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Intensive use of military vehicles in military installations create conditions favorable for gully formation. Gullies impede the access of vehicle, restrict the continuation of training, and lead to significant damage to vehicle and risk the life of soldiers. Therefore, it is critical to correctly identify the locations of gullies for continuous training mission. In this study, Fort Riley (FR) military installation was chosen as the study area. LiDAR derived 1 m resolution digital elevation model (DEM) acquired on 2010 was used to map the gullies. A procedure that measures local topographic position, i.e., difference from mean elevation (DFME) along with its integration to the land surface having high surface curvature values was employed. Two high spatial resolution WorldView-2 images of 2010 and field gully data collected in 2010 were utilized for accuracy assessment. Results showed that: (1) A total of 237 small and 166 large gullies were detected and most of them dominated the central west and northwest parts of the installation; (2) Based on the visual interpretation in the WorldView-2 images, there was no statistically significant difference between the detected and observed numbers of gullies; (3) Gullies measured in the field were well detected with an overall accuracy of 78%. [ABSTRACT FROM AUTHOR]

Abstract: A great challenge the US military land managers are often faced with is how to optimize road networks in order to maintain roads (including all roads, trails, and paths) for the purpose of military training and reducing negative impacts on environment. In this study, a methodology was developed to identify superfluous roads for being closed in terms of both sustainable military land carrying capacity and environment for Fort Riley. In this method, Geographic Information Systems (GIS), remote sensing, and landscape analysis technologies were combined to derive various spatial data layers of factors that had significant impacts on both military training and environment. The factors included maintenance cost of roads, road access area, military training intensity, soil erosion, water quality, landscape fragmentation, and noise production. The factors were quantified and normalized. A spatial multicriteria decision was then developed to obtain the weights of the factors, combine the data layers, and derive a priority map of all the roads for being closed. This map summarized the negative and positive impacts of the factors on environment and military land carrying capacity and can provide the US military land managers with useful guidelines and tools for determining superfluous roads in terms of both sustainable military training and environment. It is expected this effort can provide a method to quickly ascertain which roads are most cost-effective for being closed without hindering the mission and at the same time with benefits for environmental protection and thus provide the land managers with a comprehensive analysis and assessment of alternatives at their disposal. [Copyright &y& Elsevier]

Abstract: We assessed short-term impacts of changes in military training load on vegetative cover at Fort Hood, TX. From 1989 to 1995, permanent field transects were monitored for vegetative cover and land use disturbance using standard army monitoring methods [Land Condition Trend Analysis (LCTA)]. Land use intensity (training load) was quantified and used to develop a model to predict future vegetation cover values. We found that standard Army monitoring methods detected changes in installation resources associated with changes in training load. Increased training loads were associated with increased measures of disturbance, decreased ground cover, and decreased aerial vegetative cover. We found that the spatial pattern of disturbance and vegetation cover remained relatively constant over the study period despite large variations in overall training load. Our model used the consistency in spatial cover patterns over time and the strong relationship between training load and vegetation cover to predict the impact of future training loads on vegetation. [Copyright &y& Elsevier]