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@ARTICLE{Schroeder:52478,
      author       = {Schroeder, T. and Cohen, W. and Song, C. and Canty, M. J.
                      and Yang, Z.},
      title        = {{R}adiometric correction of {M}ulti-temporal {L}andsat data
                      for characterization of early successional forest patterns
                      in western {O}regon},
      journal      = {Remote sensing of environment},
      volume       = {103},
      issn         = {0034-4257},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {PreJuSER-52478},
      pages        = {16 - 26},
      year         = {2006},
      note         = {Record converted from VDB: 12.11.2012},
      abstract     = {Detecting and characterizing continuous changes in early
                      forest succession using multi-temporal satellite imagery
                      requires atmospheric correction procedures that are both
                      operationally reliable, and that result in comparable units
                      (e.g., surface reflectance). This paper presents a
                      comparison of five atmospheric correction methods (2
                      relative, 3 absolute) used to correct a nearly continuous
                      20-year Landsat TM/ETM+ image data set (19-images) covering
                      western Oregon (path/row 46/29). In theory, full absolute
                      correction of individual images in a time-series should
                      effectively minimize atmospheric effects resulting in a
                      series of images that appears more similar in spectral
                      response than the same set of uncorrected images.
                      Contradicting this theory, evidence is presented that
                      demonstrates how absolute correction methods such as Second
                      Simulation of the Satellite Signal in the Solar Spectrum (6
                      s), Modified Dense Dark Vegetation (MDDV), and Dark Object
                      Subtraction (DOS) actually make images in a time-series
                      somewhat less spectrally similar to one another. Since the
                      development of meaningful spectral reflectance trajectories
                      is more dependant on consistent measurement of surface
                      reflectance rather than on accurate estimation of true
                      surface reflectance, correction using image pairs is also
                      tested. The relative methods tested are variants of an
                      approach referred to as "absolute-normalization", which
                      matches images in a time-series to an atmospherically
                      corrected reference image using pseudo-invariant features
                      and reduced major axis (RMA) regression. An advantage of
                      "absolute-normalization" is that all images in the
                      time-series are converted to units of surface reflectance
                      while simultaneously being corrected for atmospheric
                      effects. Of the two relative correction methods used for
                      "absolute-normalization", the first employed an automated
                      ordination algorithm called multivariate alteration
                      detection (MAD) to statistically locate pseudo-invariant
                      pixels between each subject and reference image, while the
                      second used analyst selected pseudo-invariant features (PIF)
                      common to the entire image set. Overall, relative correction
                      employed in the "absolute-normalization" context produced
                      the most consistent temporal reflectance response, with the
                      automated MAD algorithm performing equally as well as the
                      handpicked PIFs. Although both relative methods performed
                      nearly equally in terms of observed errors, several reasons
                      emerged for preferring the MAD algorithm. The paper
                      concludes by demonstrating how "absolute-normalization"
                      improves (i.e., reduces scatter in) spectral reflectance
                      trajectory models used for characterizing patterns of early
                      forest succession. (c) 2006 Elsevier Inc. All rights
                      reserved.},
      keywords     = {J (WoSType)},
      cin          = {ICG-IV / STE},
      ddc          = {050},
      cid          = {I:(DE-Juel1)VDB50 / I:(DE-Juel1)VDB64},
      pnm          = {Terrestrische Umwelt / Nachhaltige Entwicklung und Technik},
      pid          = {G:(DE-Juel1)FUEK407 / G:(DE-Juel1)FUEK408},
      shelfmark    = {Environmental Sciences / Remote Sensing / Imaging Science
                      $\&$ Photographic Technology},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000238901000002},
      doi          = {10.1016/j.rse.2006.03.008},
      url          = {https://juser.fz-juelich.de/record/52478},
}