Abstract The coastline is the dividing line between the ocean and land. Monitoring coastline dynamics is of crucial importance because it provides essential information for understanding the coastal response to contemporary climate change and human impacts. Real-time monitoring of coastlines has increasingly depended on remote sensing images at high temporal and spatial resolutions. In this study, a method combining the tasseled cap transformation (TCT) and feature knowledge is proposed to extract coastline information. Because traditional remote sensing methods cannot be used where total suspended sediment content is high, land and ocean separation was carried out using the wetness component of the Otsu method. A mathematical morphology analysis was used to remove the influence of lakes, rivers, and ships. Coastlines with shorter length were eliminated, and intermittent coastlines were connected based on a coordinate geometry description (such as length, distance, and direction). For the period of 2000–2018, Landsat-5 TM (Thematic Mapper) and Landsat-8 OLI (Operational Land Imager) images of the Zhoushan Islands, China, were used to extract coastline information using a method combining TCT and feature knowledge. A comparison of the results with reference data obtained by visual interpretation shows that the average of producer accuracy and user accuracy are 93.62% and 96.15%, respectively, and the average of length error was 2.45%. Therefore, the proposed method was effective in delineating the boundary between land and ocean and more effective than traditional methods in areas with high suspended sediment concentrations.


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    Title :

    The application of the tasseled cap transformation and feature knowledge for the extraction of coastline information from remote sensing images


    Contributors:
    Chen, Chao (author) / Bu, Juan (author) / Zhang, Yuhuan (author) / Zhuang, Yue (author) / Chu, Yanli (author) / Hu, Jiachen (author) / Guo, Biyun (author)

    Published in:

    Advances in Space Research ; 64 , 9 ; 1780-1791


    Publication date :

    2019-07-19


    Size :

    12 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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