Integrated Terrain Forecasting for Military Operations in Deserts: Geologic Basis for Rapid Predictive Mapping of Soils and Terrain Features

Eric V. McDonald*, Steven N. Bacon, Scott D. Bassett, Rivka Amit, Yehouda Enzel, Timothy B. Minor, Ken McGwire, Onn Crouvi, Yoav Nahmias

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Scopus citations


During the past three decades, the U.S. armed forces have been called on repeatedly to operate in the deserts of the Middle East and southwest Asia. Avoiding locations susceptible to extreme dust emissions and other terrain-related hazards requires the ability to predict soil and terrain conditions, often from limited information and under dynamic environmental conditions. This paper reports the approach used to develop an integrated, predictive tool for forecasting terrain conditions to support military operations in desert environments at strategic, operational, and tactical scales. The technical approach relies on the systematic integration of desert landform parameters in geomorphic models for predicting terrain conditions. This integrated effort is performed in a geographic information system (GIS) framework using expert-based analysis of airborne and spaceborne imagery to identify terrain elements. Advances in earth science research have established that unique, predictable relations exist among landscape position, soils, vegetation, and geology. Furthermore, new instrumentation allows the collection of a wide range of environmental information to characterize surface and subsurface conditions. By integrating models and methods from geomorphology, soil science, climatology, and atmospheric science with remote sensing and other technologies, a predictive model can be developed to support military operations.

Original languageAmerican English
Title of host publicationAdvances in Military Geosciences
Number of pages23
StatePublished - 2016

Publication series

NameAdvances in Military Geosciences
ISSN (Print)2522-8315
ISSN (Electronic)2522-8323

Bibliographical note

Publisher Copyright:
© 2016, Springer Science+Business Media New York.


  • Landforms
  • Predictive mapping
  • Soils
  • Terrain hazards


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