Skip to main content

Advertisement

Log in

Spatio-temporal variation in land use/land cover pattern and channel migration in Majuli River Island, India

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Precise land use and land cover (LULC) change information of a land surface is significant for sustainable development programs as the earth’s surface is undergoing rapid changes. Majuli Island is located in the upper reach of the Brahmaputra River in India. It is continuously changing its shape by the action of erosion of the Brahmaputra River, incurring both tangible and intangible losses. This study aims to find out the changes that occurred in the island by analyzing the land use/land cover along with channel migration in the Brahmaputra River that occurred in that area over the period 1973 to 2019. This paper assesses the changes and present status of Majuli River Island from 1973 to 2019 using Landsat MSS (1973), TM (1985, 1995), ETM + (2009), and OLI (2019) satellite imageries. Here, the maximum likelihood classification (MLC) technique for LULC analysis and their temporal changes and normalized difference vegetation index (NDVI) technique for the vegetation characteristics have been processed and analyzed with the help of the geospatial information system (GIS). From the results, it is found that area of vegetation has gradually decreased from 365.59 (26.85%) in 1973 to 262.79 km2 (19.29%) in 2019. In contrast, the barren land had increased from 4.82 (0.35%) in 1973 to 31.88 km2 (2.34%) in 2019. Other LULC categories like agricultural lands, built-up areas, water bodies, and sand deposition also have changed significantly. The NDVI values are also changed due to channel shifting, soil erosion, and deforestation. The accuracy assessment for the supervised classification of LULC classes for all years showed excellent results in all six classes.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

The authors confirm that all the data used in the present study are mentioned within the article.

References

  • Allam, M., Bakr, N., & Elbably, W. (2019). Multi-temporal assessment of land use/land cover change in arid region based on Landsat satellite imagery: Case study in Fayoum Region. Egypt. Remote Sensing Application S: Society and Environment, 14, 8–19.

    Article  Google Scholar 

  • Anderson, J. R. (1976). A land use and land cover classification system for use with remote sensor data (Vol. 964). US Government Printing Office.

  • Bhagabati, A. K. (2001). Biodiversity and associated problems in the islands of the Brahmaputra. Assam. Geographical Review of India, 63(4), 330–343.

    Google Scholar 

  • Bhakal, L., Dubey, B., & Sarma, A. K. (2005). Estimation of bank erosion in the River Brahmaputra near Agyathuri by using Geographic Information System. Journal of the Indian Society of Remote Sensing, 33(1), 81–84.

    Article  Google Scholar 

  • Bhaskar, B. P., Baruah, U. T. P. A. L., Vadivelu, S., Raja, P., & Sarkar, D. E. E. P. A. K. (2010). Remote sensing and GIS in the management of wetland resources of Majuli Island, Assam. India. Tropical Ecology, 51(1), 31–40.

    Google Scholar 

  • Chakraborty, S., & Datta, K. (2013). Causes and consequences of channel changes–a spatio-temporal analysis using remote sensing and GIS—Jaldhaka-Diana River System (Lower Course), Jalpaiguri (Duars), West Bengal. India. Journal of Geography & Natural Disasters, 3(1), 1–13.

    Google Scholar 

  • Chamling, M., & Bera, B. (2020). Spatio-temporal patterns of land use/land cover change in the Bhutan–Bengal foothill region between 1987 and 2019: Study towards geospatial applications and policy making. Earth Systems and Environment, 1–14.

  • Congalton, R. G., & Green, K. (2019). Assessing the accuracy of remotely sensed data: Principles and practices. CRC press.

  • Dutta, M. K., Barman, S., & Aggarwal, S. P. (2010). A study of erosion-deposition processes around Majuli Island, Assam. Earth Science India, 3(4).

  • Enderle, D. I., & Weih, R. C., Jr. (2005). Integrating supervised and unsupervised classification methods to develop a more accurate land cover classification. Journal of the Arkansas Academy of Science, 59(1), 65–73.

    Google Scholar 

  • Fonji, S. F., & Taff, G. N. (2014). Using satellite data to monitor land-use land-cover change in North-eastern Latvia. Springerplus, 3(1), 61.

    Article  Google Scholar 

  • Gandhi, G. M., Parthiban, S., Thummalu, N., & Christy, A. (2015). Ndvi: Vegetation change detection using remote sensing and gis–A case study of Vellore District. Procedia Computer Science, 57, 1199–1210.

    Article  Google Scholar 

  • Giri, C., Zhu, Z., & Reed, B. (2005). A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets. Remote Sensing of Environment, 94(1), 123–132.

    Article  Google Scholar 

  • Gogoi, C., & Goswami, D. C. (2013). A study on bank erosion and bank line migration pattern of the Subansiri River in Assam using remote sensing and GIS technology. International Journal of Engineering Science, 2(9), 1–6.

    Google Scholar 

  • Hussain, S., Mubeen, M., Ahmad, A., Akram, W., Hammad, H. M., Ali, M., & Nasim, W. (2019). Using GIS tools to detect the land use/land cover changes during forty years in Lodhran district of Pakistan. Environmental Science and Pollution Research, 1–17.

  • Jensen, J. R. (2009). Remote sensing of the environment: An earth resource perspective 2/e. Pearson Education India.

  • Kanga, S., Sharma, L. K., Pandey, P. C., & Nathawat, M. S. (2014). GIS Modelling approach for forest fire risk assessment and management. International Journal of Advancement in Remote Sensing, GIS and Geography, 2(1), 30–44.

    Google Scholar 

  • Kantakumar, L. N., & Neelamsetti, P. (2015). Multi-temporal land use classification using hybrid approach. The Egyptian Journal of Remote Sensing and Space Science, 18(2), 289–295.

    Article  Google Scholar 

  • Kummu, M., Lu, X. X., Rasphone, A., Sarkkula, J., & Koponen, J. (2008). Riverbank changes along the Mekong River: Remote sensing detection in the Vientiane-Nong Khai area. Quaternary International, 186(1), 100–112.

    Article  Google Scholar 

  • Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. biometrics, 159–174.

  • Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote sensing and image interpretation. John Wiley & Sons.

    Google Scholar 

  • Li, S., Yang, H., Lacayo, M., Liu, J., & Lei, G. (2018). Impacts of land-use and land-cover changes on water yield: A case study in Jing-Jin-Ji. China. Sustainability, 10(4), 960.

    Article  Google Scholar 

  • Lu, D., Li, G., Moran, E., & Hetrick, S. (2013). Spatiotemporal analysis of land-use and land-cover change in the Brazilian Amazon. International Journal of Remote Sensing, 34(16), 5953–5978.

    Article  Google Scholar 

  • Nanson, G. C., & Hickin, E. J. (1986). A statistical analysis of bank erosion and channel migration in western Canada. Geological Society of America Bulletin, 97(4), 497–504.

    Article  Google Scholar 

  • Omo-Irabor, O. O., & Oduyemi, K. (2007). A hybrid image classification approach for the systematic analysis of land cover (LC) changes in the Niger Delta region. School of Contemporary Sciences, University of Abertay, Scotland, UK.

    Google Scholar 

  • Piao, S., Fang, J., Zhou, L., Guo, Q., Henderson, M., Ji, W., & Tao, S. (2003). Interannual variations of monthly and seasonal normalized difference vegetation index (NDVI) in China from 1982 to 1999. Journal of Geophysical Research: Atmospheres, 108(D14).

  • Pellikka, P., Clark, B., Hurskainen, P., Keskinen, A., Lanne, M., Masalin, K., ... & Sirviö, T. (2004, October). Land use change monitoring applying geographic information systems in the Taita Hills, SE-Kenya. In Proceedings of the 5th AARSE Conference (pp. 18–21).

  • Pontius, R. G. (2000). Quantification error versus location error in comparison of categorical maps. Photogrametric Engineering and Remote Sensing, 66(8), 1011–1016.

    Google Scholar 

  • Ranjan, A. K., Sivathanu, V., Verma, S. K., Murmu, L., & Kumar, P. B. S. (2017). Spatio-temporal variation in Indian part of Sundarban Delta over the years 1990–2016 using Geospatial Technology. International Journal of Geomatics and Geosciences, 7(3), 275–292.

    Google Scholar 

  • Sahay, A., & Roy, N. (2017). Shrinking Space and expanding population: Socioeconomic impacts of Majuli's changing geography. Focus on Geography, 60–2.

  • Santhiya, G., Lakshumanan, C., & Muthukumar, S. (2010). Mapping of landuse/landcover changes of Chennai coast and issues related to coastal environment using remote sensing and GIS. International Journal of Geomatics and Geosciences, 1(3), 563–576.

    Google Scholar 

  • Sarma, J. N., & Acharjee, S. (2012). A GIS based study on bank erosion by the river Brahmaputra around Kaziranga National Park, Assam. India. Earth System Dynamics Discussions, 3(2), 1085–1106.

    Google Scholar 

  • Sarma, A. (2014). Landscape degradation of river Island Majuli, Assam (India) due to flood and erosion by river Brahmaputra and its restoration. Journal of Medical and Biological Engineering, 3(4).

  • Skidmore, A. K. (1999). Accuracy assessment of spatial information. In Spatial statistics for remote sensing (pp. 197–209). Springer, Dordrecht.

  • Sreelekha, M., & Reddy, S. N. (2019). Accuracy Assessment of Supervised and Unsupervised Classification using NOAA Data in Andhra Pradesh Region. International Journal of Engineering Research and Technology, 8(12), 60–64.

    Google Scholar 

  • Story, M., & Congalton, R. G. (1986). Accuracy assessment: A user’s perspective. Photogrammetric Engineering and Remote Sensing, 52(3), 397–399.

    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.

    Article  Google Scholar 

  • Thomas, J., Kumar, S., & Sudheer, K. P. (2020). Channel stability assessment in the lower reaches of the Krishna River (India) using multi-temporal satellite data during 1973–2015. Remote Sensing Applications: Society and Environment, 17, 100274.

  • Ullah, R., Malik, R. N., & Qadir, A. (2009). Assessment of groundwater contamination in an industrial city, Sialkot, Pakistan. African Journal of Environmental Science and Technology, 3(12).

  • Usman, M., Liedl, R., Shahid, M. A., & Abbas, A. (2015). Land use/land cover classification and its change detection using multi-temporal MODIS NDVI data. Journal of Geographical Sciences, 25(12), 1479–1506.

    Article  Google Scholar 

  • Wilkie, D. S., & Finn, J. T. (1996). Remote sensing imagery for natural resources monitoring. Columbia University Press.

    Google Scholar 

  • Xiubin, L. (1996). A review of the international researches on land use/land cover change [J]. Acta Geographica Sinica, 6.

  • Xie, Y., Zhao, X., Li, L., & Wang, H. (2010). Calculating NDVI for Landsat7-ETM data after atmospheric correction using 6S model: A case study in Zhangye city, China. In 2010 18th International Conference on Geoinformatics (pp. 1–4). IEEE.

  • Yang, X., Damen, M. C., & Van Zuidam, R. A. (1999). Satellite remote sensing and GIS for the analysis of channel migration changes in the active Yellow River Delta, China. International Journal of Applied Earth Observation and Geoinformation, 1(2), 146–157.

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge all the stakeholders supporting this research work in every aspect.

Author information

Authors and Affiliations

Authors

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pathan, S.A., Ashwini, K. & Sil, B.S. Spatio-temporal variation in land use/land cover pattern and channel migration in Majuli River Island, India. Environ Monit Assess 193, 811 (2021). https://doi.org/10.1007/s10661-021-09614-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-021-09614-w

Keywords

Navigation