Forest Cover Change Analysis in Sundarban Delta Using Remote Sensing Data and GIS

  • K. KunduEmail author
  • P. Halder
  • J. K. Mandal
Part of the Studies in Computational Intelligence book series (SCI, volume 784)


The present study deals with change detection analysis of forest cover in Sundarban delta during 1975–2015 using remote sensing data and GIS. Supervised maximum likelihood classification techniques are needed to classify the remote sensing data, and the classes are water body, barren land, dense forest, and open forest. The study reveals that forest cover areas have been increased by 1.06% (19.28 km2), 5.80% (106.82 km2) during the periods of 1975–1989 and 1989–2000, respectively. The reversed tendency has been observed during 2000–2015, and its areas have been reduced to 5.77% (111.85 km2). The change detection results show that 63%–80% of dense forest area and 66%–70% of open forest area have been unaffected during 1975–2015 and 1975–2000, respectively, while during the interval 2000–2015, only 36% of open forest area has been unaltered. The overall accuracy (86.75%, 90.77%, 88.16%, and 85.03%) and kappa statistic (0.823, 0.876, 0.842, and 0799) have been achieved for the year of 1975, 1989, 2000, and 2015 correspondingly to validate the classification accuracy. Future trend of forest cover changes has been analyzed using the fuzzy logic techniques. From this study, it may be concluded that in future, the forest cover area has been more declined. The primary goal of this study is to notify the alteration of each features, and decision-maker has to take measurement, scrutinize, and control the natural coastal ecosystem in Sundarban delta.


Change detection Remote sensing data Dense forest Open forest Fuzzy logic Sundarban 



This research activity has been carried out in the Dept. of CSE, University of Kalyani, Kalyani, India. The authors acknowledge the support provided by the DST PURSE Scheme, Govt. of India at the University of Kalyani.


  1. 1.
    Ghosh, A., Schmidt, S., Fickert, T., Nüsser, M.: The Indian Sundarban mangrove forests: history, utilization, conservation strategies and local perception. Diversity 7, 149–169 (2015)Google Scholar
  2. 2.
    Alongi, D.M.: Mangrove forests: resilience; protection from tsunamis; and responses to global climate change. Estuar. Coast. Shelf Sci. 76, 1–13 (2008)CrossRefGoogle Scholar
  3. 3.
    FSI: India State of Forest Report 2011. Forest Survey of India, Ministry of Environment and Forests, Dehradun (2011)Google Scholar
  4. 4.
    Jha, C.S., Goparaju, L., Tripathi, A., Gharai, B., Raghubanshi, A.S., Singh, J.S.: Forest fragmentation and its impact on species diversity: an analysis using remote sensing and GIS. Biodivers. Conserv. 14, 1681–1698 (2005)CrossRefGoogle Scholar
  5. 5.
    Giri, C., Pengra, B., Zhu, Z., Singh, A., Tieszen, L.L.: Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuar. Coast. Shelf Sci. 73, 91–100 (2007)CrossRefGoogle Scholar
  6. 6.
    Pan, Y., Birdsey, R.A., Fang, J., Houghton, R., Kauppi, P.E., Kurz, W.A., et al.: A large and persistent carbon sinks in the world’s forests. Science 333(6045), 988–993 (2011)CrossRefGoogle Scholar
  7. 7.
    Giri, C., Long, J., Sawaid Abbas, R., Murali, M., Qamer, F.M., Pengra, B., Thau, D.: Distribution and dynamics of mangrove forests of South Asia. J. Environ. Manage. 148, 1–11 (2014)Google Scholar
  8. 8.
    Ostendorf, B., Hilbert, D.W., Hopkins, M.S.: The effect of climate change on tropical rainforest vegetation pattern. Ecol. Model. 145(2), 211–224 (2001)CrossRefGoogle Scholar
  9. 9.
    Giriraj, A., Shilpa, B., Reddy, C.S.: Monitoring of Forest cover change in Pranahita Wildlife Sanctuary, Andhra Pradesh, India using remote sensing and GIS. J. Environ. Sci. Technol. 1(2), 73–79 (2008)CrossRefGoogle Scholar
  10. 10.
    Jayappa, K.S., Mitra, D., Mishra, A.K.: Coastal geomorphological and land-use and land cover study of Sagar Island, Bay of Bengal (India) using remotely sensed data. Int. J. Remote Sens. 27(17), 3671–3682 (2006)CrossRefGoogle Scholar
  11. 11.
    Mitra, D., Karmekar, S.: Mangrove classification in Sundarban using high resolution multi spectral remote sensing data and GIS. Asian J. Environ. Disast. Manage 2(2), 197–207 (2010)CrossRefGoogle Scholar
  12. 12.
    Giri, S., Mukhopadhyay, A., Hazra, S., Mukherjee, S., Roy, D., Ghosh, S., Ghosh, T., Mitra, D.: A study on abundance and distribution of mangrove species in Indian Sundarban using remote sensing technique. J Coast Conserv. 18, 359–367 (2014)CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringGovernment College of Engineering & Textile TechnologySerampore, HooghlyIndia
  2. 2.Department of Computer Science and EngineeringPurulia Government Engineering CollegePuruliaIndia
  3. 3.Department of Computer Science and EngineeringUniversity of KalyaniKalyani, NadiaIndia

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