• DocumentCode
    990447
  • Title

    Spectral vegetation indices and uncertainty: insights from a user´s perspective

  • Author

    Van Leeuwen, WillemJ D. ; Orr, Barron J.

  • Author_Institution
    Dept. of Geogr. & Regional Dev., Univ. of Arizona, Tucson, AZ
  • Volume
    44
  • Issue
    7
  • fYear
    2006
  • fDate
    7/1/2006 12:00:00 AM
  • Firstpage
    1931
  • Lastpage
    1933
  • Abstract
    The primary objectives of this response communication are to provide insight into the use of spectral vegetation indices (SVIs) and a user´s perspective on the uncertainty in SVI values, especially when these are derived from multiple sensors. We review how two papers in this special issue address uncertainty, and we explore two practical applications of SVI products and how comprehensive quantification and spatially explicit visualization of uncertainty could enhance their use. Although researchers identify the causes of uncertainties in SVIs, there has been little advancement in connecting and integrating the associated uncertainties inherent to all steps of the processing and model chains (e.g., data capture, data input and SVI generation). Cross-comparison of uncertainty assessment is challenging to the end-product user because reporting of uncertainty tends to be research or data product-specific with limited emphasis on facilitating the interpretation of uncertainty associated with algorithm and processing quality for use by managers or decision makers. Consequently, the confidence in these data is often based on experience and visual confirmation of the spatial and temporal consistency in SVI imagery and time-series data. Although the level of accuracy required varies depending on use, overall product quality assurance and a comprehensive, site-specific uncertainty assessment bundled with SVI data fields could mean the difference between using SVIs to report on spatial-temporal patterns versus using these data to make natural resource management decisions
  • Keywords
    geophysical signal processing; vegetation mapping; SVI generation; SVI vaue uncertainty; data capture; data input; natural resource management decisions; spatial-temporal patterns; spectral vegetation indices; uncertainty assessment; Collaborative tools; Data visualization; Geography; Joining processes; Knowledge management; Quality assurance; Quality management; Resource management; Uncertainty; Vegetation mapping; Remote sensing; uncertainty; vegetation;
  • fLanguage
    English
  • Journal_Title
    Geoscience and Remote Sensing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0196-2892
  • Type

    jour

  • DOI
    10.1109/TGRS.2006.873688
  • Filename
    1645293