• DocumentCode
    575910
  • Title

    Synergetic use of TerraSAR-X and Radarsat-2 time series data for identification and characterization of grassland types - a case study in Southern Bavaria, Germany

  • Author

    Metz, Annekatrin ; Schmitt, Andreas ; Esch, Thomas ; Reinartz, Peter ; Klonus, Sascha ; Ehlers, Manfred

  • Author_Institution
    Inst. for Geoinf. & Remote Sensing, Univ. of Osnabruck, Osnabruck, Germany
  • fYear
    2012
  • fDate
    22-27 July 2012
  • Firstpage
    3560
  • Lastpage
    3563
  • Abstract
    In the context of global change, alteration of landscapes and loss of biodiversity, the monitoring of habitats, vegetation types and their changes have become extraordinary important. In this paper, first results from a study that analyses the differentiability of NATURA2000 habitats and HNV grassland with imaging radar data are presented. Therefore, Kennaugh elements derived from TerraSAR-X and Radarsat-2 dual pol (VV/VH) time series data are used, both separately and in combination, to model the distribution of these classes with the Maximum-Entropy principle. The preliminary results show that the multi-frequency approach enables - compared to single frequency analyses - a finer differentiation between scatterers in the size of 3-6 cm (e.g. 7120, 7230 and HNV grassland).
  • Keywords
    maximum entropy methods; radar imaging; remote sensing by radar; synthetic aperture radar; time series; vegetation; vegetation mapping; Germany; HNV grassland; Kennaugh elements; NATURA2000 habitats; Radarsat-2 time series data; Southern Bavaria; TerraSAR-X time series data; biodiversity; differentiability; grassland types; imaging radar data; landscapes; maximum-entropy principle; single frequency analyses; synergetic use; Biomedical optical imaging; Data models; Monitoring; Optical imaging; Remote sensing; Time series analysis; Vegetation mapping; Maximum-Entropy; Radarsat-2; TerraSAR-X; grassland; polarimetry;
  • 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.2012.6350649
  • Filename
    6350649