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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]

This study investigates applications and efficiencies of remotely sensed data and the sensitivity of grid spacing for the sampling and mapping of a ground and vegetation cover factor in a monitoring system of soil erosion dynamics by cokriging with Landsat Thematic Mapper (TM) imagery based on regionalized variable theory. The results show that using image data can greatly reduce the number of ground sample plots and sampling cost required for collection of data. Under the same precision requirement, the efficiency gain is significant as the ratio of ground to image data used varies from 1: 1 to 1: 16. Moreover, we proposed and discussed several modifications to the cokriging procedure with image data for sampling and mapping. First, directly using neighbouring pixels for image data in sampling design and mapping is more efficient at increasing the accuracy of maps than using sampled pixels. Although information among neighbouring pixels might be considered redundant, spatial cross-correlation of spectral variables with the cover factor can provide the basis for an increase in accuracy. Secondly, this procedure can be applied to investigate the appropriate spatial resolution of imagery, which, for sampling and mapping the cover factor, should be 90 m × 90 m - nearly consistent with the line transect size of 100 m used for the ground field survey. In addition, we recommend using the average of cokriging variance to determine the global grid spacing of samples, instead of the maximum cokriging variance. [ABSTRACT FROM AUTHOR]

Abstract: Military training activities disturb ground and vegetation cover of landscapes and increases potential soil erosion. To monitor the dynamics of soil erosion, there is an important need for an optimal sampling design in which determining the optimal spatial resolutions in terms of size of sample plots used for the collection of ground data and the size of pixels for mapping. Given a sample size, an optimal spatial resolution should be cost-efficient in both sampling costs and map accuracy. This study presents a spatial variability-based method for that purpose and compared it with the traditional methods in a study area in which a soil erosion cover factor was sampled and mapped with multiple plot sizes and multi-sensor images. The results showed that the optimal spatial resolutions obtained using the spatial variability-based method were 12 and 20m for years 1999 and 2000, respectively, and were consistent with those using the traditional methods. Moreover, the most appropriate spatial resolutions using the high-resolution images were also consistent with those using ground sample data, which provides a potential to use the high-resolution images instead of ground data to determine the optimal spatial resolutions before sampling. The most appropriate spatial resolutions above were then verified in terms of cost-efficiency which was defined as the product of sampling cost and map error using ordinary kriging without images and sequential Gaussian co-simulation with images to generate maps. [Copyright &y& Elsevier]

Abstract: When a ground and vegetation cover factor related to soil erosion is mapped with the aid of remotely sensed data, a cost-efficient sample design to collect ground data and to obtain an accurate map is required. However, the supports used to collect ground data are often smaller than the desirable pixels used for mapping, which leads to complexity in developing procedures for sample design and mapping. For these purposes, a sampling and mapping method was developed by integrating stratification and an up-scaling method in geostatistics — block cokriging with Landsat Thematic Mapper imagery. This method is based on spatial correlation and stratified sampling. It scales up not only the ground sample data but also the uncertainties associated with the data aggregation from smaller supports to larger pixels or blocks. This method uses the advantages of both stratification and block cokriging variance-based sample design, which leads to sample designs with variable grid spacing, and thus significantly increases the unit cost-efficiency of sample data in sampling and mapping. This outcome was verified by the results of this study. [Copyright &y& Elsevier]