• DocumentCode
    15110
  • Title

    Accuracy Assessment of a Remote Sensing-Based, Pan-European Forest Cover Map Using Multi-Country National Forest Inventory Data

  • Author

    Kempeneers, P. ; McInerney, Daniel ; Sedano, F. ; Gallego, Jaime ; Strobl, P. ; Kay, Steven ; Korhonen, K.T. ; San-Miguel-Ayanz, J.

  • Author_Institution
    Centre for Remote Sensing & Earth Obs. Processes (TAP), Flemish Inst. for Technol. Res. (VITO), Mol, Belgium
  • Volume
    6
  • Issue
    1
  • fYear
    2013
  • fDate
    Feb. 2013
  • Firstpage
    54
  • Lastpage
    65
  • Abstract
    A pan-European forest cover map (FMAP2006) was produced using a novel automated classification approach using remotely sensed data from fine resolution satellite instruments. In contrast to previous classification accuracy assessments of such continental scale land cover products, the current study aimed for a reliable assessment at different geographical levels: pan-European, regional and local level. A unique data set consisting of detailed field inventory plots was provided via a collaboration with the national forest inventories (NFIs) in Europe. Close to 900,000 field plots were available for the assessment. The fine spatial resolution of the FMAP2006 facilitated the label assignment of the field plots to subsets of mapped pixels for the accuracy assessment process, thereby overcoming scale and definition difficulties encountered in previous studies with coarser resolution products. An overall accuracy of 88% was achieved at pan-European level based on the field plots of the NFIs. It is demonstrated that important differences exist for the class accuracies in different geographical regions, particularly at the regional and local level.
  • Keywords
    geophysical image processing; image classification; vegetation; vegetation mapping; FMAP2006; coarser resolution products; continental scale land cover products; fine resolution satellite instruments; fine spatial resolution; geographical levels; mapped pixel subsets; multicountry national forest inventory data; novel automated classification approach; pan-European forest cover map; remote sensing-based accuracy assessment; remotely sensed data; Accuracy; Earth; Europe; Remote sensing; Satellites; Systematics; Vegetation; Forestry; image classification; vegetation mapping;
  • fLanguage
    English
  • Journal_Title
    Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
  • Publisher
    ieee
  • ISSN
    1939-1404
  • Type

    jour

  • DOI
    10.1109/JSTARS.2012.2236079
  • Filename
    6414603