Remotely-sensed vegetation classification as a snow depth indicator for hydrological analysis in sub-arctic Finland

Abstract

The relationship between vegetation and snow depth has been studied with a view to subsequent incorporation in hydrological modelling. Study areas were designated at Kilpisjärvi and Kevo, northern Finland, to represent vari­ations in altitude. Landsat MSS data for Kilpisjärvi were processed through a series of interim classifications using simple density slicing of a band 7/5 ratio, incorporating ground radiometric data and vegetation identification progressively to refine the classification. The Kevo data were processed through a contrasting approach using principal components analysis. The resulting classifications were evaluated by further ground radiometry, and the snow retention capabilities of the individual classes were assessed by field snow surveys. The analysis established that MSS data provided suf­ficient discrimination to allow six distinct vegetation classes plus areas of water to be identified. The ground surveys of snow depth confirmed that these classes had clearly distinguishable snow retention properties, ranging from 5 cm for medium altitude heath to 85 cm for birch forest. The technique developed relies upon the availability of field data, but is efficient in the sense that limited data permit wide extrapolation of the estimates. It was con­cluded that despite the relative crudity of the classifications, they did offer a viable basis for rapid estimation of basin water equivalent storage in sub­arctic areas.