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This article in SSSAJ

  1. Vol. 73 No. 5, p. 1682-1692
    Received: May 1, 2007

    * Corresponding author(s): xun.shi@dartmouth.edu


Integrating Different Types of Knowledge for Digital Soil Mapping

  1. X. Shi *a,
  2. R. Longb,
  3. R. Dekettb and
  4. J. Philippeb
  1. a Department of Geography, Dartmought College, 6017 Fairchild, Hanover, NH 03755
    b USDA-NRCS, 481 Summer Street, Suite 202, St. Johnsbury, VT 05819


Analysis of the soil scientists' knowledge provides guidelines for the development of knowledge-based digital soil mapping (DSM) methodologies and software tools. Literature addressing the analysis and integration of different types of soil scientists' knowledge is limited. We analyze the knowledge from the perspectives of scale and space. We distinguish global knowledge that covers the entire mapping area and local knowledge that is only applicable to certain local regions. We also distinguish knowledge represented by environmental values in parametrical space and knowledge represented by locations in geographical space. Rule-based reasoning (RBR) is proposed for handling the global knowledge in parametrical space, global case-based reasoning (CBR) for the global knowledge in geographical space, and local CBR for the local knowledge in geographical space. The final soil mapping products should represent an integration of knowledge and inferences of all different types. A software tool, named Soil Inference Engine (SIE), was developed to facilitate an eight-step integrated RBR-CBR DSM process. The SIE was tested in a pilot project in northern Vermont and proved to be effective. The soil scientist working on the project was generally satisfied with the results from SIE, in terms of both quality and cost.

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