High-Mountain Landscape Classification to Analyze Patterns of Land Use and Potential Natural Vegetation

Tim Theissen, Annette Otte and Rainer Waldhardt
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Tim Theissen: Center for International Development and Environmental Research (ZEU), Justus Liebig University Giessen, 35390 Giessen, Germany
Annette Otte: Center for International Development and Environmental Research (ZEU), Justus Liebig University Giessen, 35390 Giessen, Germany
Rainer Waldhardt: Center for International Development and Environmental Research (ZEU), Justus Liebig University Giessen, 35390 Giessen, Germany

Land, 2022, vol. 11, issue 7, 1-20

Abstract: In Georgia’s Lesser Caucasus, extremely species rich wooded grasslands are still used as pastures or meadows. These silvopastoral systems are one of the oldest land-use types in Europe, hosting both light-demanding and shade-tolerant species. However, in Europe silvopastoral systems have decreased over the past centuries. The aim of this study is to map, quantify, and classify the local land use and forest types in comparison to the potential natural vegetation to analyze and evaluate the high-mountain landscape pattern. Therefore, we mapped a 223 km 2 study area and classified this mountainous terrain by topographical variables in a cluster analysis. Our results revealed a small-scale pattern of agriculture and forest in the study area, both strongly interlinked. The forest pattern strongly depends on altitude and aspect. The mentioned wooded grassland consists of forests with varying canopy covers connecting the settlement-near pastures and meadows in the montane belt with the natural open grassland in the alpine belts. The forest is in a near-natural condition compared with the potential natural vegetation. However, the quantifications revealed shrub encroachment indicating land-use abandonment. The compiled GIS-maps and the spatial classification of the landscape can be used to support sustainable management strategies in forestry and agriculture.

Keywords: landscape structure; Lesser Caucasus; potential natural vegetation; GIS; cluster analysis (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
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