Abstract

Lichen is a major forage resource for reindeer and may constitute up to 80% of a reindeer's winter diet. The reindeer grazing area in Sweden covers almost half of the country, with reindeer using mountainous areas in the summer and forested areas in the winter. Knowledge about the spatial distribution of ground lichens is important for both practical and sustainable decision-making purposes. Since the early 1980s, remote sensing research of lichen cover in northern environments has focused on reindeer grazing issues. The objective of the present study was to use lichen information from the Swedish Forest Inventory (NFI) for classification of satellite data into ground lichen classes. The classification procedure was focused on using of NFI plots as training sets for supervised classification of the ground lichen cover in purpose to classify areas with different lichen coverage. The present research has shown the advantage of use forest inventory plot data by assessment of three methods: mahalanobis distance (MD) classification, maximum likelihood (ML) classification and spectral mixture analysis (SMA). The results of this study demonstrate high classification accuracy of SPOT imagery in distinction between lichen-abundant and lichen-poor areas by mahalanobis distance classifier (overall accuracy 84.3%, kappa=0.68). The highest classification accuracy for Landsat scene was achieved by maximum likelihood classification (overall accuracy 76.8%, kappa=0.53). The continuation research on more detailed fragmentation of lichen cover into fractions is proposed.

Keywords

Lichen classification; National forest inventory; SPOT-5; Landsat 7 ETM+

Published in

International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences
2010, Volume: 38-4-8, number: 2W, pages: 146-152
eISBN: *****************
Publisher: ISPRS

Conference

Conference on Core Spatial Databases - Updating, Maintenance and Services - from Theory to Practice, MAR 15-17, 2010, Haifa, ISRAEL

SLU Authors

• Gilichinsky, Michael

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

• Sandström, Per

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

• Reese, Heather

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

• Nilsson, Mats

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Forest Science
Remote Sensing


Permanent link to this page (URI)

https://res.slu.se/id/publ/118152