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Airborne Laser Scanning for the Site Type Identification of Mature Boreal Forest Stands

Mikko Vehmas,K. Eerikäinen,J. Peuhkurinen,P. Packalen,M. Maltamo

Published 2011 in Remote Sensing

ABSTRACT

In Finland, forest site types are used to assess the need of silvicultural operations and the growth potential of the forests and, therefore, provide important inventory information. This study introduces airborne laser scanner (ALS) data and the k-NN classifier data analysis technique applicable to the site quality assessment of mature forests. Both the echo height and the intensity value percentiles of different echo types of ALS data were used in the analysis. The data are of 274 mature forest stands of different sizes, belonging to five forest site types, varying from very fertile to poor forests, in Koli National Park, eastern Finland. The k-NN classifier was applied with values of k varying from 1 to 5. The best overall classification accuracy achieved for all the forest site types and for a single type, were 58% and 73%, respectively. The conclusion is that when conducting large-scale forest inventories ALS-data based analysis would be a useful technology for the identification of mature boreal site types. However, the technique could still be improved and further studies are needed to ensure its applicability under different local conditions and with data representing earlier stages of stand development.

PUBLICATION RECORD

  • Publication year

    2011

  • Venue

    Remote Sensing

  • Publication date

    2011-01-10

  • Fields of study

    Computer Science, Environmental Science

  • Identifiers
    DOI 10.3390/rs3010100
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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