Spatiotemporal Dynamics of Riverbank Erosion and Accretion of the Surma River in Sunamganj District, Bangladesh: A Remote Sensing and GIS-Based Assessment (2000–2025)

Authors

Amisha Das Roy

Department of Geography and Environment, Shahjalal University of Science and Technology, Sylhet, Bangladesh (Bangladesh)

Article Information

DOI: 10.51584/IJRIAS.2026.11080082

Subject Category: Education

Volume/Issue: 11/8 | Page No: 1117-1126

Publication Timeline

Submitted: 2026-08-18

Accepted: 2026-08-25

Published: 2026-09-08

Abstract

Riverbank erosion and accretion are important geomorphological processes that continuously modify river channels and adjacent floodplain environments. In Bangladesh, these processes are particularly significant because of extensive alluvial river systems, monsoonal hydrology, sediment dynamics, and frequent flooding. This study investigates the spatial and temporal dynamics of erosion and accretion along the Surma River in Sunamganj District, northeastern Bangladesh, over the period 2000–2025 using remote sensing and Geographic Information System (GIS) techniques. Multi-temporal Landsat satellite imagery was used to delineate river boundaries for different periods, while GIS-based overlay analysis was applied to identify areas of erosion, accretion, and unchanged river extent. The results demonstrate substantial temporal variability. During 2000–2005, accretion was considerably greater than erosion, with reported values of 40.205123 and 0.947521, respectively. Erosion became dominant during 2005–2010 and 2010–2015. A pronounced increase in accretion occurred during 2015–2020, when the reported accretion value reached 968.039589 while erosion declined to 0.224532. During 2020–2025, both erosion and accretion increased substantially, reaching 273.911314 and 975.462243, respectively, indicating intensified channel adjustment and lateral migration. The findings demonstrate that the Surma River is a highly dynamic fluvial system characterized by alternating phases of erosion and deposition. Remote sensing and GIS provide useful tools for monitoring riverbank changes and supporting river management, land-use planning, and erosion-risk reduction.

Keywords

Riverbank erosion; accretion; Surma River; Sunamganj; remote sensing; GIS; Landsat; channel migration; Bangladesh

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References

1. Haque, S. E., Nahar, N., Chowdhury, N. N., Sayanno, T. K., & Haque, M. S. (2024). Geomorphological changes of river Surma due to climate change. International Journal of Energy and Water Resources, 9, 113–132. https://doi.org/10.1007/s42108-023-00275-8 [Google Scholar] [Crossref]

2. Hasan, I., Dey, J., Munna, M. M. R., Preya, A., Nisanur, T. B., Memy, M. J., & Zeba, M. Z. S. (2024). Morphological changes of river bank erosion and channel shifting assessment on Arial Khan River of Bangladesh using Landsat satellite time series images. Progress in Disaster Science, 24, 100381. https://doi.org/10.1016/j.pdisas.2024.100381 [Google Scholar] [Crossref]

3. Hasan, J., Uz Zaman, M. A., & Faridatul, M. I. (2026). Assessing spatiotemporal trends of riverbank erosion using a GIS-DSAS-based integrated approach. Geology, Ecology, and Landscapes, 10(1), 331–349. https://doi.org/10.1080/24749508.2025.2451450 [Google Scholar] [Crossref]

4. Nawar, Q. A., Alam, R., Akhter, J., & Hasan, G. M. J. (2026). Assessing riverbank transformations of the major rivers in Bangladesh: A remote sensing and GIS based study of erosion and accretion patterns from 1988–2023. Marine Geodesy, 49(1), 26–63. https://doi.org/10.1080/01490419.2025.2494695 [Google Scholar] [Crossref]

5. Miah, M. D., Chowdhury, N. A., & Ali, Y. (2025). Monitoring multi-decadal riverbank dynamics of the Padma river in Bangladesh using geospatial analysis and DSAS model. Discover Geoscience, 3, 238. https://doi.org/10.1007/s44288-025-00353-w [Google Scholar] [Crossref]

6. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. https://doi.org/10.1177/001316446002000104 [Google Scholar] [Crossref]

7. Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46. https://doi.org/10.1016/0034-4257(91)90048-B [Google Scholar] [Crossref]

8. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0 [Google Scholar] [Crossref]

9. U.S. Geological Survey. (2024). Landsat Collection 2 Surface Reflectance. U.S. Department of the Interior. https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance [Google Scholar] [Crossref]

10. U.S. Geological Survey, EROS Center. (2020). Landsat 8–9 Operational Land Imager/Thermal Infrared Sensor Level-2, Collection 2. U.S. Geological Survey. https://doi.org/10.5066/P9OGBGM6 [Google Scholar] [Crossref]

11. Alam, M. M., et al. (2024). Assessing river bank erosion and accretion along the lower part of the Meghna River using GIS and remote sensing techniques. Journal of Bangladesh University of Engineering and Technology. (2024). [Google Scholar] [Crossref]

12. Rahman, M., et al. (2025). Brahmaputra riverbank morpho dynamic changes: A multi-temporal remote sensing and GIS-based assessment. Progress in Disaster Science. (2025). [Google Scholar] [Crossref]

13. Miah, M. D., Chowdhury, N. A., & Ali, Y. (2025). Monitoring multi-decadal riverbank dynamics of the Padma River in Bangladesh using the Google Earth Engine and digital shoreline analysis system. Discover Geoscience, 3, 238. https://doi.org/10.1007/s44288-025-00353-w [Google Scholar] [Crossref]

14. Rana, M. F., et al. (2026). Socioeconomic consequences of riverbank erosion and accretion in Bangladesh. Discover Geoscience. (2026). [Google Scholar] [Crossref]

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