• DocumentCode
    2012509
  • Title

    Detection of seagrass in optical shallow water with Quickbird in Xincun Bay of Hainan province, China

  • Author

    Yang, Dingtian

  • Author_Institution
    LED, Chinese Acad. of Sci., Guangzhou
  • fYear
    2009
  • fDate
    11-12 May 2009
  • Firstpage
    187
  • Lastpage
    192
  • Abstract
    Seagrass, mainly distributed in the coastal water along South China Sea, is very important for coastal ecosystem. In order to obtain precise data about seagrass distribution in optical shallow water in Xincun Bay of Hainan province, Quickbird data was used in the paper. Radiance transfer model in optical shallow water was used to retrieve the information of bottom reflectivity, and supervise classification was used to retrieve the distribution and density of submerged seagrass; Results showed that seagrass distributed in the northeast coast of Xincun Bay with the pattern of stripe can be clearly detected. Density gradient was very clear, coverage under 20% was mainly distributed at the out range of seagrass bed and coverage greater than 80% distributed in the centre of the seagrass bed. The accuracy of seagrass retrieval was more than 80% and the density of seagrass can also be distinguished evidently.
  • Keywords
    image classification; oceanography; remote sensing; seafloor phenomena; China; Hainan province; Quickbird data; South China Sea; Xincun Bay; bottom reflectivity; coastal ecosystem; coastal water; optical shallow water; radiance transfer model; seagrass bed; seagrass detection; seagrass distribution; sediments; submerged seagrass density; supervise classification; Ecosystems; Image resolution; Information retrieval; Light emitting diodes; Optical imaging; Optical sensors; Remote sensing; Satellites; Sea measurements; Spatial resolution; Quickbird; Seagrass; Xincun Bay;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Imaging Systems and Techniques, 2009. IST '09. IEEE International Workshop on
  • Conference_Location
    Shenzhen
  • Print_ISBN
    978-1-4244-3482-4
  • Electronic_ISBN
    978-1-4244-3483-1
  • Type

    conf

  • DOI
    10.1109/IST.2009.5071630
  • Filename
    5071630