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The military extensively utilizes simulation and virtual training to prepare troops for combat. The OneSAF (One Semi-Automated Forces) computer generated simulation is one of the U.S. Armys next generation systems that can represent a full range of military operations, systems, and control processes. It is an entity-level simulation, meaning that it can simulate the activities of an individual or groups of individual combatants or weapons platforms. Additionally, OneSAF also provides the appropriate representations of the physical environment (e.g., terrain features, weather, and illumination) and its effect on simulated activities and behaviors. The ability of this software program to provide improved training realism within a simulation environment that is closely related to the actual conditions in the field provides an opportunity for land managers and other environmental decision makers to predict land use patterns before disturbance events actually occur or to track events as they occur. This would provide a proactive means for installation land managers to mitigate land use impacts. The development of such a tool would be invaluable, because this capability does not currently exist. As a precursor to an effort to extend OneSAF software capabilities to encompass land management activities, this presentation will present a methodology to harness fielded simulation and training systems to generate information on vehicle mobility density throughout a landscape, utilizing among other resources the Deployable Force-On-Force Instrumented Range System (DFIRSTTM), shown in Figure 1. For land managers, the major benefit to this approach is to rapidly identify those locations that are intensely utilized and therefore subject to land degradation. Additionally, this approach provides a means to readily rank and quantify those impacts. The benefits and limitations of this approach will be discussed to guide future work in this area and determine the refinements needed to adjust the tracking and simulation systems to accurately capture land use impacts.