Advertisement

A Multi-Sensor Approach for Assessing Mangrove Biophysical Characteristics in Coastal Odisha, India

  • Abhishek Kumar
  • Patricia Stupp
  • Subash Dahal
  • Caren Remillard
  • Roger Bledsoe
  • Austin Stone
  • Christopher Cameron
  • Gurdeep Rastogi
  • Rabindro Samal
  • Deepak R. Mishra
Research Article
  • 164 Downloads

Abstract

Mangroves around the world play a major role in coastal ecosystem processes by mitigating erosion and serving as a barrier against storm surges. India holds approximately 5% of the world’s mangroves, over half of which are found along its east coast. Situated in the state of Odisha, Chilika Lagoon and Bhitarkanika Wildlife Sanctuary are two wetlands of local importance in need of effective management. This study demonstrated the use of Terra, Landsat, and Sentinel-1 satellite data for spatio-temporal monitoring of mangrove health at these two sites. Several indices, including Normalized Difference Vegetation Index and Enhanced Vegetation Index, were examined to develop biophysical prediction tools and derive a 17-year time-series (from 2000 to 2016) of leaf chlorophyll (CHL), Leaf Area Index (LAI), and Gross Primary Productivity (GPP). The long-term analysis revealed phenological patterns in the biophysical characteristics such as high values during wet season and low values during the dry season. Correlations between biophysical characteristics and meteorological factors revealed a time lag exists in response to precipitation and associated runoff. In contrast, surface temperature did not show any lag in response time. This study also utilized Sentinel-1 radar data for the first time for Odisha mangroves to show seasonal variability in LAI, GPP, and CHL. The results from radar data were consistent with optical sensors and proved useful for capturing the rainy season, where data were limited due to cloud cover. This study revealed the advantages of using a multi-sensor approach for monitoring mangrove health and defining future monitoring protocols.

Keywords

Remote sensing MODIS Landsat Sentinel-1 radar Chilika Lagoon Bhitarkanika Wildlife Sanctuary 

Notes

Acknowledgements

Funding and support for this project was provided by the NASA DEVELOP National Program and the Geography Department, University of Georgia. Authors would like to thank NASA DEVELOP Lead Science Advisor, Marguerite Madden, at the University of Georgia for their advisement and expertise throughout this project. Authors also like acknowledge the scientists at the Government of Odisha’s Chilika Development Authority, for their involvement with this work. Authors thank members of the Eastern India Ecological Forecasting DEVELOP team, María José Rivera Araya and Jessica Staley.

References

  1. 1.
    Lovelock CE, Simpson LT, Duckett LJ, Feller IC (2015) Carbon budgets for Caribbean mangrove forests of varying structure and with phosphorus enrichment. Forests 6:3528–3546CrossRefGoogle Scholar
  2. 2.
    Giri C, Ochieng E, Tieszen LL, Zhu Z, Singh A, Loveland T, Masek J, Duke N (2010) Status and distribution of mangrove forests of the world using earth observation satellite data. Glob Ecol Biogeogr 20:154–159CrossRefGoogle Scholar
  3. 3.
    Kauffman JB, Donato DC (2012) Protocols for the measurement, monitoring and reporting of structure, biomass and carbon stocks in mangrove forests. Center for International Forestry Research, BogorGoogle Scholar
  4. 4.
    Azam N (2011) The importance of mangrove forests management. Dissertation, International Islamic University MalaysiaGoogle Scholar
  5. 5.
    Das S, Vincent JR (2009) Mangroves protected villages and reduced death toll during Indian super cyclone. Proc Natl Acad Sci USA 106:7357–7360ADSCrossRefGoogle Scholar
  6. 6.
    Pattanaik C, Reddy CS, Murthy MSR, Swain D (2008) Assessment and monitoring the coastal wetland ecology using RS and GIS with reference to Bhitarkanika Mangroves of Orissa, India. Monit Model Lakes Coast Environ.  https://doi.org/10.1007/978-1-4020-6646-7_17 Google Scholar
  7. 7.
    The Ramsar Convention Secretariat (Ed.) (2014) Ramsar. Retrieved March 29, 2017, from http://www.ramsar.org/
  8. 8.
    Peetabas N, Panda RP (2015) Conservation and management of bioresources of Chilika Lake, Odisha, India. Int J Sci Res Publ 5:2250–3153Google Scholar
  9. 9.
    Chauhan R, Ramanathan AL (2008) Evaluation of water quality of Bhitarkanika mangrove system, Orissa, east coast India. Indian J Mar Sci 37:153–158Google Scholar
  10. 10.
    Behera DP, Nayak L (2013) Floral diversity of Bhitarkanika, East Coast of India and its potential uses. J Chem Biol Phys Sci 3:1863–1874Google Scholar
  11. 11.
    Hussain SA, Badola R (2010) Valuing mangrove benefits: contribution of mangrove forests to local livelihoods in Bhitarkanika Conservation Area, East Coast of India. Wetlands Ecol Manag 18:321–331CrossRefGoogle Scholar
  12. 12.
    Reddy SC, Murthy M (2007) Assessment and monitoring of mangroves of Bhitarkanika Wildlife Sanctuary, Orissa, India using remote sensing & GIS. Curr Sci 92:1409–1415Google Scholar
  13. 13.
    Kamal M, Phinn S, Johansen K (2015) Object-based approach for multi-scale mangrove composition mapping using multi-resolution image datasets. Remote Sens 7(4):4753–4783ADSCrossRefGoogle Scholar
  14. 14.
    Madden M (2004) Remote sensing and geographic information system operations for vegetation mapping of invasive exotics. Weed Technol 18:1457–1463CrossRefGoogle Scholar
  15. 15.
    Ibharim NA, Mustapha MA, Lihan T, Mazlan AG (2015) Mapping mangrove changes in the Matang Mangrove Forest using multi temporal satellite imageries. Ocean Coast Manag 114:64–76CrossRefGoogle Scholar
  16. 16.
    Ishtiaque A, Soe WM, Wang C (2016) Examining the ecosystem health and sustainability of the world’s largest mangrove forest using multi-temporal MODIS products. Sci Total Environ 569–570:1241–1254CrossRefGoogle Scholar
  17. 17.
    Pastor-Guzman J, Atkinson PM, Dash J, Rioja-Nieto R (2015) Spatiotemporal variation in mangrove chlorophyll concentration using Landsat 8. Remote Sens 7:14530–14558ADSCrossRefGoogle Scholar
  18. 18.
    Feng M, Huang C, Channan S, Vermote EF, Masek JG, Townshend JR (2012) Quality assessment of Landsat surface reflectance products using MODIS data. Comput Geosci 38(1):9–22ADSCrossRefGoogle Scholar
  19. 19.
    Feng M, Sexton JO, Huang C, Masek JG, Vermote EF, Gao F, Narasimhan R, Channan S, Wolfe RE, Townshend JR (2013) Global surface reflectance products from Landsat: assessment using coincident MODIS observations. Remote Sens Environ 134:276–293ADSCrossRefGoogle Scholar
  20. 20.
    Ke Y, Im J, Lee J, Gong H, Ryu Y (2015). Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in situ observations. Remote Sens Environ. http://dx.doi.org/10.1016/j.rse.2015.04.004
  21. 21.
    Roy D, Wulder M, Loveland T, Woodcock C, Allen R, Anderson M et al (2014) LANDSAT-8: science and product vision for terrestrial global change research. Remote Sens Environ 145:154–172ADSCrossRefGoogle Scholar
  22. 22.
    Flores-de-Santiago F, Kovacs JM, Flores-Verdugo F (2013) The influence of seasonality in estimating mangrove leaf chlorophyll-a content from hyperspectral data. Wetlands Ecol Manag 21:193–207CrossRefGoogle Scholar
  23. 23.
    Flores-de-Santiago F, Kovacs J, Flores-Verdugo F (2012) Seasonal changes in leaf chlorophyll a content and morphology in a sub-tropical mangrove forest of the Mexican Pacific. Mar Ecol Prog Ser 444:57–68ADSCrossRefGoogle Scholar
  24. 24.
    Upadhyay VP, Mishra PK (2010) Phenology of mangroves tree species on Orissa coast, India. Trop Ecol 51(2):289–295Google Scholar
  25. 25.
    Upadhyay VP, Mishra PK (2014) An ecological analysis of mangroves ecosystem of Odisha on the Eastern Coast of India. Proc Indian Natl Sci Acad 80(3):647–661CrossRefGoogle Scholar
  26. 26.
    Singh HS (1996) Successional stages of mangroves in the Gulf of Kutch. Indian For 122:212–219ADSGoogle Scholar
  27. 27.
    Rouse JW, Haas RH, Schell JA, Deering DW (1974) Monitoring vegetation systems in the Great Plains with ERTS. In: Third ERTS-1 Symposium, NASA, Washington, DC, pp 309–317Google Scholar
  28. 28.
    Liu HQ, Huete A (1995) A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Trans Geosci Remote Sens 33:457–465ADSCrossRefGoogle Scholar
  29. 29.
    Jiang Z, Huete AR, Didan K, Miura T (2008) Development of a two-band enhanced vegetation index without a blue band. Remote Sens Environ 112:3833–3845ADSCrossRefGoogle Scholar
  30. 30.
    Gitelson AA, Kaufman YJ, Merzlyak MN (1996) Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens Environ 58:289–298ADSCrossRefGoogle Scholar
  31. 31.
    Jordan CF (1969) Derivation of leaf-area index from quality of light on forest floor. Ecology 50(4):663–666CrossRefGoogle Scholar
  32. 32.
    Gao BC (1996) NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58:257–266ADSCrossRefGoogle Scholar
  33. 33.
    Heenkenda MK, Maier SW, Joyce KE (2016) Estimating mangrove biophysical variables using world view-2 satellite data: rapid creek, northern territory, Australia. J Imaging 2:24CrossRefGoogle Scholar
  34. 34.
    Gilabert MA, Sanchez-Ruiz S, Moreno S (2017) Annual gross primary production from vegetation indices: a theoretically sound approach. Remote Sens 9:193ADSCrossRefGoogle Scholar
  35. 35.
    Carter GA (1998) Reflectance wavebands and indices for remote estimation of photosynthesis and stomatal conductance in pine canopies. Remote Sens Environ 63:61–72ADSMathSciNetCrossRefGoogle Scholar
  36. 36.
    Vogelmann JE, Rock BN, Moss DM (1993) Red-edge spectral measurements from sugar maple leaves. Int J Remote Sens 14:1563–1575.  https://doi.org/10.1080/01431169308953986 CrossRefGoogle Scholar
  37. 37.
    Horler DNH, Dockray M, Barber J (1983) The red-edge of plant leaf reflectance. Int J Remote Sens 4:273–288CrossRefGoogle Scholar
  38. 38.
    Bhar S, Chakraborty D, Ram SS, Das D, Chakraborty A, Sudarshan M, Santra SC (2013) Spatial variation of chlorophyll integrity in a mangrove plant (Excoecaria agallocha) of Indian Sundarban, with special reference to leaf element and water salinity. J Environ Sci Toxicol Food Technol 3(5):24–31Google Scholar
  39. 39.
    Mitra A, Banerjee K (2010) Pigments of Heritiera fomes seedlings under different salinity conditions: perspective sea level rise. Mesop J Mar Sci 25(1):1–10Google Scholar
  40. 40.
    Parida A, Das AB, Das P (2003) NaCl stress cause changes in photosynthetic pigments, proteins, and other metabolic components in the leaves of a true mangrove, Bruguiera parviflora, in hydroponic cultures. J Plant Biol 45(1):28–36CrossRefGoogle Scholar
  41. 41.
    Rodda SR, Thumaty KC, Jha CS, Dadhwal VK (2016) Seasonal variations of carbon dioxide, water vapor and energy fluxes in tropical Indian mangroves. Forests.  https://doi.org/10.3390/f7020035 Google Scholar
  42. 42.
    Tian Y, Woodcock CE, Wang Y, Privette JL, Shabanov NV, Zhou L, Zhang Y, Buermann W et al (2002) Multiscale analysis and validation of the MODIS LAI product I. Uncertainty assessment. Remote Sens Environ 83:414–430ADSCrossRefGoogle Scholar
  43. 43.
    Blasco F, Gauquelin T, Rasolofoharinoro M, Denis J, Aizpuru M, Caldairou V (1998) Recent advances in mangrove studies using remote sensing data. Mar Freshw Res 49:287–296CrossRefGoogle Scholar
  44. 44.
    Díaz BM, Blackburn GA (2003) Remote sensing of mangrove biophysical properties: evidence from a laboratory simulation of the possible effects of background variation on spectral vegetation indices. Int J Remote Sens 24:53–73CrossRefGoogle Scholar
  45. 45.
    Barik KK, Mitra D, Annadurai R, Tripathy JK, Nanda S (2016) Geospatial analysis of coastal environment: a case study on Bhitarkanika Mangroves, East coast of India. Int J Mar Sci 45(4):492–498Google Scholar
  46. 46.
    Clinton N, Yu L, Fu H, He C, Goung P (2014) Global-scale associations of vegetation phenology with tainfall and temperature at a high spatio-temporal resolution. Remote Sens 6:7320–7338ADSCrossRefGoogle Scholar

Copyright information

© The National Academy of Sciences, India 2017

Authors and Affiliations

  • Abhishek Kumar
    • 1
    • 5
  • Patricia Stupp
    • 2
  • Subash Dahal
    • 3
  • Caren Remillard
    • 1
    • 5
  • Roger Bledsoe
    • 4
  • Austin Stone
    • 1
  • Christopher Cameron
    • 5
  • Gurdeep Rastogi
    • 6
  • Rabindro Samal
    • 6
  • Deepak R. Mishra
    • 1
  1. 1.Center for Geospatial Research, Department of GeographyUniversity of GeorgiaAthensUSA
  2. 2.Department of Science, Technology, and International AffairsGeorgetown UniversityWashingtonUSA
  3. 3.Department of Crop and Soil SciencesUniversity of GeorgiaAthensUSA
  4. 4.College of Environment and DesignUniversity of GeorgiaAthensUSA
  5. 5.NASA Develop National ProgramUniversity of GeorgiaAthensUSA
  6. 6.Wetland Research and Training CentreChilika Development AuthorityBalugaonIndia

Personalised recommendations