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Volume 14, issue 5 | Copyright
Ocean Sci., 14, 1085-1092, 2018
https://doi.org/10.5194/os-14-1085-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 25 Sep 2018

Research article | 25 Sep 2018

Monitoring of seasonal variability and movement of suspended sediment concentrations along the Thiruvananthapuram coast, southern India, using the Landsat OLI sensor

Bismay Ranjan Tripathy1, Kaliraj Seenipandi1, Haroon Sajjad2, Pawan Kumar Joshi3, Bhagwan Singh Chaudhary4, and Pavan Kumar2 Bismay Ranjan Tripathy et al.
  • 1National Centre for Earth Science Studies, Ministry of Earth Sciences, Govt. of India, Thiruvananthapuram, 695011, India
  • 2Department of Geography, Jamia Millia Islamia, New Delhi, 110025, India
  • 3School of Environmental Science, Jawaharlal Nehru University, New Delhi, 110067, India
  • 4Department of Geophysics, Kurukshetra University, Kurukshetra, Haryana, 136119, India

Abstract. Studies on suspended sediment concentrations at a seasonal scale play a vital role in understanding coastal hydrodynamic processes in an area. Assessment of spatio-temporal changes in suspended sediments in nearshore areas has gained complexity due to the utilization of conventional methods; this issue can be successfully solved nowadays using multi-temporal remotely sensed images with the help of advanced image processing techniques. The present study is an attempt to demonstrate the model algorithm used to extract suspended sediment concentrations using Landsat 8 OLI (Operational Land Imager) sensor images. The study was executed in a near-offshore area of the Thiruvananthapuram coast, southern India, and focused on the extraction of suspended sediment concentrations and their seasonal variability during pre-monsoon and post-monsoon periods. The OLI images were pre-processed to obtain the actual reflectance using the FLASSH module of the ENVI v5.5 software. The generic model developed herein is designed to compute the spectral reflectance variability between coastal water and suspended sediments and to differentiate the spatial accumulation of the suspended sediment concentrations from the coastal water at the pixel scale. Maximum (0.8% in near-infrared bands) and minimum (0.6% in blue bands) spectral reflectance indicates the occurrence of suspended sediments in the coastal water. The model-derived results revealed that the suspended sediment concentration gradually decreased with increasing depth and distance from the shoreline. Higher sediment concentrations accumulated at lower depths in coastal water due to wave and current action that seasonally circulated the sediments. This higher concentration of the suspended sediment load was estimated to be 0.92mgL−1 at the shallow depths (< 10m) of the coastal waters and 0.30mgL−1 at a depth of 30m. Seasonal variability of suspended sediments was observed in a north–south direction during the pre-monsoon; the reverse was noted during the post-monsoon period. The spatial variability of suspended sediments was indirectly proportional to the depth and distance from the shoreline, and directly proportional to offshore wave and littoral current activity. This study proves that the developed model coupled with the provided computational algorithm can be used as an effective tool for the estimation of suspended sediment concentrations using multi-temporal OLI images; furthermore, the output may be helpful for coastal zone management and conservation planning and development.

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This paper explored the suitability of the Landsat OLI sensor for monitoring concentration and movement of suspended particles along a coastal landscape of Thiruvananthapuram. Suspended sediments moved north–south during pre-monsoon and south–north during post-monsoon seasons under the influence of monsoonal winds. It was found that the spatial variability of suspended sediments was indirectly proportional to the depth and distance from the shoreline and directly proportional to wave action.
This paper explored the suitability of the Landsat OLI sensor for monitoring concentration and...
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