Abstract

Remote sensing provides a viable alternative for mapping vegetation in the Arctic because it allows for the mapping of discontinuous distribution of cover types over different spatial scales. In this paper we present a statistical method to map the distribution of important cover types for the reindeer Rangifer tarandus during summer in northernmost Sweden using IRS 1D-LISS satellite imagery. We exemplify our method with modeling of the distribution of snowbed vegetation, the cover type used most intensively by the reindeer in the study area. An autologistic regression model that incorporates the spatial structure of the data is used to combine the field data and the satellite image data. The terrain effects in the satellite image are accounted for in the regressions using a digital elevation model (DEM). We produced a fine-scaled coverage depicting the probability of occurrence of snowbed vegetation as a continuous variable at the pixel level. The accuracy of mapping snowbed vegetation was 69-77%, depending on the data used. We conclude that small-scale, pixel-wise classification modeling may be useful for depicting sparsely occurring cover types, some of which may be important determinants of range quality for reindeer

Published in

Arctic, Antarctic, and Alpine Research
2003, Volume: 35, number: 2, pages: 150-157 Publisher: INST ARCTIC ALPINE RES

SLU Authors

• Sandström, Per

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

• Edenius, Lars

  • Department of Animal Ecology, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Environmental Sciences related to Agriculture and Land-use


Publication identifier

DOI: https://doi.org/10.1657/1523-0430(2003)035[0150:CSIAAD]2.0.CO;2

Permanent link to this page (URI)

https://res.slu.se/id/publ/618