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Applied and Environmental Soil Science
Volume 2011, Article ID 421904, 12 pages
http://dx.doi.org/10.1155/2011/421904
Research Article

Digital Soil Mapping in the Absence of Field Training Data: A Case Study Using Terrain Attributes and Semiautomated Soil Signature Derivation to Distinguish Ecological Potential

Jornada Experimental Range, Agriculture Research Service, U.S. Department of Agriculture, P.O. Box 30003, MSC 3JER, NMSU, Las Cruces, NM 88003-8003, USA

Received 15 November 2010; Revised 10 February 2011; Accepted 26 February 2011

Academic Editor: Nicolas Baghdadi

Copyright © 2011 Dawn M. Browning and Michael C. Duniway. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Spatially explicit data for soil properties governing plant water availability are needed to understand mechanisms influencing plant species distributions and predict plant responses to changing climate. This is especially important for arid and semiarid regions. Spatial data representing surrogates for soil forming factors are becoming widely available (e.g., spectral and terrain layers). However, field-based training data remain a limiting factor, particularly across remote and extensive drylands. We present a method to map soils with Landsat ETM+ imagery and high-resolution (5 m) terrain (IFSAR) data based on statistical properties of the input data layers that do not rely on field training data. We then characterize soil classes mapped using this semiautomated technique. The method distinguished spectrally distinct soil classes that differed in subsurface rather than surface properties. Field evaluations of the soil classification in conjunction with analysis of long-term vegetation dynamics indicate the approach was successful in mapping areas with similar soil properties and ecological potential.