Advertisement

A Sediment Dynamic Modelling of Landsat OLI Image for Suspended Sediment Drift Along the Southwest Coast of India

  • Meenu Rani
  • S. Kaliraj
  • Raihan Ahmed
  • Biswajit Tripathy
  • Bismay Ranjan Tripathy
  • Gajendra Singh Pippal
Chapter

Abstract

The movement of suspended sediment along the coastal water is an indicator of erosion and deposition of the coastal landforms. The current study deals with the spatio-temporal movement of suspended sediments in the shallow along the southwest coast of Thiruvananthapuram district, Kerala state in India. The customized model here systematically analyses the spectral properties of multiple bands to mapping the suspended sediments at various concentration and spatial distributions. The study on sediment drift and its impacts on the coast through conventional method are difficult; meanwhile, multi-temporal images may provide effective results for studying sediments concentration and their movement along the coastal water. The geoprocessing modelling of sediment dynamics has executed mathematical algorithm on Landsat OLI image to retrieve SSC from coastal water and demarcate movement of sediments along the coast during pre- and post-monsoon. The study reveals that sediment concentration has estimated at higher rate along the offshore area with depth lower than 30 m, and this is gradually decreasing towards the sea at the depth beyond 30 m. It is observed that the suspended sediment drift produces depositional landforms at the low wave-energy zone and erosional landforms at high wave-energy zone. This study proves the effectiveness of geospatial technology to estimate sediment concentration and transportation in the shallow coastal water.

Keywords

Suspended sediment drifts (SSD) Seasonal movement of sediment Landsat OLI image Single band model algorithm GIS and remote sensing 

Notes

Acknowledgement

The authors declare that there is no conflict of interests regarding the publication of this paper. BT and KR designed and proposed the research; BT processed data; PK, KR and BT implemented techniques; PK and BT analysed results; and PK and JSR drafted and edited the article.

Reference

  1. Byers AC (1992) Soil loss and sediment transport during the storms and landslides of May 1988 in Ruhengeri prefecture, Rwanda. Nat Hazards 5(3):279–292CrossRefGoogle Scholar
  2. Curran PJ, Novo EMM (1988) The relationship between suspended sediment concentration and remotely sensed spectral radiance, a review. J Coast Res 4(3):351–368Google Scholar
  3. Gerald KM (1980) Satellite remote sensing of water turbidity/Sonde de télémesure par satellite de la turbidité de l’eau. Hydrol Sci Bull 25(4):407–421CrossRefGoogle Scholar
  4. Gordon HR, Morel AY (1994) Remote assessment of ocean colour for interpretation of satellite visible imagery, a review. Lecture Notes Coast Estuar Stud 4:114Google Scholar
  5. Islam MR, Yamaguchi Y, Ogawa K (2001) Suspended sediment in the Ganges and Brahmaputra River in Bangladesh, observation from TM and AVHRR data. Hydrol Process 15(3):493–509CrossRefGoogle Scholar
  6. Kaliraj S (2016) Geospatial analysis of coastal geomorphological vulnerability on South West coast of Kanyakumari using Remote sensing Technology. Unpublished Ph.D. Thesis, Manonmaniam Sundaranar University, Tirunelveli, pp 83–110Google Scholar
  7. Kaliraj S, Chandrasekar N (2012) Spectral recognition techniques and MLC of IRS P6 LISS III image for coastal landforms extraction along south west coast of Tamil Nadu, India. BonfringInt J Adv Image Process 2(3):01–07CrossRefGoogle Scholar
  8. Kaliraj S, Chandrasekar N, Magesh NS (2013) Impacts of wave energy and littoral currents on shoreline erosion/accretion along the south-west coast of Kanyakumari, Tamil Nadu using DSAS and geospatial technology. Environ Earth Sci.  https://doi.org/10.1007/s12665-013-2845-6 CrossRefGoogle Scholar
  9. Kaliraj S, Chandrasekar N, Magesh NS (2015) Evaluation of coastal erosion and accretion processes along the south-west coast of Kanyakumari, Tamil Nadu using geospatial techniques. Arab J Geosci 8(1):239–253CrossRefGoogle Scholar
  10. Kaliraj S, Chandrasekar N, Ramachandran KK (2016) Mapping of coastal landforms and volumetric change analysis in the south west coast of Kanyakumari, South India using remote sensing and GIS techniques. Egypt J Remote Sens Space Sci.  https://doi.org/10.1016/j.ejrs.2016.12.006 CrossRefGoogle Scholar
  11. Kaliraj S, Chandrasekar N, Ramachandran KK, Srinivas Y, Saravanan S (2017) Coastal landuse and land cover change and transformations of Kanyakumari coast, India using remote sensing and GIS. Egypt J Remote Sens Space Sci.  https://doi.org/10.1016/j.ejrs.2017.04.003 CrossRefGoogle Scholar
  12. Katlane R, Nechad B, Ruddick K, Zargouni F (2013) Optical remote sensing of turbidity and total suspended matter in the Gulf of Gabes. Arab J Geosci 6:1527–1535CrossRefGoogle Scholar
  13. Marcus WA, Fonstad MA (2010) Remote sensing of rivers, the emergence of a sub discipline in the river sciences. Earth Surf Process Landf 35(15):1867–1872CrossRefGoogle Scholar
  14. Nechad B, Ruddick K, Park Y (2010) Calibration and validation of a generic multi-sensor algorithm for mapping of total suspended matter in turbid waters. Remote Sens Environ 114:854–866CrossRefGoogle Scholar
  15. Neukermans G, Ruddick K, Bernard E, Ramon D, Nechad B, Deschamps PY (2009) Mapping total suspended matter from geostationary satellites, a feasibility study with SEVIRI in the southern North Sea. Opt Express 17:14029–14052CrossRefGoogle Scholar
  16. Ontowirjo B, Paris R, Mano A (2013) Modeling of coastal erosion and sediment deposition during the 2004 Indian Ocean tsunami in Lhok Nga, Sumatra, Indonesia. Nat Hazards 65(3):1967–1979CrossRefGoogle Scholar
  17. Panwar S, Agarwal V, Chakrapani GJ (2017) Morphometric and sediment source characterization of the Alaknanda river basin, headwaters of river ganga, India. Nat Hazards:1–23Google Scholar
  18. Qu L (2014) Remote sensing suspended sediment concentration in the Yellow River. Ph.D. dissertation paper 383, University of Connecticut, website accessed Dec 2014 at http,//digitalcommons.uconn.edu/dissertations/383/
  19. Rawat PK, Tiwari PC, Pant CC, Sharama AK, Pant PD (2011) Modelling of stream run-off and sediment output for erosion hazard assessment in lesser Himalaya, need for sustainable land use plan using remote sensing and GIS, a case study. Nat Hazards 59(3):1277–1297CrossRefGoogle Scholar
  20. Sinha PC, Guliani P, Jena GK, Rao AD, Dube SK, Chatterjee AK, Murty T (2004) A breadth averaged numerical model for suspended sediment transport in Hooghly estuary, East Coast of India. Nat Hazards 32(2):239–255CrossRefGoogle Scholar
  21. Tassan S (1998) A procedure to determine the particulate content of shallow water from thematic mapper data. Int J Remote Sens 19:557–562CrossRefGoogle Scholar
  22. Vanhellemont Q, Neukermans G, Ruddick K (2014) Synergy between polar-orbiting and geostationary sensors, remote sensing of the ocean at high spatial and high temporal resolution. Remote Sens Environ 146:49–62CrossRefGoogle Scholar
  23. Wang JJ, Lu XX (2010) Estimation of suspended sediment concentrations using Terra MODIS, an example from the lower Yangtze River, China. Sci Total Environ 408(5):1131–1138CrossRefGoogle Scholar
  24. Wang JJ, Lu XX, Liew SC, Zhou Y (2009) Retrieval of suspended sediment concentrations in large turbid rivers using Landsat ETM plus, an example from the Yangtze River, China. Earth Surf Process Landf 34:1082–1092CrossRefGoogle Scholar
  25. Warrick JA, Merters LAK et al (2004a) Estimating suspended sediment concentrations in turbid coastal waters of the Santa Barbara Channel with SeaWiFS. Int J Remote Sens 25:1995–2002CrossRefGoogle Scholar
  26. Warrick JA, Merters LAK, Siegel DA, Mackenzie C (2004b) Estimating suspended sediment concentrations in turbid coastal waters of the Santa Barbara Channel with SeaWiFS. Int J Remote Sens 25:1995–2002CrossRefGoogle Scholar
  27. Whitelock CH, Witte WG, Taly TA, Morris WD, Usry JW, Poole LR (1981) Research for reliable quantification of water sediment concentrations from multispectral scanner remote sensing data, US National Aeronautics and Space Administration, Langley Research Center, Hampton, VA, NASA-TM-82372, 243–255Google Scholar
  28. Yanjiao W, Feng Y, Peiqun Z, Wenjie D (2007) Experimental research on quantitative inversion models of suspended sediment concentration using remote sensing technology. Chin Geogr Sci 17(3):243–249CrossRefGoogle Scholar
  29. Zhang Y, Pulliainen J, Koponen S, Hallikainen M (2003) Water quality retrievals from combined Landsat TM data and ERS-2 SAR data in the Gulf of Finland. IEEE Trans Geosci Remote Sens 41:622–629CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Meenu Rani
    • 1
  • S. Kaliraj
    • 2
  • Raihan Ahmed
    • 3
  • Biswajit Tripathy
    • 4
  • Bismay Ranjan Tripathy
    • 5
  • Gajendra Singh Pippal
    • 6
  1. 1.Department of GeographyDSB Campus, Kumaun UniversityNainitalIndia
  2. 2.Central Geomatics Laboratory (CGL), ESSO-National Centre for Earth Science Studies (NCESS)Ministry of Earth Sciences, Government of IndiaThiruvananthapuramIndia
  3. 3.Department of GeographyJamia Millia IslamiaNew DelhiIndia
  4. 4.Project Engineer ElectricalSupreme Infrastructure (I) LtdKolkataIndia
  5. 5.Remote Sensing and GISKumaun UniversityAlmoraIndia
  6. 6.Punjab Remote Sensing centreLudhianaIndia

Personalised recommendations