Advertisement

Enhancing the Use of Remotely-Sensed Data and Information for Digital Soilscape Mapping

  • L. Le Du-Blayo
  • P. Gouéry
  • T. Corpetti
  • K. Michel
  • B. Lemercier
  • C. Walter

Abstract

The lack of soil maps in Brittany in the north west of France, leads to an approach based on the inference of soilscape units which can be delimited and characterised with relatively fewer field observations than conventional survey. Whereas geology and landform are generally used data to map soilscape units, natural and agricultural landscapes indicate relevant information on soils within them. Remote sensing is obviously the main source of data to map landscape units at regional scale, but one must look carefully how to analyse landscape units, including soil properties, without simply focusing on land-use class. The proposed method for landscape classification is based on a specific classification system developed at regional and local scales, including the role of landscape patterns using object-oriented classification. Post-classification processing is then developed to generalise the results and define mixed landscapes. Finally fusion techniques are tested to examine the probability of common soilscape boundaries arising from different environmental factors (geology, elevation, landscape).

Keywords

Mass Function Landscape Pattern Landscape Unit Paradoxical Information Digital Soil Mapping 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bou Keir R., Girard M.C., Khawlie M., 2004. Utilisation d’une classification structurale OASIS pour la cartographie d’unités de paysage dans une région représentative du Liban. J. Can. Télédétection 30, 617–630.Google Scholar
  2. Canevet C., Laurent L., Le Queau J.-R., Le Rhun P.-Y., Mounier J., Pihan J., Prioul C., Remy F., Le Moignic G., 1990. Atlas de Bretagne, Rennes, coédition Institut culturel de la Bretagne – Skol Vreizh – INSEE, 64 p.Google Scholar
  3. Corgne S., Hubery-Moy L., Dezert J., Mercier G., 2003. Land cover change prediction with a new theory of plausible and paradoxical reasoning. Fusion 2003 Conference, Cairns, Australia, 8–11 July 2003, 1141–1148.Google Scholar
  4. Girard M.C., Girard C.M., 2003, Processing of remote sensing data. Balkema, The Netherlands, 487 p.Google Scholar
  5. Gaddas F., 2001. Proposition d’une méthode de cartographie des pédopaysages – application à la moyenne vallée du Rhône. Thèse de l’Institut National Agronomique de Paris-Grignon, 212pp. http://www.inapg.inra.fr/ens_rech/ager/recherche/theses/these_gaddas.pdf.Google Scholar
  6. Lagacherie P., Robbez-Masson J.M., Nugyen-The N., Barthès J.P., 2001. Mapping of reference area representativity using a mathematical soilscape distance. Geoderma 101, 105–107.CrossRefGoogle Scholar
  7. Lecerf R., Hubert-Moy, L., Corpetti, T., Dubreuil, V., 2006. Détermination et suivi de la couverture hivernale des sols à l’échelle régionale par télédétection: évaluation des données EOS/MODIS en paysage fragmenté, Colloque Interaction Nature–Société, analyses et modèles, UMR 6554 LETG Nantes.Google Scholar
  8. Le Du L., 2000. Unités de paysage et télédétection. In Action paysagère et acteurs territoriaux, GESTE mathrmnˆ1, Université de Poitiers, pp. 109–119.Google Scholar
  9. Le Du-Blayo L., 2007. Le paysage en Bretagne, enjeux et défis, 351 p.Google Scholar
  10. McBratney A., Mendonça Santos M.L., Minasny B., 2003. On digital soil mapping. Geoderma 117, 3–52.CrossRefGoogle Scholar
  11. Robbez-Masson J.-M., Foltete J.-C., Cabello L., Flitti M., 1999. Prise en compte du contexte spatial dans l’instrumentation de la notion de paysage – application à une segmentation géographique assistée. Revue Internationale de Géomatique 9, 173–195.Google Scholar
  12. Serra J., 1982. Image analysis and mathematical morphology Vol. I. Academic Press, London, 600 p.Google Scholar
  13. Serra J., 2004. http://cmm.ensmp.fr/∼serra/cours.htm (Lectures on Mathematical Morphology).Google Scholar
  14. Shafer G.A., 1976. Mathematical theory of evidence. Princeton University Press, Princeton, New Jersey.Google Scholar
  15. Smarandache F., Dezert J., 2004. Advances and applications of DSmT for information fusion: from evidence to plausible and paradoxical reasoning. American Research Press, Rehoboth.Google Scholar
  16. Whiteside T., Ahmad D W., 2005. A comparison of object-oriented and pixel-based methods for mapping land cover in northern Australia. Proceedings of SSC2005 Spatial Intelligence Innovation and Praxis: The National Biennial Conference of the Spatial Science Institute, September 2005, Melbourne, pp. 1225–1331.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • L. Le Du-Blayo
    • 1
  • P. Gouéry
  • T. Corpetti
  • K. Michel
  • B. Lemercier
  • C. Walter
  1. 1.Equipe COSTEL, UMR CNRS 6554 (Littoral Environnement Télédétection et Géomatique)Université de Rennes 2, place du recteur H. le Moal35 043 Rennes cedex

Personalised recommendations