Abstract
Mapping of above-ground phytomass provides a baseline for monitoring climate-induced changes, especially in the northern regions. This is important for practical applications, such as assessing quality of pastures and defining reindeer migration routes. Use of very high resolution (1 m and better) aerial and satellite images is of particular interest, because changes at the level of individual trees can be monitored over comparatively large areas. The goals of this study were to: i) establish relations between phytomass values and structure and spectral reflectance derived from ground research and ii) upscale from ground data to QuickBird satellite imagery to compile maps of above-ground phytomass for key sites. As a result, the study has produced a preliminary map of the above-ground phytomass of lichens for a test site in the Tuliok Valley, Khibiny Mountains, central Kola Peninsula, Russia, with phytomass values well in line with fieldwork data.
Highlights
Vegetation is the most informative component in studies of geosystems at different scales
We have identified lichen tundra areas on the preliminary classification map which was compiled by maximum likelihood classification of the QuickBird image
Optical properties of forest-tundra and tundra landscapes of the Kola Peninsula were primarily determined by reflective properties characteristic of their moss, lichen, dwarf shrub, shrub, and tree components
Summary
Vegetation is the most informative component in studies of geosystems at different scales. Vegetation defines geosystem features and their structural and functional organization including processes of creation, transformation, and migrations of matter, energy, and information. Large-scale mapping of vegetation may be a useful tool in analysis of biodiversity and in monitoring of vegetation productivity. Cartographic methods facilitate identification of spatial patterns and structure; they help to define the nature of vegetation changes and their trends, to determine vegetation productivity, and to develop cartographic models that describe situations arising from impacts of natural and anthropogenic factors. Large-scale mapping and remote sensing data (RSD) enable the most accurate (depending on image resolution) representation of vegetation structure. RSD can facilitate studies of spatial structure of vegetation and of natural and anthropogenic factors that influence phytomass while minimizing labor-intensive fieldwork
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