• DocumentCode
    576119
  • Title

    Automated detection of storm damage in forest areas by analyzing TerraSAR-X data

  • Author

    Thiele, Antje ; Boldt, Markus ; Hinz, Stefan

  • Author_Institution
    Inst. of Photogrammetry & Remote Sensing (IPF, Karlsruhe Institue of Technol. (KIT), Karlsruhe, Germany
  • fYear
    2012
  • fDate
    22-27 July 2012
  • Firstpage
    1672
  • Lastpage
    1675
  • Abstract
    Fast mapping of storm-damaged forest areas is in great demand. In general, airborne platforms are called into action to get a quick impression and to record high-resolution data. However, such storm events come often along with bad weather conditions that limit acquisition of optical data as well as flying by airplane. In this case, the new generation of high-resolution spaceborne SAR sensors (e.g., TerraSAR-X) can be used to acquire rapidly image data. The new generation of high-resolution spaceborne sensors increases the expectation of more promising results. In this paper, we focus first on the border line extraction of forest areas to enable a fast estimation of wind-thrown areas, whereby the pre-event forest border is derived from multi-spectral data. Second, clean-up operations are monitored in the affected forest area by applying a change detection operator.
  • Keywords
    remote sensing by radar; storms; vegetation; TERRASAR-X data; airborne platforms; bad weather conditions; border line extraction; clean-up operations; forest areas; high-resolution data; high-resolution spaceborne SAR sensors; image data; multispectral data; optical data acquisition; pre-event forest border; storm damage automated detection; storm events; storm-damaged forest areas; wind-thrown areas; Data mining; Image edge detection; Remote sensing; Sensors; Spaceborne radar; Storms; Synthetic aperture radar; RADAR; change detection; forest areas; remote sensing; storm event;
  • 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.2012.6351205
  • Filename
    6351205