An Estimation of the particle-size Distribution in gravel bed river Using Image Processing

Document Type : Research Paper


1 M.Sc. Student, Department of Civil Engineering, Jundi-Shapur University of Technology, Dezful, Iran.

2 Assistant Professor, Department of Civil Engineering, Jundi-Shapur University of Technology, Dezful

3 Professor, Department of Water and Science Engineering, Shahid Chamran University of Ahvaz, Iran.


Distribution pattern of the river bed particles grading creates important issues in investigation of the hydraulic, geomorphological and ecological behavior of the river.. For example, surface grain-size variability is crucial for illustrating sediment transport (Hoey and Ferguson, 1994; Russ, 1999; Joyce et  al., 2001).
Particle size characteristics that are dependent on particle size distribution are estimated in different ways, such as sieving method, sampling techniques in the field, photographic print method, and the other methods that have been suggested so far (Aberle and Nikora, 2006). The most prevalent method is the sieving method that obtains particle size distribution curve using cumulative weight of passing aggregation,.. It is obvious that, measurement of the particle size  distribution based on field methods are time consuming, overwhelming and non-economic, so developing a fast and accurate method for measuring the particle size distribution of the river bed has a significant effect on civil and environmental engineering. Nowadays, this process is possible using image processing methods to automatically extract particle size using digital images of river bed. Various methods have been reported which aim to provide robust and automated estimates of grain size from images, falling under two broad categories classified by (Buscombe et al., 2010) as, respectively, ‘geometrical’ and ‘statistical’. Both techniques require imagery where the smallest grains are resolved by at least a few pixels. Statistical methods characterize grain size using a measure sensitive to image texture. These approaches have used autocorrelation (Warrick et al., 2009), semi variance or one of the several other methods, including fractals (Buscombe, 2013) and grey-level co-occurrence matrices. Geometrical methods use image processing techniques (principally, threshold and segmentation) to isolate and measure the visible axes (or portions of whole axes) of each individual grain (e.g. Graham et al., 2005; Chang &Chung, 2012). This research showed that new methods of image processing have an adequate potential to replace with previous traditional methods. Also, the percentage of human bias in methods such as field sampling or Sticky layer is very high for sampling of the Armor layer of the river bed, while the image processing method has enough power to take all the details and analysis of the data and it has a desirable accuracy compared to traditional methods.
This research, with using image processing toolbox in MATLAB software,  tried to improve the detection filters and finally determined the size distribution of sedimentary particles of armor layer in the alluvial river bed.


Main Subjects

1-    برقعی، آ.، 1385. راهنمای عملیات صحرایی نمونه‌برداری مواد رسوبی رودخانه‌ها و مخازن سدها . نشریه شماره 349، وزارت نیرو سازمان مدیریت و برنامه‌ریزی کشور معاونت امور فنی.
2-    حسن­نژاد شریفی، ف.، صمدی، آ. و آ.، عزیزیان. 1395. ارزیابی عملکرد روش پردازش تصاویر در تخمین ضریب زبری مانینگ در لایه سطحی بستر رودخانه‌ها، مجله تحقیقات آب و خاک ایران، 47(4):722-711.
3-    عبدشریف اصفهانی، م.، کرباسی، م.، رجبی هشجین، م. و ا. کیاسالاری، 1384.  معرفی روش عکس برداری شبکه ای از بستر رودخانه در تعیین دانه بندی لایه محافظ یک بستر درشت دانه (مطالعه موردی: رودخانه کرج). پنجمین کنفرانس هیدرولیک ایران، دانشگاه شهید باهنر کرمان.
4-    Aberle, J. and V. Nikora, 2006. Statistical properties of armored gravel bed surfaces. Water Resources Research, 42(11):114-128.
5-    Barrett, P.J. 1980. The shape of rock particle. A Critical Review. 27: 291-303.
6-    Beggan, C. and C.W. Hamilton, 2010. New image processing software for analyzing object size-frequency distributions, geometry, orientation, and spatial distribution. Computers and Geosciences, 36(4): 539–549.
7-    Buscombe, D. 2008. Estimation of grain size-distributionsand associated parameters from digital images of sediment. Sedimentary Geology, 210(1): 1–10.
8-    Buscombe, D. 2013. Transferable wavelet method for grain-size distribution from images of sediment surfaces and thin sections, and other natural granular patterns. Sedimentology. 60(7): 1709-1732.
9-    Butler, J.B., Lane, S.N. and J.H. Chandler. 2001. Automated extraction of grain-size data from gravel surfaces using digital image processing. Journal of Hydraulic Research, 39 (5): 519–529.
10- Chang, F.J. and C.H. Chung. 2012. Estimation of riverbed grain-size distribution using image processing techniques. Journal of Hydrology, 440:102–112.
11- Gonzalez, R.C. and R.E. Woods. 2007. Digital image processing. Prentice Hall, Upper Saddle River, New Jerscy ., pp. 675–683.
12- Graham, D.J., Reid, I. and S.P. Rice, 2005. Automated sizing of coarse-grained sediments: image-processing procedures. Mathematical Geology, 37 (1): 1–28.
13- Graham, D.J., Rollet, A. J., Rice, S.P. and H. Piegay. 2012. Conversions of surface grain-size samples collected and recorded using different procedures. Journal of Hydraulic Engineering, 138(10): 839–849.
14- Hoey, T.B. and R. Ferguson. 1994. Numerical-simulation of downstream fining by selective transport in gravel-bed rivers-model development and illustration. Water Resources Research, 30(7): 2251–2260.
15- Krumbein, W.C. 1941. Measurment and geological significance of shape and roundness of sedimentary particles. Journal of Sedimentary Petrology, 11(2): 64-72.
16-   Liu, Y., Yu, Y., Zhou, X. and C. Wang. 2016. A new automatic threshold selecting criteria for spectroscopy data processing, Chemometrics and Intelligent Laboratory Systems, 161: 8–14.
17- Mao, L. and N. Surian, 2010. Observations on sediment mobility in a large gravel-bed river. Geomorphology, 114 (3): 326–337.
18- Mao, L., Cooper, J.R. and L.E. Frostick, 2011. Grain size and topographical differences between static and mobile armour layers. Earth Surface Processes and Landforms, 36 (10): 1321–1334.
19- Meyer, F. and S, Beucher. 1990. Morphological segmentation. Journal of Visual Communication and Image Representation, 1(1): 21–46.
20-   Rice, S. P., Greenwood, M. T., and Joyce, C. B. 2001. Tributaries, sediment sources, and the longitudinal organisation of macroinvertebrate fauna along river systems. Canadian Journal of Fisheries and Aquatic Sciences, 58(4):801- 824.
21- Rittenhouse, G. 1943. A visual method of estimating two dimensional sphericity. Journal of Sedimentary Petrology, 13(2): 79-81.
22- Russ, J. C. 1999. The image processing handbook. 3rd edition: CRC Press, Boca Raton, Florida.
23- Rubin, D.M., Chezar, H., Harney, J.N., Topping, D.J., Melis, T.S. and C.R. Sherwood. 2007. Underwater microscope for measuring spatial and temporal changes in bedsediment grain size. Sedimentary Geology, 202 (2): 402–408.
24- Strom, K.B., Kuhns, R. D. and H.J. Lucas, 2010. Comparison of automated image-based grain sizing to standard pebble-count methods. Journal of Hydraulic Engineering, 136(8): 461-473.
25-   Vincent, L. and P. Soille. 1991. Watersheds in digital spaces – an efficient algorithm based on immersion simulations. Institute of Electrical and Electronics Engineers Transactions on Pattern Analysis and Machine Intelligence, 13(6): 583–598.
26- Wadell, H. 1932. Volume shape and roundness of rock particles. Journal of Geology, 40(5): 443-451.
27- Warrick, J.A., Rubin, D.M., Ruggiero, P., Harney, J., Draut, A.E. and D. Buscombe. 2009. Cobble cam: Grain-size measurements of sand to boulder from digital photographs and autocorrelation analyses. Earth Surface Processes and Landforms, 34(13): 1811–1821.
Volume 40, Issue 4
February 2018
Pages 125-139
  • Receive Date: 11 April 2016
  • Revise Date: 31 January 2017
  • Accept Date: 02 September 2016
  • First Publish Date: 21 January 2018