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Pixel-based classification techniques for automated shoreline extraction on open sandy coast using different optical satellite images

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Abstract

The coastline and their landscapes are at constant risk due to various natural hazards. Shoreline monitoring and mapping are essential for proper coastal zone planning, development, management, and divisive conservation schemes. Digitizing shoreline position manually is a time-consuming process, and manual errors are a concerning factor. The present study proposed a pixel-based classification technique on optical images to extract the proxy-based (wet/dry) shorelines on an open sandy coast. A composite technique, which is a combination of band ratio (green/mid-infrared band) and histogram thresholding (near-infrared band), was applied on Landsat-7 (ETM + — Enhanced Thematic Mapper Plus) image. An unsupervised classification (ISODATA — Iterative Self-Organizing Data Analysis Technique) technique using multi-spectral (green, red and near-infrared) bands were applied to the Resourcesat-2 (LISS-IV — Linear Imaging Self-Scanning Sensor) image. Pre-processing techniques such as geometric correction and noises from the images were reduced, and then the proposed techniques were implemented to extract the shoreline position from the raster images automatically. The robustness and accuracy of the automated shoreline are compared and validated with the in situ field measured data. The comparison shows that 87.5% of the transects fall less than 2 m accuracy (less than half of a pixel of LISS IV — 5.8 m), whereas K-mean, NDWI, and maximum likelihood methods show the accuracy of 83%, 79%, and 75%, respectively. Compared to other remote sensing techniques, the shoreline positions extracted from the ISODATA techniques produce a consistent result with open sandy coast. This study provides a comprehensive, reliable, and standard, repeatable automatic method for extracting the shoreline position from optical satellite images for a similar type of coastal landscape, which ultimately reduces the time duration and leads to rapid monitoring of the coast.

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The data source of each satellite images used is mentioned in the manuscript.

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Acknowledgements

We sincerely thank the Secretary, Ministry of Earth Sciences (MoES), Government of India (GoI) and the Director, National Centre for Coastal Research (NCCR) and project stuffs for their kind support and valuable suggestions for this work.

Funding

Present study is a part of research work being carried out in National Centre for Coastal Research (NCCR), Ministry of Earth Sciences (MoES).

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Authors and Affiliations

  1. National Centre for Coastal Research (NCCR), MoES, Chennai, India, 600100

    Chenthamil Selvan Sekar, Roop Singh Kankara & Prabhu Kalaivanan

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  1. Chenthamil Selvan Sekar
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Contributions

Dr. S. Chenthamil Selvan: original manuscript preparation, methodology preparation, data analysis, interpretation and field investigation. Dr. R. S. Kankara: conceptualization, data interpretation, correction and finalizing Manuscript. MR. Prabhu K: involved in testing and maps creation, field investigation.

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Correspondence to Chenthamil Selvan Sekar.

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Sekar, C.S., Kankara, R.S. & Kalaivanan, P. Pixel-based classification techniques for automated shoreline extraction on open sandy coast using different optical satellite images. Arab J Geosci 15, 939 (2022). https://doi.org/10.1007/s12517-022-10239-7

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