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Assessing impact of climate change on Mundra mangrove forest ecosystem, Gulf of Kutch, western coast of India: a synergistic evaluation using remote sensing

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Abstract

Mangrove cover changes have globally raised the apprehensions as the changes influence the coastal climate as well as the marine ecosystem services. The main goals of this research are focused on the monitoring of land cover and mangrove spatial changes particularly for the Mundra forest in the western coast of Gujarat state, India, which is famous for its unique mangrove bio-diversity. The multi-temporal Indian Remote Sensing (IRS) Linear Imaging Self Scanning (LISS)-II (IRS-1B) and III (IRS P6/RESOURCESAT-1) images captured in the year 1994 and 2010 were utilized for the spatio-temporal analysis of the area. The land cover and mangrove density was estimated by a unique hybrid classification which consists of K means unsupervised following maximum likelihood classification (MLC) supervised classification-based approach. The vegetation and non-vegetation layers has been extracted and separated by unsupervised classification technique while the training-based MLC was applied on the separated vegetation and non-vegetation classes to classify them into 11 land use/land cover classes. The climatic variables of the area involves wind, temperature, dew point, precipitation, and mean sea level investigated for the period of 17 years over the site. To understand the driving factors, the anthropogenic variables were also taken into account such as historical population datasets. The overall analysis indicates a significant change in the frequency and magnitude of sea-level rise from 1994 to 2010. The analysis of the meteorological variables indicates a high pressure and changes in mangrove density during the 17 years of time, which reveals that if appropriate actions are not initiated soon, the Mundra mangroves might become the victims of climate change-induced habitat loss. After analyzing all the factors, some recommendations and suggestions are provided for effective mangrove conservation and resilience, which could be used by forest official to protect this precious ecosystem.

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References

  • Adeel Z, Pomeroy R (2002) Assessment and management of mangrove ecosystems in developing countries. Trees-Struct and Funct 16(2):235–8

    Article  Google Scholar 

  • Alongi DM (2002) Present state and future of the world’s mangrove forests. Environ Conserv 29(3):331–49

    Article  Google Scholar 

  • Alongi DM (2008) Mangrove forests: resilience, protection from tsunamis, and responses to global climate change. Estuar Coast Shelf Sci 76(1):1–13

    Article  Google Scholar 

  • Arzandeh S, Wang J (2003) Monitoring the change of Phragmites distribution using satellite data. Can J Remote Sens 29(1):24–35

    Article  Google Scholar 

  • Banerjee R, Srivastava PK (2013) Reconstruction of contested landscape: detecting land cover transformation hosting cultural heritage sites from central India using remote sensing. Land Use Policy 34:193–203. doi:10.1016/j.landusepol.2013.03.005

    Article  Google Scholar 

  • Banerjee R, Srivastava P (2014) Remote sensing based identification of painted rock shelter sites: appraisal using advanced wide field sensor, neural network and field observations. In: Srivastava PK, Mukherjee S, Gupta M, Islam T (eds) Remote sensing applications in environmental research. Springer International Publishing, Society of Earth Scientists Series, pp 195–212. doi:10.1007/978-3-319-05906-8_11

    Chapter  Google Scholar 

  • Barrett EC, Curtis LF (1999) Introduction to environmental remote sensing. Routledge

  • Bishop YMM, Fienberg SE, Paul W (1975) Holland. Discrete multivariate analysis: theory and practice. MIT Press, Cambridge

    Google Scholar 

  • Boening C, Willis JK, Landerer FW, Nerem RS, Fasullo J (2012) The 2011 La Niña: So strong, the oceans fell. Geophysical Research Letters 39 (19)

  • Bricknell IR, Dalesman SJ, O’Shea B, Pert CC, Mordue Luntz AJ (2006) Effect of environmental salinity on sea lice Lepeophtheirus salmonis settlement success. Dis Aquat Org 71(3):201

    Article  Google Scholar 

  • Chandra S (2013) Incidence and consequences of mangrove exploitation in the Sunderbans. JCE JCE 4(1):23

    Google Scholar 

  • Chen A, Warren J (2008) Paving the path for India’s growth. Far East Econ Rev 171(2):8

    Google Scholar 

  • Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37(1):35–46

    Article  Google Scholar 

  • Cronk JK, Fennessy MS (2001) Wetland plants: biology and ecology. CRC press

  • Dahdouh-Guebas F, Jayatissa LP, Di Nitto D, Bosire JO, Lo Seen D, Koedam N (2005) How effective were mangroves as a defence against the recent tsunami? Curr Biol 15(12):R443–7

    Article  Google Scholar 

  • DasGupta R, Shaw R (2013) Changing perspectives of mangrove management in India—an analytical overview. Ocean & Coastal Manag 80:107–18

    Article  Google Scholar 

  • Dewidar KHM (2004) Detection of land use/land cover changes for the northern part of the Nile delta (Burullus region), Egypt. Int J Remote Sens 25(20):4079–89

    Article  Google Scholar 

  • Di Nitto D, Neukermans G, Koedam N, Defever H, Pattyn F, Kairo J, Dahdouh-Guebas F (2013) Mangroves facing climate change: landward migration potential in response to projected scenarios of sea level rise. Biogeosci Discuss 10(2)

  • Ellison JC (1994) Climate change and sea level rise impacts on mangrove ecosystems. Impacts Clim Change Ecosyst and Species: Mar and Coastal Ecosyst IUCN, Gland:11–30

  • Ellison AM, Farnsworth EJ (1997) Simulated sea level change alters anatomy, physiology, growth, and reproduction of red mangrove (Rhizophora mangle L.). Oecologia 112(4):435–46

    Article  Google Scholar 

  • Ellison JC, Stoddart DR (1991) Mangrove ecosystem collapse during predicted sea-level rise: Holocene analogues and implications. J Coastal Res 7:151–165

    Google Scholar 

  • FAO (1994) Mangrove forest management guidelines, FAO Forestry, 117, Rome, 319 pp.

  • Field C (1999) Rehabilitation of mangrove ecosystems: an overview. Mar Pollut Bull 37(8):383–92

    Article  Google Scholar 

  • Foody GM, Campbell N, Trodd N, Wood T (1992) Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification. Photogramm Eng Remote Sens 58(9):1335–41

    Google Scholar 

  • Gilman E, Ellison J, Coleman R (2007) Assessment of mangrove response to projected relative sea-level rise and recent historical reconstruction of shoreline position. Environ Monit Assess 124(1–3):105–30. doi:10.1007/s10661-006-9212-y

    Article  Google Scholar 

  • Gilman EL, Ellison J, Duke NC, Field C (2008) Threats to mangroves from climate change and adaptation options: a review. Aquat Bot 89(2):237–50

    Article  Google Scholar 

  • Gregory J, Oerlemans J (1998) Simulated future sea-level rise due to glacier melt based on regionally and seasonally resolved temperature changes. Nature 391(6666):474–6

    Article  Google Scholar 

  • Griffiths M, Drysdale R, Gagan M, Zhao J-X, Ayliffe L, Hellstrom J, Hantoro W, Frisia S, Feng Y-X, Cartwright I (2009) Increasing Australian–Indonesian monsoon rainfall linked to early Holocene sea-level rise. Nat Geosci 2(9):636–9

    Article  Google Scholar 

  • Gupta M, Srivastava PK (2010) Integrating GIS and remote sensing for identification of groundwater potential zones in the hilly terrain of Pavagarh, Gujarat, India. Water Int 35(2):233–45

    Article  Google Scholar 

  • Halpern BS, Selkoe KA, Micheli F, Kappel CV (2007) Evaluating and ranking the vulnerability of global marine ecosystems to anthropogenic threats. Conserv Biol 21(5):1301–15

    Article  Google Scholar 

  • Hobbs RJ (2000) Land-use changes and invasions. Invasive species in a changing world: 55–64

  • Hoegh-Guldberg O, Bruno JF (2010) The impact of climate change on the world’s marine ecosystems. Science 328(5985):1523–8

    Article  Google Scholar 

  • Hong PN (1993) Mangroves of Vietnam, vol 7. IUCN

  • Hutchings PA, Saenger P (1987) Ecology of Mangroves. University of Queensland Press, pp 388

  • Im J, Jensen JR (2005) A change detection model based on neighborhood correlation image analysis and decision tree classification. Remote Sens Environ 99(3):326–40

    Article  Google Scholar 

  • Ishak A, Remesan R, Srivastava P, Islam T, Han D (2013) Error correction modelling of wind speed through hydro-meteorological parameters and mesoscale model: a hybrid approach. Water Resour Manag 27(1):1–23. doi:10.1007/s11269-012-0130-1

    Article  Google Scholar 

  • Khambete A, Jagdish S (2010) Role of mangroves on domestic wastewater discharge: case study at Jamnagar costal region, India In. IEEE, pp 359–362

  • Klemas VV (2001) Remote sensing of landscape-level coastal environmental indicators. Environ Manag 27(1):47–57

    Article  Google Scholar 

  • Krauss KW, From AS, Doyle TW, Doyle TJ, Barry MJ (2011) Sea-level rise and landscape change influence mangrove encroachment onto marsh in the Ten Thousand Islands region of Florida, USA. J Coastal Conservation:1–10

  • Krauss KW, McKee KL, Lovelock CE, Cahoon DR, Saintilan N, Reef R, Chen L (2013) How mangrove forests adjust to rising sea level. Phytologist, New

    Google Scholar 

  • Kumar R (2000) Conservation and management of mangroves in India, with special reference to the state of Goa and the Middle Andaman Islands. Unasylva 51(203):41–6

    Google Scholar 

  • Lillesand TM, Kiefer RW, Chipman JW (2004) Remote sensing and image interpretation. vol Ed. 5. John Wiley & Sons Ltd

  • Lin G, Sternberg LS (1992) Effect of growth form, salinity, nutrient and sulfide on photosynthesis, carbon isotope discrimination and growth of red mangrove (Rhizophora mangle L.). Funct Plant Biol 19(5):509–17

    Google Scholar 

  • MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. California, USA, p 14

  • McGuffie K, Henderson-Sellers A, Zhang H, Durbidge T, Pitman A (1995) Global climate sensitivity to tropical deforestation. Glob Planet Chang 10(1–4):97–128

    Article  Google Scholar 

  • McLeod E, Salm RV (2006) Managing mangroves for resilience to climate change. Switzerland, World Conservation Union (IUCN) Gland

    Google Scholar 

  • Meehl GA, Washington WM, Collins WD, Arblaster JM, Hu A, Buja LE, Strand WG, Teng H (2005) How much more global warming and sea level rise? Science 307(5716):1769–72

    Article  Google Scholar 

  • Meier MF, Dyurgerov MB, Rick UK, O’Neel S, Pfeffer WT, Anderson RS, Anderson SP, Glazovsky AF (2007) Glaciers dominate eustatic sea-level rise in the 21st century. Science 317(5841):1064–7

    Article  Google Scholar 

  • Mukherjee S, Shashtri S, Singh C, Srivastava P, Gupta M (2009) Effect of canal on land use/land cover using remote sensing and GIS. J Indian Soc Remote Sens 37(3):527–37

    Article  Google Scholar 

  • Nicholls RJ, Marinova N, Lowe JA, Brown S, Vellinga P, De Gusmao D, Hinkel J, Tol RS (2011) Sea-level rise and its possible impacts given a ‘beyond 4 C world’ in the twenty-first century. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 369 1934:161–181

  • Ojima D, Galvin K, Turner B (1994) The global impact of land-use change. Bioscience 44(5):300–4

    Article  Google Scholar 

  • Omo-Irabor O, Olobaniyi S, Akunna J, Venus V, Maina J, Paradzayi C (2011) Mangrove vulnerability modelling in parts of Western Niger Delta, Nigeria using satellite images, GIS techniques and Spatial Multi-Criteria Analysis (SMCA). Environ Monit Assess 178(1–4):39–51. doi:10.1007/s10661-010-1669-z

    Article  Google Scholar 

  • Otukei JR, Blaschke T (2010) Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. Int J Appl Earth Obs 12:S27–S31. doi:10.1016/j.jag.2009.11.002

    Article  Google Scholar 

  • Overpeck JT, Otto-Bliesner BL, Miller GH, Muhs DR, Alley RB, Kiehl JT (2006) Paleoclimatic evidence for future ice-sheet instability and rapid sea-level rise. Science 311(5768):1747–50

    Article  Google Scholar 

  • Patel D, Srivastava P (2013) Flood hazards mitigation analysis using remote sensing and GIS: correspondence with town planning scheme. Water Resour Manag 27(7):2353–68. doi:10.1007/s11269-013-0291-6

    Article  Google Scholar 

  • Patel DP, Dholakia MB, Naresh N, Srivastava PK (2011) Water harvesting structure positioning by using geo-visualization concept and prioritization of mini-watersheds through morphometric analysis in the lower Tapi Basin. J Indian Soc Remote Sensing:1–14

  • Patel DP, Gajjar CA, Srivastava PK (2013) Prioritization of Malesari mini-watersheds through morphometric analysis: a remote sensing and GIS perspective. Environ Earth Sci 69(8):2643–56

    Article  Google Scholar 

  • Pernetta JC (1993) Mangrove forests, climate change and sea level rise: hydrological influences on community structure and survival, with examples from the Indo-West Pacific. IUCN

  • Phinn SR, Menges C, Hill GJE, Stanford M (2000) Optimizing remotely sensed solutions for monitoring, modeling, and managing coastal environments. Remote Sens Environ 73(2):117–32

    Article  Google Scholar 

  • Rahmstorf S (2007) A semi-empirical approach to projecting future sea-level rise. Science 315(5810):368–70

    Article  Google Scholar 

  • Richards JA, Jia X (2006) Remote sensing digital image analysis: an introduction. Springer, Verlag

    Google Scholar 

  • Rönnbäck P (1999) The ecological basis for economic value of seafood production supported by mangrove ecosystems. Ecol Econ 29(2):235–52

    Article  Google Scholar 

  • Saenger P, Hegerl E, Davie JD (1983) Global status of mangrove ecosystems. Global status of mangrove ecosystems

  • Saha A, Arora M, Csaplovics E, Gupta R (2005) Land cover classification using IRS LISS III image and DEM in a rugged terrain: a case study in Himalayas. Geocarto Int 20(2):33–40

    Article  Google Scholar 

  • Satapathy DR, Krupadam RJ, Kumar LP, Wate SR (2007) The application of satellite data for the quantification of mangrove loss and coastal management in the Godavari estuary, east coast of India. Environ Monit Assess 134(1–3):453–69. doi:10.1007/s10661-007-9636-z

    Article  Google Scholar 

  • Semeniuk V (1994) Predicting the effect of sea-level rise on mangroves in northwestern Australia. J Coastal Res 10:1050–1076

    Google Scholar 

  • Shah D (2009) Special economic zones in India: a review of investment, trade, employment generation and impact assessment. Indian J Agric Econ 64(3):431

    Google Scholar 

  • Singh H (2003) Potential impact of climate change on mangroves in India

  • Singh S, Srivastava PK, Gupta M, Mukherjee S (2012) Modeling mineral phase change chemistry of groundwater in a rural–urban fringe. Water Sci Technol 66(7)

  • Singh SK, Srivastava PK, Pandey AC, Gautam SK (2013) Integrated assessment of groundwater influenced by a confluence river system: concurrence with remote sensing and geochemical modelling. Water Resour Manag 27(12):4291–313

    Article  Google Scholar 

  • Singh S, Srivastava P, Gupta M, Thakur J, Mukherjee S (2014) Appraisal of land use/land cover of mangrove forest ecosystem using support vector machine. Environ Earth Sci 71(5):2245–55. doi:10.1007/s12665-013-2628-0

    Article  Google Scholar 

  • Song C, Woodcock CE, Seto KC, Lenney MP, Macomber SA (2001) Classification and change detection using Landsat TM data: when and how to correct atmospheric effects? Remote Sens Environ 75(2):230–44

    Article  Google Scholar 

  • Srivastava PK, Mukherjee S, Gupta M (2010) Impact of urbanization on land use/land cover change using remote sensing and GIS: a case study. Int J Ecol Econ Stat 18(S10):106–17

    Google Scholar 

  • Srivastava PK, Gupta M, Mukherjee S (2011) Mapping spatial distribution of pollutants in groundwater of a tropical area of India using remote sensing and GIS. Applied Geomatics 4:21–32

  • Srivastava PK, Mukherjee S, Gupta M, Singh S (2011b) Characterizing monsoonal variation on water quality index of river Mahi in India using geographical information system. Water Qual, Expo Health 2(3):193–203. doi:10.1007/s12403-011-0038-7

    Article  Google Scholar 

  • Srivastava PK, Han D, Gupta M, Mukherjee S (2012a) Integrated framework for monitoring groundwater pollution using a geographical information system and multivariate analysis. Hydrol Sci J 57(7):1453–72. doi:10.1080/02626667.2012.716156

    Article  Google Scholar 

  • Srivastava PK, Han D, Ramirez MR, Bray M, Islam T (2012b) Selection of classification techniques for land use/land cover change investigation. Adv Space Res 50(9):1250–65

    Article  Google Scholar 

  • Srivastava PK, Han D, Rico Ramirez MA, Islam T (2013a) Comparative assessment of evapotranspiration derived from NCEP and ECMWF global datasets through weather research and forecasting model. Atmos Sci Lett 14(2):118–25. doi:10.1002/asl2.427

    Article  Google Scholar 

  • Srivastava PK, Singh SK, Gupta M, Thakur JK, Mukherjee S (2013b) Modeling impact of land use change trajectories on groundwater quality using remote sensing and GIS. Environ Eng Manag J 12(12):2343–55

    Google Scholar 

  • Valiela I, Bowen JL, York JK (2001) Mangrove forests: one of the world’s threatened major tropical environments. Bioscience 51(10):807–15

    Article  Google Scholar 

  • Walker B, Steffen W (1999) The nature of global change. Terrestrial Biosph Glob Chang: Impl Nat Manag Ecosyst:1–18

  • Wang FG, Xu YJ (2010) Comparison of remote sensing change detection techniques for assessing hurricane damage to forests. Environ Monit Assess 162(1–4):311–326. doi:10.1007/s10661-009-0798-8

    Article  Google Scholar 

  • Wang L, Sousa W, Gong P (2004) Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. Int J Remote Sens 25(24):5655–68

    Article  Google Scholar 

  • Woodcock CE, Macomber SA, Pax-Lenney M, Cohen WB (2001) Monitoring large areas for forest change using Landsat: generalization across space, time and Landsat sensors. Remote Sens Environ 78(1–2):194–203

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank Head, Department of Remote Sensing and Geoinformatics, Birla Institute of Technology, Mesra, Ranchi, India for their support. The authors are also thankful to T.P. Singh, Director, Bhaskaracharya Institute for Space Applications and Geo-Informatics (BISAG), Gandhinagar, Gujarat, India. The views expressed here are those of the authors solely and do not constitute a statement of policy, decision, or position on behalf of NOAA/NASA or the authors’ affiliated institutions.

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Authors and Affiliations

  1. Hydrological Sciences, NASA Goddard Space Flight Center, Greenbelt, MD, USA

    Prashant K. Srivastava

  2. Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA

    Prashant K. Srivastava

  3. Department of Remote Sensing and Geoinformatics, Birla Institute of Technology, Ranchi, India

    Abhinav Mehta

  4. Gujarat Institute of Desert Ecology, Bhuj, Gujarat, India

    Abhinav Mehta

  5. Department of Civil Engineering, Indian Institute of Technology Delhi, New Delhi, India

    Manika Gupta

  6. K. Banerjee Centre of Atmospheric and Ocean Studies, IIDS, Nehru Science Centre, University of Allahabad, Allahabad, 211002, India

    Sudhir Kumar Singh

  7. NOAA/NESDIS Center for Satellite Applications and Research, College Park, MD, USA

    Tanvir Islam

  8. Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, USA

    Tanvir Islam

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Srivastava, P.K., Mehta, A., Gupta, M. et al. Assessing impact of climate change on Mundra mangrove forest ecosystem, Gulf of Kutch, western coast of India: a synergistic evaluation using remote sensing. Theor Appl Climatol 120, 685–700 (2015). https://doi.org/10.1007/s00704-014-1206-z

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