• DocumentCode
    576220
  • Title

    Submerged macrophytes height estimation by echosounder data sample

  • Author

    da Silva Rotta, L.H. ; Imai, Nilton Nobuhiro

  • Author_Institution
    Postgrad. Course in Cartographic Sci., Sao Paulo State Univ., Presidente Prudente, Brazil
  • fYear
    2012
  • fDate
    22-27 July 2012
  • Firstpage
    808
  • Lastpage
    811
  • Abstract
    Traditional methods of submerged aquatic vegetation (SAV) survey last long and then, they are high cost. Optical remote sensing is an alternative, but it has some limitations in the aquatic environment. The use of echosounder techniques is efficient to detect submerged targets. Therefore, the aim of this study is to evaluate different kinds of interpolation approach applied on SAV sample data collected by echosounder. This study case was performed in a region of Uberaba River - Brazil. The interpolation methods evaluated in this work follow: Nearest Neighbor, Weighted Average, Triangular Irregular Network (TIN) and ordinary kriging. Better results were carried out with kriging interpolation. Thus, it is recommend the use of geostatistics for spatial inference of SAV from sample data surveyed with echosounder techniques.
  • Keywords
    geographic information systems; hydrological techniques; remote sensing; rivers; vegetation; Brazil; SAV sample data; Uberaba river; aquatic environment; echosounder data sample; echosounder techniques; geographic information systems; interpolation methods; kriging interpolation; optical remote sensing; ordinary kriging; submerged aquatic vegetation survey; submerged macrophytes height estimation; triangular irregular network; Artificial neural networks; Estimation; Interpolation; Remote sensing; Software; Tin; Vegetation mapping; Geographic Information Systems; Interpolation; Rivers; Submerged aquatic vegetation; Underwater acoustics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International
  • Conference_Location
    Munich
  • ISSN
    2153-6996
  • Print_ISBN
    978-1-4673-1160-1
  • Electronic_ISBN
    2153-6996
  • Type

    conf

  • DOI
    10.1109/IGARSS.2012.6351439
  • Filename
    6351439