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Vehicle use during military training activities results in soil disturbance and vegetation loss. The capacity of lands to sustain training is a function of the sensitivity of lands to vehicle use and the pattern of land use. The sensitivity of land to vehicle use has been extensively studied. Less well understood are the spatial patterns of vehicle disturbance. Since disturbance from off-road vehicular traffic moving through complex landscapes varies spatially, a spatially explicit nonlinear regression model (disturbance model) was used to predict the pattern of vehicle disturbance across a training facility. An uncertainty analysis of the model predictions assessed the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty. For the most part, this analysis showed that mapping and modeling process errors contributed more than 95% of the total uncertainty of predicted disturbance, while satellite imagery error contributed less than 5% of the uncertainty. When the total uncertainty was larger than a threshold, modeling error contributed 60% to 90% of the prediction uncertainty. Otherwise, mapping error contributed about 10% to 50% of the total uncertainty. These uncertainty sources were further partitioned spatially based on other sources of uncertainties associated with vehicle moment, landscape characterization, satellite imagery, etc. [Copyright &y& Elsevier]

Two types of demographic analyses, perturbation analysis and uncertainty analysis, can be conducted to gain insights about matrix population models and guide population management. Perturbation analysis studies how the perturbation of demographic parameters (survival, growth, and reproduction parameters) may affect the population projection, while uncertainty analysis evaluates how much uncertainty there is in population dynamic predictions and where the uncertainty comes from. Previously, both perturbation analysis and uncertainty analysis were conducted on the long-term population growth rate. However, the population may not reach its equilibrium state, especially when there is management by harvesting or hunting. Recently, there has been an increased interest in short-term transient dynamics, which can differ from asymptotic long-term dynamics. There are currently techniques to conduct perturbation analyses of short-term transient dynamics, but no techniques have been proposed for uncertainty analysis of such dynamics. In this study, we introduced an uncertainty analysis technique, the general Fourier Amplitude Sensitivity Test (FAST), to study uncertainties in transient population dynamics. The general FAST is able to identify the amount of uncertainty in transient dynamics and contributions by different demographic parameters. We applied the general FAST to a mountain goat (Oreamnos americanus) matrix population model to give a clear illustration of how uncertainty analysis can be conducted for transient dynamics arising from matrix population models. [Copyright &y& Elsevier]

Abstract: Off-road vehicles increase soil erosion by reducing vegetation cover and other types of ground cover, and by changing the structure of soil. The investigation of the relationship between disturbance from off-road vehicles and the intensity of the activities that involve use of vehicles is essential for water and soil conservation and facility management. Models have been developed in a previous study to predict disturbance caused by off-road vehicles. However, the effect of data on model quality and model performance, and the appropriate structure of models have not been previously investigated. In order to improve the quality and performance of disturbance models, this study was designed to investigate the effects of model structure and data. The experiment considered and tested: (1) two measures of disturbance based on the Vegetation Cover Factor (C Factor) of the Revised Universal Soil Loss Equation (RUSLE) and Disturbance Intensity; (2) model structure using two modeling approaches; and (3) three subsets of data. The adjusted R-square and residuals from validation data are used to represent model quality and performance, respectively. Analysis of variance (ANOVA) is used to identify factors which have significant effects on model quality and performance. The results of the ANOVA show that subsets of data have significant effects on both model quality and performance for both measures of disturbance. The ANOVA also detected that the C Factor models have higher quality and performance than the Disturbance models. Although modeling approaches are not a significant factor based on the ANOVA tests, models containing interaction terms can increase the adjusted R-squares for nearly all tested conditions and the maximum improvement can reach 31%. [Copyright &y& Elsevier]

When using remote sensing and geographic information systems, accurately scaling-up spatial data of a variable and their uncertainties from a finer to a coarser spatial resolution is widely required in mapping and managing natural resources and ecological and environmental systems. In this study, four up-scaling methods were derived based on simple and ordinary cokriging estimators and a sequential Gaussian cosimulation algorithm for points and blocks. Taking spatial variability of variables into account in the up-scaling process made it possible to simultaneously and accurately obtain estimates and estimation variances of larger blocks from sample and image data of smaller supports. With the aid of Thematic Mapper imagery, these methods were compared in a case study where overall vegetation and tree covers were sealed up from a spatial resolution of 30 × 30 m² to 90 × 90 m² with a stratification method at 90 × 90 m². The results showed that the methods Point simple coKriging-Point co-Simulation scaling UP (PsK-PSUP) and PsK-Block co-Simulation (PsK-BS) led to smaller errors and better reproduced spatial distribution and variability of the variables than the other methods. Choosing PsK-PSUP or PsK-BS depends on the users' emphasis on accuracy of estimates and variances, computational time, etc. The methods can be applied to multiple continuous variables that have any distribution. It is also expected that the general idea behind the methods can be expanded to scaling-up spatial data for categorical variables. [ABSTRACT FROM AUTHOR]

Focuses on the levels of nitrate in Mississippi River. Correlation between nitrate flux to anthropogenic nitrogen input (NANI); Description about NANI; Improvement of adaptive management of conservation policies.