Modeling Earth Systems and Environment

, Volume 5, Issue 1, pp 217–226 | Cite as

Mapping and monitoring of mangrove along the Odisha coast based on remote sensing and GIS techniques

  • Santanu RoyEmail author
  • Manik Mahapatra
  • Abhishekh Chakraborty
Original Article


Digital image processing techniques using multi-temporal satellite imagery are widely used to understand landscape dynamics. In this study, we analysed the spatio-temporal changes of mangrove along the Odisha coast, India using Landsat satellite images of four different time periods, i.e., Landsat-5 Thematic Mapper satellite images of 1990 and 2009, Landsat-7 Enhanced Thematic Mapper Plus satellite images of 2000 and Landsat-8 Operational Land Imager satellite images of 2015. Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI) and Normalized Difference Wetland Index (NDWI) are used to extract and classify the mangroves into three classes such as dense mangrove, sparse mangrove and open mangrove. The study reveals that total mangrove of the study area increased from 18573.49 ha (1990) to 23871.49 ha (2015) during the last three decades. This may be attributed to mangrove plantation, restoration and proper coastal zone management plan. The paper highlights the importance of digital change detection techniques using NDVI, SAVI and NDWI for mangrove mapping and monitoring along the Odisha coast. This study aids Odisha coastal zone management authority to take timely decisions for conservation of mangrove and future mangrove plantation plans with help of satellite image processing in GIS environment.


Mangrove Remote sensing NDVI SAVI NDWI Odisha 



The authors express their sincere gratitude to Dr. Ashis Kumar Paul Professor, Vidyasagar University for providing valuable guidance, support and constant encouragement. The authors are also thankful to Dr. Ramkrrishna Maiti, Professor, Vidyasagar University and Dr. Jatisankar Bandyopadhyay, Assistant Professor, Vidyasagar University for providing valuable guidance.


  1. Alongi DM (2008) Mangrove forests: resilience, protection from tsunamis, and responses to global climate change. Estuar Coast Shelf Sci 76(1):1–13pCrossRefGoogle Scholar
  2. Alsaaideh B, Al-Hanbali A, Tateishi R, Kobayashi T, Hoan NT (2013) Mangrove forests mapping in the southern part of Japan using Landsat ETM+ with DEM. J Geogr Inf Syst 5(4):369Google Scholar
  3. Andriamparany R, Francois F (2010) Dynamics of mangrove forests in the Mangoky River Delta, Madagascar, under the influence of natural and human factors. For Ecol Manag 259:1161–1169CrossRefGoogle Scholar
  4. Bedawi N, Kamel M (2010) Segmentation of very high resolution remote sensing imagery of urban areas using particle swarm optimization algorithm. In: Image analysis and recognition: lecture notes in computer science. Springer, New YorkGoogle Scholar
  5. Chander G, Markham BL, Helder DL (2009) Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens Environ 113(5):893–903CrossRefGoogle Scholar
  6. Dahdouh-Guebas F, Verheyden A, De Genst W, Hettiarachchi S, Koedam N (2000) Four decade vegetation dynamics in Sri Lankan mangroves as detected from sequential aerial photography: a case study in Galle. Bull Mar Sci 67:741–759Google Scholar
  7. Danielsen F et al (2005) The Asian tsunami: a protective role for coastal vegetation. Science 310:643CrossRefGoogle Scholar
  8. Das S, Vincent JR (2009) Mangroves protected villages and reduced death toll during Indian super cyclone. Proc Natl Acad Sci 106(18):7357–7360CrossRefGoogle Scholar
  9. Everitt JH, Yang C, Judd FW, Summy KR (2010) Use of archive aerial photography for monitoring black mangrove populations. J Coast Res 26:649–653CrossRefGoogle Scholar
  10. Ferreira MA, Andrade F, Banderiba SO, Cardoso P, Nogueira Mendes R, Paula J (2009) Analysis of cover change (1995–2005) of Tanzania/Mozambique transboundary mangroves using Landsat imagery. Aquat Conserv 19:38–45CrossRefGoogle Scholar
  11. Gao BC (1996) NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58(3):257–266CrossRefGoogle Scholar
  12. Ghorai D, Mahapatra M, Paul AK (2016) Application of remote sensing and GIS techniques for decadal change detection of mangroves along Tamil Nadu Coast, India. J Remote Sens GIS 7(1):42–53Google Scholar
  13. Gilman E, Ellison J, Duke NC, And Field C (2008) Threats to mangroves from climate change and adaptation options: a review. Aquat Bot 89:237–250CrossRefGoogle Scholar
  14. Giri C, Pengra B, Zhu Z, Singh A, Tieszen L (2007) Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuar Coast Shelf Sci 73:91–100CrossRefGoogle Scholar
  15. Giri C, Zhu Z, Tieszen LL, Singh A, Gillette S, Kelmelis JA (2008) Mangrove forest distributions and dynamics (1975–2005) of the tsunami-affected region of Asia. J Biogeogr 35:519–528CrossRefGoogle Scholar
  16. Green EP, Clear CD, Mumby PJ, Edwards AJ, Ellis AC (1998) Remote sensing techniques for mangrove mapping. Int J Remote Sens 19:935–956CrossRefGoogle Scholar
  17. Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25(3):295–309CrossRefGoogle Scholar
  18. Jensen JR, Lin H, Yang X, Ramset E, Davis BA, Thoemke CW (1991) The measurement of mangrove characteristics in south-west Florida using SPOT multispectral data. Geocartogr Int 2:13–21CrossRefGoogle Scholar
  19. Kristensen E, Bouillon S, Dittmar T, Marchand C (2008) Organic carbon dynamics in mangrove ecosystems: a review. Aquat Bot 89(2):201–219CrossRefGoogle Scholar
  20. Kuenzer C, Bluemel A, Gebhardt S, Vo Quoc T, Dech S (2011) Remote sensing of mangrove ecosystems: a review. Remote Sens 3(5):878–928CrossRefGoogle Scholar
  21. Liu GR, Liang CK, Kuo TH, Lin TH, Huang SJ (2004) Comparison of the NDVI, ARVI and AFRI vegetation index along with their relations with the AOD using SPOT 4 vegetation data. TAO 15(1):15–31Google Scholar
  22. Mas JF (1999) Monitoring land-cover changes: a comparison of change detection techniques. Int J Remote Sens 20(1):139–152CrossRefGoogle Scholar
  23. Mas JF (2004) Mapping land use/cover in a tropical coastal area using satellite sensor data, GIS and artificial neural networks. Estuar Coast Shelf Sci 59:219–230CrossRefGoogle Scholar
  24. Nagelkerken I, Blaber SJM, Bouillon S, Green P, Haywood M, Kirton LG, Somerfield PJ (2008) The habitat function of mangroves for terrestrial and marine fauna: a review. Aquat Bot 89(2):155–185CrossRefGoogle Scholar
  25. NASA (ed) (2011) Landsat 7 Science data users handbook landsat project science office at NASA’s Goddard Space Flight Center in Greenbelt, 186. Accessed 10 April 2016
  26. National Wetland Atlas, SAC/EPSA/ABHG/NWIA/ATLAS/34/2011 (2011) Space Applications Centre (ISRO), Ahmedabad, India. Accessed 6 Jan 2013
  27. Nayak S (2002) Application of remote sensing to coastal zone managmrnt in India. Int Arch Photogramm Remote Spat Inf Syst 34:371–377 Part 7.Google Scholar
  28. SAC (2012) Coastal Zones of India. Space Applications Centre (ISRO), pp 601. ISBN: 978-81-909978-9-8Google Scholar
  29. Selvam V, Ravichandran KK, Gnanappazham L, Navamuniyammal M (2003) Assessment of community based restoration of Pichavaram mangrove wetland using remote sensing data. Curr Sci 85:794–798Google Scholar
  30. Seto KC, Fragkias M (2007) Mangrove conversion and aquaculture development in Vietnam: a remote sensing-based approach for evaluating the Ramsar Convention on Wetlands. Glob Environ Change 17:486–500CrossRefGoogle Scholar
  31. Tomlinson PB (1986) The botany of mangroves. Cambridge University Press, CambridgeGoogle Scholar
  32. Walters BB, Rönnbäck P, Kovacs JM, Crona B, Hussain SA, Badola R, Dahdouh-Guebas F (2008) Ethnobiology, socio-economics and management of mangrove forests: a review. Aquat Bot 89(2):220–236CrossRefGoogle Scholar
  33. Wang L, Zhang S (2014) Incorporation of texture information in a SVM method for classifying salt cedar in western China. Remote Sens Lett 5(6):501–510CrossRefGoogle Scholar
  34. Wang L, Sousa W, Gong P (2004) Integration of object-based and pixel-based classification for mangrove mapping with IKONOS imagery. Int J Remote Sens 25:5655–5668CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Remote Sensing and GISVidyasagar UniversityMidnaporeIndia
  2. 2.National Centre for Sustainable Coastal ManagementChennaiIndia

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