Showing papers on "Digital soil mapping published in 1984"

The Missing Geographic Dimension to Soil Taxonomy

Abstract: The establishment of Soil Taxonomy as the official United States system of soil classification introduced a number of innovative concepts of significance to soil classification, soil survey, and soil geography. These concepts include the pedon, the polypedon, the taxadjunct, consociation, and redefinitions of existing concepts such as the soil complex and the soil association. However, examination of the application of Soil Taxonomy in a geographic context reveals fundamental deficiencies in its ability to portray landscape pattern in a manner that concisely and effectively conveys to map users the soil surveyor's knowledge of soil distributions. Some of these problems can be illustrated by examining soils formed on the interbedded shales, sandstones, and dolomites of the Rome formation in the Central Valley of Virginia. These soils fall within four separate soil orders, arranged in such intimate landscape patterns that Soil Taxonomy seems inadequate as a vehicle for describing their geographic c...

Predicting offshore soil conditions

Abstract: This paper describes a practical method for developing predictive soil models for large offshore areas. The models are developed by synthesizing geologic background information, high-resolution seismic reflection data, and soil boring results. The geologic information provides reasonable limits for the predicted range in soil conditions. Seismic data define the three-dimensional relationships among soil units and provide a framework for relating soil borings to one another. The soil borings ''calibrate'' the model by defining soil stratigraphy and geotechnical properties at key locations. Components of a soil model are: a soil province map showing areas where soil conditions are similar; soil cross sections to show the vertical relationships among soil units; and a representative soil profile for each soil province. Soil models can be used to predict general soil conditions throughout a study area, but not exact soil conditions at specific sites. Reliability of predictive models varies according to geologic complexity; the amount, type, and quality of data used; and the experience of those who develop the model. The method described has been used successfully to develop models for several Alaskan OCS areas and other frontier areas.

Probabilistic Characterization of Nigerian Falls

Abstract: A probabilistic method of subsurface characterization is developed for sites in Southern Nigeria. This method is based on the use of statistics to analyze real field data and determine models for predicting data trends in similar environments. The method is used to confirm the validity of an existing classification scheme for the soils in the area. This scheme recognizes four major soil groups in addition to some specific or local soil types found in different locations. The models developed incorporate all factors contributing to soil property variations and can thus be used with confidence in all engineering designs and analyses.

Landform-Based Characterization of Soil Properties

Abstract: A probabilistic characterization of site soil properties can be achieved with sparse site-specific data by augmenting them with data available from other sites in the region having the same geologically charactertistic landform. This paper summarizes important concepts of landform characterization, presents pertinent updating equations, discusses soil profile development, and includes an illustrative, site-specific application of the method.