RIVER RESEARCH AND APPLICATIONS

River. Res. Applic. 24: 650–660 (2008)

Published online in Wiley InterScience

QUANTIFYING CHANNEL PLANFORM AND PHYSICAL HABITATDYNAMICS ON A LARGE BRAIDED RIVER USING SATELLITE

DATA—THE BRAHMAPUTRA, INDIA

SANCHITA BORUAH,a DAVID GILVEAR,a* PETER HUNTERa and NAYAN SHARMAb

a School of Biological and Environmental Science, University of Stirling, FK9 4LA, Scotland, UKb Department of Water Resources Development and Management, Indian Institute of Technology, Roorkee 247667, India

(www.interscience.wiley.com) DOI: 10.1002/rra.1132

ABSTRACT

This paper presents the results of a trialling of the use of Indian Remote Sensing (IRS) satellite imagery for mapping thegeomorphology and physical habitat of the Brahmaputra River in Assam, India. The study was undertaken on a 110 km reach asthe river emerges out of the Himalayas; a reach with a complex braid pattern and high levels of channel instability. Imageanalysis on four sets of IRS data encompassing a period of 13 years (1990–2002) was undertaken to detect basic changes in theextent and pattern of low flow channels, vegetated islands, exposed sand bars and floodplain vegetation. Simple unsupervisedclassification techniques were applied to the images. The water surface of the low flow braided channel network, however wasmapped more accurately using the GROW facility within ENVI 4.1; a technique based upon mapping areas within a givennumber of standard deviations in terms of pixel values within a training area. Subsequent to this unsupervised classification ofthe water surface area allowed classification of the water in to three water types based on depth and separation of isolated poolsand backwaters from the channels conveying flow. Overall classification accuracies of 82.5% were achieved in relation tomapping physical habitat; varying between 86 and 95% for the four different dates. Analysis of the nature of channel planformand habitat change showed that there has been a 3.7% increase in the active channel area consisting of exposed sediment andwater within the river corridor. This substantiated a general awareness of an increase in the width and reduction in depth of theBrahmaputra River over the last few decades. The analysis also depicts the occurrence of a major avulsion and the highlydynamic nature of the braided channel network and sand bars.

Overall the study demonstrated that simple classification methods when applied to satellite data, capturing imagery in thevisible and near infra-red, can be used to measure important changes in the geomorphology and physical habitats that make up thefluvial system of the Brahmaputra River. The approach may also be more widely applicable to large river systems comprising amosaic of water, exposed sediment and spectrally different vegetation communities. Copyright # 2008 John Wiley & Sons, Ltd.

key words: remote sensing; Brahmaputra River; image analysis; river islands; sand bars; Riparian zones

Received 30 September 2006; Revised 30 May 2007; Accepted 21 November 2007

INTRODUCTION

The utility of satellite remote sensing data to detect channel planform and channel migration on large rivers has

been recognized for many years (Muller et al., 1993; Milton et al., 1995; Gilvear et al., 1995; Leuven et al., 2002;

Gilvear and Bryant, 2003). Remotely sensed data were also a pre-requisite for developing an understanding of the

linkage between channel planform change and habitat on the world’s largest rivers. The seminal paper by Salo et al.

(1985) illustrating the linkage between channel migration and habitat diversity within the river corridor of the

Amazon was, for example, reliant upon analysis of sequential sets of satellite data. The remote sensing approach

when applied to large dynamic rivers has the advantage that it can provide greater coverage than field investigations

and provide evidence of channel dynamics prior to the period when the river became the focus of scientific

attention. Despite the apparent suitability of a remote sensing approach to the study of large rivers, for the most part,

it has been restricted to detecting rates of bank erosion and gross changes in channel planform. Analysis of the

*Correspondence to: David Gilvear, School of Biological and Environmental Science, University of Stirling, FK9 4LA, Scotland, UK.E-mail: d.j.gilvear@stir.ac.uk

Copyright # 2008 John Wiley & Sons, Ltd.

QUANTIFYING BRAIDED RIVER HABITATS DYNAMICS USING SATELLITE DATA 651

imagery using automated techniques has not specifically addressed automated detection of individual geomorphic

features and quantification of the spatial arrangement and dynamics of river and floodplain habitats. On smaller

rivers, however a number of studies have shown the utility of airborne spectral data for mapping channel

morphology (Winterbottom and Gilvear, 1997; Westaway et al., 2000; Gilvear et al., 2004a; Gilvear et al., 2004b),

in-stream habitat (Lunetta et al., 1997; Puestow et al., 2001; Legleiter, 2003; Legleiter et al., 2004; Legleiter and

Roberts, 2005; Leckie et al., 2005; Hedger et al., 2006; Gilvear et al., in press) and change in physical habitat

through time (Bryant and Gilvear, 1999). For example Whited et al. (2002) automatically mapped river habitats

using four wave bands and 0.7 m pixel data. Accuracies for islands, exposed rocks and water surfaces on the Yakima

River, USA, were greater than 99% but identification of hydraulic habitat (riffles, eddies, slack water and pools) was

less accurate (70%). The majority of misclassification relates to problems of mixed pixels, shadow cast from

riparian woodland, georectification of images and boundary definition in the presence of ecotones between major

habitats. Such problems should not be so apparent on large rivers but this claim requires thorough testing. To

quantify channel and habitat dynamics over sizeable channel lengths one of the key techniques is the ability to map

water, sediment and vegetated surfaces using automated classification techniques. Automated mapping requires the

features of interest to be at a scale much larger than the spatial resolution of the imagery and to have spectral

properties that allow differing habitats to be distinguished from each other; at the most basic level the fluvial system

is made up of water, exposed sediment, partially colonized and fully vegetated surfaces but this division is of course

simplistic.

Here we investigate the utility of remotely sensed data to quantify channel change and habitat dynamics over a

110 km highly braided reach of the Brahmaputra River, in Assam. The size and complexity of the river has

prevented to date a good understanding of the nature of channel and physical habitat change, and the linkage to

ecology, to be realized. As such a trial investigation was undertaken to ascertain the potential of existing satellite

data to provide important information relating to channel and physical habitat change on the Brahmaputra River in

India and to provide a rigorous test of the utility of a satellite-based remote sensing approach to large braided rivers.

The purpose of this paper is to demonstrate that in many cases simple image analysis techniques applied to satellite

data can reveal useful information on channel morphology and physical habitat on large braided rivers.

THE STUDY AREA

The Brahmaputra River and catchment

The Brahmaputra River drains a catchment of 580 000 km2 with more than half the area lying within China and

Tibet; the remainder being composed of parts of Bhutan, India and Bangladesh (Figure 1). In total, the river is

2906 km long with its source being the Kangklung Kang glacier in Tibet at an altitude of 4877 m. On leaving the

Himalaya it flows for 640 km within the state of Assam, before entering Bangladesh and flowing in to the Bay of

Bengal. The mean discharge of the river in Assam (measured at Pandu) is 16 682 m3 s�1 (Sarma, 2005); 80% of

which is conveyed in the rainy season from March to October. At its mouth, the flow of the Brahmaputra River is

19 830 m3 s�1 making it the fourth largest river in the world in terms of discharge (Goswami, 1985). The

Brahmaputra also has one of the highest suspended sediment loads (an estimated 402 million tonnes per year) of

any river in the world; which is assumed to be between 85 and 95% of the total load (Sarma, 2005).

The river morphology is Assam is braided with an average channel width of over 10 km and a valley floor width

of 70–80 km. The channel is also highly unstable and mobile and is gradually moving southwards due to the

effect of tectonic uplift. In the 20th century it is estimated 868 km2 of land in Assam has been lost by erosion

principally on the southern bank. The river is also reported to be wider and shallower with bank erosion rates almost

quadrupling in the 20th century (Sharma and Phukan, 2004) and bed aggradation rates of 16.8 cm per year having

been reported (Varma and Rao, 1996; Sarma, 2005). Individual anabranches, sand bars and the location of the

whole channel undergo rapid shifting (Sharma, 2004). These channel adjustments are reflected in an increase in the

braiding changing index from 6.11 in 1928 to 8.33 in 1996 (Sarma, 2005).

This instability is a major threat to artificial structures, navigation and human occupation of river margins. It is

also significant in terms of the ecology of the river and floodplain environment (Kotoky et al., 2003). The width of

Copyright # 2008 John Wiley & Sons, Ltd. River. Res. Applic. 24: 650–660 (2008)

DOI: 10.1002/rra

Figure 1. Map of the Brahmaputra River covering China, India and Bangladesh

652 S. BORUAH ET AL.

the channel and levels of channel instability may increase since climate change scenarios for the region suggest an

increase in the size of flood events (Mirza et al., 2003). Meanwhile, to stem bank erosion and floodplain inundation,

bank protection and embankments are continually being engineered possibly altering channel morphology and

physical habitat. This in turn may be reducing river biodiversity and survival of iconic species such as the river

dolphin (Dudgeon, 1992; Biswas and Boruah, 2000).

The study reach

The study reach is 110 km long situated in the northeastern part of Assam and lies just downstream of the

confluence of the Dihang, Dibang and Lohit tributaries (Figure 1). The gradient of the river as it enters Assam

changes markedly from between 4.00 and 1.0 m km�1 upstream to 0.27 and 0.13 m km�1 in the reach of interest

between Kobo and Dibrugarh. Here the channel on average is 11.4 km wide. The land use surrounding the river is a

mix of dense tropical rainforest including forest reserves and a national park, riparian woodland, wetlands and

tea gardens. The reach exhibits a complex braid pattern and has undergone changes in channel planform including a

major avulsion in 1996 and was well known to two of the authors. As such it was considered a good reach to explore

the use of satellite imagery for detecting channel morphology and physical habitat dynamics. The avulsion in 1996

occurred when a channel of Lohit River coalesced with the river Dibru and widened in the process from less than

20 m in 1995 to more than 500 m in 2002. It turned a major portion of the Dibru–Saikhowa National park into a river

island (Figure 2) with an area of about 322 km2.

Copyright # 2008 John Wiley & Sons, Ltd. River. Res. Applic. 24: 650–660 (2008)

DOI: 10.1002/rra

Figure 2. The nine-fold classification of the Brahmaputra River in (a) 1990, (b) 2002. Dark areas are water, light areas sand and intermediateshades vegetation. This figure is available in colour online at www.interscience.wiley.com/journal/rra

QUANTIFYING BRAIDED RIVER HABITATS DYNAMICS USING SATELLITE DATA 653

METHODS

Image acquisition

Cloud free Northern Hemisphere winter (December/January) Indian Remote Sensing satellite data were acquired

for 1990, 1997, 2000 and 2002. The December/January period is the low flow period and time of lowest water

turbidity; the low flow discharge for the four dates was similar enough that differences in stage were unlikely to

account for the morphological changes on the imagery. The 1990 data were acquired in four spectral bands

(0.45–0.52 0.52–0.59, 0.62–0.68 and 0.77–0.86mm) and had a ground resolution of 78 m. The later images were

acquired in four bands (0.52–0.59, 0.62–0.68, 0.77–0.86, and 1.55–1.70mm) and had a ground resolution of 23.5 m.

Image analysis

Image analysis took place in ENVI version 4.1. Images were georeferenced using ground control points such as

airstrips and location of stable relic meanders. A fixed Region of Interest (ROI) on all images was then defined by

masking out the entire scene except for a riparian corridor encompassing the river channel, river islands and

Copyright # 2008 John Wiley & Sons, Ltd. River. Res. Applic. 24: 650–660 (2008)

DOI: 10.1002/rra

Table I. The nine-fold classification of the riparian corridor of the Brahmaputra River used inthis study

Class Sub-class

Water Deep waterMedium waterShallow water

Vegetation Dense forest vegetationWell vegetationSparsely vegetation

Exposed sediment Dry sandWet sandSoils (cultivated farmland)

654 S. BORUAH ET AL.

adjacent riparian zones contained within the active floodplain (or where constrained by flood embankments) on the

north and south banks. No atmospheric correction was undertaken.

Unsupervised classification of each image into seven classes was undertaken with these subsequently grouped in

to water, exposed sediment, and vegetation for a tier 1 level of analysis (Table I). The low flow water surface, which

was eventually mapped differently to the other classes was subsequently classified into three depth classes. The

vegetation in the river corridor generally consisted of dense mature riparian forest, younger less dense riparian

woodland and pioneer grassland. Each image was subjected to the same process of ENVI’s K-means unsupervised

classification. Classification of the river water within the scene was attempted using the GROW facility. It allowed a

small training area to be established and then for contiguous areas within a specified standard deviation to be

mapped. This facility thus allowed the river channel, with all connected and partially connected anabranches to be

reliably mapped. The same approach was also used to map complex sand bars but the technique here cannot be used

to map their presence across the whole scene due to mid-channel bars being confined by water and side bars being

adjacent to water and riparian vegetation. As such the rule of contiguity was periodically broken and the mapping

technique collapsed. ENVI’s accuracy assessment was then applied to the data. A fixed ROI was plotted on all the

four classified images. The accuracy evaluation of the classified images was done by the confusion matrix function

within ENVI. Aerial photography, local knowledge of the area and changes over time and scrutiny of the images

was used to manually produce accurate data for the accuracy assessment. Some fuzziness in the data in terms of

depth categories, vegetation density and sand moisture conditions will be apparent. A number of channel

cross-sectional surveys of the river for the reach were also available for 1990 and 1997. On successful classification

of all four images the change detection algorithm was used to map change between successive dates with analysis of

the results being performed within Excel. Values were expressed as total change in area or change per annum.

RESULTS AND DISCUSSION

Geometric and classification accuracy

The georectification produced a root-mean-square error equivalent to one pixel in the images. In the classified

images an overall classification accuracy was found to be 78.8977, 78.0395, 78.4251, 83.5812% in 1990, 1997,

2000, 2002, respectively with a k-coefficient of 0.5996, 0.5848, 0.5700 and 0.6446. The specific class accuracies

are depicted in Tables II(a–d). Water was very reliably classified using unsupervised classification with shallow

water only occasionally confused with wet sand more than likely due to mixed pixels. Medium and deep water was

occasionally confused with vegetation often due to mixed pixels at the channel margin. Confusion between deep

and shallow water may be due in accuracy in producing the ground truth data. The alternative method of mapping

using the GROW facility and a standard deviation of 2 proved even more accurate at mapping the surface water area

and its pattern. Exposed sediment was also well classified with sand only being confused occasionally with shallow

Copyright # 2008 John Wiley & Sons, Ltd. River. Res. Applic. 24: 650–660 (2008)

DOI: 10.1002/rra

QUANTIFYING BRAIDED RIVER HABITATS DYNAMICS USING SATELLITE DATA 655

water as documented above. The GROW facility was good at mapping individual sands bars. Soil was occasionally

classified as vegetation and this will be where pixels at field boundaries cover both an arable and vegetated surface.

Composition and changes in morphology and physical habitat

A river corridor area of approximately 1380 km2 was covered by the area of study. In 1990, 27.9% of the scene

consisted of low flow channels and backwaters areas and 31.4% sand bars (Figure 2). The low flow channel in 1990

exhibited only moderate levels of braiding being confined mainly to one significant channel through the mid

reaches (Figure 2a). South of the Dibru–Saikhowa National park no sand bars and little evidence of a river was

present. By 2002 the percentage of low flow channels showed a net increase to 30.2% and sand bars to 32.4%

although positive and negative change had occurred through the 13 year period (Tables III and IV). Overall

vegetation loss was due to erosion of floodplain vegetation, river islands and pioneer communities on vegetating

sand bars. On the 2000 image the avulsion around the Dibru–Saikhowa National park is shown principally as a

linear strip of water with small areas of wet and dry sand. By 2002 (Figure 2b) more sinuous and braided course is

depicted with an increase in sand area. Overall the 2000 and 2002 scenes show greater lengths of anabranch

channels and a more braided low flow channel.

In terms of net turnover over the 13 year period, 88.6% of those pixels classified as vegetation in 1990 remained

as vegetation in 2002 presumably signifying no vegetation destruction took place in the interim period (Table V).

Only 18.8% of water pixels remained water demonstrating the mobility of the low flow channels and in many of

these may have become sand bars and reverted back to flowing areas. The 17.3% that had become vegetated will

have become sand bars with pioneer vegetation colonization having occurred. The 73.6% of sand pixels is an

indication that the active channel zone have remained fairly fixed in location despite some evidence of a southwards

shift. It is likely that all the sand bars have undergone destruction and construction phases over the period and the

percentage stability value is not an indication of sand patch persistence in the landscape. A fuller understanding of

turnover in the landscape and in particular the dynamics of the sand bars would require an annual image series but

the basic approach is valid.

The utility of the remote sensing approach

In common with a number of other studies, channel planform and physical habitat mapping was undertaken using

satellite data with good levels of accuracy. In this case, automated classification of channel planform and physical

habitat, to a high level of accuracy (>85%), was achieved on a large braided river. As with other remote sensing

based river habitat mapping unsupervised classification proved a technique fit for purpose. This is not necessarily

surprising because the morphology and habitats of braided rivers of key interest are principally composed of the

three spectral end members of the fluvial system, namely water, exposed sediment and vegetation. However, with

each of these end members the reflectance spectra will vary with turbidity and water depth (e.g. Winterbottom and

Gilvear, 1997), sediment type and moisture content (e.g. Rainey et al., 2003; Yates et al., 2003) and vegetation

composition and vigour, respectively; these properties in themselves also being of inherent interest to river

scientists. With regard to mapping the low flow channel pattern, which in the cases of the braided river pattern of the

Brahamputra comprised a range of anabranch types some connected at both ends while others only existing as

backwaters, a technique based on creating training areas and then mapping all areas with a pixel reflectance

distribution with two standard deviations proved most successful; the exercise having to be repeated a number of

times along the reach to build up a composite image. This technique relies on connectivity and thus isolated pools

and side arms totally disconnected from the river were mapped separately. Moreover the universal problem of

mixed pixels will also be inherent within the scene but with high spatial resolution data covering a river system the

size of the Brahmaputra, even in the presence of an intricate network of river braids, the number of pixels affected is

small.

Given the success of the investigation in mapping accurately the initial properties of interest it seems a fruitful

approach to mapping the Brahmaputra over a longer reach to determine whether the direction and nature of change

is consistent along the entire length of the river that maintains a braided form and to apply the technique to imagery

in the future to track the temporal dynamics of the river. We also believe that the technique can be applied to other

braided river environments. The data produced also provide a basis from which modelling, such as that undertaken

Copyright # 2008 John Wiley & Sons, Ltd. River. Res. Applic. 24: 650–660 (2008)

DOI: 10.1002/rra

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656 S. BORUAH ET AL.

River. Res. Applic. 24: 650–660 (2008

DOI: 10.1002/rra

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QUANTIFYING BRAIDED RIVER HABITATS DYNAMICS USING SATELLITE DATA 657

Copyright # 2008 John Wiley & Sons, Ltd.

River. Res. Applic. 24: 650–660 (2008)

DOI: 10.1002/rra

Table III. Quantification of the classes in the imagery from 1990 through 2002

Classes 1990 1997 2000 2002

Area(km2)

Percentage Area(km2)

Percentage Area(km2)

Percentage Area(km2)

Percentage

Deep water 155.8 11.3 183.7 13.3 228.2 16.9 203.8 15.2Medium depth water 165.1 11.9 141.4 10.2 138.3 10.3 150.5 11.2Shallow water 64.0 4.6 59.0 4.3 64.8 4.8 63.2 4.7Dry sand 353.5 25.7 353.8 25.6 365.6 27.2 377.8 28.2Wet sand 77.3 5.6 61.1 4.4 69.0 5.1 69.6 5.2Soil 64.6 4.6 66.6 4.8 73.9 5.5 67.2 5.0Dense forest 235.3 17.0 236.1 17.1 179.2 13.3 188.4 14.0Medium vegetation 84.6 6.1 98.1 7.1 106.2 7.9 101.4 7.6Sparse vegetation 180.6 13.2 179.9 13.3 154.9 11.5 158.9 11.8

Table IV. A summary of change in area (km2) of water, sand and vegetated surfaces for the periods 1990–1997, 1997–2000,2000–2002 and over the full 13 year period

Class 1990–1997 1997–2000 2000–2002 Net change (1990–2002)

Water þ0.6 þ47 �13.8 þ32.6 (2.4% of area)Vegetation þ13.6 �73.8 þ8.4 �51.6 (�3.7% of area)Sand �15.9 þ19.7. þ12.8 þ16.6 (1.3% of area)

Table V. A measure of turnover in the river corridor represented by the percentage change pixels from their class in 1990 to theirclass in 2002

Water Vegetation Sand

Water 18.8 17.3 67.9Vegetation 5.1 88.6 6.3Sand 20.1 6.4 73.6

658 S. BORUAH ET AL.

by Coulthard., et al (2007) on the braided Waimakirri in New Zealand, can be used to determine the causal variables

invoking the changes observed.

The nature and significance of channel change

Channel migration southwards and a major channel avulsion were mapped. Overall a general trend of increased

active channel width was identified with water and sand bars covering a greater area within the river corridor. In

terms of the composition of the main active channel of the Brahmaputra the study revealed overall that between

1990 and 2002 there was a net 2.4 and 1.3% increase in the extent of water and exposed sand with a corresponding

decrease in vegetation within the river corridor delimited for this study. As such the analysis provided important

geomorphic understanding regarding the behaviour of this reach of Brahmaputra River over the last 13 years; the

Copyright # 2008 John Wiley & Sons, Ltd. River. Res. Applic. 24: 650–660 (2008)

DOI: 10.1002/rra

QUANTIFYING BRAIDED RIVER HABITATS DYNAMICS USING SATELLITE DATA 659

overall pattern of change is consistent with findings elsewhere that the river channel is widening and moving

southwards. Previous studies using repeat cross-sectional surveys undertaken by the Indian government and bank

lines as depicted on remotely sensed images have suggested erosion on the south bank (Goswami, 1985; Sharma,

2004; Sarma, 2005) the southern migration being a function of the tectonic uplift of the Himalaya to the north. This

study confirmed the presence of a southerly shift of the river with a new anabranch channel occurring along the

southern bank turning the Dibru–Saikhowa National park into an island.

Ascertaining the natural dynamics of channel migration and planform change and human-induced channel

change whether it is via changes to hydrology or river engineering is a pre-requisite for sustainable river

management, particularly on large mobile rivers such as the Brahmaputra. Digitization of bank lines and GIS-based

analysis or manual counting of river islands does not reveal the exact pattern of braid bar construction and

destruction, channel enlargement and contraction and vegetation development on fluvial surfaces disturbed by

repeated sediment transport and erosion. Quantifying the dynamics of sand bar behaviour, migration of channels

and construction and destruction of vegetated surface, both pioneer communities and later stages of vegetation

development, is critical to understanding the system both under natural conditions and where human-induced

change occurs. The technique also potentially allows for quantification of the persistence of fluvial features in the

landscape and for patch persistence and floodplain turnover to be quantified. With an improved understanding of the

linkage between plant and animal species colonization and usage of the physical habitats present the implications of

channel change will also be able to be assessed. For example, the iconic and threatened river dolphin on the river

Brahmaputra often utilizes deep water at convergence zones between major anabranches and particularly where

one of the anabranches is highly productive biologically because it drains through floodplain forests and wetlands.

CONCLUSIONS

Unsupervised classification applied to Indian Remote Sensing satellite data proved to be a reliable mapping tool for

detecting changes in channel planform and physical habitat on the braided Brahmaputra River in Assam. In this

paper, automated classification of the imagery spanning four dates over 13 years allowed channel pattern and

physical habitat to be mapped at a basic level over a 120 km reach. Our results showed an overall accuracy of 85% in

terms of mapping river water, sand bar, and riparian vegetation. Sub-classification of the low flow channels, sand

bars and riparian vegetation also appears to be feasible allowing detailed and robust in-stream and riparian zone

mapping. To enhance the usefulness, in terms of river conservation collection of field-based information that

couples physical habitat characteristics mapped remotely to biological communities is the next logical step.

We believe that this pilot study has shown that unsupervised classification applied to satellite data is a valuable

investigative tool to map large braided river systems and quantify change over time and this yields greater

information than field-based studies and simple digitization of bank-lines such as has been previously attempted on

the Brahmaputra River. Only by using such techniques will scientists and river managers manage to achieve a full

appreciation of the response of a river to a changing environment over long enough distances for the results to be

valid. As such, we advocate greater use of satellite remote sensing in river studies on large and geomorphologically

dynamic river systems.

ACKNOWLEDGEMENTS

Dr Sanchita Boruah acknowledges a UK Royal Society three-month study visit award which was spent at the

University of Stirling, Scotland.

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DOI: 10.1002/rra