Abstract
Coastline detection has been of major interest for environmentalists and many methods have been introduced to detect coastline automatically. Remote Sensing techniques are the most promising ones to deliver a satisfactory result in this regard. In our study, the objective was to retrieve performance level of certain image processing techniques vigorously used for the purpose to delineate coastline automatically and they were tested against two images acquired almost on the same period by LISS III and LANDSAT ETM+ sensors. The algorithms used in the study are Water Index, NDVI, Complex Band Ratio, ISODATA, Thresholding, ISH Transfirmation techniques. Accuracy of the shoreline detection by classifying the image in land and water has been tried to be estimated in three ways, firstly with comparison to the visually interpreted high resolution google earth image, secondly field collected GCP data of reference points of classes and thirdly the raw image itself. But problem in temporal disparity caused the constraint doing accuracy assessment from the first two reference data and maps along the coast. As a whole although four techniques among six, show satisfactory results namely density slicing, ISODATA classification, Water Index and ISH transformation technique, in the case of LISS-III and ETM+, Water Index (with kappa value being 0.95 for LISS-III and 0.97 for ETM+) and Intensity-Hue-Saturation transformation techniques give better performance. Sensor to sensor variation might have introduced certain differences in shoreline detection in images of same season with similar tidal influence.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aedla R, Dwarakish GS et al (2015) Automatic shoreline detection and change detection analysis of Netravati-GurpurRivermouth using histogram equalization and adaptive thresholding techniques. Aquatic Procedia 4(0):563–570
Ahmad SR, Lakhan VC (2012) GIS-based analysis and modeling of coastline advance and retreat along the coast of Guyana. Mar Geod 35(1):1–15
Aktaş, Ü. R., G. Can, et al. (2012). A robust approach for shoreline detection in satellite imagery. 2012 20th Signal Processing and Communications Applications Conference (SIU)
Alesheikh AA, Ghorbanali A et al (2007) Coastline change detection using remote sensing. International Journal of Environmental Science & Technology 4(1):61–66
Anthony EJ (2005) Wave and tide-dominated coasts. In: Schwartz ML (ed) Encyclopedia of coastal science. Springer, Dordrecht
Bhattacharya A, Sarkar SK (2001) Study on salt-marsh and associated meso-tidal beach faces variation from the coastal zone of eastern India. Proc. of the International Conference on Ocean Engineering COE '96 IIT Madras, India, Chennai, Allied Publishing Ltd
Bhattacharya AK, Sarkar SK et al (2003) An assessment of of coastal modification in the low-lying tropical coast of north East India and role of natural and artificial Forcings. International Conference on Estuaries and Coasts, Chennai
Braud J, Feng W (1998) Semiautomated construction of the Louisiana coastline digital land/water boundary using Landsat thematic mapper satellite imagery. OSRAPD Technical Report Series., Louisiana, Loiusiana Coastline Digitak Spill Research and Development Program, pp 97–102
Chakrabarti P (1990) Process-response system analysis in the macrotidal estuarine and mesotidal coastal plain of eastern India. Mem Geological Survey of India 22:165–187
Chang L-Y, Chen AJ, Chen CF, Huang CM (1999) A robust system for shoreline detection and its application to coastal-zone monitoring. In 20th Asian Conference on Remote Sensing (ACRS). AARS, Hong Kong
Di, K., R. Ma, et al. (2003). Coastal mapping and change detection using high-resolution IKONOS satellite imagery. Proceedings of the 2003 annual national conference on Digital government research. Boston, MA, USA, Digital Government Society of North America: 1–4
ESRI (2016) GIS dictionary. Retrieved 5th January, 2017, from http://support.esri.com/other-resources/gis-dictionary/term/band%20ratio
Foody GM (2002) The role of soft classification techniques in the refinement of stimates of ground control point location. Photogramm Eng Remote Sens 68(9):897–903
Frey R (1975) The realm of ichnology, its strengths and limitations. In: Frey R (ed) The Study of Trace Fossils. Springer, Berlin Heidelberg, pp 13–38
Hayes MO (2005) Wave-dominated coasts. Encyclopedia of Coastal Science. M. L. Schwartz. Dordrecht, Springer: 1054–1056
Higgins M (2005) Public access to the shore: public rights and private property. America’s Changing Coasts: Private Rights and Public Trust. M. W. Diana and G. R. V. UK and USA, Edward Elgar Publishing Limited
Jana A, Bhattacharya A (2013) Assessment of coastal erosion vulnerability around Midnapur-Balasore coast, eastern India using integrated remote sensing and GIS techniques. Journal of the Indian Society of Remote Sensing 41(3):675–686
Komar PD (1976) Beach processes and sedimentation. Prentice-Hall, Englewood Cliffs, New Jersey
Krishana GM, Mitra D et al (2005) Evaluation of semi-automated image processing techniques for the identification and delineation of coastal edge using IRS, LISS-III image - a case study on Sagar Island, East Coast of India. International Journal of Geoinformatics 1(2005)
Krishna GM, Mitra D et al (2005) Evaluation of semi-automated image processing techniques for the identification and delineation of coastal edge using IRS, LISS-III image - a case study on Sagar Island, East Coast of India. International Journal of Geoinformatics 1(2005)
Kuehl SA, Levy BM et al (1997) Subaqueous delta of the Ganges-Brahmaputra river system. Mar Geol 144(1–3):81–96
Li R, Di K, Ma R (2001) A comparative study of shoreline mapping techniques. In: The Fourth International Symposium on Computer Mapping and GIS for Coastal Zone Managemnet. Halifax, Nova Scotia
Masria A, Nadaoka K et al (2015) Detection of shoreline and land cover changes around Rosetta promontory, Egypt, based on remote sensing analysis. Land 4(1):216
Santra A, Mitra D et al (2011) Spatial modeling using high resolution image for future shoreline prediction along Junput coast, West Bengal, India. Geo-spatial Information Science 14(3):157–163
Sekovski I, Stecchi F et al (2014) Image classification methods applied to shoreline extraction on very high-resolution multispectral imagery. Int J Remote Sens 35(10):3556–3578
Short AD (1983) Sediments and structures in beachnearshore environments. South E: 1st Australia. In: Mclachlan A and T Er:1Smus (eds) Sandy beaches as ecosystems. Junk, The Hague, p 145–I55
Tarmizi NM, Samad AM et al. (2014) Shoreline data extraction from QuickBird satellite image using semi-automatic technique. 2014 I.E. 10th International Colloquium on Signal Processing and its Applications
Tran Thi V, Tien Thi Xuan A et al (2014) Application of remote sensing and GIS for detection of long-term mangrove shoreline changes in Mui Ca Mau, Vietnam. Biogeosciences 11(14):3781–3795
Umitsu M (1993) Late quaternary sedimentary environments and landforms in the Ganges Delta. Sediment Geol 83(3):177–186
Winarso G, Budhiman S (2001) The potential application of remote sensing data for coastal study. Proc. 22nd. Asian Conference on Remote Sensing, Singapore. Available on: http://www.crisp.nus.edu.sg/~acrs2001
Wright LD, Coleman JM (1973) Variations in morphology of major deltas as function of ocean waves and river discharge regions. AAPG Bull 57
Author information
Authors and Affiliations
Corresponding author
Additional information
Responsible editor: H. A. Babaie
Rights and permissions
About this article
Cite this article
Mitra, S.S., Mitra, D. & Santra, A. Performance testing of selected automated coastline detection techniques applied on multispectral satellite imageries. Earth Sci Inform 10, 321–330 (2017). https://doi.org/10.1007/s12145-017-0289-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12145-017-0289-3