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Quantitative Classification of Landscapes in Northern Namibia Using an ASTER Digital Elevation Model

  • Gunter Menz
  • Jochen Richters
Chapter
Part of the Studies in Human Ecology and Adaptation book series (STHE, volume 4)

Abstract

In quantitative landscape ecology and environmental modelling, the spatial delineation of a landscape and its differentiation into landscape units is often based on subjective criteria. At the same time, the precise spatial definition of a landscape as a study area is often crucial for deriving accurate model outputs. In this study we present a promising solution to this problem by quantitatively assessing the complex term ‘landscape’ using an empirical statistical approach. This analysis is based principally on a digital elevation model (DEM) derived from stereoscopic ASTER sensor data. DEMs were produced for varying spatial subsets of two ACACIA landscapes in Northern Namibia: (1) Lower Kunene Hills (LKH) and (2) Upper Kunene Hills (UKH).

These subsets were converted into a multilayer dataset, which was then statistically transformed using a principal component analysis (PCA). We attempted to interpret the resulting landscape elements (which we term ‘landform types’) and to utilize calculated variances to explain their geomorphological contribution to establishing the individual landscape.

For the Upper Kunene Hills (UKH) landscape, the highest variance was obtained with a quadratic subset size of 50 × 50 raster elements and a spatial coverage of 50%. For the Lower Kunene Hills (LKH) landscape, the highest variance was obtained with a subset size of 25 × 25 raster elements and coverage of 25%. This difference can be explained by the different dominant landforms of the two study areas. Whereas the UKH is principally characterized by large planar geomorphological features, the LKH is dominated by smaller-scale features such as mountain ridges intersected with steep valleys. These differences in dominant landforms among the two landscapes make it necessary to establish site-specific variance thresholds in order to operationally implement this methodology and produce a final classification of the study areas.

Keywords

Digital Elevation Model Subset Size Landscape Unit Absolute Elevation Landform Type 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The digital elevation model of the central Kaokoveld region was calculated within the ACACIA-subproject E1.

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Gunter Menz
    • 1
  • Jochen Richters
  1. 1.Department of GeographyUniversity of BonnBonnGermany

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