Sains Malaysiana 46(3)(2017): 413–420

http://dx.doi.org/10.17576/jsm-2017-4603-08

 

Geospatial Techniques for Assessment of Bank Erosion and Accretion in the Marala Alexandria Reach of the River Chenab, Pakistan

(Teknik Georeruang bagi Penilaian Hakisan Tebing dan Tokokan di Rantau Marala Alexandria, Sungai Chenab, Pakistan)

 

M. HAMID CH1*, M. ASHRAF2, QUDSIA HAMID1, SYED MANSOOR SARWAR3

& ZULFIQAR AHMAD SAQIB4

 

1GIS Centre, University of the Punjab, Lahore, Pakistan

 

2Centre of Excellence in Water Resource Engineering, University of Engineering and Technology, Lahore, Pakistan

 

3Punjab University College of Information Technology, University of the Punjab, Lahore

Pakistan

 

4Institute of Soil and Environmental Sciences, University of Agriculture, Faisalabad, 38040

Pakistan

 

Diserahkan: 4 Disember 2015/Diterima: 17 Jun 2016

 

ABSTRACT

Remote Sensing (RS) and Geographical Information Systems (GIS) are widely used for change detection in rivers caused by erosion and accretion. Digital image processing techniques and GIS analysis capabilities are used for detecting temporal variations of erosion and accretion characteristics between the years 1999 and 2011 in a 40 km long Marala Alexandria reach of River Chenab. Landsat satellite images for the years 1999, 2007 and 2011 were processed to analyze the river channel migration, changes in the river width and the rate of erosion and accretion. Analyses showed that the right bank was under erosion in both time spans, however high rate of deposition is exhibited in middle reaches. The maximum erosion was 1569843 m2 and 1486160 m2 along the right bank at a distance of 24-28 km downstream of the Marala barrage in the time span of 1999-2007 and 2007-2011, respectively. Along right bank mainly there is trend of accretion but erosion is much greater between 20 and 28 km reach. Maximum accretion was 5144584 m2 from 1999-2007 and 2950110 m2 from 2007-2011 on the right bank downstream of the Marala Barrage. The derived results of channel migration were validated by comparing with SRTM data to assess the accuracy of image classification. Integration of remote sensing data with GIS is efficient and economical technique to assess land losses and channel changes in large rivers.

 

Keywords: Accretion; erosion; GIS; image processing; remote sensing

 

ABSTRAK

Pengesanan Jarak Jauh (RS) dan Sistem Maklumat Geografi (GIS) digunakan secara meluas untuk mengesan perubahan di sungai-sungai yang disebabkan oleh hakisan dan tokokan. Keupayaan teknik pemprosesan imej digital dan analisis GIS digunakan untuk mengesan variasi temporal hakisan dan tokokan antara tahun 1999 dan 2011 di sepanjang 40 km rantau Marala Alexandria di Sungai Chenab. Imej satelit Landsat bagi tahun 1999, 2007 dan 2011 telah diproses untuk analisis migrasi aliran sungai tersebut, perubahan dalam lebar sungai serta kadar hakisan dan tokokan. Analisis menunjukkan tebing di sebelah kanan terhakis pada kedua-dua tempoh masa, walau bagaimanapun kadar pemendapan yang tinggi ditunjukkan pada rantau pertengahan. Hakisan maksimum ialah 1569843 m2 dan 1486160 m2 di sepanjang tebing kanan pada jarak 24-28 km di hilir baraj Marala masing-masing dalam jangka masa 1999-2007 dan 2007-2011. Terdapat trend tokokan terutamanya di sepanjang tebing kanan tetapi hakisan adalah lebih besar antara jarak 20-28 km. Tokokan maksimum ialah 5144584 m2 dari 1999-2007 dan 2950110 m2 2007-2011 di tebing kanan hilir baraj Marala. Keputusan migrasi aliran yang diperoleh telah disahkan dengan membandingkannya dengan data SRTM untuk menilai ketepatan pengelasan imej. Integrasi data pengesanan jarak jauh dengan GIS adalah teknik yang cekap dan ekonomi untuk menilai kehilangan tanah dan perubahan aliran di sungai-sungai besar.

 

Kata kunci: GIS; hakisan; imej pemprosesan; pengesanan jarak jauh; pertambahan

RUJUKAN

Abubaker Haroun Mohamed Adam, Elhag A.M.H. & Salih, Abdelrahim. M. 2013. Accuracy assessment of land use & land cover classification (LU/LC) case study of Shomadi area-renk county-upper Nile State, South Sudan. International Journal of Scientific and Research Publications 3(5): 1-6.

Alam, J.B., Uddin, M., Ahmed, U.J., Cacovean, H., Rahman, H.M., Banik, B.K. & Yesmin, N. 2007. Study of morphological change of river old Brahmaputra and its social impacts by remote sensing. Geographia Technica 4(2): 1-11.

Arabinda Sharma & K.N. Tiwari. 2014. A comparative appraisal of hydrological behavior of SRTM DEM at catchment level. Journal of Hydrology 519(Part B): 1394-1404.

Awan, S.A. 2003. Pakistan: Flood Management-River Chenab from Marala to Khanki, Flood Forecasting Division, Pakistan Meteorological Department. pp. 123-125.

Ayman A. Ahmed & Ahmed Fawzi. 2011. Meandering and bank erosion of the River Nile and its environmental impact on the area between Sohag and El-Minia, Egypt. Arabian Journal of Geoscience 4: 1-11.

Darby, S.E., Leyland, J., Kummu, M., Räsäne, T.A. & Lauri, H. 2013. Decoding the drivers of bank erosion on the Mekong River: The roles of the Asian monsoon, tropical storms, and snowmelt. Water Resour. Res. 49(4): 2146-2163.

Engay-Gutierrez, K.G. 2015. Land cover change in the Silang- Santa Rosa River Subwatershed, Laguna, Philippines. Journal of Environmental Science and Management 18(1): 34-46.

Hussain, E., Ural, S., Malik, A. & Shan, J. 2011. Mapping Pakistan 2010 floods using remote sensing data. In Proceedings of the ASPRS Annual Conference, Milwaukee, WI, USA. pp. 1-5.

Leopold, L.B. & Wolman, M.G. 1957. River channel patterns: Braided, meandering and straight. US Geological Survey Professional 282-B: 1-85.

Lewin, J. & Brewer, P.A. 2001. Predicting channel patterns. Geomorphology 40: 329-339.

Lillesand, T.M., Kiefer, R.W. & Chipman, J.W. 2004. Remote Sensing and Image interpretation. 5 ed. New York: John Wiley & Sons Ltd.

Lupia, F. 2012. Erdas Imagine 9.2: An Overview of the Main Features and Tools. Technical Report; DOI: 10.13140/2.1.3689.9525.

Nguyen Lam Dao, Nguyen Thanh Minh, Pham Thi Mai Thy, Hoang Phi Phung & Hoang Van Huan. 2010. Analysis of changes in the riverbanks of Mekong River - Vietnam by using multi-temporal remote sensing data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, Volume XXXVIII, Part 8, Kyoto Japan.

Petts, G.E. 1995. Changing river channels: The geographical tradition. In Changing River Channels, edited by Gurnell, A.M. & Petts, G.E. Chichester: John Wiley & Sons Inc. pp. 1-23.

Puletti, N., Perria, R. & Storchi, P. 2014. Unsupervised classification of very high resolution remotely sensed images for grapevine rows detection. European Journal of Remote Sensing 47: 45-54. doi: http://dx.doi.org/10.5721/ EuJRS20144704.

Rogan, J. & Miller, J.A. 2007. Using GIS and remote sensing for ecological mapping and monitoring. In Integration of GIS and Remote Sensing, edited by Masev, V. Chichester: John Wiley and Sons.

Schumm, S.A. & Khan, H.R. 1972. Experimental study of channel patterns. Geological Society of America Bulletin 83: 1755-1770.

Shiguo Jiang & Desheng Liu. 2011. On chance-adjusted measures for accuracy assessment in remote sensing image classification. ASPRS 2011 Annual Conference Milwaukee, Wisconsin, May 1-5.

Tou, J.T. & Gonzalez, R.C. 1974. Pattern Recognition Principles. Reading, Massachusetts: Addison-Wesley Publishing Company.

Bato, V.A., Paningbatan Jr. E.P. & Bartolome, B.J. 2011. High resolution satellite data for comprehensive land-use planning. Journal of Environmental Science and Management 14(1): 12-23.

Yang, L., Meng, X. & Zhang, X. 2014. SRTM DEM and its application advances. International Journal of Remote Sensing 32(14): 3875-3896.

 

 

*Pengarang untuk surat-menyurat; email: hamid.ch@pucit.edu.pk

 

 

sebelumnya