associated with the remote classifications represented true variability at a pixel scale that wasmissed in the ground ‘truth’ data collected by field teams. Likewise, many of the ‘inaccuracies’ in wood mappingprobably represent hyperspectral detection of wood at resolutions smaller than were mapped by field teams (Marcuset al., 2003).

These concerns regarding accuracy and precision indicate that it is time to reconsider the nature of validation forremotely sensed imagery. If, as has classically been done, ground measurements are to provide validation for remotely



Figure 7. Grayscale imagery and derived cross-sections using spectral absorption techniques for the Brazos River, Texas, USA.

sensed stream parameters, what sorts of things ought to be measured on the ground? The most obvious are thosemetrics that reflect direct physical processes, such as water velocity, depth or bottom substrate size. But the resolutionof field measurements typically does not accurately reflect variations at pixel resolutions, creating mismatches that area function of scale rather than a true test of the imagery. Even more worrisome are ground measurements made usingclassification systems. Classes used by fluvial geomorphologists such as ‘pool’, ‘riffle’, ‘sand’, ‘algae’ or ‘whitewater’might be useful, but they are not ratio level measurements amenable to continuous, precision measurements. Time andagain, remote sensing observations of streams have shown continuous transitions between classes that are more ‘true’than the classified ‘ground truths’. It might be an important time for practitioners to consider broadening the notion of‘truth’, especially amongst classed information. Fuzzy set theory applied to classification (Legleiter and Goodchild,2005) is one approach to confronting this conundrum.

This is not to argue that remote measurements are consistently more accurate than ground-based work. Rather, it isto make the point that (1) classic metrics of remote sensing accuracy do not tell the whole story and (2) users shouldbalance the potential inaccuracies of remote sensing mapping techniques with the pixel-resolution, river-wide dataprovided by such techniques. There is no single answer as to which techniques are preferable. However, users shouldnot dismiss remote mapping techniques simply because classification measures of ‘percent correct’ or R2 fall belowsome arbitrary threshold. Clearly, more robust approaches for validation and accuracy assessment are needed forremote sensing mapping at the resolution and scale of rivers.

Empirical and Physical Modeling Approaches to Validation. Validation of river remote sensing results is usuallydone by documenting river features to use for ground ‘truth’ (e.g. Table I), although the image data is actually a directmeasure of light entering the optical device rather than a measure of river features. The difference between recorded



light and empirical river measurements raises the possibility (perhaps the certainty) that there are unmeasured factorsin- and outside the river environment that affect the image information. The optical geometry of a camera relative tothe ground, for example, can change the path length, focus and bidirectional reflectance (Figure 6(d)).

Researchers have tackled the issue of characterizing the light environment of rivers in several ways. One approachis to directly measure the light environment in the field with a hand-held spectrometer or similar instrument (e.g.Figure 1(a)). Another approach is physically based modeling of light through various media and optical conditions toshow what light information a particular river environment should have produced. The modeled information can thenbe compared with the image to evaluate physical controls on variations in the recorded imagery. A secondary use ofsuch algorithms is to solve the inverse problem: that is, define the physical river environment that must have existedto produce the light information being collected by the imaging instrument.

Optical modeling approaches have rarely been used in remote sensing of rivers, but they are becoming increasinglyimportant. Legleiter et al. (2004), Legleiter and Roberts (2005), and Gilvear et al. (2007) have used the Hydrolightmodel (Mobley, 1998) to examine how spectra behave in various river environments. In addition to identifyingpossible sources of error such as radiometric resolution limitations, modeling can also show where and when remotesensing of various river variables is most likely to work. One example is the modeling and experimentally basedfinding that the natural logarithm of the ratio of green over red radiance is linearly related to water depth over a verywide range of bottom substrates (Legleiter et al., 2004). This finding provides guidance on reasonable approaches tomapping water depths using color aerial imagery.

While physical models provide insight to river optics, the extraordinary complexity of river environments makes itdifficult to apply such models in the natural environment. In addition to the three-dimensional nature of the water, thespatially varying turbidity levels, particle-and-shadow relationships and unknown bottom albedo distributions, thereare physical water phenomena that are not well understood in an optical sense. Surface turbulence, for example,cannot be directly analyzed from first principles, so simulating the physics of photon–turbulence interactions can onlybe accomplished using approximations. In large water bodies, such as oceans and lakes, the amount of surfaceturbulence is low, and the geometrical properties of the water surface can be simulated through relatively simplepostulates such as symmetrical water waves of various lengths. Software packages such as Hydrolight rely on suchassumptions, and have been quite successful in these low-turbulence environments. Rivers with fully developedturbulent regimes are much more complicated. Until more sophisticated theory and measurement capabilities aredeveloped, researchers will have to work with partly empirical, partly physically based optical theory to evaluate andvalidate their results.

EthicsThe level of detail and breadth of river extent revealed by remotely sensed analyses and the increasingly widespreadavailability of high resolution imagery tremendously increases the potential for local and basin-wide over-exploitationof riverine resources. Basin-wide maps of in-stream habitats at the scale of individual spawning beds and fishingholes can be generated by students who have taken introductory remote sensing. Besides being useful to riverscientists and managers, these maps are of interest to a wide range of resource users ranging from anglers to kayakersto miners. As such, the maps will have commercial potential and can promote use of areas that previously were offthe beaten path. We have observed this very behavior in Yellowstone National Park while field validating depthmaps. Curious anglers noted the ‘giant hole’ shown on our maps about 2 km from the road and immediately hikedto that location. Before departing, they commented that they had usually stayed within about 500 m of the roadprior to realizing that such a prime fishing spot existed. They asked whether we would have maps for sale in the nearfuture.

Unfortunately, present ethical codes of conduct for remote sensing provide little, if any, guidance on issues relatedto high resolution depiction of river habitats. Instead, most codes focus on professional conduct related to dataintegrity, professional credentials and competition (e.g. ASPRS, 2007). More recently, some codes have addresseddata ownership, data access and national security threats resulting from data sharing (e.g. Federal Geographic DataCommittee, 2005). A final category of ethics codes encourages use of remote sensing to benefit of people of alleconomic backgrounds, so long as the uses are legal under national or international laws (e.g. United Nations GeneralAssembly, 1986). Finally, individual countries have a wide range of constraints on remote sensing that relate toprivacy issues. To our knowledge, none of these codes directly address potential damages to natural resources result-ing from remote imaging of the environment.

River scientists should consider developing and adopting ethics guidelines for data development and release, muchas the social sciences do with human subject surveys or census data. In some cases this may lead to restriction of dataaccess or intentional degrading of imagery in sensitive areas.



Figure 8. Nueces River imagery segmented for analysis: (a) catchment scale; (b) segment scales; (c) reach scale (× symbols areseparated by 100 m intervals). This figure is available in colour online at www.interscience.wiley.com/journal/espl

Applications at Basin Extents

Despite the obstacles and issues described above, many of the impediments to fine spatial resolution, optical mappingof rivers at watershed extents are tractable in the near term. In fact, several researchers have already mapped specificvariables continuously at basin extents. Carbonneau (2005) and Carbonneau et al. (2003, 2004, 2005a, 2005b, 2006)used optical imagery to map subaqueous sediment sizes over long distances, while Jordan and Fonstad (2005) derivedwatershed-extent maps of stream power using aerial photos. The point to be made, however, extends beyond the factthat obstacles can be overcome and that remote mapping of rivers is feasible at watershed extents. Equally importantis the recognition that watershed-extent, pixel-resolution, optically based maps provide fundamental insights intosystem form and function that cannot be readily achieved through classic field approaches. The following briefexample from the Nueces River, Texas, USA, is provided to highlight how remote sensing can offer an alternativeperspective to a classic, field-measurement-based geomorphic principle: hydraulic geometry (Leopold, 1953).

In the United States, the National Aerial Photography Program takes high resolution aerial photos of most of thecountry every few years and converts these photos to georectified digital orthophoto quadrangles. Stream gages can beused to find the discharge in these imaged rivers at the time of the aerial photos and topographic maps provide averagebed slope. Using the hydraulically assisted bathymetry (HAB) approach (Fonstad and Marcus, 2005), one can com-bine the discharge data, topographic slope and digital image brightness values with the conservation of mass law andthe Beer–Lambert law (for exponential decay of light as it passes through a medium) to derive water depth for everypixel in an image.

Figure 8 provides an overview of the 33 digital orthophoto quadrangles used with the HAB technique to derivehydraulic data for the Nueces River, Texas. Small variations in photo contrasts between images were corrected byedge-matching. Although the HAB technique can generate data for every pixel continuously along the river, datawere extracted for cross-sections at 100 m intervals to make the analysis and visualization more tractable for thisdemonstration.

High resolution analysis of the hydraulic geometry of the Nueces River shows a tremendous amount of stochasticvariation around the classical power-law trends for width and depth portrayed by hydraulic geometry analysis(Figure 9(A)). While the standard power law exponents are recovered using the remote sensing techniques, the amountof local variability overwhelms much of the systematic trends. Even if the precision errors of the water depthsapproached 40% (which is unlikely), the large sample provided by the HAB-based values generates a statisticallysignificant and accurate portrayal of major departures from hydraulic geometry. Further analyses of these trends showthat the largest deviations from the classical power-law means are positive errors and that the errors from the powerlaw are strongly skewed and, in the case of the depth, are bimodal (Figure 9(B)). One of the reasons that this level of




Figure 9. Downstream hydraulic geometry plots for the Nueces River’s low flow depth (A), and the basin-wide distribution ofdeviations from the downstream hydraulic geometry curve for depth (B).

stochasticity has not been widely recognized by the geomorphic community is that a large sample size must becollected to capture the deviations; something difficult to do with traditional techniques, but easy with remote sensingmethods.

Beyond demonstrating deviations from hydraulic geometry, the remote-sensing-based depths can be used to derivespatial maps of stream power per unit length and per pixel (see, e.g., Jordan and Fonstad, 2005). Over short distances,the deviations from watershed-wide stream power trends and the local information on depths and widths can guideinvestigations on local controls on stream energetics and structuring of stream habitats. Finally, the detailed dataenable multi-scalar exploration of patterns. Although it is not yet apparent what new empirical patterns or theories will



be generated by remote mapping of rivers, results from even this brief example suggest that continuous remotemapping will probably offer new insights into river form and function.

The potential for improved watershed-extent mapping and new insights is even larger when optical imagery iscombined with other image types such as lidar. Although optical images are the only data available for most historicalanalyses, new and future missions are increasingly coupling optical imagery with other image types, making possiblea new range of river observations (Mertes, 2002). While this article focuses on high resolution optical imagery, herewe highlight examples of how coupling optical imagery with other data types can enhance river analyses.

Lidar imaging of rivers opens the possibility that water surface elevations can be mapped at high spatial resolution(Schumann et al., 2007). In the absence of a way to measure velocity remotely, such water surface slopes are goodanalogues to the energy gradient. Combining the water surface slopes with remotely derived depth maps and two-dimensional hydraulics models (Bates et al., 1992) enables extraction of roughness coefficients such as the Darcy–Weisbach friction factor or Manning’s n (Mason et al., 2003). Particle size mapping a là Carbonneau (2005), whenadded to these maps of depth, velocity and roughness, could be used to construct fundamental relationships betweenflow dynamics and variations in sediment; the level of detail in these relationships would be similar to those devel-oped in laboratory flume experiments, but would have the added benefit of real-world scaling and real-world geo-graphical distributions. Adding high resolution, passive thermal imaging (Torgersen et al., 1999, 2001) could addtemperature data useful for determining habitat characteristics. Remapping rivers with this same suite of instrumentscould be used to monitor river changes and parameterize channel change models.

An alternative example is that of large floods, which are already monitored routinely using medium resolutionspace-borne optical imaging systems to construct relations between area of inundation and discharge (Brakenridge etal., 2005; Ip et al., 2006). Although currently limited to single missions, reduced resolution or smaller areas, new lidarand radar instruments are beginning to add to this surface extent information by providing detailed maps of watersurface elevation data (Alsdorf et al., 2000; Mertes, 2002; Alsdorf et al., 2007; Schumann et al., 2007; Smith, 1997;Sippel et al., 1998). When combined with preflood topographic data, the combined imagery can map flood volumesand, with high resolution data, more specific information such as flood waves (Brakenridge et al., 1998). Lidar andradar data can often be manipulated in various ways to extract flooding information from beneath vegetation canopiesand through clouds, problems optical imagery alone cannot solve. Large flood dynamics could be further analyzed bycoupling remotely observed suspended sediment concentrations (Mertes et al., 1993) with remotely based hydraulicdata and models (Mason et al., 2003) and maps of vegetation to parameterize flood models and to establish vegeta-tion–topography–flood relationships.

These examples are just a few of the potential uses for watershed-wide, high resolution remote sensing withmultiple image types. There is a whole suite of hydrologic, geomorphic and ecologic connections and feedback areaswhere remote sensing exploration might have dramatic effects with a modicum of investigation (Bryant and Gilvear,1999; Mertes et al., 1996).

The Future

There is cause for optimism that remotely sensed optical imagery can be used to map river parameters at watershedscales, but this optimism needs to be tempered with a dose of reality regarding the scientific and logistical barriers.Overcoming these obstacles to extend proof-of-concept studies to watershed extents and to realize the potential ofremote imagery will require a broader, collaborative framework supported by larger funding initiatives.

Stating that special funding initiatives are needed is a statement that is potentially easily dismissed; after all, whyshould remote sensing be a special case when so many deserving issues are at hand? The reasons for giving specialattention to optical remote sensing of rivers relate to pragmatic, social and theoretical concerns.

First and foremost, optical remote sensing deserves special attention because it is the only viable method formeasuring, monitoring and mapping a large suite of in-channel river parameters continuously at sub-meter resolution.Much like long-term discharge records, such data sets would be a resource for all, useful for applications ranging fromenvironmental assessment and change to modeling and theory development. Some of the immediate professionalapplications of these maps would include providing river data for advanced hydraulic models (Hardy et al., 2005),mapping of stream habitat units for stocking and for determining endangered organism status (Hedger et al., 2006),improving river visualizations (Marcus et al., 2004), and monitoring erosion and deposition (Lane et al., 2003). Anyof the applications discussed earlier in this article could potentially be mapped and monitored at basin extents to buildcatchment extent ‘river observatories’. Such observatories could be and should be at the service of larger societalgoals as is occurring with the European Union’s Water Framework Directive. However, substantially larger funding isrequired for watershed-scale studies compared with reach-scale experiments.



But why a separate initiative for remote sensing rather than folding it within the panoply of funding sources forriver science? In part, a special initiative is needed to insure that funding goes to methodological develop-ment. Numerous obstacles and needed advances in techniques have been identified in this article, but agenciestypically fund proposals on advancing process understanding, theory and predictive capability. This creates a Catch-22for research on remote sensing of rivers. Based on our professional experience and that of colleagues, proposals thataim to advance techniques are turned down because they are overly methodological in their goals, but proposalsthat aim to use remote sensing measurements to develop and test theory are turned down because the methods arenot yet accepted. Initiatives targeted specifically at advancing remote sensing of rivers are needed to address thisproblem.

Furthermore, the science of remote sensing of rivers is being commercialized and implemented rapidly. While thisprovides commercial incentives for technique development and data acquisition, it places these images and techniquesin the control of the private sector. Profit motives are likely to restrict access to imagery and applications, plus limitapplications to a sole source that cannot be evaluated and improved in an atmosphere of open inquiry. A broadlybased, collaborative research and funding framework supported by larger public–private initiatives would insure thatdata and techniques were publicly available and subject to peer review. Development of proof-of-concept catchment-extent ‘river observatories’ is a reasonable approach around which to build these larger initiatives.

As has been demonstrated by several authors and the applications presented in this article, remote sensing riverobservatories could be established today. If remotely sensed river observatories are to be useful to scientists andmanagers, however, we need to have the same confidence in the data that we have with other contemporary hydrologicdata. Developing this confidence requires moving beyond the reach-scale proof of concept typical of most previouswork to test outcomes from different research teams in multiple settings at watershed extents. The time has come todevelop and fund initiatives targeted at this goal.

AcknowledgmentsThis paper was first presented as a talk at the 2007 British Society of Geomorphology meeting on Geomorphology in the Year 2020,held at Birmingham University, UK. We wish to thank Damian Lawler and Ian Fairchild for inviting us to give an oral presentation,which laid the groundwork for this article. Stuart Lane also provided impetus, encouragement and feedback and two anonymousreviewers offered useful suggestions for improving the manuscript. Support funding was provided by the Office of the Provost andVice President for Academic Affairs, University of Oregon.

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