A pedogenic understanding raster classification model for mapping soils, Powder River Basin, Wyoming.

摘要

Vast areas of the earth need new or updated soil survey data. However, traditional methods of soil survey are inefficient, expensive, and often inaccurate. A methodology incorporating geographic information systems (GIS), remote sensing (RS), and modeling to predict and map soil distribution was developed and tested in a pilot project in the Powder River Basin, Wyoming, USA. Topographic data derived from digital elevation models (DEMs) and Landsat RS spectral data were selected to represent soil-forming factors and analyzed using ERDAS Imagine image processing software. Unsupervised and supervised classifications were used to develop representations of soil-landscape patterns and to plan locations for collection of field data. As more was learned about the survey area, a knowledge-based classification model was built based on the concept of a decision tree. Final map quality was checked using traditional qualitative means and a quantitative accuracy assessment (88% overall accuracy for eight map units).