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

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


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.


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.



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


  1. Bahrenberg, G., Giese, E., & Nipper, J. (1990). Statistische Methoden in der Geographie. Univariate und bivariate Statistik. Teubner Verlag.Stuttgart:Google Scholar
  2. Benichou, P. (1987). Annual and interannual variability of statistical relationships between precipitation and topography in a mountainous area. In 10th Conference on Probability and Statistics in Atmospheric Science,Edmonton.Canada: 273–278.Google Scholar
  3. Benichou, P. & Le Breton, O. (1987). Prise en compte de la topographie pour la cartographie des champs pluviométrique statistiques. La Météorologie, 7, (19)23–34, Paris.Google Scholar
  4. Chumura, G.L., Costanza, R., & Kosters, E.C. (1992). Modelling coastal marsh stability in response to sea level rise: A case study in coastal Louisiana, USA. Ecological Modelling, 64, (1)47–64.CrossRefGoogle Scholar
  5. DEA (2001). Digital Atlas of Namibia. (– 6.10.2001).
  6. Gossmann, H., Banzhaf, E., & Klein, G. (1993). Regionalisierung Ökologischer Daten – alte Aufgaben, neue Lösungswege. Das Freiburger Regionalisierungsmodell FREIM. Würzburger Geographische Arbeiten, 87, 399–418.Google Scholar
  7. Güssefeldt, J. (1991). Quantitative Relieftypen zur Parametrisierung geoökologischer Modelle, Manuskript (Freiburg).Google Scholar
  8. Haase, G. (1991). Theoretisch-methodologische Schlussfolgerungen zur Landschaftsforschung. In Nova acta Leopoldina. Abhandlungen der Deutschen Akademie der Naturforscher Leopoldin. a, Neue Folge, Nr. 276, Bd64, 173–186.Google Scholar
  9. Jürgens, U. & Bähr, J. (2002). Das südliche Afrika., Perthes Regionalprofile. GothaKlett-Perthes Verlag.Stuttgart:Google Scholar
  10. Kreyszig, E. (1979). Statistische Methoden und ihre Anwendungen. Vandenhoeck & Ruprecht.Göttingen:Google Scholar
  11. Leser, H. (1991). Landschaftsökologie. Ansatz, Modelle, Methodik, Anwendung: mit einem Beitrag zum Prozeß-Korrelations-Systemmodell von Thomas Mosimann. Ulmer Verlag.Stuttgart:Google Scholar
  12. Mendlesohn, J., Jarvis, R., & Robertson, T. (2002). Atlas of Namibia: A Portrait of the Land and Its People. David Philip. Cape Town:Google Scholar
  13. Mosimann, T. & Duttmann, R. (1992). Die digitale Geoökologische Karte als Ergebnis einer prozeßorientierten Landschaftsanalyse am Beispiel der Nienburger Geest. Berichte zur Deutschen Landeskunde, 66, (2)335–336.Google Scholar
  14. Passarge, S. (1933). Einführung in die Landschaftskunde. B.G. Teubner.Leipzig:Google Scholar
  15. RSI, Research Systems Inc. (1999). ENVI User Guide. RSI.Google Scholar
  16. Sander, H. & Becker, T. (2002). Klimatologie des Kaokolandes. In: M. Bollig, E. Brunotte, & Th. Becker (Eds.) Interdisziplinäre Perspektiven zu Kultur- und Landschaftswandel im ariden und semiariden Nordwest Namibia, Kölner Geographische Arbeiten, H. 77, Selbstverlag des Geographischen Instituts der Universität zu Köln, pp. 57–68.Google Scholar
  17. Yue-Hong, C., Pin-Shuo, L., & Dezzani, R. (1999). Terrain complexity and reduction of topographic data. Journal of Geographical Systems, 1, (2)179–198.CrossRefGoogle Scholar

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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