We use cookies to ensure that we give you the best experience on our website. You can change your cookie settings at any time. Otherwise, we'll assume you're OK to continue.

Durham University

Research & business

View Profile

Publication details for Dr Patrice Carbonneau

Carbonneau, P.E., Lane, S.N. & Bergeron, N.E. (2004). Catchment-scale mapping of surface grain size in gravel bed rivers using airborne digital imagery. Water Resources Research 40(7): W07202.

Author(s) from Durham


This study develops and assesses two methods for estimating median surface grain sizes using digital image processing from centimeter-resolution airborne imagery. Digital images with ground resolutions of 3 cm and 10 cm were combined with field calibration measurements to establish predictive relationships for grain size as a function of both local image texture and local image semivariance. Independently acquired grain size data were then used to assess the algorithm performance. Results showed that for the 3 cm imagery both local image semivariance and texture are highly sensitive to median grain size, with semivariance being a better predictor than image texture. However, in the case of 10 cm imagery, sensitivity of image semivariance and texture to grain size was poor, and this scale of imagery was found to be unsuitable for grain size estimation. This study therefore demonstrates that local image properties in very high resolution digital imagery allow for automated grain size measurement using image processing and remote sensing methods


Adams, J. (1979), Gravel size analysis from photographs, J. Hydraul. Div.
Am. Soc. Civ. Eng., 105(HY10), 1247–1255.
Baret, F., V. C. Vanderbilt, M. D. Steven, and S. Jacquemoud (1994), Use of
spectral analogy to evaluate canopy reflectance sensitivity to leaf optical
properties, Remote Sens. Environ., 48, 253– 260.
Bergeron, N. E. (1998), Scale-space analysis of stream-bed roughness in
coarse gravel-bed streams, Math. Geol., 28(5), 537– 561.
Borel, C. C., and S. A. W. Gerstl (1994), Nonlinear spectral mixing
models for vegetative and soil surfaces, Remote Sens. Environ., 47,
403– 416.
Bray, D. I. (1982), Flow resistance in gravel-bed rivers, in Gravel-Bed
Rivers, edited by R. D. Hey, J. C. Bathurst, and C. R. Thorne, pp. 109–
138, John Wiley, Hoboken, N. J.
Bunte, K., and S. R. Abt (2001), Sampling surface and subsurface particlesize
distributions in wadable gravel-and cobble-bed streams for analyses
in sediment transport, hydraulics and streambed monitoring, Gen. Tech.
Rep. U. S. Dep. Agric., RMRS-GTR-74.
Butler, J. B., S. N. Lane, and J. H. Chandler (2001a), Automated extraction
of grain-size data from gravel surfaces using digital image processing,
J. Hydraul. Res., 39(5), 1– 11.
Butler, J. B., S. N. Lane, and J. H. Chandler (2001b), Characterisation of
the structures of river-bed gravels using two-dimensional fractal analysis,
Math. Geol., 333, 301– 330.
Carbonneau, P. E., S. N. Lane, and N. E. Bergeron (2003), Cost-effective
non-metric close-range digital photogrammetry and its application
to a study of coarse gravel river beds, Int. J. Remote Sens., 24, 2837–
Castleman, K. R. (1996), Digital Image Processing, 666 pp., Prentice-Hall,
Old Tappan, N. J.
Church, M. A., D. G. Mclean, and J. F. Wolcott (1987), River bed gravels:
Sampling and analysis, in Sediment Transport in Gravel-Bed Rivers,
edited by C. R. Thorne, J. C. Bathurst, and R. D. Hey, John Wiley,
Hoboken, N. J.
Clifford, N. J., A. Robert, and K. S. Richards (1992), Estimation of flow
resistance in gravel-bed rivers: A physical explanation of the multiplier
of roughness length, Earth Surf. Processes Landforms, 17, 529–
Conners, R. W., M. M. Trivedi, and C. A. Harlow (1984), Segmentation of
a high resolution urban scene using texture operators, Comput. Vision
Graphics Image Processing, 25, 273– 310.
Cunjak, R. A. (1988), Behaviour and microhabitat of young Atlantic salmon
(Salmo salar) during winter, Can. J. Fish. Aquat. Sci., 45, 2156–
Fausch, K. D., C. E. Torgerson, C. V. Baxter, and H. W. Li (2002), Landscapes
to riverscapes: Bridging the gap between research and conservation
of stream fishes, BioScience, 52(6), 483–498.
Guay, J. C., D. Boisclair, D. Rioux, M. Leclerc, M. Lapointe, and
P. Legendre (2000), Development and validation of numerical habitat
models for juveniles of Atlantic salmon (Salmo salar), Can J. Fish
Aquat. Sci., 57, 2065–2075.
Haralick, R. M. (1979), Statistical and structural approaches to texture,
Proc. IEEE, 67(5), 786–804.
Haralick, R. M., K. Shanmugan, and I. Dinstein (1973), Textural features
for image classification, IEEE Trans. Syst. Man Cybern., 3(6), 610–
Heggenes, J. (1996), Habitat selection by brown trout (Salmo trutta) and
young Atlantic salmon (S. salar) in streams: Static and dynamic hydraulic
modelling, Reg. Rivers Res. Manage., 12, 155– 169.
Hey, R. D., and C. R. Thorne (1983), Accuracy of surface samples from
gravel bed material, J. Hydraul. Eng., 109(6), 842– 851.
Ibbeken, H., and R. Schleyer (1986), Photo sieving: A method for grainsize
analysis of coarse-grained, unconsolidated bedding surfaces, Earth Surf.
Processes Landforms, 11, 59– 77.
Institute of Electrical and Electronics Engineers (IEEE) (1990), IEEE Standard
Glossary on Image Processing and Pattern Recognition Terminology,
610. 4, IEEE Press, Piscataway, N. J.
Legleiter, C. J.,W. A. Marcus, and R. L. Lawrence (2002), Effects of sensor
resolution on mapping in-stream habitats, Photogramm. Eng. Remote
Sens., 68(8), 801– 807.
Middleton, G. V., and J. B. Southard (1984), Mechanics of Sediment Movement,
SEPM Short Course, 3, 401 pp.
Miranda, F., and J. Carr (1994), Application of the semivariogram textural
classifier for vegetation discrimination using SIR-B data of Borneo, Int.
J. Remote Sens., 13, 2349–2354.
Otsu, N. (1979), A threshold selection method from gray-level histograms,
IEEE Trans. Syst. Man Cybern., 9(1), 62– 66.
Rice, S., and M. Church (1996), Sampling superficial fluvial gravels: The
precision of size distribution percentile estimates, J. Sediment. Res.,
66(3), 654–665.
Rice, S., and M. Church (1998), Grain size along two gravel-bed rivers:
Statistical variations, spatial patterns and sedimentary links, Earth Surf.
Processes Landforms, 23, 345–363.
Rimmer, D. M., U. Paim, and R. L. Saunders (1983), Changes in the
selection of microhabitat by juvenile Atlantic salmon (Salmo salar) at
the summer-autumn transition in a small river, Can. J. Fish. Aquat. Sci.,
41, 469– 475.
Robert, A. (1991), Fractal properties of simulated bed profiles in coarsegrained
channels, Math. Geol., 23(3), 367– 382.
Rossi, R. E., D. J. Mulla, A. G. Journel, and E. H. Franz (1992), Geostatistical
tools for modeling and interpreting ecological spatial dependence,
Ecol. Monogr., 62, 277– 314.
Verdu´ , J. M., R. J. Batalla, and J. A. Martinex-Cassasnovas (2003), Estimating
grain size distributions of a gravel riverbed at reach scale from
detailed aerial photos, geostatistics and digital image processing (Isabena
River, Spain), paper presented at the Braided Rivers Conference, Br.
Geomorphol. Res. Group, London, April.
Whitman, S. M., E. H. Moran, and R. T. Ourso (2003), Photographic
techniques for characterizing streambed particle sizes, Trans. Am. Fish.
Soc., 132, 605– 610.
Wiberg, P. L., and J. D. Smith (1987), Calculations of the critical shear
stress for motion of uniform and heterogeneous sediments, Water Resour.
Res., 23, 1471– 1480.
Winterbottom, S. J., and D. J. Gilvear (1997), Quantification of channel bed
morphology in gravel-bed rivers using airborne multispectral imagery
and aerial photography, Regul. Rivers, 13(6), 489– 499.
Wolman, M. G. (1954), A method of sampling coarse bed material, Eos
Trans. AGU, 35, 951–956.
Wright, A., W. A. Marcus, and R. Aspinall (2000), Evaluation of multispectral,
fine scale digital imagery as a tool for mapping stream morphology,
Geomorphology, 33(2), 107– 120.
Wulder, M., and B. Boots (1998), Local spatial autocorrelation characteristics
of remotely sensed imagery assessed with the Getis statistic, Int. J.
Remote Sens., 19, 2223– 2231.
Zhang, Y. (2000), A method for continuous extraction of multispectraly
classified urban rivers, Photogramm. Eng. Remote Sens., 66(8), 991–