Skip to main content
SpringerLink
Log in

Otsu-BRSG: An Effective Algorithm for River Bank Line Detection and Monitoring in the Challenging Terrains of Kaziranga National Park

  • Research Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

The topography of the Kaziranga National Park (KNP), situated in Central Assam, India, is undergoing enormous erosion and accretion as a result of unforeseen changes in the Brahmaputra River’s course. Since monsoon rains and subsequent floods inundate the National Park area annually, the geographical analysis of the riverfront region surrounding the Park has been a laborious effort. The task of detecting the river bank-line becomes quite difficult as the topography of the landscape is continuously changing over the time period. The river course and vegetative parts are primarily affected by the heavy seasonal rainfall in the area. The region of interest consists of bare land which includes sandbar (also known as shoal), different types of vegetation, and water bodies. In such a scenario, extraction of edges using classical edge detection algorithms is quite challenging. A list of multispectral Landsat and Sentinel 2 imagery is used here to determine the river bank line of Brahmaputra which represents the northern boundary of the KNP using the smoothening capability of Savitzky-Goley filter followed by a suitable edge plotting method which is dependant on the locations of the edge pixels with respect to the other pixels in the image. The method suggests the suitability of an Otsu segmentation-based boundary refining algorithm (Otsu-BRSG) that examines the evolution of the above-mentioned bank line region between 2008 and 2018 and also the south-west boundary of Majuli Island as an additional case study between 2018 and 2022. This study explored a detailed comparative analysis of different vegetations in the highly affected regions from the viewpoint of multisource earth observation sensors using changes in bank line edges to measure soil erosion across the study period (2008–2018) along with the changes in riparian vegetation. The analysis revealed a net loss of 540.63 ha of wooded area in the vulnerable region, revealing an alarming trend of erosion.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  • Aghdami-Nia, M., Shah-Hosseini, R., Rostami, A., & Homayouni, S. (2022). Automatic coastline extraction through enhanced sea-land segmentation by modifying standard U-Net. International Journal of Applied Earth Observation and Geoinformation, 109, 102785. https://doi.org/10.1016/j.jag.2022.102785

    Article  Google Scholar 

  • Artiola, J., Pepper, I. L., & Brusseau, M. L. (Eds.). (2004). Environmental monitoring and characterization. Academic Press.

    Google Scholar 

  • Basumatary, H., Sah, R. K., & Das, A. K. (2019). Analyzing area dynamics on a protected floodplain using long-term sequential data: The case of Kaziranga National Park. Journal of the Indian Society of Remote Sensing, 47, 1557–1566. https://doi.org/10.1007/s12524-019-01000-x

    Article  Google Scholar 

  • Basumatary, H., Sah, R. K., & Das, A. K. (2021). Bankline dynamics and their effects on protected areas along the Brahmaputra river. CATENA, 197, 104947. https://doi.org/10.1016/j.catena.2020.104947

    Article  Google Scholar 

  • Billa, L., & Pradhan, B. (2011). Semi-automated procedures for shoreline extraction using single RADARSAT-1 SAR image. Estuarine, Coastal and Shelf Science, 95(4), 395–400. https://doi.org/10.1016/j.ecss.2011.10.009

    Article  Google Scholar 

  • Biron, P. M., Buffin-Bélanger, T., Larocque, M., Choné, G., Cloutier, C. A., Ouellet, M. A., & Eyquem, J. (2014). Freedom space for rivers: A sustainable management approach to enhance river resilience. Environmental Management, 54(5), 1056–1073. https://doi.org/10.1007/s00267-014-0366-z

    Article  Google Scholar 

  • Bravard, J. P., Amoros, C., Pautou, G., Bornette, G., Bournaud, M., Creuzé des Châtelliers, M., & Tachet, H. (1997). River incision in south-east France: Morphological phenomena and ecological effects. Regulated Rivers: Research & Management: an International Journal Devoted to River Research and Management, 13(1), 75–90. https://doi.org/10.1002/(SICI)1099-1646(199701)13:1%3C75::AID-RRR444%3E3.0.CO;2-6

    Article  Google Scholar 

  • Buffin-Bélanger, T., Biron, P. M., Larocque, M., Demers, S., Olsen, T., Choné, G., & Eyquem, J. (2015). Freedom space for rivers: An economically viable river management concept in a changing climate. Geomorphology, 251, 137–148. https://doi.org/10.1016/j.geomorph.2015.05.013

    Article  Google Scholar 

  • Buono, A., Nunziata, F., Mascolo, L., & Migliaccio, M. (2014). A multipolarization analysis of coastline extraction using X-band COSMO-SkyMed SAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(7), 2811–2820. https://doi.org/10.1109/JSTARS.2014.2320366

    Article  Google Scholar 

  • Cao, W., Zhou, Y., Li, R., & Li, X. (2020). Mapping changes in coastlines and tidal flats in developing islands using the full time series of Landsat images. Remote Sensing of Environment, 239, 111665. https://doi.org/10.1016/j.rse.2020.111665

    Article  Google Scholar 

  • Das, J. D., Dutta, T., & Saraf, A. K. (2007). Remote sensing and GIS application in change detection of the Barak river channel, NE India. Journal of the Indian Society of Remote Sensing, 35(4), 301–312. https://doi.org/10.1007/BF02990786

    Article  Google Scholar 

  • Ding, X., & Li, X. (2014). Coastline detection in SAR images using multiscale normalized cut segmentation. In 2014 IEEE geoscience and remote sensing symposium (pp. 4447–4449). IEEE.

  • Gausman, H. W., Allen, W. A., Cardenas, R., & Richardson, A. J. (1973). Reflectance discrimination of cotton and corn at four growth Stages 1. Agronomy Journal, 65(2), 194–198. https://doi.org/10.2134/agronj1973.00021962006500020003x

    Article  Google Scholar 

  • Gilvear, D. J. (1993). River management and conservation issues on formerly braided river systems; the case of the River Tay, Scotland. Geological Society, London, Special Publications, 75(1), 231–240. https://doi.org/10.1144/GSL.SP.1993.075.01.14

    Article  Google Scholar 

  • Gilvear, D., Winterbottom, S., & Sichingabula, H. (2000). Character of channel planform change and meander development: Luangwa River, Zambia. Earth Surface Processes and Landforms: THe Journal of the British Geomorphological Research Group, 25(4), 421–436. https://doi.org/10.1002/(SICI)1096-9837(200004)25:4%3C421::AID-ESP65%3E3.0.CO;2-Q

    Article  Google Scholar 

  • Goswami, U., Sarma, J. N., & Patgiri, A. D. (1999). River channel changes of the Subansiri in Assam India. Geomorphology, 30(3), 227–244. https://doi.org/10.1016/S0169-555X(99)00032-X

    Article  Google Scholar 

  • Jardim, R., & Morgado-Dias, F. (2020). Savitzky-Golay filtering as image noise reduction with sharp color reset. Microprocessors and Microsystems, 74, 103006. https://doi.org/10.1016/j.micpro.2020.103006

    Article  Google Scholar 

  • Jensen, J. R. (1996). Introductory digital image processing: A remote sensing perspective. New York: Prentice-Hall Inc.

    Google Scholar 

  • Kittler, J., & Illingworth, J. (1985). On threshold selection using clustering criteria. IEEE Transactions on Systems, Man, and Cybernetics, 5, 652–655. https://doi.org/10.1109/TSMC.1985.6313443

    Article  Google Scholar 

  • Kotoky, P., Bezbaruah, D., Baruah, J., & Sarma, J. N. (2005). Nature of bank erosion along the Brahmaputra river channel, Assam India. Current Science, 88, 634–640. http://www.jstor.org/stable/24110266

    Google Scholar 

  • Laliberte, A. S., Johnson, D. E., Harris, N. R., & Casady, G. M. (2001). Stream change analysis using remote sensing and geographic information systems (GIS). Journal of Range Management, 54(2), 204–204.

    Google Scholar 

  • Lee, J. S., & Jurkevich, I. (1990). Coastline detection and tracing in SAR images. IEEE Transactions on Geoscience and Remote Sensing, 28(4), 662–668. https://doi.org/10.1109/TGRS.1990.572976

    Article  Google Scholar 

  • Liu, H., & Jezek, K. C. (2004). Automated extraction of coastline from satellite imagery by integrating Canny edge detection and locally adaptive thresholding methods. International Journal of Remote Sensing, 25(5), 937–958. https://doi.org/10.1080/0143116031000139890

    Article  Google Scholar 

  • Madej, M. A., Weaver, W. E., & Hagans, D. K. (1994). Analysis of bank erosion on the Merced river, Yosemite valley, Yosemite National Park, California, USA. Environmental Management, 18(2), 235–250. https://doi.org/10.1007/BF02393764.

    Article  Google Scholar 

  • Mentaschi, L., Vousdoukas, M. I., Pekel, J. F., Voukouvalas, E., & Feyen, L. (2018). Global long-term observations of coastal erosion and accretion. Scientific Reports, 8(1), 1–11. https://doi.org/10.1038/s41598-018-30904-w.

    Article  CAS  Google Scholar 

  • Mishra, I., Bhateja, Y., Gaur, N., & Sharma, V. (2018). Spatial enhancement of SWIR band from Resourcesat-2A by preserving spectral details for accurate mapping of water bodies. J. Geomat, 13(5).

  • Misra, I., Rohil, M. K., Moorthi, S. M., & Dhar, D. (2023). SPRINT: Spectra preserving radiance image fusion technique using holistic deep edge spatial attention and Minnaert guided Bayesian probabilistic model. Signal Processing: Image Communication, 113, 116920.

    Google Scholar 

  • Mondal, R., Mukherjee, J., & Mukhopadhyay, J. (2020). Automated coastline detection from Landsat 8 Oli/Tirs images with the presence of inland water bodies in andaman. In IGARSS 2020–2020 IEEE international geoscience and remote sensing symposium (pp. 57–60). IEEE. https://doi.org/10.1109/IGARSS39084.2020.9324366.

  • Nogueira, K., Dalla Mura, M., Chanussot, J., Schwartz, W. R., & Dos Santos, J. A. (2019). Dynamic multicontext segmentation of remote sensing images based on convolutional networks. IEEE Transactions on Geoscience and Remote Sensing, 57(10), 7503–7520. https://doi.org/10.1109/TGRS.2019.2913861.

    Article  Google Scholar 

  • Nunziata, F., Buono, A., Migliaccio, M., & Benassai, G. (2016). Dual-polarimetric C-and X-band SAR data for coastline extraction. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(11), 4921–4928.

    Article  Google Scholar 

  • Otsu, N. (1979). A threshold selection method from Gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66.

    Article  Google Scholar 

  • Roy, N., Pandey, B. W., & Rani, U. (2020). Protecting the vanishing geo-cultural heritage of India: Case study of Majuli Island in Assam. International Journal of Geoheritage and Parks, 8(1), 18–30. https://doi.org/10.1016/j.ijgeop.2020.03.003.

    Article  Google Scholar 

  • Salembier, P., Oliveras, A., & Garrido, L. (1998). Antiextensive connected operators for image and sequence processing. IEEE Transactions on Image Processing, 7(4), 555–570. https://doi.org/10.1109/83.663500.

    Article  CAS  Google Scholar 

  • Savitzky, A., & Golay, M. J. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8), 1627–1639. https://doi.org/10.1021/ac60214a047

    Article  CAS  Google Scholar 

  • Seale, C., Redfern, T., Chatfield, P., Luo, C., & Dempsey, K. (2022). Coastline detection in satellite imagery: A deep learning approach on new benchmark data. Remote Sensing of Environment, 278, 113044. https://doi.org/10.1016/j.rse.2022.113044

    Article  Google Scholar 

  • Sheng, G., Yang, W., Deng, X., He, C., Cao, Y., & Sun, H. (2012). Coastline detection in synthetic aperture radar (SAR) images by integrating watershed transformation and controllable gradient vector flow (GVF) snake model. IEEE Journal of Oceanic Engineering, 37(3), 375–383. https://doi.org/10.1109/JOE.2012.2191998.

    Article  Google Scholar 

  • Sohn, H. G., & Jezek, K. C. (1999). Mapping ice sheet margins from ERS-1 SAR and SPOT imagery. International Journal of Remote Sensing, 20(15–16), 3201–3216. https://doi.org/10.1080/014311699211705.

    Article  Google Scholar 

  • Tajima, Y., Wu, L., Fuse, T., Shimozono, T., & Sato, S. (2019). Study on shoreline monitoring system based on satellite SAR imagery. Coastal Engineering Journal, 61(3), 401–421. https://doi.org/10.1080/21664250.2019.1619252.

    Article  Google Scholar 

  • Tajima, Y., Wu, L., & Watanabe, K. (2021). Development of a shoreline detection method using an artificial neural network based on Satellite SAR imagery. Remote Sensing, 13(12), 2254. https://doi.org/10.3390/rs13122254.

    Article  Google Scholar 

  • Thakur, P. K., Laha, C., & Aggarwal, S. P. (2012). River bank erosion hazard study of river Ganga, upstream of Farakka barrage using remote sensing and GIS. Natural Hazards, 61(3), 967–987. https://doi.org/10.1007/s11069-011-9944-z

    Article  Google Scholar 

  • Tian, J., & Philpot, W. D. (2015). Relationship between surface soil water content, evaporation rate, and water absorption band depths in SWIR reflectance spectra. Remote Sensing of Environment, 169, 280–289. https://doi.org/10.1016/j.rse.2015.08.007

    Article  Google Scholar 

  • Tockner, K., & Stanford, J. A. (2002). Riverine flood plains: Present state and future trends. Environmental Conservation, 29(3), 308–330. https://doi.org/10.1017/S037689290200022X

    Article  Google Scholar 

  • Van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., & Yu, T. (2014). Scikit-image: Image processing in Python. PeerJ, 2, e453.

    Article  Google Scholar 

  • Vandebroek, E., Lindenbergh, R., Van Leijen, F., De Schipper, M., De Vries, S., & Hanssen, R. (2017). Semi-automated monitoring of a mega-scale beach nourishment using high-resolution Terrasar-x satellite data. Remote Sensing, 9(7), 653.

    Article  Google Scholar 

  • Venkateswarlu, E., Sivannarayana, T., & Ratna Kumar, K. V. (2014). A comparative analysis of Resourcesat-2 LISS-3 and Landast-8 OLI imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40, 987–989.

    Article  Google Scholar 

  • Vincent, L. (1993). Grayscale area openings and closings, their efficient implementation and applications. In 1st Workshop on mathematical morphology and its applications to signal processing (pp. 22–27).

  • Wang, N., Chen, F., Yu, B., & Qin, Y. (2020). Segmentation of large-scale remotely sensed images on a Spark platform: A strategy for handling massive image tiles with the MapReduce model. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 137–147. https://doi.org/10.1016/j.isprsjprs.2020.02.012.

    Article  Google Scholar 

  • Wang, Y., & Allen, T. R. (2008). Estuarine shoreline change detection using Japanese ALOS PALSAR HH and JERS-1 L-HH SAR data in the Albemarle-Pamlico Sounds, North Carolina, USA. International Journal of Remote Sensing, 29(15), 4429–4442. https://doi.org/10.1080/01431160801932525.

    Article  Google Scholar 

  • Ward, J. V., Tockner, K., & Schiemer, F. (1999). Biodiversity of floodplain river ecosystems: Ecotones and connectivity 1. River Research and Applications, 15(1–3), 125–139. https://doi.org/10.1002/(SICI)1099-1646(199901/06)15:1/3%3C125::AID-RRR523%3E3.0.CO;2-E

    Article  Google Scholar 

  • Weier, J., & Herring, D. (2000). Measuring vegetation (NDVI & EVI). NASA Earth Observatory.

    Google Scholar 

  • Wu, L., Tajima, Y., Yamanaka, Y., Shimozono, T., & Sato, S. (2019). Study on characteristics of SAR imagery around the coast for shoreline detection. Coastal Engineering Journal, 61(2), 152–170. https://doi.org/10.1080/21664250.2018.1560685

    Article  Google Scholar 

  • Xie, S., & Tu, Z. (2015). Holistically-nested edge detection. In Proceedings of the IEEE international conference on computer vision (pp. 1395–1403).

  • Yao, Z., Xiao, J., Ta, W., & Jia, X. (2013). Planform channel dynamics along the Ningxia-Inner Mongolia reaches of the Yellow River from 1958 to 2008: Analysis using Landsat images and topographic maps. Environmental Earth Sciences, 70(1), 97–106. https://doi.org/10.1007/s12665-012-2106-0

    Article  Google Scholar 

  • Yasir, M., Sheng, H., Fan, H., Nazir, S., Niang, A. J., Salauddin, M., & Khan, S. (2020). Automatic coastline extraction and changes analysis using remote sensing and GIS technology. IEEE Access, 8, 180156–180170.

    Article  Google Scholar 

  • Yin, Y., Guo, Y., Deng, L., & Chai, B. (2023). Improved PSPNet-based water shoreline detection in complex inland river scenarios. Complex & Intelligent Systems, 9(1), 233–245. https://doi.org/10.1007/s40747-022-00793-8

    Article  Google Scholar 

  • Zhang, D., Van Gool, L., & Oosterlinck, A. (1994). Coastline detection from SAR images. In Proceedings of IGARSS'94–1994 IEEE international geoscience and remote sensing symposium (vol. 4, pp. 2134–2136). IEEE.

Download references

Acknowledgements

The first author acknowledges the Center for Soft Computing Research and Machine Intelligence Unit, ISI Kolkata, India for providing the facilities required in carrying out the research.

Funding

The research has been funded by DST Woman Scientist-A scheme of the Govt. of India (DST/SR/WOS-A/ET-145/2016).

Author information

Author notes

  1. Srutiparna Neogi and Geetanjali Aich have equal contribution to this work.

Authors and Affiliations

  1. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

    Srutiparna Neogi & Kuntal Ghosh

  2. Center for Soft Computing Research, Indian Statistical Institute, Kolkata, India

    Geetanjali Aich & Kuntal Ghosh

  3. Department of Computer Science and Engineering, MSIT, Kolkata, India

    Aniruddha Dey

  4. Theoretical and Applied Sciences Unit, North-East Centre, Indian Statistical Institute, Tezpur, India

    Sanjit Maitra

  5. Department of CSE, Indian Institute of Information Technology, Kalyani, India

    Oishila Bandyopadhyay

Authors
  1. Srutiparna Neogi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Geetanjali Aich
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aniruddha Dey
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sanjit Maitra
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Oishila Bandyopadhyay
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kuntal Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SN*: Problem identification, Writing, Investigation, Visualization, Methodology, Analysis, Supervision, Data Curation, Funding acquisition. GA*: Writing, Investigation, Visualization, Methodology, Software. AD: Writing, Supervision. SM: Problem identification, Writing, Data curation, Supervision. OB: Review and editing, Supervision, KG: Review and editing, Supervision.

Corresponding author

Correspondence to Kuntal Ghosh.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Neogi, S., Aich, G., Dey, A. et al. Otsu-BRSG: An Effective Algorithm for River Bank Line Detection and Monitoring in the Challenging Terrains of Kaziranga National Park. J Indian Soc Remote Sens (2024). https://doi.org/10.1007/s12524-024-01843-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12524-024-01843-z

Keywords

Search

Navigation