Abstract
A shoreline is a highly dynamic part of the earth’s surface. Advanced remote sensing (RS) and geographic information system (GIS) techniques are being used for detection of shoreline position and change analysis. In this paper, a new methodology for automatic shoreline extraction is demonstrated and analyzed using Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) images. The methodology involves several stages consisting of preprocessing of satellite images, band selection, coastal water index (CWI) preparation, normalization of binary images, Otsu thresholding technique (named after Nobuyuki Otsu) for the land and water separation, image noise correction with morphological filter (image morphology), seawater separation from waterbody, vectorization of classified binary image, polyline conversion from polygon vector, shoreline selection, and generalization of the final shoreline. The positional accuracy of the final shoreline is evaluated with expert captured shoreline. It was observed that the average positional difference between computer generated shoreline and expert digitized shoreline was less than a pixel resolution. The proposed methodology is very helpful in any coastal application where the shoreline is used as a parameter. It also reduces the time of human intervention.
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20 January 2020
The original version of this article unfortunately contained some mistakes. At Section 2.3 Image Classification, the following sentences are technically not correct.
References
Aedla R, Dwarakish GS, Reddy DV (2015) Automatic shoreline detection and change detection analysis of netravati-gurpur river mouth using histogram equalization and adaptive thresholding techniques. Aquat Procedia 4:563–570
Altinuc SO, Keceli AS, Sezer EA (2014) Semi-Automated Shoreline Extraction in Satellite Imagery and Usage of Fractals as Performance Evaluator. Int J Comput Theory Eng 6(2)
Brown GW (1982) Standard deviation, standard error: which 'standard' should we use? Am J Dis Child 136(10):937–941
Congedo, L. (2015). Semi-automated classification plugin documentation, release 4.8.0.1. QGIS plugin
Cracknell AP (1999) Remote sensing techniques in estuaries and coastal zones - an update. Int J Remote Sens 20(3):485–496
Di K, Wang J, Ma R, Li R (2003) Automatic shoreline extraction from high-resolution IKONOS satellite imagery. In: Proceeding of ASPRS 2003 Annual Conference, vol 3
Dolan R, Hayden B, May P, May SK (1980) The reliability of shoreline change measurements from aerial photographs. Shore Beach 48(4):22–29
Frazier PS, Page KJ (2000a) Water body detection and delineation with Landsat TM data. Photogramm Eng Remote Sens 66:147–167
Frazier PS, Page KJ (2000b) Water body detection and delineation with Landsat TM data. Photogramm Eng Remote Sens 66(2):1461–1467
Ghorai D (2018) Geo-environmental Appraisal and Assessment of Development of the Coastal Zone: A Case Study of Tamil Nadu Coast using Geo-informatics. Ph.D. Thesis, Department of Remote Sensing & GIS, Vidyasagar University, India
Gouillart E, Varoquaux G (2019) Image manipulation and processing using Numpy and Scipy, Image Processing, content number 2.6
Kamavisdar P, Saluja S, Agrawal S (2013) A survey on image classification approaches and techniques. Int J Adv Res Comput Commun Eng 2(1):1005–1009
Karantzalos K, Argialas D (2007) Automatic shoreline mapping from panchromatic satellite images. In: proceedings of 8th pan-Hellenic geographic conference. Greek Geographical Society (in Greek)
Khushbu, Vats I (2017) Otsu image segmentation algorithm: a review. Int J Innov Res Comput Commun Eng 5(6)
Kuleli T (2010) Quantitative analysis of shoreline changes at the Mediterranean coast in Turkey. Environ Monit Assess 167:387–397
Kuleli T, Guneroglu A, Karsli F, Dihkan M (2011) Automatic detection of shoreline change on coastal Ramsar wetlands of Turkey. Ocean Eng 38:1141–1149
Loos E. A. and Niemann K. O. (2002). Shoreline feature extraction from remotely-sensed Imagery.IEEE, 3417-3419
Mahapatra M, Ratheesh R, Rajawat AS (2014) Shoreline change analysis along the coast of South Gujarat, India, using digital shoreline analysis system. Journal of the Indian Society of Remote Sensing, 42(4):869–876
Maragos P, Pessoa LF (1999) Morphological filtering for image enhancement and detection. The Image and Video Processing Handbook, 135–156
Mokhtar NR, Harun NH, Mashor MY, Roseline H, Mustafa N, Adollah R et al (2009) Image enhancement techniques using local, global, bright, dark and partial contrast stretching for acute leukemia images. In: Proceedings of the world Congress on Engineering, vol 1, pp 1–3
Moran MS, Jackson RD, Slater PN, Teillet PM (1992) Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output. Remote Sens Environ 41(2–3):169–184
Naik BC, Anuradha B (2018) Water body extraction using images from two different satellites over Chilika Lake. Int J Electr Eng 10(1):140–145
Otsu N (1979) A threshold selection method from gray-level histogram. IEEE trans. Sys., man. Cyber. 9(1):62–66
Pandey RK, Mathurkar SS (2017) A review on morphological filter and its implementation. Int J Sci Res 6(1):69–72
Paravolidakis V, Ragia L, Moirogiorgou K, Zervakis M (2018) Automatic coastline extraction using edge detection and optimization procedures. Geosciences 8(11):407
QGIS (2019) Documentation QGIS 2.18. https://docs.qgis.org/2.18/en/docs/user_manual/working_with_vector/editing_geometry_attributes.html, retrieved on 17th Sept. 2019
Rasuly A, Naghdifar R, Rasoli M (2010) Monitoring of Caspian Sea coastline changes using object-oriented techniques. Procedia Environ Sci 2:416–426
Scott JW, Moore LR, Harris WM, Reed MD (2003) Using the Landsat 7 enhanced thematic mapper tasseled cap transformation to extract shoreline. Open-file report OF 03-272 U.S. Geol Surv
Serra J, Vincent L (1992) An overview of morphological filtering. Circuits Syst Signal Process 11(1):47–108
Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13(1):146–165
Song C, Woodcock CE, Seto KC, Lenney MP, Macomber SA (2001) Classification and change detection using Landsat TM data: when and how to correct atmospheric effects? Remote Sens. Environ 75:230–244
Spanner MA, Pierce LL, Peterson DL, Running SW (1990) Remote sensing of temperate coniferous forest leaf area index: the influence of canopy closure, understory vegetation and background reflectance. Int J Remote Sens 11(1):95–111
Sreedhar K, Panlal B (2012) Enhancement of images using morphological transformation. arXiv preprint arXiv:1203.2514
Wang KDJ, Ma R, Li R (2003) Automatic shoreline extraction from high resolution Ikonos satellite imagery. In: proceedings of the ASPRS annual conference. Anchorage, Alaska
Welikala RA, Dehmeshki J, Hoppe A, Tah V, Mann S, Williamson TH, Barman SA (2014) Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification. Comput Methods Prog Biomed 114:247–261
William KP (2007) Digital Image Processing, Los Altos
Wu ST, Marquez MRG (2003) A non-self-intersection Douglas-Peucker algorithm. In 16th Brazilian symposium on computer graphics and Image Processing (SIBGRAPI 2003) (pp. 60–66). IEEE
Yang K, Li M, Liu Y, Cheng L, Duan Y, Zhou M (2014) River delineation from remotely sensed imagery using a multi-scale classification approach. IEEE J Select Top Appl Earth Observ Remote Sens. https://doi.org/10.1109/JSTARS.2014.2309707
Yang K, Li M, Liu Y, Cheng L, Huang Q, Chen Y (2015) River detection in remotely sensed imagery using Gabor filtering and path opening. Remote Sens 2015(7):8779–8802
Zakariya R, Rosnan Y, Saidin SA, Yahaya MH, Kasawani I, Lokman H (2006) Shoreline detection and changes for Terengganu river mouth from satellite imagery (Landsat 5 and Landsat 7). J Sustain Sci Manag 1:47–57
Acknowledgments
The authors express their sincere gratitude to Dr. Ashis Kumar Paul, Head of the department of Geography, Vidyasagar University, Midnapore for providing valuable guidance, support, and constant encouragement. The authors are thankful to the reviewers for their valuable suggestions. The authors would also like to thank the various data sources for the free accessibility of data.
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"The original version of this article was revised: "The 2 sentences at Section 2.3 Image Classification, ”lf the binary..program.” and “To reduce...levels.” are not correct.
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Ghorai, D., Mahapatra, M. Extracting Shoreline from Satellite Imagery for GIS Analysis. Remote Sens Earth Syst Sci 3, 13–22 (2020). https://doi.org/10.1007/s41976-019-00030-w
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DOI: https://doi.org/10.1007/s41976-019-00030-w